NAT L INST. OF STAND & TECH

NISI

PUBLICATIONS

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100

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NO .355

2001

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AUBE 01

12TH INTERNATIONAL

CONFERENCE AUTOMATIC

FIRE DETECTION

March 25 - 28, 2001

National Institute Of Standards and Technology

Gaithersburg, Maryland U.S.A.

PROCEEDINGS

Editors: Kelhe Beall, Wilham Grosshandler and Heinz Luck

Nisr

National Institute of Standards and Technology

Technology Administration, U.S. Department of Commerce
ACKNOWLEDGEMENT

The 12th International Conference on Automatic Fire Detection was organized through the

cooperative effort of an international steering committee co-chaired by Prof. Dr.-Ing. H. Luck of

Gerhard-Mercator-Universitiit, Duisburg, Germany, and Dr. William Grosshandler of the National

Institute of Standards and Technology (NIST), Gaithersburg, USA. The co-chairs wish to acknowledge the assistance provided by the steering committee members and their institutions: Mr.

Richard Bukowski, NIST Gaithersburg; Prof. Dr. Ingolf Willms, Gerhard-Mercator-Universitiit,

Duisburg; Dr. Markus Loepfe, Siemens Building Technologies AG, Cerberus Division, Miinnedorf,

Switzerland; Prof. Hiromitsu Ishii, Department of Electrical Engineering, Nihon University, Tokyo,

Japan; and Dr. Yoshiyuki Matsubara, National Research Institute of Fire and Disaster, Tokyo. Japan.

The co-chairs especially wish to acknowledge the organizational skills and numerous editing tasks

performed by Ms. Kellie Beall of NIST. Gaithersburg.

The endorsement of AUBE '01 by the European Society for Automatic Alarm Systems (EUSAS),

Zurich, Switzerland, is acknowledged. Partial support for the conference came from

• National Electrical Manufacturers Association (NEMA), USA

• Hughes Associates, Incorporated. Baltimore, MD, USA

• FM Global, Norwood, MA, USA

The financial contributions of these organizations to the success of the conference are gratefully

acknowledged.

ii
Table of Contents

A Histoiy of NBS/NIST Research on Fire Detejctors, Richard Bukowski 1 NIST, Gaithersburg USA

European Standards and Certification Procedure 13

Reinhard Conrads, VdS. Cologne GERMANY

SMOKE CHARACTERIZATION

An Apparatus for Light Scattering Studies of Smoke Particles 31

Darryl Weinert, George Mulholland, NIST, Gaithersburg USA

Calibration of Fire Sensors in the Sub Micron Range 43

A. Trampe, H. Fissan, Gerhard-Mercator-Universitat, Duisburg GERMANY

Size Distribution and Light Scattering Properties of Test Snwkes 58

Darryl Weinert, Thomas Cleary, George Mulholland, NIST, Gaithersburg USA

DETECTION BY RADIATED EMISSION

Smoldering Fire Detection by Image Processing 71

Daisuke Kozeki, National Research Institute of Fire and Disaster, Tokyo JAPAN

Smoke Detection in Tunnels Using Video Images 79

Dieter Wieser, Thomas Brupbacher, Siemens Building Technologies, Mannedorf SWITZERLAND

Characterization of Spectral Radiation Intensities from Standard Test Fires for Fire Detection 91

Woo Kim, Yudaya Sivathanu. Jay P. Gore, Purdue University, West LaFayette USA

Is Microwave Radiation Usefidfor Fire Detection? 107

Thomas Kaiser, Thorsten Kempka, Gerhard-Mercator-Universitat, Duisburg GERMANY

DETECTION SYSTEMS AND ALGORITHMS

An Energy Model in Fire Detection and Integrated Analysis on False Alarms 122

Shu Yan, Shu Wang, Zheng Dou, Huazhong University of Science and Technology, Wuhan PRC

A New Approach to Fire Detection Algorithms Based on the Hidden Markov Model 129

Hans-Christian Miiller, Gerhard-Mercator-Universitat, Duisburg GERMANY

Distributed Sensor Fire Detection 139

Thomas Cleary, Kathy Notamanni, NIST, Gaithersburg USA

Spot and Aspirated Laser Smoke Detection in Telecommunications Facilities........ 151

Daniel Gottuk. Lawerence McKenna, Jr., Hughes Associates, Inc., Baltimore USA

iii
i

Strategies for the Development of Detection Algorithms

Rainer Siebel, Gerhaid-Mercator-Universitat, Duisburg GERMANY

Real-Time Probabilistic Neural Network Performance and Optimization for Fire Detection and

Nuisance Alarm Rejection j|7( Susan Rose-Pehrsson, Sean Hart, Thomas Street, Patricia Tatem, Frederick Williams, Naval

Research Laboratory, Washington DC USA; Mark Hammond, NOVA Research, Alexandria USA;

Daniel Gottuk, Mark Wright, Jennifer Wong, Hughes Associates, Baltimore USA

A New Type of Neural Fuzzy System and its Application in Automatic Fire Detection ^ )1 ShuWang, Shu Yan, Zheng Dou, Huazhong University of Science and Technology of China, Wuhan

PRC

A New Algorithm for Adaptive Alarm Threshold in Fire Detection System

Milan Blagojevich, Dejan Petkovich, Djordje Simich, Faculty of Occupational Safety, Nish

YUGOSLAVIA

WIRELESS SYSTEMS & ELECTROMAGNETIC COMPATIBILITY

Electromagnetic Compatibility and Radio-linked Systems

Pascal Dubois, CNPP, Saint Marcel FRANCE

Radio Module Characteristics and Their Relevance to Fire Detection Systems

Karlheinz Schreyer, Siemens Gebaudesicherheit, Munich, GERMANY

Internet Technology: New Perspectives for Alarm Systems

George Schmitz, Peter Witschital, Siemens, Gebaudesicherheit, Munich, GERMANY

Technical State of 868-870 MHz Radio Modules in the SRD Band

Bjoem Silberberg, Siemens Gebaudesicherheit, Munich GERMANY

TEST METHODS AND INSTRUMENTATION

\

Testing Methods for Gas Sensor Based Fire Detectors

Oliver Linden, Hans H lemann. University of Wuppertal GERMANY

A Modular Data Acquisition Systemfor the Measurement of Fire Characteristics j( Wolfgang Kriill, Gerhard-Mercator-Universitat, Duisburg GERMANY

Response-Time Comparisons of Ionization and Photoelectric/Heat Detectors \ J.R. Qualey, III, L. Desmarais, J. Pratt, Simplex Time Recorder, Co., Westminster USA [

The Performance of Mains-Powered Residential Smoke Alarms with a Backup Energy Source

'

J. N. Smithies, FRS Building Research Establishment Ltd. Watford, UK; M. J. Spearpoint,

University of Canterbury, Christchurch NEW ZEALAND

The Fire Emulator/Detector Evaluator: Design, Operation, and Performance '. Thomas Cleary, Michelle Donnelly, William L. Grosshandler, NIST, Qaithersburg USA

Fire Protection Systems for Traffic Tunnels Under Test '. Rudolf, Magerle, Siemens Building Technologies, Mannedorf SWITZERLAND

iv
Concepts for the Test of Volumetric Fire Detectors 338

Ingolf Willms, Gerhard-Mercator-Universitat Duisburg GERMANY

GAS & MULTI-ELEMENT DETECTION

Enhanced Residential Fire Detection by Combining Smoke and Carbon Monoxide Sensors 346

Thomas Cleary. NIST, Gaithersburg USA; Takashi Ono, Nihon University, Tokyo JAPAN

A Diode Laser Multigas Ancdyzerfor Advanced Detection of Fires 358

David Bomse, Southwest Sciences, Inc.. Santa Fe USA

Requirements to Gas Sensors in Fire Alarms for Residential Use 370

A. Pfefferseder. Bosch Telecom. Ottobrunn GERMANY

A Rugged Led-Based Sensorfor Fire Detection 378

Jeffrey Goldmeer, Southwest Sciences, Inc., Santa Fe USA

Measuring Results of a Combined Optical, Thermal and CO Detector in Real Sites and Classifying

the Signals 390

U. Oppelt, Bosch Telecom, Ottobrunn GERMANY

Measurements for Fire Detection by Means of Gas Sensors in an Insulation Material Factory 403

J. GroBer, C. Kubon, O. Linden, H. H lemann. University of Wuppertal, GERMANY

Early Detection and Distinction of Fire Gases with a Gas Sensor Microarray 416

M. Harms, J. Goschnick, Forschungszentrum, Karlsruhe GERMANY

Fire Location Estimation Using Temperature Sensor Arrays 432

Martin Berentsen, Thomas Kaiser, Gerhard-Mercator-Universitat, Duisburg GERMANY

MODELING AND COMPUTER SIMULATION

Field Modeling of an Initial Stage of Fire in a Compartment:

Comparison with a Fire Experiment Measured in an Enclosure 444

N. Miyamoto. T. Someya, T. Omori, Tokyo Gas Co., Ltd., Tokyo, JAPAN

Fire Detector Performance Predictions in a Simulated Multi-Room Configuration 455

Thomas Cleary, Michelle Donnelly, George Mulholland, NIST, Gaithersburg USA; Bakhtier Farouk,

Drexel University, Philadelphia USA

Fire Sensor Modelling and Simulation 470

Frank Gockel, Gerhard-Mercator-Universitat, Duisburg GERMANY

Simulation of Smoke Transport and Coagulation for a Standard Test Fire 482

B. Farouk, Drexel University, Philadelphia USA; G. W. Mulholland, K. B. McGrattan, NIST,

Gaithersburg USA

A Sensor-Driven Fire Model 494

William Davis, Glenn Forney, NIST, Gaithersburg USA

v
Revisiting Modelling of Fluid Penetration into Smoke Detectors Revisitedfor Low

Speed Ceiling Jets 506

O. Keski-Rahkonen, VTT Building and Transport, Espoo FINLAND

A General Approach for Simulating Signals of Scattered Light Detectors 517

Claudia Rexfort, Thomas Kaiser, Gerhard-Mercator-Universitat Duisburg GERMANY

Fire Detection Modeling - The Research-Application Gap 529

Robert Schifiliti, R. P. Schifiliti Associates, Inc. Reading USA

INTEGRATED FIRE DETECTION/BUILDING CONTROL SYSTEM

Development of Fire Detection Systems in the Intelligent Building 561

Z. Liu, J. Makar, A. K. Kim, Institute for Research in Construction, Ottawa CANADA

Using High Reliability Detection for Fire Service Response in Buildings 574

Walter Jones, Richard Bukowski, NIST, Gaithersburg USA

An Open Distributed Fire Detection System 592

Xiangyang Li, Weihau Liu, Aizhong Wang, Jing Su, Gulf Security Tech. Co., Qinhuangdao PRC

Analysis on Data Stream Model of Network Image Fire Detection System 602

Tao Chen, Longbiao Wu, Weicheng Fan, Weiguo Song, University of Science and Technology of

China, Hefei PRC

FIRE DETECTION IN AIRCRAFT, TRANSPORT SYSTEMS AND SPECIAL HAZARDS

Initial Development of Improved Aircraft Cargo Compartment Fire Detection

Certification Criteria 615

David Blake, FAA Technical Center, Atlantic City USA; Stefan Domino, Walt Gill, Louis Gritzo,

Jill Williams, Sandia National Laboratories, Albuquerque USA

Aircraft Fire Detection: Requirements, Qualification, and Certification Aspects 630

Klaus Schmoetzer, EADS Airbus GmbH, Bremen GERMANY

New Approaches to Aircraft Fire Protection 641

A. Freiling, EADS Airbus GmbH, Bremen GERMANY

Fire Detection for Aircraft Cargo Compartments, Reduction of False Alarms 653

Phillipe Mangon, Cerberus S. A., Buc FRANCE

The Cargo Fire Monitoring System (CFMS)for the Visualisation of Fire Events in Aircraft

Cargo Holds 665

T. Wittkopp, C. Hecker, D. Opitz, VIDAIR-AG, Monchengladbach GERMANY

Two Dimentional Multi Detection Fire Sensor, System Architecture and Performances 677

G. Boucourt, Latecoere, Toulouse FRANCE

Aircraft Cargo Compartment Fire and Nuisance Source Test in the FE/DE 689

Thomas Cleary, Michelle Donnelly, NIST Gaithersburg USA

vi
Richard W. Bukowski

National Institute of Standards and Technology, Gaithersburg, MD 20899 USA

A History of NBS/NIST Research on Fire Detectors

Early History of Fire Research at NBS

The celebration of the National Bureau of Standards/National Institute of Standards and

Technology's (NBS/NIST) centennial year has led to many looks back over a century of

research activities. The fire research program at NBS began shortly after the agency's

founding in 1901 with the initial funded studies focused on fire resistance and

flammability properties of materials beginning with the hiring of Simon Ingberg in

1914. In those days the primary objective of fire research was to prevent fires from

burning down large sections of cities. A summary of the early work at NBS was

prepared by Dan Gross for the third International Association of Fire Safety Science

(lAFSS) Conference in 1991 1.

The earliest studies at NBS of the performance of detectors were conducted in the

1920's and 30's. In the 1950's pioneering work was conducted by McCamy on flame

detectors for aircraft engine nacelles^ in which he published data on both ultraviolet

(UV) and infrared (IR) signatures and proposed coupling IR sensors with flame flicker

circuits to discriminate hot objects from actual flame.

Operation Breakthrough

In the late 1960's the US Department of Housing and Urban Development (HUD)

instituted a major, innovative housing demonstration project called "Operation

Breakthrough^." Intended to facilitate the development of novel approaches to design,

materials, and construction techniques of use in addressing low income housing issues,

the program included not only the submission of concepts but also the actual

construction of a home using the proposed techniques. Recognizing that traditional,

prescriptive building codes could not deal effectively with these innovative methods

I
and materials NBS developed one of the earliest performance-based code approaches

not too dissimilar from those now being promulgated globally.

At the time of Breakthrough, fire alarm systems in homes were rare, and where installed

used commercial detectors and panels designed by the rules applied to commercial

properties. Here heat detectors were used in most occupiable spaces. In commercial

installations smoke detectors were usually only used to protect high value items, so they

were rare in home systems. The typical residential system cost as much (in 1968

dollars) as residential sprinkler systems cost today. The (single-station) smoke alarm

had been developed in 1965 but sales were low and availability poor for the few models

being marketed.

One of NBS's fire protection engineers, Mr. Richard (Dick) Bright, had been impressed

with an article published by Canada's National Research Council in 1962. John

McGuire and Brian Ruscoe^ studied 342 residential fire deaths in Ontario from 1956-

1960 and judged the life saving potential of a heat detector in every room or a single,

smoke detector outside the bedrooms and at the head of the basement stairs (if the home

had a basement). Their judgement was that the heat detectors would have reduced the

fatalities by 8% and the smoke detectors by 41%.

NBS included in its Breakthrough criteria^ (essentially the building code applied to the

demonstration houses) a requirement for smoke detectors located in accordance with the

McGuire and Ruscoe guidelines. Since only one of each of the selected homes were

built and none were ever occupied, no fire experience with these detectors was ever

gained.

Hurricane Agnes

In 1971 Hurricane Agnes followed a track up the Chesapeake bay destroying many

homes in central Pennsylvania and lower New York. HUD mounted a federal disaster

relief effort (this was long before FEMA) including the provision of temporary housing

for many poor residents of the region. HUD purchased 17,000 mobile homes (later

2
called manufactured homes) and asked NBS to appl\- some of the lessons of

Breakthrough to the purchase specification. NBS included a requirement for a (singlestation) smoke detector outside the bedrooms of each unit. The order for 17,000 smoke

detectors had to be split among fi\'e manufacturers because at the time no single

companx had the production capacity to fill the order. Today, one manufacturer could

do so with two da\s" production.

The 17,000 homes were delivered to several sites and were used by families until they

could rebuild or find alternati\-e accommodations. Most li\ ed in the homes for a year

but some were still occupied three \ ears later. The fire safety statistics were surprising.

While there w ere nearh the statistically expected number of fires, there were no fire

deaths and few injuries. The smoke detectors were credited with getting occupants out

before the\ became trapped - just as McGuire and Ruscoe surmised.

This w as the first, large installation of residential smoke detectors and the results

con\ inced the manufactured housing industry to adopt the first smoke detector

"ordinance."" In 1975 it became the polic\' of the Mobile Home Manufactunng

Association (the predecessor of today's Manufactured Housing Institute) that one smoke detector located outside the bedrooms be provided m e\ ery manufactured home

produced b\ a member company.

Developing Standards

UL217

The large procurement of smoke detectors for the Agnes homes piqued Dick Bright"

s

cunosit} about just how w ell these devices performed in detecting fires. He modified a

spare protot\ pe of the NBS Smoke Chamber (that later became ASTM D662) to

generate smoke from a small source and circulate it with a small bar heater. Hanging

production smoke detectors m the box he was appalled to see the ""power on"" light on many disappear m the smoke w ithout a sound from the detector.

3
Further tests revealed a problem with smoke entry into the outer housing at low

convective flow rates. The smoke box test used by Underwriters Laboratories (UL) at

the time had two large fans pointed directly at the detector forcing the smoke in - a not

so realistic condition. This experience led Bright and his supervisor Irwin Benjamin to

conclude that the potential of residential smoke detectors would not be realized unless

there were effective product approval standards that assured their proper performance

and reliability.

Bright and Benjamin approached UL about participation in a cooperative project under

NBS' Industry Research Associate program where UL would assign an employee to work at NBS for a year to develop the basis for such a standard that UL would then

promulgate. I was selected by UL for the one-year assignment, beginning in the Fall of

1973.

One of the unique aspects of this project was that it was conducted in close cooperation

with the residential smoke detector industry, who themselves were working with an

immature technology. Companies provided samples of current product and were very

grateful for constructive criticism. Company engineers began to visit with prototypes of

models under development that were jointly evaluated and improved. This cooperative

environment led to rapid improvements in the performance of detectors that benefitted

the public and the industry.

The work that year covered a number of issues identified as problems (or potential

problems) that were corrected by the industry and incorporated in the suggested

standard that was presented to UL and formed the basis for the first edition of their

Safety Standard for Single- and Multiple-Station Smoke Detectors, UL217. These

included:

• Identification and quantification of low velocity smoke entry problems into detector

housings or sensor assemblies and the associated Variable Velocity and

Directionality tests in the new Standard.

4
• Design of a new smoke box for sensitivity testing with improvements to the flow

characteristics and instrumentation that is now used for all smoke detectors.

• Effects of the condensation of moisture on sensor or circuit boards that could cause

false alarms or non-operation and the Humidity Plunge test placed in the

Standard to address this issue.

• Development of an electrical transients test to improve reliability by reducing the

susceptibility of detectors to damage from transients.

• The application of the "full-scale fire tests" to all smoke detectors where they had

previously been used only for ionization type.

• Agreement on the policies of minimum one-year battery life, including the battery

with the detector at purchase, the use of commonly available batteries,

functional testing features, and others.

In the Fall of 1974 I returned to UL and completed the development and adoption of

UL217. Dick Bright had been appointed Chair of the National Fire Protection

Association (NFPA) Committee on Household Fire Warning Equipment that developed

the NFPA 74 Standard on the Installation, Maintenance, and Use of Household Fire

Warning equipment. First published in 1967 as a guide for homeowners this document

reflected the philosophy of the times that homes should be protected in the same way as

commercial businesses - with a heat detector in every room wired to a fire alarm panel

and alarm bells. The cost of such a residential fire alarm system for an average home

was about $1500 so they were rare.

NFPA 74

Since the installation of residential fire alarm equipment was voluntary (and no one

thought that requiring fire safety equipment in homes would ever happen). Bright felt

that homeowners should be given the opportunity to choose a minimum system that

5
provided some protection at low cost, like that suggested by McGuire and Ruscoe. The

committee proposed a system of four "Levels of Protection" in the 1974 edition of

NFPA 74. These were:

• Level 4 was a smoke detector outside the bedrooms and at the head of any basement

stairs from MeGuire and Ruscoe.

• Level 3 added heat or smoke detectors in living or family rooms which had the

highest statistical likelihood of residential fire initiation.

• Level 2 added heat or smoke detectors in the bedrooms that were next on the list of

fire initiation.

• Level 1 was the full system of a heat or smoke detector in every room.

This unique concept was presented to the NFPA Membership for adoption at the May

1974 meeting in Miami Beach and it was strongly opposed by the fire service (the Fire

Marshals and Fire Chiefs). Their concern was that they saw no evidence that anything

less than "complete protection" (Level 1) was adequate. They were correct - the levels

were solely based on the judgement of the committee and that of McGuire and Ruscoe.

Full-scale tests of Smoke Detector Performance

The Indiana Dunes Tests^

While the Levels of Protection concept was adopted at that meeting the concern

expressed by the fire service were not taken lightly. Bright proposed that NBS fund a

research project to assess the effectiveness of the Levels of Protection and this contract

was awarded to IIT Research Institute and UL. The Principle Investigators were Tom

Waterman of IITRI, and William Christian and myself from UL.

The idea was to take detectors currently available on the market and install them in

actual homes that were to be demolished. Fires involving actual residential contents

would be used and instruments would monitor conditions within the homes to judge

6
when unassisted escape out doors (but not jumping out windows) would no longer be

practical.

The research involved 76 experiments conducted over two years in three homes

scheduled for demolition as part of an expansion of the Indiana Dunes National

Lakeshore Park. The data showed that in fact, the optimum performance was obtained

with a smoke detector on every floor level of the home, mostly because smoke flow up

stairs could be impeded by flows induced by HVAC systems, especially air

conditioning. A closed door at the top of the basement stairs could create a dead air

space that delayed response. The home was better protected from fires starting in the

basement by a smoke detector on the basement ceiling near the stairway.

The report presented results in a unique way, in terms of the escape time (time between

detector alarm and reaching one of the tenability limits defined by the study) provided

by the detectors. These escape times were used to produce a probability plot of the

percent of experiments in which a given amount of escape time was provided. Thus the

reader could select a time needed and determine the percent of cases in which that (or

more) time was available.

In an independent analysis of the first year results, a fire safety panel advising the

governor of Massachusetts on a statewide detector law an arbitrary 3 minute escape

time assumption was applied. This resulted in the observation that a smoke detector on

every level would provide the required 3 minutes in 89% of the cases and a smoke

detector in every room would only increase that to 93%.

Manufactured Homes

In 1978 the US Department of Housing and Urban Development (HUD) commissioned

a study similar to the Indiana Dunes Tests to be conducted in a manufactured home'^.

HUD was preparing to promulgate their federal Manufactured Home Construction and

Safety Standards (49CFR3280) and this work provided the basis fo the smoke detector

requirements therein.
Smoke Detector Regulations

The Indiana Dunes tests had a strong and immediate impact and soon various

jurisdictions began to adopt laws requiring the provision of every level smoke detectors

in new residential housing. More surprising to many was the adoption by some of

regulations requiring the installation of smoke alarms in existing residences. This ran

counter to the U.S. tradition of "a man's home is his castle" and most opposition was

not to the smoke detectors but to the change in this tradition. Montgomery County

Maryland was one of the first to adopt such an ordinance in 1975, effective in 1978.

Even more startling later was the immediate impact of the law. As implementation

began the residential fire death rate that had been steady for some years around 32 per

year began to drop precipitously. After the law was effective fatalities hit zero in

compliant homes and stayed there for several years, convincing others to adopt similar

laws.

Successes like Montgomery County led to the rapid adoption of mandatory smoke

detectors in most state or provincial building codes in the U.S. and Canada. Codes at

the city or county level often went further to require the installation of smoke detectors

in existing residential properties. Coupled with effective marketing campaigns by

major appliance manufacturers such as GE and Gillette, and retailers like Sears,

compliance with these regulations was unusually high - typically above 90 %. The

result was a decline in U.S. fire deaths by 50 % between 1975 and 1998 that has been

largely attributed to the smoke detector.

Quantifying Escape Time Needs

The "Indiana Dunes Tests" and other similar studies conducted in the 1970's and 80's

clearly demonstrated that the occupants of most homes with every level smoke detectors

could expect 3 to 5 minutes of escape time for most fires. However there were several

human factors questions such as how effective smoke detectors were at awakening

8
sleeping people and how much time was needed for a family, especially with young

children, to escape.

To address these issues NBS awarded a grant to Prof. E. Harris Nober at the University

of Massachusetts at Amherst to conduct a study. Prof. Nober had a sleep lab on campus

and experience in this field although like most sleep researchers had focused on

insomnia as opposed to awakening.

Prof. Nober' s work^ began in the laboratory but soon moved into homes in order to

provide more realism and to address the questions of families. They developed a

protocol in which they installed a smoke detector in a test home that could be activated

with a radio transmitter from the street. Waiting several weeks to avoid biasing the trial

the researchers showed up in the middle of the night and activated the alarm. The

subjects were instructed to turn on a bedroom light immediately on awakening (this

gave a measure of awakening time) and then place a call to the Amherst Fire

Department (who were part of the study and provided a time for the call). Then the

family all evacuated outside to a pre-arranged meeting place in front of the house.

Through these experiments it was determined that the three minutes assumed almost a

decade prior was a typical value for families.

Detection for Special Applications

In the early I980's NBS decided that the residential smoke detector issues had largely

been addressed and the technology matured. Product approval standards (UL217) and

installation standards (NFPA74) were in place and the combination of regulatory and

voluntary installations were at a pace that soon nearly every home would be equipped.

Thus NBS decided to apply its limited resources in other areas.

The result was limited studies mostly aimed at improving detector performance in

Q

'0 special applications. The applications addressed included health care facilities^'

(especially reducing the incidence of nuisance alarms that were affecting system

credibility), fire protection for atria ^

^ (these had become a common architectural

9
feature), and even spacecraft 12. NASA had begun advanced planning for their 21^'

Century projects including Space Station and wanted to explore innovative techniques

for fire detection.

Computational Studies

In the 1990's NIST (formerly NBS) pioneered the use of computational experiments to

study the performance of, and to develop guidelines for the installation of smoke

detectors. In a project funded through a public/private consortium through the

(National) Fire Protection Research Foundation, NIST researchers evaluated the effects

of both geometry and physical barriers, and the interaction with mechanical ventilation

systems on smoke and heat detector activation times. While others have used

computational techniques to design specific installations, this was the first time anyone

performed parametric calculations designed like a series of experiments to provide

systematic information on a hypothesis..

The results of the study were revealing; confirming some common practice and

indicating that some assumptions may be wrong. The results had a direct and

significant effect on the code requirements. ^ 3' 15- 16

Current and Future Activities

NIST is still involved in detector research. One project involves the development of an

apparatus for evaluating the performance of multi-sensor devices. Called the Fire

Emulator/Detector Evaluator (or FE/DE), the apparatus shows real promise for

international standardization

With links to the Indiana Dunes Tests, NIST is conducting a new evaluation of

residential smoke detectors (now commonly refened to as smoke alarms). This work

intends to re-examine the installation and siting rules, the efficacy of current sensor

technologies, examine nuisance alarm sources, and develop data With which alarm

algorithms might be developed for multi-sensor devices.

10
Finally, NIST is using its experience in computational fire models to develop a "sensordriven'' or "inverse" model Where traditional fire models start with the heat release

rate of the fire and predict the fire's impact on the building this model takes the analog

signal from fire sensors and predicts the heat release rate of the fire most likely to be

producing those signals. This model holds promise in allowing fire alarm systems to

produce real time data of significant use to the fire service in making tactical decisions,

as well as evaluating detector signals for consistency with fire chemistry and physics

and determining the level of threat to people and property.

Fire detectors and the systems to which they connect play a significant role in the

reduction of fire losses. Thus the NIST fire program will continue to conduct research

on detection as a means to achieve its goals of reducing the burden of fire.

References

^ Gross, D., Fire Research at NBS: The First 75 Years, Fire Safety Science

- Proc of the Third International Symposium, Elsevier Applied Science, London,

pp 119-133, 1991.

2 McCamy, C.S., A Five-Band Recording Spectroradiometer, NBS J. Res.,

56,5,293, 1956.

^ Feedback. Operation Breakthrough. Phase 1. Design and Development

of Housmg Systems. Department of Housing and Urban Development,

Washmgton. DC, HUD-RT-28; 261 p. 1973.

4 McGuire, J.H. and Ruscoe, B.E., The Value of a Fire Detector in the

Home, National Research Council of Canada, Fire Study No. 9 of the Division

of Building Research, Ottawa, Canada, 1962.

5 Benjamin, I. A., Criteria for Fire Safety in Operation Breakthrough,

National Bureau of Standards, Washington, DC, Building Standards, Vol. 40,

No. 6, 33-36, November/December 1971.

6 Bukowski, R.W., Field Investigation of Residential Smoke Detectors,

NBSIR 76-1126, 1976; md Fire Journal, 71, No. 2, pp 18-41, 1977.

^ Bukowski, R. W., Investigation of the Effects of Heating and Air

Conditioning on the Performance of Smoke Detectors in Mobile Homes, NBSIR

79-1915, 1979.

8 Nober, E. H.; Peirce, H.; Well, A. D., Waking Effectiveness of

Household Smoke and Fire Detection Devices. Final Report. Massachusetts

Univ., Amherst, National Bureau of Standards, Gaithersburg, MD, Available

11
from National Technical Information Service NBS GCR 83-439; 92 p. July

1983.

^ Bukowski, R. W., Tests on the Performance of Automatic Fire Detectors

in Health Care Occupancies - A Preliminary Report, NBSIR 79-1739, 1979.

^0 Bukowski, R. W. and Istvan, S. M., A Survey of Field Experience with

Smoke Detectors in Health Care Facilities, NBSIR 80-2130, 1980.

^ ' Bukowski, R. W., Smoke Detectors, Alarms, and Controls, NFPA Round

table on Fire Safety in Atriums - Are the Codes Meeting the Challenge?,

December 15-16, 1988, pp 1-19, 1988.

^2 Bukowski, R. W., Techniques for Fire Detection, Proceedings of the

NASA Workshop on Spacecraft Fire Safety, Nat. Aeronautics and Space Admin.

(US)pp 9-29, 1987.

1-^ Forney, G. P., Bukowski, R. W., and Davis, W. D., Fire Modeling:

Effects of Flat Beamed Ceilings on Detector and Sprinkler Response.

International Fire Detection Research Project. Technical Report. Year 1. 59p.,

1993.

*4 Davis, W. D., Forney, G. P., and gukowski, R. W., Field Modeling:

Simulating the Effect of Sloped, Beamed Ceilings on Detector and Sprinkler

Response, International Fire Detection Research Project.Technical Report. Year

2. 34p., 1994.

15 Klote, J.H., Forney, G.P., Davis, W.D., and Bukowski, R.W., Field

Modeling: Simulating the Effects of HVAC Induced Air Flow from Slot

Diffusers on Detector Response, Year 3 Technical Report, National Fire

Protection Research Foundation, Quincy, MA 02269, 1996.

16 Klote, J.H., Davis, W.D., Forney, G.P., and Bukowski, R.W., Field

Modeling: Simulating the Effects of HVAC Induced Airflow From Various

Diffusers and Returns on Detector Response, Technical Report, Year 4, National

Fire Protection Research Foundation, Quincy, MA 02269, 1998.

1^ Grosshandler, W. L., Towards the Development of a Universal Fire

Emulator-Detector Evaluator. National Institute of Standards and Technology,

Gaithersburg, MD . Fire Safety Journal, Vol. 29, 113-127, 1997. University of

Duisburg. International Conference on Automatic Fire Detection "AUBE ^5",

10th. April 4-6, 1995, Duisburg, Germany, Luck, H., Editor, 368-380 pp, 1995.

1^ Davis, W. D.; Forney, G. P. Sensor-Driven Inverse Zone Fire Model.

National Institute of Standards and Technology, Gaithersburg, MD Research

and Practice: Bridging the Gap. Fire Suppression and Detection Research

Application Symposium. Proceedings. Fire Protection Research Foundation.

February 23-25, 2000, Orlando, FL, 204-21 1 pp, 2000.

12
Reinhard Conrads, Head of the Certification Department (VdS Cert)

VdS Schadenverhiitung Koln (VdS Loss Prevention Cologne — Germany)

European Standards and Certification Procedure

1 Introduction

The protection of hfe and real values is too important to leave it to the free

interplay of forces of economy. In a world getting more complex every day

the people, authorities, trade and industry and insurers expect a proved and

confirmed function and reliability when thinking about useful protection

measures against fire, explosion, intrusion, hold-up and other hazards. The

introduction of a test and certification scheme for products and services in

the field of fire protection and security technology [1] was the consequence

of this request in most of the European countries.

The following article will describe this system of testing and certification of

products and crosses questions regarding the co-operation of different test

houses and certification bodies within Europe which is becoming more and

more one unit in some sectors.

2 Importance of testing and certification

One of the characteristics of all security and protection measures against

fire, intrusion and hold-up in practical use is the fact that normal users

unfortunately are not able to verify the functionality and reliability of such

systems. Users also cannot differ between useful systems of high quality

and systems which are to the benefit of the salesman only.

Another important aspect is the fact that faulty functions of protection

measures may cause losses in another area or at least big trouble with

people, neighbours etc. The unintentional flooding of an area which is

13
protected by an automatic sprinkler system is one example, the need for

evacuation of an area flooded by mistake with COg-gas by an automatic CO^-

extinguishing system another one. But also the faulty function of a detection

system like an automatic fire detection system may cause serious problems

if you think about the jeopardising which is caused by the vehicles of the fire

brigade which are driving very fast through the night. Additionally the

confidence in protection measures giving false alarms is sinking rapidly.

Which policeman is reacting to an alarm of an intruder alarm system when

he has made the experience that this is typically a false alarm?

It is one of the greatest challenges for the technical development

departments in the industry that safety installations on the one hand in the

case of an actual hazard have perfectly to work mostly in short times; on the

other hand these installations have to survive huge periods in a "stand-by"

mode under most different environmental conditions, probably with

changing modes for use for the protected premises, other operators etc.

Therefore it is a good and long tradition in Europe that products in the fire

protection and security technology market are tested by independent Test

Laboratories (Test Labs) and certified by independent Certification Bodies

(CB). The initiative for these activities mainly came from the insurers.

Already at the end of the 19"" century they knew that the assurance in much

cases is more reasonable than just to insure. First emphasis was given on

products for fire prevention. Later on also the field of protection against

intrusion and hold-up was considered. In Germany for example the insurers

against losses who gathered in associations founded a department of loss

prevention in 1906. Same activities started in countries like France or Great

Britain even before this date.

The assessment of products first was made on-side during installation.

Later - e. g. in Germany — universities made this assessment on the basis of

14
product samples. There one concentrated first on the key functions of a

product, e. g. is the sprinkler system extinguishing fire and then on the

options against false function. In the 60ies this activity was no more of

interest for the universities because their work is not focused on typetesting. The insurers — in front of the decision to give up the item

prevention - decided to found own laboratories which took over this activity

in 1967. In other European organisations the development was similar.

A further aspect should be raised here: the installation of specified and

qualified protection measures as e. g. a certified automatic sprinkler system

may lead to enormous reductions in insurance rates. So the loss prevention

is not only useful but also cost reducing.

Finally the advantage for users when changing to another insurer cannot be

denied because everyone knows what measures are existent when

standardised and certified products are used and a new assessment of the

measures on side is not necessary.

3 Activities of the insurers and other organisations

As mentioned above testing in the past was first concentrated on the key

functions of a product, later in addition on the key options against false

function of that product. Beginning with the 70ies the idea of "type testing"

was initiated; this meant that samples of the products were sent to the

laboratories for testing. The insurers' laboratories together with their

associations and in co-operation with universities - as an example in

Germany the University of Aachen and later Duisburg, developed test

schedules for the different products. A lot of these test schedules were the

predecessors of national and international standards (example: the

insurers' test schedules for heat and smoke detectors are now a part of the

European standard EN54) [2].

15
Products which have passed the testing with success got a "letter of

conformity " with the remark that this product has been approved by the

insurance association. Restrictions in function or time limitation of this

"approval" were unknown in the first years, neither terms like complete test

report nor quality assurance. In the 70ies the test schedules got more

complete, also the problems of avoiding faulty function were more and more

tackled and solved. What was furthermore found out was the fact that

testing on samples of products or even only one specimen handed in by the

manufacturer is not sufficient as a sample test for all products.

Given this idea further requirements as the testing of several samples, the

visit of the manufacturers' plant, single re-tests of approved products and a

kind of product surveillance of the installed systems which also have been

assessed by the staff of the insurers' department of loss prevention were

described. The rules and standards of the insurers changed in the same

way; those, often versions lying in the office tables of the laboratories in the

80ies, became specifications equivalent to "regular" standards.

In the meantime the information on the approval was given not only by

letter but the laboratories also issued approval certificates which were

limited timely and which constrained both parties to re-check - normally

after 4 years - if the characteristics of the product are still actual.

Furthermore the tested and approved products were listed accordingly.

The described procedure had a great advantage; solutions for problems

which were detected during visits on side — or in struggles with the failure

of protection systems - or the bypassing of intrusion prevention measures

by more skilled intruders - could directly be introduced in the testing

schemes; not in every case to the pleasure of the manufacturers. At the end

16
of the 80ies - at least in Germany - a satisfying level of the tools for loss

prevention against fire and intrusion was reached.

The industry was not sleeping that time. Especially those companies who

wanted to stand out from the majority and wanted to have confirmed the

quality of their products made big efforts towards a standardization of

performance and quality characteristics for fire prevention and security

technology. First traces of this standardisation can be found e. g. in

Germany in the year 1913 [3]. In other countries there were similar

developments and in the TOies and 80ies all major countries in Europe had

own national standards in the field of fire prevention and security. This

trend was continued by the European standardisation bodies GEN

(European Committee for Standardization) and GENELEG (European

Committee for Electrotechnical Standardization).

4 Certification and accreditation against European standards £N

45 000 ff. A decisive turning point in the activities of loss prevention was introduced

by the activities of the European Community (EG) regarding a combined

European market. These activities started in the middle of the 80ies. The

aim of the harmonisation of the European market, anchored in the

foundation act of the EEC already in the middle of the SOies (so-called

Treaties of Rome), was blocked at that time by a series of trade restrictions

between the member states. These restrictions were mainly given by:

• different interstate legal and administrative regulations

• different or missing technical standards

• different test, certification and sur\'eillance procedures

The commission of the EC decided that all these trade restrictions should be

cleared up, and as a consequence the standardisation bodies in Europe

17
should begin to transform national standards and specifications into

European standards. The different test, certification and surveillance

procedures were to be standardised. Today, in the year 2001, we can notice

that we are still far away from reaching this aim, e g. in the area of intruder

alarm systems. It has, however, to be considered that it is not possible to

equalise very strong habits which have grown in long decades and even

centuries just "by order" The different languages represent further

difficulties. Insider only know the difficulties of a group of people discussing

about complex items when everyone of them is talking in another language.

Also the actual use of standards is different: in some countries standards

are used seriously like laws, in others they are more or less advices.

As far as the different test procedures, certification and surveillance

procedures are taken into account it was easier to come to an agreement.

For testing and certification one has to distinguish between the so-called

legally controlled area, e. g. where safety in electrical installations or safety

of machines is required (see also clause 7) and the voluntary area to which

at the moment all in clause 1 mentioned products count. In order to create

the conditions for a trustful co-operation the European Organisation for

Testing and Certification - EOTC - [4] was founded in April 1990 by the

European Commission, the European Free Trade Association (EFTA) and

the European Standards bodies. The EOTC was charged with the

elaboration of transparent and trustworthy accreditation and certification

models for Europe in order to reach the aim of one-stop-testing.

In order that all institutions like laboratories, certification bodies and

institutions for product surveillance are working according to the same

rules, a European standard series was elaborated valid for all these

institutions [5] [6] [71. Furthermore accreditation systems in the member

states of the EU were established with the aim to check on side if all

participating institutions are working according to the same rules. The so18
called sectorial committees within the national accreditation bodies

elaborated product-by-product specific standards, against which

laboratories, certification bodies were assessed.

In this connection also the requirement to give proof of a certified quality

management system (QMS) according to the ISO 9000 series of standards

came up (8] [9|. These requirements represented also a reaction on the

activities of the Japanese in the 80ies regarding a higher quality of products

compared with the rest of the world.

The realisation of the requirements of the accreditation bodies was not very

easy; a lot of ingrained structures had to be adopted to the requirements of

the standards; standards existing only on the paper had to be brought to

applied standards and specifications; testing procedures had to be described

in detail. Now there is more exactness but in a lot of cases the spontaneity

was lost, as e. g. the change of standards regarding requirements which

have become outdated.

A major problem was created by the requirement for a certified quality

management system for smaller companies - especially in Germany where

traditionally personnel enjoys a long-lasting training (e. g. 3 to 4 years for a

junior handicraftsman), before beginning to work without steady

observation. This is in contradiction to the philosophy in other countries

where staff is employed without training and is then working according to

detailed and unambiguous instructions. But it was mentioned already that

the European unification is not a very easy task.

Taken all these arguments into consideration the constraint for

accreditation of institutions which deal seriously with testing and

certification of products in the fields of fire protection and security

technology has lead to an amelioration of results in many areas [101. The

whole procedure has become more formulised, there are real test reports

* 19
and real documented certificates now. Also the items testing (that means

the measurement of facts) and certification (that means the confirmation of

conformity with a technical specification) were divided into two independent

procedures to allow a qualified verification and evaluation of the test results

which have been ascertained in the laboratories.

Lists of all certified products had to be published regularly, the rules of the

insurers became quasi-standards. Also requirements as e. g. certified

quality management system, regular product surveillance lead to the fact

that not only the few tested and certified products (the test samples) were

good but - what can be assumed by the additional measures - all certified

products which are leaving the factory.

In principle the certification — or approval - now is based on three equal

pillars:

• Successful testing of test specimen

• Operation of a certified quality management system according to ISO

9000 by the manufacturer

• Regular product surveillance and - if necessary - re-testing of samples.

But now each member state of the European Union (EU) felt forced to found

laboratories and certification bodies for the field of fire protection and

security technology - a disadvantage as suddenly for those economically

small areas more laboratories and certification bodies than manufacturers

were existing. And due to the different mentalities and the legal

administrations in Europe the organisations of accreditation bodies had

different characteristics. Some of them worked thoroughly and tested each

step of the certification bodies and laboratory, others concentrated on formal

items. So, for the time being the Europeans cannot be satisfied with what

has been reached until now. There is still a lot of work to do.

20
5 Active certification bodies in Europe

Due to the variety of the languages in Europe it is not easy to get an

overview of the nowadays existing test and certification schemes for

products and services in the field of fire protection and security technology

and their efficiency in the market. According to the knowledge of the author - which may possibly not be complete - the situation for this field of activity

in the most important countries of the European Economic Area represents

as follows:

Italy: Certification of intruder and automatic fire alarm systems, safes and

strongrooms

France: Certification of gas and water extinguishing systems, safes and

strongrooms, intruder and automatic fire alarm systems, access control

systems

Belgium: Certification of intruder alarm systems and automatic fire

detection systems

Germany: Certification of intruder and automatic fire alarm systems, access

control systems, gas and water extinguishing systems, safes and

strongrooms, physical security equipment and locks, portable fire

extinguishers

Great Britain: Certification of intruder and automatic fire alarm systems,

gas and water extinguishing systems, safes and strongrooms, physical

security equipment, portable fire extinguishers

Denmark: Certification of automatic fire alarm systems

Sweden: Certification of safes and strongrooms, physical security and locks

Switzerland: Certification of intruder and automatic fire alarm systems

Austria: Certification of intruder alarm systems and safes and strongrooms

The meaning of the different certification marks in the market seems to be

very different; in some countries there is already no chance to get an

21
allowance to use a building by the authorities without the use of certified

products in the area of fire protection, in other countries the insurers are

very strict: that means no underwriting in specific risks without sufficient

certified protection against intrusion. But certification marks also may only

be an item "nice to have" or a marketing instrument of the sales

department.

6 Testing and Certification in a united Europe

The great differences of the certification systems and test specifications in

Europe are causing series of trade restrictions between the individual states

in Europe. This may lead to the fact that a system which is seen positively

and in accordance with the standards in one country may not be applied in

another country - and in the worst case - has to be changed and re-tested

and re-certified. In the legally controlled area these differences will be

abandoned by the introduction of the CE-marking (see clause 7). However,

the CE-marking represents the smallest common factor for requirements of

a product. In case trade restrictions are not based on liabilities as we know

it in the legally controlled area but on the free interplay of forces, the

governmental authorities cannot take direct nor indirect influence. This

situation can be avoided by two measures: on the one hand the technical

specifications which are included in the national certification schemes have

to be harmonised. On the other hand agreements between the certification

bodies should be made in order to ensure the mutual acceptance of test

results and the additional requirements like a certified quality management

system in accordance with ISO 9000, regular product surveillance, sampling

and re-testing of products.

Agreements between certification bodies, however, are only possible if

impartial fundaments for a trustful co-operation between the certification

22
bodies do exist. This ideal situation is not yet completely achieved in the

higher sophisticated European unification procedure. '

Therefore the „European Fire and Security Group" (EFSG) was founded in

1991 I 111 with the aim to avoid unnecessary duplication of testing,

assessment and certification work - but to keep this work on the proven

high level. EFSG started with the three members: APSAD (Assemblee

Pleniere des Societes d 'Assurances Dommage — today Centre National de

Prevention et de Protection (CNPP) in France, Loss Prevention Council

(LPC) - today BRE Certification Ltd. in Great Britain, and Verband der

Sachversicherer e.V. - today VdS Schadenverhiitung (VdS) in Germany.

Today EFSG has 8 members in 6 European countries [12] and some other

organisations are waiting to get accepted as members

A former aim of EFSG was the introduction of a harmonised European

certification mark (EES Mark). But is has been abandoned because the

mark was not requested from the market nor was it possible to remedy the

almost insuperable obstacles caused by the missing of harmonised

standards and specifications. Furthermore there were and are reservations

of the national certification bodies regarding the loss of their independence.

Another important issue is the fact that Certification Bodies carry the full

responsibility for the decision of a certification. From the legal point of view

the certification body may get great problems in case of damages caused by

a product tested and/or certified by another organisation.

In order to come closer to a mutual recognition of test results without

jeopardising the autonomy of the certification bodies the basic philosophy of

EFSG has been renewed in the year 2000:

Now the testing and certification procedures are left to the relevant national

certification bodies or associated laboratories. The certification bodies stay

23
autonomous in future and will not be forced to issue certificates on base of a

test result without any further condition. Agreements on a product-byproduct basis are made between the single certification bodies on base of

which certifications may be performed. These agreements may be bilateral

or even multilateral agreements and may cover a complete test and

certification scheme for a specific product or parts of it only.

These agreements may be similar to the Memorandum of Understanding

(MoU) which exists already between BRE and VdS and works satisfactory

since August 1997. Under this MoU test results in the area of automatic fire

detection systems have been exchanged between BRE and VdS without

problems. Personal of the laboratories work close together e. g. in

standardisation groups and regulary exchange information important for

the daily work. An extention of the MoU to other areas is planned.

Members of EFSG - the certification bodies - have to fulfil the

requirements specified in the Terms of Reference (a kind of statutes of

EFSG), as e. g. the accreditation according to EN 45011, qualified personell,

regular training of the staff, round-robin tests. These are some of the

requirements for a trustful co-operation between the EFSG members. Same

severe requirements are to be fulfilled by the associated laboratories. A

Certification Body may nominate one or more laboratories as associated,

which then acts as subcontractors to perform the tests necessary for the

certification. The fulfilment of these requirements for certification bodies

and associated laboratories is checked in form of audits by EFSG.

Finally it must be stated that EFSG now is associated member of the

European Fire and Advisory Council (EFSAC) [13], an umbrella

organisation of the manufacturers in the field of intrusion and fire

protection. Membership within EFSAC brings together European industrial

24
associations, the Confederation of Fire Protection Associations (CFPA) and

the European Insurance Committee (CEA) [14].

7 CE-marking versus testing and certification

As already mentioned in clause 6 the commission of the European Union

(EU) [15] tries to harmonise the technical specifications and

test/certification systems in areas which match health and safety with legal

influence to level out trade restrictions between the single states in Europe.

Now the main set of products used in the field of fire prevention will become

part of the „Construction Product Directive" (CPD) and are therefore part of

the legally controlled area which requires CE-marking in the future.

As soon as the so-called harmonised standards for these products are

available - and after a transition period of 1 - 2 years - all these products

have to be CE-marked by the manufacturer and in addition show a

„Certificate of Conformity" issued by a „Notified Body". Notified Bodies have

to be listed in Brussels (at EU level) for the field of activities testing,

certification and surveillance.

From the point of view of the insurers the oncoming CE-marking does not

guarantee in every case the same level of product quality as known today;

the CE-mark is more or less not more than a „passport" allowing products to

cross borders and to demonstrate that the products are conform with

European laws. Very often „CE" represents the smallest common factor for

requirements of a product. Also a „system", that means the combined

function of different products (e.g. the chain: fire detectors - fire alarm

control and indicating equipment - alarm transmission equipment), will not

be tested for CE-marking. But the main concern is the fact that Notified

Bodies will come into the market which are not active in the field of testing

25
and certification of products in the area of fire protection until today. This

may cause great differences in quahty of the products marked with "CE".

The estabhshed certification bodies working together in EFSG will continue

to work on a level of quality of testing and certification as known today.

Therefore the mentioned „Certificate of Conformity" for CE-marking will be

a subset of the certificate which is issued today. The certification marks of

today (e.g. CNPP, LPCB, VdS) will continue to guarantee a high level of

product quality above the level of the CE-marking.

8 Conclusion

The third party testing and certification of products and services of the fire

protection and security technology has been applied in Europe since decades

with success. Only the impartial judgement assures that user, insurer and

authorities can trust on the functionality and reliability of these services

and products.

All certification bodies which are united in the „European Fire and Security

Group" (EFSG) work in order to reduce the expenses for testing, quality

assurance and product surveillance on the base of European standards and

specifications - but to keep the quality of the certified products on a high

level. Also they would like to stay independent certification bodies with

their own independent decision and their own certification marks.

Basis of these activities on one hand is a trustful co-operation between the

certification bodies on the base of equal conditions - on the other hand the

availability of harmonised and comparable requirements and test methods.

Here big deficits still exit, especially in the area of security. Automatic fire

detection technology is in a slightly better situation.

26
But the oncoming CE-marking in the field of fire prevention will not

supersede marks of qualified certification bodies because of lack of quality

in technical specifications and in the confidence of the issuing bodies.

References

111 Fire protection: e.g. automatic fire alarm systems, water extinguishing

systems (sprinkler systems), gas extinguishing systems (COg), smoke and

heat exhausting and ventilation systems (SHEVS), portable fire

extinguishers

Security technology: e.g. intruder alarm systems, hold-up alarm systems,

access control systems, safes and strongrooms, high security locks, physical

security equipment, locks

[2] EN 54 series of Standards of Fire Detection and Fire Alarm Systems

[3] E. g. German standard DIN 14 675 Feuermelde- und Alarmanlagen (Fire

signalling and alarm systems), VDE 0800 Bestimmungen fur

Fernmeldeanlagen (Regulations for communication systems), first edition

pubhshed 1913)

[4] EOTC - European Organisation for Conformity Assessment

Rue d'Egmont 15, B-1000 Brussels Web: http://www.eotc.be

[5] EN 45 001 General criteria for the operation of testing laboratories

[6] EN 45 Oil General requirements for bodies operating product

certification systems

[7] EN 45 012 General requirements for certification bodies operating

Quality System

certification

27
[8] EN ISO 9001 Quality systems - model for quality assurance in

design/development, production, installation and servicing

[9] EN ISO 9002 Quality systems - model for quality assurance in

production, installation and servicing

[10] The laboratories and certification bodies of most of the members of

EFSG were accredited at beginning of the 90ties; e. g. VdS

Schadenverhiitung was accredited for testing and certification of products in

the area of fire protection and security technology in 1992.

[11] „European Fire and Security Group" (EFSG)

The secretary:

European Fire and Security Group

Amsterdamer Str. 172- 174

D-50735 Koln Germany

Telephone: +49-221-7766-375

Telefax: +49-221-7766-377

Email: gspaeth@efsg.or.g

Internet: http://www.efsg.org

[12] „European Fire and Security Group" (EFSG) - members December

2000

BRE Certification Ltd. - Great Britain

Building 3, Bucknalls Lane, Garston, Watford, Hertfordshire GB-WD25

9XX

e-Mail-Adresse: FieldP@bre.co.uk

Centre National de Prevention et de Protection (CNPP) - France

BP 2265, F-27950 St. IMarcel

e-Mail-Adresse: Isabelle.Glasner@cnpp.com

- Forschungs- und Priifgemeinschaft Geldschranke und Tresoranlagen

(FuP) - Germany

Lyoner Str. 18, D-60528 Frankfurt

e-Mail-Adresse: fup@vdma.org

28
Svensk Brand- och Sakerhetscertifiering AB (SBSC) - Sweden

Tegeluddsvagen 100, S-11587 Stockholm

e-Mail-Adresse: kjell.nordahl@sbsc.se

- Instituto di Certificazione Industriale per la Meccanica (ICIM) - Italy

Piazza Diaz 2, 1-20123 Milano

e-Mail-Adresse: icimorg@tin.it

- Instituto Italiano del Marchio di qualita (IMQ) - Italy

Via Quintiliano 43, 1-20138 Milano

e-Mail-Adresse: baggio@imq.it

- VdS Schadenverhiitung GmbH - Germany

Amsterdamer Str. 172- 174, D-50735 Koln

e-Mail-Adresse: hschuengel@vds.de

- Schweizerisches Institut zur Forderung der Sicherheit (SI)

Switzerland

Niischelerstrasse 45, CH-8001 Zurich

e-Mail-Adresse: safetv@swissi.ch

[13] „European Fire and Security Advisory Council" (EFSAC)

The secretary:

c/o Hans Schiingel

Amsterdamer Str. 172

D-50735 Koln Germany

Telephone: +49-221-7766-185

Telefax: +49-221-7766-150

Email: info@efsac.org

Internet: http://www.efsac.org

[14] „European Fire and Security Advisory Council" (EFSAC) - members

January 2001-01

European Federation of Associations of Lock and Builders Hardware

Manufacturers - ARGE , Postbus 190, NL-2700 AD Zoetermer

29
Comite Europeen des Assurances - CEA

3 Bis, rue de la Chaussee d'Antin, F-75009 Paris

Confederation of fire protection associations - CFPA

Niischelerstrasse 45, CH-8001 Zurich

European Association of Manufacturers of Fire and Intruder Alarm

Systems - Euralarm

HofmannstraBe 51, D-81379 Munchen

European Committee of the Manufacturers of Fire Protection Equipment

and Fire Fighting Vehicles - Eurofeu

c/o Minimax GmbH, IndustriestraBe 10 - 12, , D-20840 Bad Oldesloe

European Committee of Safe Manufacturers Associaton - Eurosafe

Merwedestraat 48, NL-3300 AB Dordrecht

European Doors and Shutters Federation - EDSF

c/o Verband Tore-Tiiren-Zargen, HochstraBe 113, D-58095 Hagen

[15] The „European Union" (EU) is the successor of the „European

Community" (EC), which was founded 1957 in Rome as „European Economic

Community" (EEC) by France, Italy, Germany, Netherlands, Belgium and

Luxemburg in order to push the economical and political integration of

Europe.

30
Dairy1 W. Weinert* and George W. Mulholland

Building and Fire Research Laboratory, National Institute of Standards and Technology,

Gaithersburg, MD 20899, USA

*Guest Researcher on leave from the Center for Environmental Safety and Risk

Engineering, Victoria University, Melbourne, PO Box 14428, VIC 8001, AUSTRALIA

An apparatus for light scattering studies of smoke particles

1. Introduction

There is great interest in being able to distinguish between flaming, non-flaming and

nuisance alarm aerosols using light scattering methods. Commercially available light

scattering smoke detectors are not able to make such distinctions. Past studies have

examined various aspects of light scattering from smoke and nuisance aerosols. Two

such studies [1] have examined the light scattering from smoke generated by flaming

and non-flaming fuels using fixed position detectors. Loepfe et al. [2] made use of

polystyrene spheres for calibration of their instrument and both studies had the

instruments installed in a fire test room. Meacham and Motevaili [1] examined the

detector response signals at individual angles, while Loepfe et al. [2] discriminated

between smokes using the degree of linear polarization as a function of angle. We have

developed a facility for measuring the light scattered by smokes as a function of the

scattering angle and hnear polarization. By using monosize polystyrene calibration

particles and filter collection for gravimeteric measurement, we were able to measure for

the first time the differential (angular distribution) scattering cross section per mass of a smoke aerosol. This differential mass scattering cross section is a measure of the light

scattering su-ength of a particle in units of area per mass.

In this paper we focus on the optical design of the light scattering facility, the calibration

method and results for acetylene soot. This method is applied to two non-flame

generated smokes and three flame generated smokes and presented in another paper [3].

31
2. Apparatus

This study was carried out by adapting the existing large agglomerate optical facility

(LAOF) developed at the National Institute of Standards and Technology (NIST), MD,

USA. The LAOF was originally [4] used to measure the mass specific extinction

coefficient for flame generated smoke and the total scattering coefficient using a

reciprocal nephelometer [5], which has a cosine sensor/photomultiplier tube set at the

center of the upper cell in Figure 1 . For the differential light scattering experiments the

long upper glass section with the tapered inlet was replaced with a short column. This

column has opposing inlets on either side of the column to promote mixing of the

aerosol when it

Figure 1: A schematic of the basic LAOF set up.

enters the LAOF. The particles are carried by the air flow to the scattering cell in the

midsection of the column where the light scattering occurs. The particles then flow to

the bottom of the LAOF where they are collected on a 47 mm filter at a known

32
volumetric flow rate. The mass concentration of the aerosol is determined from the

mass of particles on the filter and the volume flow rate. A constant flow rate of

10 L min' is maintained with a mass flow controller followed by a vacuum pump.

3. Generation of Calibration and Smoke Aerosol

Nebulizing a suspension of monosize (nominally) 496 nm diameter PS spheres in water

produces a monosize polystyrene latex (PS) aerosol which was used as the calibration

aerosol. This size was chose for its similarity to non-flaming smoke particle sizes and its Mie scattering pattern, which is free of high frequency variation. The suspension is

prepared by adding about 2 ml of the concentrated (0.01 volume fraction) suspension to

50 ml of particle free water. Nebulized droplets containing a single sphere evaporate as

they flow through a diffusion drier, resulting in a PS aerosol. The drier consists of a 500

mm long, 70 mm E) tube with a 10 mm diameter screen tubing surrounded by silica

gel. A majority of the particles are singlets but there is a small fraction of doublets

resulting from the drying of a droplet containing two spheres. Once the spheres have

exited the diffusion drier some dilution air is added to the flow at a rate of about 5 L

min'^ and this aerosol stream is passed into a 3 L volume for final mixing. The flow is

then introduced into the LAOF. A by-pass exists between the mixing volume and the

LAOF to maintain nearly atmospheric pressure in the LAOF. A filter is connected to the

by-pass line to prevent PS spheres from escaping into the atmosphere if the flow rate of

PS aerosol should be greater than the vacuum flow rate. Also the by-pass filter will

prevent ambient particles from entering the system if the PS aerosol flow rate is less

than the vacuum flow rate.

The acetylene smoke used in this study was produced in a steady-state and repeatable

manner by a co-flow laminar diffusion burner [6]. The smoke concentration was

adjusted to the required range by adding dilution air to the small amount of soot that is

sampled from the burner before it enters the scattering cell.

4. Optical System

The light scattering cell is located in the midsection of the LAOF and fabricated from

33
10 cm inner diameter glass tubing ending in ground glass flanges. The flanges allow

connection to the upper and lower neighbors, which are also fabricated from glass. The

scattering cell has an entrance window for the incident light beam and a light trap

directly opposite. The scattering plane is defined to be the plane in which the incident

and scattered beams lie; for our experiments this is the horizontal plane. The scattering

angle, 6, is given by the angle measured from the direction of propagation, so that ^ = 0 ° is the forward direction of the incident beam. The incident beam originates from

a HeNe laser (wavelength X - 632.8 nm) in continuous wave (CW) mode which has a

500:1 linear polarization ratio. The CW beam is immediately passed through a rotating

chopper ( / = 80 Hz) to reduce background noise when measuring low signals. The

incident beam then passes through an optical train of polarizing elements that prepare

the optical properties of the beam. This optical train consists of a linear Glan-Taylor

type generating polarizer orientated with its optical axis at an angle = 0 °, where (pp

is the angle of the optical axis of the polarizer from the scattering plane. A subscript H

then is then used to denote this polarization as the horizontal polarization. This

generated polarization essentially eliminates the residual perpendicular or vertical

(subscript V) polarization of the laser. The next optical element is a quartz zero-order

half-waveplate retarder. This is the key element in the incident optical train as a 45°

rotation of its fast axis from the scattering plane induces a rotation of the beam from

horizontal to vertically polarized light. In this way an incident polarization of either

horizontal or vertical orientation can be chosen. The next optical element before the

scattering volume is the entrance window, which is set at a slight angle to avoid the

multiple reflection of the incident beam. This angle does have an effect of reducing the

intensity of the incident beam, as do all the other elements of the incident light optical

train. After passing through the window, the laser beam passes through a 10 cm

pathlength of uniformly distributed smoke or calibration aerosol. The light scattering is

apparent as a red line of light with a few bright spots arising from large agglomerate

particles in the case of the acetylene smoke.

The scattered light first passed through the wall of the LAOF's scattering cell with a

slight loss of light due to reflection. The opposite side of the scattering cell to the

34
detectors view was coated in a base layer of matte black paint and then a layer of carbon

paint was added onto this. Reducing reflection of scattered light entering the detector

field of view. This was particularly important near the entrance window and the light

trap as reflections and stray light were appreciable in these areas. The entrance window

itself had to be kept to a small diameter, so that plasma light from the laser and beam

dispersion did not create a large area of reflection bigger than the entrance of the light

trap, which was 5 mm. The effect of this stray light at the entrance window and near the

light trap entrance would otherwise add significantly to the scattered light signal in the

forward and 135° directions respectively.

To align the polarizers optical axes correctly, the first generating polarizer is set in a

precision mount and reference to a polarizing beam splitter. A null or crossed polarizer

approach is then used to determine the positions for the half waveplate and the analyzer,

so that horizontal incident and horizontal scattered polarized light (HH) can be produced

and similarly, vertical-vertical polarization (VV). The analyzer is a linear polarizer used

to measure the vertical or horizontal scattered light incident on the detector. The

generating polarizer was set at either = 90 ° or =0 to cross the analyzer during

alignment, but was not changed from the (pp =0° position during an experiment. Once

the polarizer and analyzer were aligned, the half waveplate retarder was placed in the

optical train and aligned. The analyzer, (p^ , and retarder, , angles are changed

manually as required when conducting an experiment. These changes were found to

cause a standard uncertainty of about 3 % (1 std. deviation) in the measured signal. In

this paper the standard uncertainty refers to one standard deviation about the mean value

of a series of measurements. The combined uncertainty refers to the combined standard

uncertainties of the variables using a root-sum-of-squares method. All uncertainties are

expressed as a percentage of the mean value.

The scattered light was detected by a series of optical elements mounted on a rotation

stage with stepper motor drive and encoder feedback. PC control of the rotation stage

was accomplished by a computer program and interfaced through an indexer-board in

the PC. This program also monitored the detector signal via an analogue-to-digital

35
converter board. The rotation stage would move the detector and its optical elements in

A^ = 5° steps in the scattering plane from 5° to 135" to measure the scattered light.

Essentially any angle in the range could be chosen for a measurement; for example, an

extra measurement is usually made at ^ = 8°. Alignment of the detector zero position

was also automated but required the placement of neutral density filters in the incident

beam and removal of the light trap. The rotation stage was then positioned manually

until the detector indicated a peak intensity was found. The computer program then

made the rotation stage scan a range of ±2.5° about this peak position in 0.25° steps.

Tlie peak signal detected was then redefined as zero and another fine step scan was

conducted from ± 1° of this position in 0.1° steps; again the peak position was redefined

if necessary and this became the ^ = 0 ° position. The detector would then move out of

the beam to the 4° resting position to await the beginning of an experimental scan.

The first optical element carried by the detector rotation stage was the analyzing

polarizer, a Glan-Taylor prism, which was aligned to analyze either the horizontal,

0^ =0°, or vertical polarization, =90° component of the scattered light for a given

scan. Following the analyzer was a 5 mm entrance aperture of the detector housing.

The detector housing is a sealed tube containing (in order) a laser line filter, an

achromatic lens, a 1 mm diameter aperture stop, a difftiser and finally the side mounted

photomultiplier tube. The filter reduces the background by allowing only the laser

wavelength to pass. The achromatic lens with the aperture at the focal point of the lens

is positioned to limit the scattered light to ±0.3° of the scattering angle. This small

acceptance angle is important for minimizing the forward scattering bias at small angles.

A large area diffuser is positioned between the aperture stop and PMT to spread the

initially focused beam over the active area of the PMT and reduce the effects of PMT

polarization dependence.

The signal from the PMT was taken through a variable resister to a lock-in amplifier

unit. The lock-in used a chopper to provide the lock-in frequency and the sensitivity of

the amplifier was chosen manually when required as the detector is moved to a new

position. The output signal from the lock-in was then passed through a voltage divider

36
to the analogue-to-digital converter in the controlling PC. In a series of calibration and

smoke experiments the sensitivity of the amplifier is the only parameter in the detection

circuit that is changed.

As the detector scans through the scattering angles, the scattering volume changes in

proportion to l/sin(^) . This angular dependence results in more than a factor of 10

change in the detector's view of the scattering volume as the scattering angle increases

from 5° to 90°. To correct for the effects of angular dependence of the scattering

volume geometry as well as signal scaling, a calibration needs to be performed.

5. Calibration

By scattering light from the PS calibration aerosol and then normalizing the detected

signal by the theoretical signal for theses spheres we can determine a calibration

function, iir(^). This calibration function corrects for the scattering volume, detector

effects, and the small error effects of the optical train. In this study the calibration

function also includes the scaling due to the incident beam irradiance, as we have

assumed that it is constant during the experiments. The object of these calibrations is to

determine the differential (angular distribution) mass scattering cross section, (7(0), of

the smoke particles to be examined.

From Debey-Mie theory [7] the differential scattering cross section, C,;, (6) , for a single

496 nm polystyrene sphere is calculated. This is then converted to a theoretical

differential scattering cross section per mass,

cr,M-C,M/ypPp^ (1)

by dividing by the particles mass; here the values and Pp are the particle volume

and density (1.05 g cm"^ for PS spheres) respectively. The calibration experiment is

conducted by introducing the PS spheres into the scattering volume and making detector

signal measurements, Up(d), for a given polarization. The detector signal, Up(0), has

a dependence on the scattering volume that varies as l/sin(^) to the first order, this can

be corrected by calculating Up[6) = Up(6)sin[d) . The mass concentration, M^, of

37
calibration particles is determined from the filter mass. The theoretical value, (7,^(0) , is

used to normalize the detector signal to determine the calibration function, K(0) , such

that

where p is the subscript for the calibration particle values. The calibration function is

0.01 - I I I I

I I I I I I

I I I I

I M I I I I I I I

I

I I I I

I

I I I I

I I I I II

I I I I

I

I M I II I I I

0 20 40 60 80 100 120 140 160 180 200

Scattering Angle (0°)

38
Figure 2: Light scattering results, VV and HH polarization, for nominally 802 nm

diameter PS spheres.

determined from Eq. (2), for each angle of measurement and for two different

polarization states: incident and detected vertical, Kyy(d), and incident and detected

horizontal polarization, K„„[$). The angular average, Kyy and ^r^^^ , of the calibration

function is then determined and referred to as the calibration factor. Averaging reduced

overcompensation caused by using the respective calibration functions, which weighted

minima in the calibration particles scattering cross section too strongly. When an

experiment with an aerosol is conducted both the volume corrected detector signal,

u(d), and the mass concentration of particles, M, are determined to then give the

particles mass scattering cross section for a given polarization (polarization notation

suppressed),

<r{0) = ^. (3)

The calibration factor would be determined from calibration experiments directly

preceding an aerosol experiment.

In Figure 2 the application of an average calibration factor to the scattering signal from

802 nm nominal diameter PS spheres has been shown. The difference between

experiment and theory is about 20 % of the Mie theory for cr^y (0) and less than about

10 % for cThh(^)' ^® larger deviation in (7vv(^) ^ "^iss alignment of the

optical elements in this case, but the agreement between the data sets is still quite clear.

Error bars are shown for sample points only, and indicate combined uncertainty of a measurement when considering the variables of Eq. 2 and 3. The largest component of

this combined uncertainty is due to the use of the angular average of the calibration

function, which have standard uncertainty typically about 10 % in the HH polarization

and 15 % in the VV polarization. The standard uncertainty of a detector signal

measurement, of which 10 are made at given angle in an experiment, varies between

2 % and 6 %, approaching the higher value for smaller scattering signals. Specific

effects due to doublets of polystyrene sphere are unknown for our experiments.

39
6. Smoke Results

Before each set of VV and HH measurement of the hght scattered from smoke particles,

a set of caHbration experiments would be completed. The calibration factor from these

experiments would then be applied to the detector signal for the smoke particle

experiments that followed. A second set of caHbration experiments was completed after

the smoke to ensure that no change in system calibration had occurred. Figure 3 shows

the first measurement of the mass scattering cross section for acetylene from a laminar

co-flow diffusion burner, the general shape of the data curve is consistent with that

reported by others [8]. However, our results go beyond previous studies by including

the cross section per mass concentration. Such information is important in comparing

measurement and theory. The repeatability of the measurements is demonstrated by the

presence of a second experiment's results. The minima at (T^//(90) is a result of

Rayleigh-like scattering of the small primary particles that make up the agglomerate.

10 f

e

.9

'«->

o^

•n ®

tou

(/)CO

C3

0.1 -.

0.01

0.001

oV

Acetylene Run 1 ^HH Acetylene Run 1

Acetylene Run 2

^HH Acetylene Run 2

0 20 40 60 80 100 120 140 160

Scattering Angle (0°)

Figure 3: Differential mass scattering cross section results for acetylene soot.

The influence of the far field interference between the light scattered by individual

40
primary particles in the soot agglomerate is apparent at the forward scattering angles,

and the 2 to 4 order of magnitude changes in the scattering cross sections over the

angular range. The light scattered by the soot agglomerate is related to the morphology

of the soot agglomerate and thus its fractal geometry [6].

With the differential mass scattering cross section determined it is then possible to

determine the truncated (5° to 135°) total mass scattering cross section, C^^^, of the

particles, which is given by:

C..=24'''(^^^M±^,„(,),,. (4)

The integration was performed numerically and calculated cross sections are shown in

Table 1. The results are also compared to Debye-Mie theory and results from other

studies.

Particles Total Mass Scattering Cross Uncertainty

Section (mVg) (mVg)

Mie Theory for 802 imi diameter 7.49

802 nm diameter PS Spheres 6.3, 6.1 ±1.6

Acetylene Soot* 1.95 ±0.08

Acetylene Soot 1.8, 2.0 ±0.5

Table 2: Truncated total mass scattering cross sections for the particles investigated in

this study. The results for our multiple experiments (Runs 1 & 2) are shown. *This

result is a measured total quantity, not truncated [4].

7. Conclusion

This study has demonstrated the application of polystyrene spheres suspended in air as a

calibration technique for measurements of the differential scattering cross section per

mass, (y{9). Introduced for the first time was the quantification of aerosol particle

scattering in terms of the differential scattering cross section per mass, which is made

possible by gravimetric measurement of the particle mass concentration. This will allow

9 41
direct comparison between measurements and light scattering theory. The light

scattering apparatus used in these measurements has provided repeatable results over a dynamic signal range of 3 to 4 orders of magnitude. Uncertainties lie in the range of

10 % to 20 % and are mainly due to the uncertainties in the calibration factor. The

method was then applied to acetylene smoke, which was produced in a consistent and

reproducible manner by a co-flow laminar diffusion burner, facilitating the experimental

measurements.

Reference List

[1] Meacham BJ, Motevalli V: Qiaracterization of smoke from smoldering

combustion for the evaluation of light scattering type smoke detector response.

Journal ofFire Protection Engineering 1992; 4:17-28

[2] Loepfe M, Ryser P, Tomkin C, Wieser D: Optical properties of fires and non-fire

aerosols. Fire Safety Journal 1997; 29:185-194

[3] Weinert DW, Cleary T, Mulholland GW: Size distribution and light scattering

properties of test smokes. Submitted: AUBE 2001

[4] Mulholland, G. W. and Choi, M. Y. Measurement of the mass specific extinction

coefficient for acetylene and ethylene smoke using the large agglomerate optics

facility. The Combustion Institute. Twenty-Seventh Symposium (international)

on Combustion. 1515-1522. 1998. Pittsburgh, The Combustion Institute.

[5] Mulholland GW, Bryner NP: Radiometric model of the transmission cellreciprocal nephelometer. Atmospheric Environment 1994; 28:873-887

[6] Samson RJ, Mulholland GW, Gentry JW: Structural analysis of soot

agglomerates. Langmuir 1987; 3:272-281

[7] Bohren CF, Huffman DR: Absorption and scattering of light by small particles

New York, John Wiley & Sons, 1983

[8] Koylu UO, Faeth GM: Optical properties of overfine soot in buoyant turbulent

diffusion flames at long residence times. Journal of Heat Transfer 1994;

116:152-159

42
A. Trampe, H. Fissan

Process- and Aerosol Measurement Technology Division, Gerhard Mercator University

Duisburg, Germany

Kalibrierung von Brandmeldern im Sub - Mikron Bereich

1 Einleitung

Zur Detektion von Branden werden unterschiedliche Melder eingesetzt, die auf

Zustandsanderungen im Raum reagieren. Mehrere Melderarten reagieren auf die beim

Brand sich andemden Parti kelanzahlkonzentrationen, gewichtet mit unterschiedlichen

Partikel - Durchmesserfunktionen. Ausgangspunkt zur Beschreibung der Reaktion

eines Melders ist zuniichst die Partikelanzahlkonzentration in Abhangigkeit vom

Partikeldurchmesser. Bei einem beginnenden Brand wird das bereits im Raum

vorliegende Hintergrundaerosol durch das Brandaerosol uberlagert, das aus der

Dampfphase gebildet wird und deswegen zunachst aus kleinsten Partikeln

(Nanopartikel) besteht, die aufgrund nachfolgend wirkender aerosoldynamischer

Prozesse vergroBert werden. Da die Nanopartikel bei einem beginnenden Brand das

erste Brandsignal darstellen, ist ihre Erfassung bei der Charakterisierung von

Brandaerosolen von besonderer Bedeutung. Die bisher eingesetzten Verfahren sind aber

in ihrer unteren Nachweisgrenze in Hinblick auf die PartikelgroBe begrenzt. Im Rahmen

dieses Beitrages wird eine Weiterentwicklung des Differentiellen Mobiiitatsanalysators

(DMPS), der die Erfassung der GroBenverteilung bis in den Nanometerbereich mit

hoher Auflosung erlaubt, vorgestellt. Eine hohe Integration der Geratekomponenten

erhoht die Feldtauglichkeit dieses Gerates.

Auch die eingesetzten Brandmelder haben eine untere Nachweisgrenze im Hinblick auf

die PartikelgroBe, die fiir die Reaktionszeit eines Melders auf einen beginnenden Brand

maBgeblich ist. Zur Verbesserung der Beurteilung des Reaktionsverhaltens eines

Melders ist die Bestimmung dieser unteren Nachweisgrenze von zentraler Bedeutung.

Sie kann mit Hilfe wohl definierter, monodisperser Aerosole, insbesondere im

Nanometerbereich, bestimmt werden. Eine Methode zur Bestimmung der unteren

Nachweisgrenze wird hier vorgestellt.

43
2 Aerosoldynamik

Brandmelder, die auf Partikel reagieren, konnen als Parti kelsensoren betrachtet werden,

deren Ausgangssignal von der Partikelanzahlkonzentration, von der

PartikelgroBenverteilung und der Partikelart abhangt. Solange kein Brand vorliegt, wird

von dem Sensor die am Ort der Probennahme vorliegende naturliche

Hintergrundbelastung an Partikeln gemessen. Das sich ergebende Signal liegt unterhalb

des Schwellwertes fUr einen Brand. Werden von mehreren Stellen mit Hilfe eines

Leitungssystems Proben zum Sensor gefiihrt, so entsteht ein Gemisch der Aerosole der

einzelnen Probenahmestellen. In der Abbildung 1 ist die AnzahlgroBenverteilung fiir

eine saubere landliche sowie fiir eine durchschnittliche stadtische Umgebung gezeigt.

1,000,000 -

10,000 -

0.000001 - Clean Rural

Average Urban

1 1 1 0.01 0.1 1 (jjm) 10 50

Dp FT2.dsf

Abbildung 1: PartikelgroBenverteilung als Hintergrundbelastung fiir Brandsensoren

44
Der naturliche GroBenbereich der Partikeldurchmesser erstreckt sich von weinigen

Nanometem bis ca. 100 /xm. Ohne auBeren Einfluss bzw. ohne besondere Ereignisse

stellt sich in der Raumluft eine relativ stabile GrdBenverteilung ein. Anderungen

vollziehen sich eher langsam. Trotzdem enthalt jedes Aerosol eine gewisse Dynamik.

Diese Dynamik fuhrt zu Veranderungen in den so genannten Nukelations- und

Akkumuiationsmoden. Diese Moden in der PartikelgroBenverteilung erkennt man erst,

wenn aus der Anzahlverteilung unter Annahme kugelformiger Partikel die

Vol umenvertei lung berechnet wird. Abbildung 2 zeigt quantitativ eine typische

Volumenverteilung.

7-

6-

Q

O) >

<

^ 3

1 -

Nucleation

DGV=0.018

Mechanically

Generated

0.002

Nuclei Mode

Geometric Diameter Dp

Accumulation Mode Coarse Mode

Fine-Mode Particles Coarse- Mode Particles

FTI.dsf

Abbildung 2 Typische Volumenverteilung

Der mechanische Mode bleibt stabil. Es kommt zu keinem Austausch von Partikeln mit

den beiden Moden im submikronen Bereich. Die beiden unteren Moden hingegen

unterliegen standigen Veranderungen insbesondere bei Branden aufgrund

Partikelbildung aus der Dampfphase (Nukleation und Kondensation) bzw.

Partikelkoagulation bei hohen Anzahlkonzentrationen. Die Partikel des

Nukleationsmode wachsen praktisch in den Akkumulationsmode hinein. Diese

Vorgange sind in der Abbildung 3 bildlich verdeutlicht.

45
Evaporation Primary panicle growth by

collision and full coalescence

Formation of irregular aggregates h\

cogulation and partial sintering during

gas cooling and primary particle growth

monomers spherical particles aggregates

V

Abbildung 3 Paitikelbildung durch Nukleation, Kondensation und Koagulation

Aus der Dampfphase entstehen Keime in sehr hohen Anzahlkonzentration, die durch

Koiiisionen untereinander und durch Dampfkondensation weiter wachsen. Die

Anzahlkonzentration in dieser Phase ist immer noch so hoch, so dass die Parti kei

aufgrund ihrer Bewegung im Gas haufig kollidieren. Die Partikel bleiben nach der

Koilision aneinander haften und wachsen dadurch sehr schneil weiter an. Die

Anzahlkonzentration im Aerosol nimmt wahrend der Koagulation stetig ab. wenn keine

neuen Partikel zugefuhrt werden. Dieser Vorgang selzt sich fort, bis die

Wahrscheinlichkeit einer Koilision aufgrund der niedrigeren Anzahlkonzentration sehr

klein geworden ist. Das Aerosol ist dann relativ stabil. Die Koagulation kann sich uber

einen Zeitraum von Bruchteilen einer Sekunde bis zu Minuten erstrecken.

Ahnliche Vorgange spielen sich bei Beginn eines Brandes ab. Es entstehen in

unmittelbarer Nahe des Brandes sehr viele Partikel im Nanometerbereich. Mit

zunehmendem Alter des Aerosols verschiebt sich die GroBenverteilung zu groBeren

Partikeln. in der Abbildung 4 sind die GroBenverteilungen fiir verschiedene

Testbrandaerosole [1] zu verschiedenen Zeiten nach Beginn des Brandes gezeigt.

46
O

o

<

Abbildung 4 GroBenverteilungen von Testbrandaerosolen

Bei offenem Holzbrand erkennt man den Nukleationsmode, dessen Maximum vom

Messsystem nicht vollstandig erfasst werden konnte. Die untere Nachweisgrenze des

verwendeten DMPS reichte dazu nicht aus. Das Aerosol des Holzbrandes bildet ab

100 nm einen zweiten Mode, hier noch mit einer geringen Anzahlkonzentration. Der

Kunststoffbrand zeigt schon deutlicher den Akkumulationsmode. Fiir die Beurteilung

des Brandfortschrittes bzw. ftir die friihzeitige Erkennung eines entstehenden Brandes

ist es also besonders wichtig, die GroBenverteilung, beginnend mit Partikeln im

Nanometerbereich, in ihrem zeitlichen Verlauf zu messen. Die oben gezeigten

Messungen wurden mit einem DMPS aufgenommen. Das System kann Partikel in

einem Bereich von 20 nm bis ca. 1 fim mit einer nichtausreichenden Zeitauflosung von

20 Minuten messen. Ab einem Parti keldurchmessem von 100 nm bis zur unteren

Nachweisgrenze von 20 nm zeigen sich aber schon groBere Fehler in der Messung.

Diese Fehler sind u. a. auf Verluste im Messgerat und auf die mit abnehmender

Parti kelgroBe abnehmende GroBenauflosung zuriickzufiihren.

47
3. Entwicklung eines DMPS mit erweitertem Messbereich

Die geschilderten Unzulanglichkeiten fiihrten zur Entwicklung eines verbesserten

Gerates [2]. Ziel der Neuentwicklung war in erster Linie eine Erweiterung des

Messbereichs bis zur kleinsten Parti kelgroBe von 3 nm und eine Minimierung der

Partikelverluste im Gerat. Die wesentlichen Komponenten des Messsystems sind in der

Abbildung 5 gezeigt.

Aerosol in

Im pactor

Corona

Charger

DMA

Electron! eter

Aerosol out

Abbildung 5 Prinzipdarstellung des Wide Range DMPS

Das zu untersuchende Aerosol wird durch einen Impaktor in seinem GroBenbereich

oberhalb 1 /Ltm quasi abgeschnitten und anschlieBend durch einen Auflader in einen

definierten elektrischen Ladungszustand gebracht. Die elektrisch geladenen Partikel

werden dann anschlieBend in dem elektrischen Feld einer Kondensatoranordnung

(DMA) fraktioniert. Die Partikel bewegen sich dabei senkrecht zu dem elektrischen

Feld in der Anordnung. Die Kraftwirkung des elektrischen Feldes fuhrt zu Bahnkurven,

auf der die Partikel sich in Richtung einer Elektrode bewegen. Nur Partikel einer

bestimmten elektrischen Mobilitat bewegen sich auf einer Bahnkurve, die zum Auslass

fiihrt. Die Partikel, die diesen Auslass erreichen, werden gezahlt. Somit ist die Anzahl

der Partikel im Aerosol mit einer bestimmten Mobilitat bekannt. Aus der Mobilitat lasst

sich auf den Partikeldurchmesser schlieBen [3]. Durch Variation der elektrischen

IIP <(!=I> User

48
Feldstarke im DMA wird die Mobilitat der Partikel geandert, die zum Auslass und

damit zum Partikelzahler gelangen. Damit wird erreicht, dass der gesamte Mobilitatsbzw. PartikelgroBenbereich erfasst wird. _

Um die Anforderungen an das verbesserte Gerat zu erfullen, wurden maBgeblich zwei

Losungsansatze umgesetzt:

• Integration aller Komponenten zu einem Gerat. Dies minimiert die Verluste und

verbessert die Feldtauglichkeit.

• Verwendung einer radialen DMA-Anordnung mit zwei Einlassschlitzen und

unterschiedlichen Volumenstromverhaltnissen, um den gesamten GroBenbereich

abdecken zu konnen.

In der Abbildung 6 ist das entwickelte Konzept dargestellt.

Abbildung 6 Konzept des neuen DMPS fiir submikrone Aerosole

Aus der Querschnittsdarstellung ist direkt zu erkennen, dass alle Komponenten zu einer

sehr kompakten Gesamteinheit integriert wurden. Die besondere Stromungsftihrung mit

minimalen Querschnitts- und Richtungsanderungen sorgt fiir minimale Verluste durch

Abscheidung. Die einzelnen Komponenten sind in der Reihenfolge der Durchstromung

erklart.

Der Impaktor gewahrleistet, dass keine Partikel in das System gelangen, deren GroBe

oberhalb der oberen Messbereichsgrenze liegen. Die auf diese Weise erfolgte

Festlegung der oberen Nachweisgrenze des Gerates erlaubt die Korrektur des Fehlers

49
verursacht durch mehrfach geladene Parti kel. Der Impaktor besitzt zwei

Abscheidestufen. Die erste Stufe ist ein Vorabscheider, der die nachfolgenden Stufen vor einer moglichen Verstopfung schiitzt, indem Partikel groBer 5 /xm schon

abgeschieden werden. Bei der nachfolgenden Stufe lasst sich die Abscheidegrenze

wahlweise zwischen 1,4 fxm und 0,4 fim umschalten. Die Notwendigkeit ergibt sich aus den Messbereichsgrenzen des weiter unten erlauterten DMA's.

Zur Aufladung der Partikel wurde ein elektrischer Auflader gewahlt. Der Auflader ist

leicht steuerbar und benotigt keine radioaktiven Quellen. Das Prinzip basiert auf der

Koronaentladung zur Erzeugung freier Ladungstrager. Die im elektrischen Feld der

koaxialen Elektroden emittierten Elektronen werden in den Aerosolstrom geleitet. In

dieser Aufladungszone kollidieren die Partikel mit den Elektronen. Durch eine gezielte

Steuerung der Anzahlkonzentration der Elektronen und der Verweildauer der Partikel in

der Aufladungszone lasst sich die Ladungsverteilung des Aerosols in weiten Grenzen

einstellen. Fiir dieses Gerat wurde das Prinzip nach [4] optimiert.

Die Fraktionierung der geladenen Partikel erfolgt im differentiellen

Mobilitatsanalysator (DMA), wie oben beschrieben. Der Messbereich eines DMA's

wird durch zwei Effekte begrenzt. Fiir die Fraktionierung groBer Partikel werden hohe

Feldstarken benotigt, die begrenzt werden durch die Durchschlagsfeldstarken der

Tragergase. Aus diesem Grund benotigt man DMA Anordnungen mit langen

Bahnkurven, damit die groBen und somit trageren Partikel noch bei realisierbaren

Feldstarken fraktioniert werden konnen. Bei kleinen Partikeldurchmessem und langen

Bahnkurven - und damit langen Verweilzeiten im DMA - erhoht sich der Einfluss der

Diffusion. Die Bahnkurven einer bestimmten Mobilitat enden nicht immer am Auslass,

sondem verteilen sich um den Auslass. Die Auflosung, also die Fahigkeit eines DMA's

unterschiedliche Parti kel mobilitaten zu fraktionieren, wird somit fiir kleinere Partikel

immer schlechter. Die Verweilzeit im DMA muss daher fiir kleine Partikel so kurz wie

moglich sein. Diese beiden kontraren Anforderungen fiir kleine, bzw. groBe Partikel,

lassen sich nur durch die Integration zweier DMA's in einer Konstruktion befriedigen.

In der Abbildung 7 ist das Prinzip eines solchen DMA's gezeigt.

50
Excess

Aerosol Inlet Aerosol Inlet

Aerosol Outlet

Abbildung 7 Prinzipdarstellung eines radialen DMA's mit zwei Einlassen

Der neue DMA ist als so genannter radialer DMA ausgefiihrt. Er besitzt zwei parallele

kreisrunde Elektroden, zwischen denen ein homogenes, elektrisches Feld besteht. In

radialer Richtung stromt das Aerosol durch einen schmalen Ringspalt in den DMA

hinein und durch eine zentrale Offnung heraus. Damit der groBe geforderte Messbereich

realisiert werden kann, wurden hier erstmalig zwei Einlassspalte mit unterschiedlichen

Durchmessem realisiert. Der innere Spalt fiihrt zu kurzen Verweilzeiten des Aerosols

im DMA und damit zu einer hohen Auflosung bis zu 3 nm Partikeldurchmesser. Stromt

das Aerosol durch den auBeren Spalt, ist die Verweilzeit langer, und bei gleicher

maximaler Feldstarke konnen Partikel bis 1000 nm fraktioniert werden. Eine spezielle

Ventilscheibe gibt altemativ einen der beiden Einlassspalte frei. Bei der Konstruktion wurde erreicht, dass die Stromung des Aerosols vollig frei von

einer tangentialen Komponente ist. Nur so bilden sich rein zentrisch gerichtete

Bahnkurven aus. Dies ist eine Voraussetzung fiir die Berechnung der Parti kelgroBen.

Die Stromungsverhaltnisse an den kritischen Einlassspalten wurden mit Hilfe

dreidimensionaler Modelle simuliert. Es zeigten sich bei der optimierten

Stromungsfiihrung keine Turbulenzen, welche die Bahnkurven storen konnten. Die

Auflosung des neuen Gerates ist im Nanometer-Bereich besser als alle zur Zeit

verfiigbaren DMA's.

Als Parti kelzahler ist ein Elektrometer entwickelt worden. Gegeniiber den haufig

verwendeten Kondensationskemezahlem besitzt das Elektrometer die Vorteile der

weitgehenden Unabhangigkeit von dem thermodynamischen Zustand der Aerosole und

51
dem Fehlen von Betriebsflussigkeiten wie Butanol o. a.. Damit ist das System

uberhaupt erst feldtauglich. Die fraktionierten Partikel werden auf einem Filter in einem

Faradayschen Kafig abgeschieden. Dieser nnuss von seinem Prinzip her feldfrei sein.

Damit dies erfiillt ist, flieBt ein dem Partikelstrom entsprechender Ausgleiciis- bzw.

Verschiebestrom. Dieser sehr kleine elektrische Strom wird gemessen und daraus die

Anzahl der abgeschiedenen Partikel pro Zeiteinheit berechnet. Diese Anzahl der

Partikel muss mit der bekannten Ladungsverteilung gewichtet werden. Der kleinste,

messbare, elektrische Strom entspricht einer Konzentration von 25 einfach geladenen

Partikeln pro cm^ bei einem Volumenstrom von 1,5 1/min.

Das Messsystem benotigt einige periphere Komponenten, die die Gasfliisse realisieren

und regeln, Temperatur, Druck und Feuchtigkeit erfassen und wandeln und die diversen

elektrischen Spannungen bereitstellen. Die Steuerung und Erfassung aller Daten des

Gerates werden zentral durch einen PC durchgefuhrt.

Mit diesem Gerat ist es moglich, Brandaerosole in dem interessierenden GroBenbereich

von 3 nm bis 1 fxm schnell zu messen, insbesondere bei Verwendung einer noch zu

modifizierenden Software, wie sie im SMPS eingesetzt wird.

4 Kalibrierung von Brandmeldern

Aus den oben stehenden Ausfuhrungen wird klar, dass fiir die Entscheidung, ob ein

Brand vorliegt oder nicht, die entstehenden Partikel im Nanometerbereich eine

besondere Bedeutung haben. Bedingt durch die Grenzen der Technik besitzt jeder

Brandmelder eine untere Nachweisgrenze bezuglich der PartikelgroBe. Diese Grenze

muss bekannt sein, damit beurteilt werden kann, inwieweit der Sensor uberhaupt einen

Brand in der Entstehung detektieren kann oder nicht.

Die Kennlinie optischer Brandsensoren kann mit Hilfe der Mie Theorie oder

weiterfiihrenden numerischen Ansatzen berechnet werden. Fiir einen lonisationsmelder

kann man in erster Naherung einen linearen Zusammenhang zwischen dem

Ausgangssignal und der PartikelgroBe sowie der Partikelanzahlkonzentration ableiten.

In der Abbildung 8 sind die Kennlinien fiir beide Melderarten qualitativ dargestellt.

52
0 1 I

I M l l

|

\ 1—I

I

I I ll| 1 1—I

I

I II I

1

1 '

I

I IN

I

1

1 I

I

I M I

0.002 0.01 0.1 10 //m 100

p Bild_8.dsf

Abbildung 8 Kennlinien optischer Brandsensoren normiert auf die

Partikelanzahlkonzentration

Fiir jeden individuellen Sensor konnen die Kennlinien auf der Abszisse verschoben

sein. Bei den lonisationsmeldem kann je nach Typ des Melders die Steigung der

Geraden variieren. Bei den optischen Sensoren ist es wesentlich komplexer, da der

Verlauf der Kennlinie nicht nur von der Geometrie des Sensors und seinen

Komponenten abhangt, sondem auch von der Partikelform und dem Partikelmaterial.

Der Bereich der Kennlinien im Hinblick auf den erfassten PartikelgroBenbereich wird

durch die obere und untere Nachweisgrenze des Sensors im Hinblick auf die

PartikelgroBe festgelegt. Die obere Nachweisgrenze ist hier nicht von Bedeutung, weil

ein beginnender Brand kaum die GroBenverteilung des Aerosols im Bereich groBer

Partikel verandert. Das Gegenteil trifft fiir die untere Nachweisgrenze zu. Zur

Festlegung der unteren Nachweisgrenze verwendet man den sog. Zahlwirkungsgrad, der

angibt, welcher Anteil der Partikel gezahlt wird. Der Verlauf des Zahlwirkungsgrades

wird maBgeblich durch das Rauschen in den Komponenten des Sensors gepragt.

Weiterhin wird der Zahlwirkungsgrad des Brandmelders im gesamten Messbereich

durch die Verluste - bei der Probenahme beeinflusst. Die Verluste in

Rohrleitungssystemen sind in [5] betrachtet worden. Man kann den Zahlwirkungsgrad

53
mit dem Wirkungsgrad des Probenahmesystems gewichten und erhalt dann den

Wirkungsgrad des Gesamtsystems. In der Abbildung 9 sind die verschiedenen

Wirkungsgrade qualitativ dargestellt.

w

c

.2 0.5 E

U 0.4

0.2

0.1

•Sampling Efficiency

Optical Sensor

loniazition Sensor

0.01 0.1 Dp

10

Abbildung 9 Zahlwirkungsgrad fur optische Brandsensoren und lonisationsmelder

Zur Bestimmung der unteren Nachweisgrenze muss ein monodisperses Aerosol

geniigend hoher Konzentration bereitgestellt werden. Der typische Aufbau zur Erzeugung der Aerosole ist in der Abbildung 10 gezeigt.

FTS.dsl Exhaust

monodispers Aerosol

Abbildung 10 Aufbau zur Erzeugung von monodispersen Testaerosolen

54
Das Parti kel materia] wird in einem Rohrofen verdampft und durch die Gasstromung aus dem Ofen transportiert. Hinter dem Ofen kondensiert das Material aus der Dampfphase.

Die GroBe der Partikel lasst sich durch die gezielte Abklihlung z. B. durch Beimischung

von kaltem Gas beeinflussen. Es entsteht ein stabiles aber polydisperses

Feststoffaerosol. Analog zur Klassierung der Partikel bei der Messung der

Brandaerosole wird auch hier durch definierte Aufladung und Fraktionierung im

elektrischen Feld (DMA) ein monodisperses Aerosol erzeugt. Durch die Variation der

Spannung am DMA lasst sich die PartikelgroBe des monodispersen Aerosols einstellen.

Mit diesen Testaerosolen werden dann die Sensoren beaufschlagt und ihre Signale

gemessen. Bei Meldem. die Einzelpartikel zahlen, wird die Partikelanzahlkonzentration

mit einen unabhangigen Prartikelz abler ermittelt, dessen Zahlwirkungsgrad in dem

betrachteten PartikelgroBenbereich nachweislich 100 % betragt. Das Verhaltnis der

gemessenen Konzentrationen gibt den Wirkungsgrad an. In der Abbildung 11 werden

die Kurven des Zahlwirkungsgrades fur einen optischen Sensor gezeigt.

50 100 150 200 250 300 nm 350

Abbildung 11 Experimentell ermittelte Kurven des Zahlwirkungsgrades fur optische

Sensoren

Die Ergebnisse smd mit einem optischen Sensor, der Einzelpartikel bis zu

Konzentrationen \on 10 cm"" zahlt ermittelt worden. Die unterschiedlichen Verlaufe

des Zahlwirkungsgrades beruhen auf der Anwendung verschiedener Algorithmen zur

55
Signalauswertung. Es ist deutlich zu erkennen, dass mit Hilfe der Signalauswertung die

untere Nachweisgrenze zu kleineren Partikeln verschoben werden kann [6].

Fiir Brandmelder, die das Partikelkollektiv messen, muss ein validiertes Modell

herangezogen werden, um das zu erwartende Signal fiir die gegebende

Partikelanzahlkonzentration zu berechnen. Der Wirkungsgrad ergibt sich dann aus dem

Verhaltnis des gemessenen Signals zum berechneten Signal.

5. Zusammenfassung

Mit Hilfe der hier vorgestellten Technik lassen sich Brandaerosole im Bereich von 3 nm

bis 1 fim schnell und mit hoher Auflosung messen. Das ermoglicht die Bestimmung

charakteristischer Merkmale von Brandaerosolen, auch in ihrem zeitlichen Verlauf. Mit

diesen Kenntnissen konnen Kriterien zur Alarmauslosung weiter verbessert werden.

Sollen Brandmelder diese Merkmale sicher messen konnen, muss im Rahmen einer

Kalibrierung sichergestellt werden, dass der Melder diese Brandaerosole auch vor dem

Hintergrund der natiirlichen Aerosolbelastung messen kann. Dazu muss die untere

Nachweisgrenze der Brandmelder bestimmt werden. Fiir diese Aufgabe ist das

dargestellte System zur Erzeugung von monodispersen Testaerosolen in dem

GroBenbereich von 3 nm bis 100 nm geeignet.

6 References

[1] Fissan, H.; Helsper, C; Franzen, H. Particle Size Distribution of Combustion

Aerosols Atmospheric Pollution. M. M. Benarie (Ed.), Studies in Environmental

Science 1987, 1: 263-266

[2] Muschenbom, P.; Otten, F.;Trampe, A.;Luo, J.;Neumann, S.;Fiassan, H

Development of a new wide-range Differential Mobility Particle Sizer. 3.ETHWorkshop „Nanoparticle-Measurement"; ETH, Zurich, Swiss, Aug 1999

[3] Hinds W. C. Aerosol Technology. Wiley-Interscience Publication, New York

1982, 288

56
[4] Buscher, P.; Schmidt-Ott, A.; Wiedensohler, A. Performance of a Unipolar

"Square Wave" Diffusion Charger with Variable nt-Product. J. Aerosol Sci.1994, 25:

651-663

[5] Fissan, H.; Otto, E.; Dixkens, J. Particle Transport in Sampling Lines used in Fire

Detection Systems. Fire Safty Journal 1997, 29; 205-215

[6] Borggrafe, P. Erweiterung des Messbereiches von optischen Partikelzahlem durch

gezielte Reduzierung der Storquellen und mit digitalen Signalverarbeitungsmethoden.

Dissertation, Gerhard-Mercator-Universitat Duisburg, 1999

57
Darryl W. Weinert*, Thomas Cleary, and George W. Mulholland

National Institute of Standards and Technology, Gaithersburg, MD, U.S.A.

*Guest Researcher on leave from the Center for Environmental Safety and Risk

Engineering, Victoria University, Melbourne, PO Box 14428, VIC 8001, AUSTRALIA

Size Distribution and Light Scattering Properties of Test Smokes

L Introduction

It is well documented that the response of a detector to smoke depends on the detector

design and the particular smoke that it is exposed to. Detailed measurements of smoke

particle size distributions and optical properties from fires could yield a better

understanding of existing detector designs and facilitate design improvements; NIST is making such measurements now on smokes produced in the fire emulator/detector

evaluator (FE/DE) [1,2]. One flaming (propylene) and two non-flaming tests smokes

(cotton smolder and wood pyrolysis) are studied in this paper. The smokes produced by

the non-flaming fuels are similar to two of the smokes produced by the EN54 test fires.

The work presented here complement previous measurements of test smoke size

distributions and various moments of the distributions [3,4], as well as light scattering

studies [5,6]. Our results provide the size distribution using a cascade impactor and an

optical particle counter. Our light scattering measurements provide the first differential

cross sections for these three smokes on a per mass basis. In addition light scattering

data for ethylene and acetylene smoke are expressed in terms of the scattering parameter

q to assess the generality of the fractal description of such smokes. An approach based

on the scattering parameter for discrimination between smoke particles from flaming

and non-flaming fires is discussed.

2. Smoke Generation and Sampling

The smokes are generated in the FE/DE and extracted for size distribution and light

scattering analysis. A detailed description of the FE/DE and its operation is presented in

another paper at this conference [2]. The flaming test smoke is soot from a propylene

58
diffusion flame burner attached to the FE/DE duct. Due to the high soot yield from

propylene diffusion flames, the burner can output a large amount of soot at moderate

fuel flow. Varying the burner fuel flow and the amount of smoke directed to the duct

controls smoke concentration in the FE/DE. The smoke concentration is constant over

the aerosol extraction time. A staged-wick-ignition-smolder device inside the FE/DE

generates one of the non-flaming fuel smokes, cotton wicks used in EN54 (part 9, test

fire 3). Wicks are smoldered and smoke was collected when the light extinction

measurement indicated a steady smoke concentration in the duct. The wood pyrolysis

smoke is generated by heating wood blocks identical to those specified in EN54 (part 9,

test fire 2) on an electrically heated hot plate. The rate of smoke evolution from the

wood is characterized by a gradually increasing concentration as the wood block heats

up followed by a period of quasi-steady-state smoke production.

For size distribution measurements of the smokes two instruments were used; an optical

particle counter (OPC) and a cascade impactor. The smoke produced in the FE/DE is

sampled directly into a MOUDI cascade impactor [7] at 30 L min"' for determining the

mass distribution, while for the other measurements the smokes were collected in a

particle-free drum (0.24 m^) by drawing smoke from the test section of the FE/DE into

the drum. For the OPC about 20 L of smoke was drawn in to the drum at a nominal rate

of 10 L min"' and in the case of the scattering cross section, about 120 L of smoke was

sampled into the drum at a rate of 20 L min '. The smoke filled drum was then

transported to the laboratory where the light scattering instruments used in the smoke

characterization were situated. These instruments were an OPC used to measure the

number distribution and the large agglomerate optical facility (LAOF) developed at NIST [8] for measuring differential scattering cross sections of smoke particles. During

the 10 to 30 minutes for obtaining the light scattering data there was little change in the

differential scattering cross section.

3. Size Distribution Measurements

The optical particle counter (OPC) utilizes an active cavity laser scattering cell and

focused jet of particles to determine the particle's number size distribution from the

59
scattered light signal of individual particles. The instrument is calibrated with

monodisperse polystyrene spheres to relate the detector output to the particle size. An

additional dilution by about a factor of 50 was required to dilute the smoke

concentration below the lO'* particles cm'^ operating threshold; above this level there

would be more than one particle in the scattering volume causing coincidence errors. The cascade impactor makes use of an airborne particle's inertia to impact large

particles in preference to small particles as they pass through the impactor. Each stage

of the impactor collects the particles of a given range of aerodynamic diameter, which is

the diameter of a sphere of unit density having the same settling velocity as the particle.

The impactor stages are covered by aluminum foil, which is weighed before being

placed on the stage. After the smoke is sampled through the impactor the foils are removed and their weight measured again. The weight of the collected particles is

divided by AlogD of the two adjacent 50 % cut points, then plotted against the stages

mid-range diameter to give the mass distribution as a function of aerodynamic diameter.

Figure 1 shows the mass distribution results from the impactor and the calculation of

the volume distribution from the number distribution data of the OPC. The large spikes

in the OPC volume distribution are a result of changes in the width of the size channel

from as small as 0.005 |J.m for 0.07 |im particles to 0. 1 jim width for 0.7 |J.m particles.

Almost half, 48 %, of the total mass of the cotton wick smoke was found to be below the

last stage of the impactor, i.e. less than 0.056 )Lim in aerodynamic diameter, implying

that the cotton lamp wick smoke is bimodal. The volume distribution OPC data and

impactor mass distribution, both shown in Figure 1, agree well for cotton wick smoke

for particle sizes larger than 0.07 jim. In the case of the wood smoke, the comparison is

incomplete because of the lack of the OPC data above 1 |Lim. The fact that only 2.2 % of

the total wood smoke mass was found below 0.056 |Lim indicates that the mass size

distribution is unimodal.

The wood smoke examined in these experiments were found to have a mass mean

aerodynamic diameter (MMAD) of about 1.6 |a,m and a geometric standard deviation

(GSD) of about 2. If the mode of the distribution of the cotton wick smoke is treated as

60
a single log-normal distribution then it has a MMAD of about 0.3 \im and a GSD of

about 1.9. The GSD values are typical of polydisperse aerosol systems. The cotton wick

smoke has a substantially smaller mean aerodynamic diameter compared to the wood

smoke when considering the single mode of cotton wick smoke measured. The number

Figure 1: Mass distribution from impactor data (*- outhned bar graph, separate scales

on left) and Volume distribution calculated from OPC data (** - line connects data

points, scale on right) for non-flaming wood and cotton lamp wick smokes.

distribution from the OPC (not shown) gives a mean number diameter for wood and

cotton wick smokes as 0.14 \im. Helsper et al. [3] used a bimodal log-normal

distribution to describe the size distribution of smoke from EN 54 test fuels. Our results

are close to the median size of the second (i.e. highest) mode of the distribution of [3],

0.14 |j.m and 0.13 |im for smoldering wood and smoldered cotton wick respectively.

Our cascade impactor data is consistent with [3] for the cotton wick as it indicates a

second peak below 0.056 |J.m, but is inconclusive when considering a second peak

below 0.056 |J.m for the wood smoke.

61
The cotton wick smoke particles less than 0.056 |J,m will contribute very little to light

extinction or light scattering signals; however, these small particles are expected to

contribute significantly to an ionization smoke detector signal, which is proportional to

the product of the number concentration and mean diameter. This was verified in the

FE/DE by measuring the response of a measuring ionisation chamber (MIC) to both

smokes along with light extinction measurements [2]. It was found that the ionization

response was about 3.4 times greater for cotton wick smoke compared to smoke from

pyrolysed wood for the average extinction coefficient over the time of smoke collection.

In Figure 2 transmission electron micrographs of smoke particles from flaming

acetylene and ethylene can be seen [9]. These are chain like structures, called

agglomerates as they are made up of an agglomeration of small primary particles,

typically 20 nm to 80 nm in diameter depending on fuel and combustion conditions. It

is also apparent from the TEM images, which have the same magnification, that the

primary spheres for the acetylene smoke are larger (about 30 %) than the ethylene

primary spheres. The size distribution of the acetylene agglomerates ranged from about

0.04 jim to 20 |im [9].

Figure 2: Transmission Electron Microscope image of acetylene and ethylene [10],

both images are at the same magnification, 50,000x. The scale bar represents 0. 1 |Lim.

62
4. Light Scattering Measurements

The light scattering properties of the smoke particles were examined using the LAOF's

differential scattering system and methodology described elsewhere [8]. The differential

(angular distribution) scattering study presented here has examined the

c

.2

4—

»

o

u

C/0

o

00

c

o

o

C/0

•S ®

b "I

o

(4

10

0 20 40 60 80 100 120 140 160

1 :

0.1

10

1

0.1

0.01 i

0.001

-i—I—1—1—I—I—I—I—I—I—I—1—I—I—1—I—I—I—I—I—I—1—I—I—

r

•

• Wood

^ Cotton Lamp Wick

Propylene

• A

A

• A

• A

• ^ A

I I I

I I I I

I

I—I I II I I I

I I I I I

I I I I I

I I—I

I I I I V

V o^ A 2 g

o

V "6

o Wood

^ Cotton Lamp Wick

V Propylene

2?^2oQOoOO° A A A A A A

V V

0 20 40 60 80 100 120 140 160

Scattering Angle (0°)

Figure 3: Differential mass scattering cross section for wood, cotton lamp wick and

propylene smoke particles generated in the Fire Emulator/ Detector Evaluator at NIST.

63
scattering of linearly polarized light for the scattering angles between 5"^ and 135°, and

determined the smoke particle's differential mass scattering cross sections, CFyy($) and

^hh(^) ' for different polarizations. The first subscript denotes the incident polarization

and the second denotes scattered polarization measured: V for vertical and H for

horizontal to the scattering plane. The differential mass scattering cross section is

determined in units of area (m ) per particle mass (g) at a given scattering angle, 0.

Figure 3 shows the differential mass scattering cross section for the three smokes

produced in the FE/DE at NIST. The fractional combined uncertainty (based on one

standard deviation) of the differential mass scattering cross sections varies but is

typically in the range of 10 % to 20 % due mainly to the calibration with monosize

polystyrene spheres [8]. The effect of larger mean size of the wood smoke particles on

the scattering cross section, (Tvv(^) » be seen in the steeper slope in the forward

direction as opposed to the less steep slope for the cotton wick results. The comparison

of the two non-flaming fuel smokes for HH polarization shows that their values are

similar at most angles except the at forward angles and near 135°. The smooth curves

for the non-flaming fuel smoke particles seen in Figure 3 are a result of the polydisperse

nature of these smoke aerosols. Scattering from polydisperes ensembles of particles

averages out the detail seen in single particle scattering data.

5. Treatment of Polarization

The information in Figure 3 can be rearranged to give the degree of linear

polarization, P(6) , (with angular notation suppressed)

The quantity P[6) has previously been measured for the EN54 test smokes over an

angular range of 5° to 165° [6]. The degree of polarization for the FE/DE generated

smokes is shown in Figure 4, along with the theoretical curve expected from Rayleigh

scattering. The deviation from P(0) =0 in Figure 4 is due to polarization dependent

errors in the optical system, particularly for the propylene. The other results are mainly

influenced by the uncertainty in the calibration [8]. The results in Figure 4 compare well

64
with [6] as they found the highest degree of polarization corresponded to the soot

particles, a medium value existed for the cotton wick smoke, and that the lowest values

were found for the pyrolyzed wood smoke.

Another widely used and related measure is the polarization ratio, defined as

0 20 40 60 80 100 120 140 160

Scattering Angle (0°)

Figure 4: Degree of linear polarization, P(0) , for the FE/DE produced smoke particles.

Table 1 includes the polarization ratio at 90° for the FE/DE smokes and two other soots,

acetylene and ethylene, generated using a laminar co-flow diffusion burner [8]. The

degree of polarization or the polarization ratio in Table 1 indicates that two detectors

positioned perpendicular to each other, with incident linear polarized light, could

distinguish between smoke from flaming or non-flaming fires. The greatest difference

65
in P(d) or p(0) for the flame generated smoke versus non-flaming occurs at ^ = 90°.

The ratio of polarization is also sensitive to the fuel for flame generated smoke particles

(see Table 1), which is related to the soot agglomerates different primary particle

diameters. Previous measurement [9] indicate that the fuel type affects the primary

sphere size, and this may account for the fuel dependence of p(0)

.

6 Fractal Analysis of Scattered Light

It has been stated that the high values of the degree of polarization (i.e. near 9 = 90°,

P[6) I ) for flame generated smokes is due to their particle size being small

compared to the wavelength of the incident beam [6]. This is misleading in that it

implies the soot particles scatter light according to Rayleigh theory. The scattering from

agglomerates with sizes up to 20 jxm [9] is more complex than Rayleigh theory.

Fuel Degree of Polarization Polarization Ratio

P(90/120) P(90/120)* p(90°)

Wood (flaming) -- 0.94 /0.5

Wood (pyrolysed) -0.07 / -0. 15 -0. 12 / -0.25 1 . 16

Cotton wick 0.27/0.05 0.3/0.13 0.58

Propylene 0.90/0.43 ~ 0.05

Acetylene 0.96/0.53 ~ 0.023

Ethylene 0.97/0.51 ~ 0.013

Table 1: Ratio of linear polarization at 9= 90°, for various smoke particles: wood,

cotton wick and propylene generated in the FE/DE ; while acetylene and ethylene were

generated in a co-flow laminar diffusion burner [11]. * Data has been taken from [6].

The total scattering from soot agglomerates can be represented as (angular notation

suppressed)

'^^=(S.,-S.)^ but (3)

where 5, is the Rayleigh-like scattering component and X denotes a given polarization.

The term S arises from the interference in the far field of the Rayleigh scattering by

66
the indi\ idual primary particles making up the agglomerate. The term, 5^^, can vary by

three orders of magnitude (Figure 3) while for Rayleigh scattering. (-Sj^^. . is

independent of angle. Since S^^ is independent of the polarization direction it is

effectively reduced in the polarization ratio. p{0) . which then reflects the Rayleigh-like

scattering from the primary spheres.

The general formalism of agglomerate light scattering [12] can be expressed in terms of

the scattering parameter, q , which follows a power law relation that reduces to

(7^-^-[9] oc for q>R~^\ \shere R is the agglomerate characteristic radius;

q = AkX'^ sm[6l2] is the elastic scattering wa\evector magnitude or scattering

parameter: and D is the fractal dimension characterizing the agglomerate. Various

studies for in-flame and post-flame soot have reported fractal dimensions to be about 1.7

to 1.85 using laser scattering techniques [13]. This general behavior is related to the fact

that all soot shares the same fundamental mechanisms of agglomeration growth [14].

Fuel Fractal Dimension. D

Acetylene 1.6 1.85*

Ethylene 1.7 1.84*

polypropylene 1.8 1.83*

Table 2: Fractal dimensions determined from Figure 5. for q>5.\ um"' and (*) results

reported by [15].

Figure 5 shows the log-log plot for mass scattering cross section (vertical-vertical

polarization) as a function of the scattering parameter, q

. for the three soots, ethylene.

acetylene and propylene, as well as the smoke from wood and cotton wick. The slopes

for the soots at ^ > 5.1 um'' are tabulated in Table 2 and compared \^ ith another study

[15]. The low value for acetylene might be due to the presence of super-agglomerates

(»50|j.m).

67
Figure 5: Mass scattering cross section (VV) as plotted against the scattering

wavevector, q.

We also observe in Figure 5 that there is a qualitative difference between the q

dependence for soot compared to non-flaming smoke particles. The higher forward

scattering for the wood smoke is apparent and the slope for scattering angles

corresponding to^> 12 |Lim'' > 75°) yield slopes of about 3 and 4.6 for wood and

cotton wick smoke respectively. The exact relationship in the context of Mie theory

between these qualitative differences and the morphology, size and refractive index of a

scattering particle is only recently being considered [11]. A detector with a diode array

in the back scattering angles could be used to compare the slope of the signal when

treated as a function of q. Large integer values of the slope, >3, would indicate

compact scattering particles while small values, < 2 , would indicate open agglomerates.

There is a need to obtain data for a wide range of non-flaming smokes as well as

nuisance aerosol results to assess the utility of this approach.

68
6 Conclusion

This study presents results for the first time of the mass scattering cross section (m^ g'')

of smokes similar to those used in smoke detector standards EN54 and UL 217.

Smokes were generated in the fire emulator/detector evaluator (FE/DE) at NIST and

characterized in terms of their size and optical properties. The size characterization

shows that the smoke from pyrolysed wood blocks is a unimodal distribution with a MMAD of 1.6 |Lim, while the smoldered cotton lamp wick fuel has a bimodal

distribution with a MMAD of 0.3 |im for its measurable mode. It was found that 48 %

of the mass of the cotton wick smoke was below 0.056 |j.m. The difference in

mass/aerodynamic diameter is also observed in the differential mass scattering cross

sections for near forward scattering angles, where the wood smoke scatters light more

strongly than cotton wick smoke particles due to its larger mean size.

The ratio of polarization and the degree of linear polarization have been demonstrated as

a means of distinguishing between soot from flaming fires and smoke particles from

non-flaming fires. The ratio of polarization is shown to be more sensitive than the

degree of polarization to soots from different flaming fuels, probably due to the different

primary particle size. All three flame generated smokes are shown to have similar " q

"

plots, which are easily distinguished from the two " q

" plots for the non-flaming fuels

smokes.

Reference List

[1] Grosshandler WL: Towards the development of a universal fire emulatordetector evaluator. Fire Safety Journal 1997; 29:113-127

[2] Qeary, T., Donnelly, M., and Grosshandler, W. The Fire Emulator/Detector

Evaluator: Design, Operation and Performance. 12th International Conference

on Automatic Fire Detection. AUBE, 2001

[3] Helsper C, Fissan HJ, Muggli J, Scheidweiler A: Particle number distributions of

aerosols from test fires. Journal ofAerosol Science 1980; 1 1:439-446

[4] Tamm E, Mirme A, Sievert U, Franken D: Aerosol particle concentration and

69
size distribution measurements of test-fires as a background for fire detector

modeling. Internationale Konferenz uber Automatische Brandentedekung,

AUBE '99 1999;150-159

[5] Meacham BJ, Motevalli V: Characterization of smoke from smoldering

combustion for the evaluation of light scattering type smoke detector response.

Journal of Fire Protection Engineering 1992; 4:17-28

[6] Loepfe M, Ryser P, Tomkin C, Wieser D: Optical properties of fires and non-fire

aerosols. Fire Safety Journal 1997; 29:185-194

[7] Marple VA, Rubow KL, Behm SM: A Microorifice Uniform Deposit Impactor

(MOUDI): Description, calibration and use. Aerosol Science and Technology

1991; 14:434-446

[8] Weinert, D. and Mulholland, G. W. An apparatus for light scattering studies of

smoke particles. 12th International Conference of Automatic Fire Detection

AUBE, 2001.

[9] Mulholland, G. W. and Choi, M. Y. Measurement of the mass specific extinction

coefficient for acetylene and ethylene smoke using the large agglomerate optics

facility. The Combustion Institute. Twenty-Seventh Symposium (international)

on Combustion. 1515-1522. 98. Pittsburgh, The Combustion Institute.

[10] Zhu J and Choi MY, University of Illinois, Personal Correspondence, 2000

[11] Sorensen CM, Fischbach DJ: Patterns in Mie Scattering. Optics

Communications 2000; 173:145-153

[12] Sorensen CM: Light Scattering from Fractal Aggregates. To be submittedfor

publication

[13] Sorensen CM, Feke GD: The morphology of macroscopic soot. Aerosol Science

and Technology 1996; 25:328-337

[14] Mountain RD, Mulholland GW: Light scattering from simulated smoke

agglomerated. Langmuir 1988; 4:1321-1326

[15] Koylu UO, Faeth GM, Farias TL, Carvalho MG: Fractal and projected structure

properties of soot agglomerates. Combustion and Flame 1995; 100:621-633

70
D. Kozeki

National Research Institute of Fire and Disaster, Tokyo, Japan

Smoldering Fire Detection by Image-processing

Abstract

Since fire detectors are not installed in all buildings, too many victims and

property damages are caused in building fires every year in the world. One reason of

non-installation is the fire detectors currently on sale are not always reliable mainly due

to the so-called false alarms. Therefore the author has been examining many

possibilities of new reliable fire detection system, including a possibility of multielement fire sensor. At the AUBE'99 in Duisburg, the author reported another newly

developing fire detection system using a thermal video camera system, related to the

heat image due to fires, where the author proposed a method to eliminate non fire

sources. In this report, the author has further advanced the image processing method to

detect smoldering fires, together with the verification of software by experiments. And

it has been indicated that this system is very effective in detecting smoldering fires very

early and reliably.

1. Introduction

Too many victims and property damage are caused in building fires every year

in the world. It is well known that fire detectors can reduce building fires, but

unfortunately fire detectors are not always widely installed in buildings. One reason is

the problem of price of fire detectors and installation. Another reason is the fire

detectors currently on sale are not always reliable mainly due to the so-called false

alarms, since most current fire detectors are based on a too primitive sensing element

and logic. The author has longly studied the method of how to reduce the false alarms of

fire detectors. One possibility developed by the author is to use the newly developed

three sensing element fire detector (including new CO sensor) and artificial intelligence

logic to judge between a real fire and a false fire[l]. It has been found that this new fire

detector is very effective to reduce false alarms, therefore increasing the reliability of

fire detection. The new three-element fire detector has, however, still a problem of price

71
to manufacture particularly on CO sensing element at present. Therefore, the author has

been examining other possibilities of new reliable fire detection systems also. At the AUBE'99 in Duisburg, the author reported another newly developed fire

detection system using a thermal video camera system, which is related to the analysis

of heat images[2]. This newly developed fire detection system is particularly effective

in detecting smoldering fires. As well known, it is comparatively easy to detect flaming

fires by infrared or ultraviolet sensors or CCD cameras[3], particularly in the air

conditioned rooms. On the other hand it is not always easy to detect smoldering fires,

since there is no ways to detect smoldering fires by remote sensors at present. Although

there have already been some studies to detect forest fires using infrared cameras[4],

those methods are not always in common. Thus there is a possibility that the detection

of a smoldering fire is delayed. Keeping this in mind, studies to develop a method to

detect smoldering fires has been made, by introducing a thermal video camera. Thermal

images are resistive against the influence by the airflow from the air conditioners or

high-temperature layer in the room. To improve the disadvantage of current smoke

detectors in smoldering fire, the author has been developing a new fire detection system

using thermal images. In this report, the author deals with image processing software for

smoldering fire detection, together with the verification by experiments.

2. Algorithm of Image Processing to Detect Smoldering Fires

2.1 Preprocessing of thermal images to eliminate noises

In order to detect fires correctly, it is important to eliminate false signals by

preprocessmg of thermal images. Generally video signals ofi:en import unwanted noises

due to miscellaneous sources, including electric ones and thermal ones. There are many

sources, which emit thermal radiation, for example by thermal reflection or something warm. Therefore, those noises should be removed. The procedure employed here to

eliminate noises is as shown in Fig. 1 . Details are as follows.

(1) An infrared thermo-graphic camera (picture (a) in Fig.l), with semiconductor

imaging cell consisting of 256 x 256 resolution, imports 30 images per second.

(2) With 30 pictures per second, six imported thermal images per minute are stored into

a computer memory as 8-bit data (picture (b) (a part of picture (c)) and picture (c) in

Fig. 1). Figs. 1(b) ,(c),(d),(f),(g) are related to electric radiant heater as an example.

(3) By comparing a preset threshold value, 8-bit data are transformed into binary data

72
(picture (d) in Fig.l). A threshold is placed between high and low temperatures.

Many threshold \-alues were tested. It was found that the thermal \'alue of 200'C was best. And then all thermal data are transformed into binarv'. If a pixel \'alue is

larger than a preset ^alue. the pixel changed mto "I" and if smaller, changed into

"0". However original 8-bit data are also kept for another purpose, to be explained

later.

(4) High temperature pixels surrounded by low temperamre pixels are defined as "high

temperature cluster". Fig. 1(e) shows how to define the high temperature cluster. Number of pixels and centroid of each high temperamre cluster within the thermal

image are measured. b\" judging the connection way bet^veen the neighboring pixels.

High temperamre clusters are extracted from the binan.' image using the well known

"eight-neighborhood connected component processing method""[5]. .-\fter that, the

identification number is labeled to each high temperamre cluster. Then calculated is

the number of pixels and centroid as shown m Fig. 1(f).

(5) Next processed is the remo\ al of thermal noises by the way that smaller clusters are

ignored. Thermal noises are eliminated m the pixel domain, by counting the number

of pixels consisting of a cluster and comparing the preset value. Here a cluster

consisting of 10 pixels is eliminated (picmres (g) in Fig. 1 ).

2.2 Image processing to eliminate non-fire cluster

(1 ) Follow up of increase and decrease of high temperature clusters

Here, non-fire clusters are defined as the tools such as cooking sto\'e. room

heater and smoothing iron, which remain at almost the same size with time. Other

sources can not be considered, since the temperamre of 200 'C is too high in normal

conditions. Therefore each cluster size is follo\\"ed up on the basis of discriminating

separation or fusion of high temperamre cluster from time series images, and the

time varving size and location are calculated. Detailed following up procedure is as

follows, needed is the information, such as identification number of each cluster,

number of pixels, centroid and intensity (namely 8-bit image data as mentioned

above) of all pixel obtained from extraction process mentioned in section 2. l-(4) for

each image. This information is named "Cluster ID Information". In the smoldering

fires, time dependent shift of the centroid is ven,- small. It is easy to identify- the

moved clusters using the Cluster ID Information between Uvo deferent images.

73
(2) Discriminate separation or fusion of high temperature cluster

In this method, the discrimination between the real smoldering fire and the nonfire is based on the time-dependent variation of the centroid and size of the high

temperature cluster. Differently from non-fires, the centroid and size of the high

temperature cluster are highly time dependent. If two separate clusters reach close to

each other and merge in one cluster, the centroid location and cluster size will

suddenly be changed. Similarly to this, if one cluster separates into multiple clusters,

the same situation will happen. In Fig. 2, one example of fusion of 6 clusters in

Fig. 2(b) into 3 clusters in Fig.2(d) is shown, namely clusters C and D in Fig.2(b)

fused into the cluster H in Fig. 2(d). Details are shown in Table 1

.

Here many trials were made to identify which clusters fuse into one cluster and

employed method is a simulation to search most appropriate combinations on the

centroid shift and pixel numbers. In the case of clusters C and D in Fig.2-(b) ftised

into the cluster H in Fig.2-(d), the centroid location of cluster H is 355 in X

direction and 228 in Y direction while the average centroid location of cluster C and

D is 354 in X direction and 228 in Y direction, as shown in Table 1. Both are very

close and it was judged that clusters C and D fiised into cluster H. Based on the

procedure mentioned above, a software to detect smoldering fires and to eliminate

non-fires is developed using C language.

3. Verification of the Software by Detection Tests of smoldering fire

Using the newly developed software, based on the procedure mentioned in

section 2, experimental verification tests were repeatedly made. Here particularly

focused in the tests are the elimination of the heat from cooking instruments or room

heater, and also the accurate detection of the smoldering fires. It was found that heat

from cooking instruments or room heater could easily be discriminated by the

calculation of centroid coordinates and the size of high temperature cluster. The

threshold value was varied from 100 °C to 300 °C. If the threshold value is too low, too

many clusters appeared. And if it is too high, the cluster size differs only a little with

time. Then the value employed here was 200 °C as most appropriate value.

Fig. 3 shows the changing high temperature cluster of electric radiant heater as a

non-fire test material. In this case, the centroid and the size were almost the same for more than 5 minutes after the initial one minute rapid increase as shown in Figs.3 and 4,

74
where the pixel number was fixed at about 7500 after one minute and at the same time

the centroid did not shift. Next examined was a smoldering fire using silk cotton

cushion. Fig. 5 shows the changing high temperature cluster of smoldering silk cotton

cushion. Fig. 6 shows the time dependent pixel number of smoldering cushion. As

shown in Figs. 5 and 6 the heated area, namely number of pixels, is rapidly increasing,

differently from the case of electric radiant heater. Additional verification tests using

other smoldering ropes were made. It has been found that the developed software is

very useftil to detect smoldering fires and to eliminate non-fires. The detection of

flaming fires is comparatively easy, as mentioned earlier. The threshold value of 200 °C

employed here is not always fixed, but can be flexible depending on the circumstances

of rooms, using artificial intelligent processing.

4. Conclusions

In this study an image processing algorism has been proposed to detect

smoldering fires and to eliminate non-fires. It is verified that the image processing

software works correctly for smoldering stage of silk cotton cushion and experiments

using an electric radiant heater.

References

[l]Satoh, K., Takemoto, A., Kozeki, D. Intelligent Fire Detection System using Multisensor Fire Detector (including CO sensor) and Fuzzy Expert System. Technical

Report of Fire Research Insfitute of Japan No.26 (1991).

[2] Kozeki, D. : Observation of Early Stage Fires by a Multi Spectrum Camera.

Proceedings of AUBE'99 1999: 180-186.

[3] Chen, X., Wu, J., Yuan, X., Zhou, H. Principles for a Video Fire Detection System.

University of Science and Technology of China, People's Republic of China, Fire

Safety Journal, July 1999: Vol. 33, No. 1, 57-69, .

[4] MURILLO, J J., OLLERO, A., et al. A hybrid infrared/visual system for improving

reliability of fire detection systems. Univ. Sevilla, Sevilla, ESP,, IFAC Symposium

on Fault Detection, Supervision and Safety for echnical Processes, Fault Detect

Superv Saf. Tech. Process 1997 Vol 2

[5] Hasegawa, J., Koshimizu, H., Yamanaka, A., Yokoi, S. Image Processing on

Personal Computer. Gijyutsu-Hyoron Co., Ltd.

75
(1) Import thermal images

(2) Store thermal images

into a computer

(3) Transform 8-bit data

into binary data

(4) Extract the high

temperature cluster

(a) Thermal camera

(b) Thermal imag( (c) visible view

(d) Binary Imagf

high temperature,

cluster

n low temperature pixel

hig h temperature pixel

high temperature

cluster

(e) Definition of High

Temperature Cluster

(f) Calculation of centroid of the cluster. (cross point of X mark is centroid)

(5) Eliminate noises in the

binary images

iscriminate separation or fusion high temperature cluster 5

-A

(g) Binary image (eliminated noises)

Fig. 1 Algorithm of Image Processing to Detect Smoldering Fires

76
(c) 8-bit image (1 8 sec.) (d) Binary image (1 8 sec.)

Fig. 2 Discriminate separation or fusion of high temperature cluster

(Number of second is elapsed time from turn on the heater)

Table 1 Centroid and pixel number of clusters

in Fig.2 (b) and (d)

Cluster ID

Centroid

Pixel number X Y

A 347 172 225

B 349 192 55 C 351 220 117 D 357 238 97

E 411 169 2650

F 420 216 2847

G 401 171 3836

A+B+E 405 170 2930

H 355 228 456

C+D 354 228 214

I 420 216 3465

F 420 216 2847

77
(a) 1 0 sec. (b) 60 sec. (c) 1 80 sec. Fig.3 Growth of high temperature cluster of electric radiant heater

(Number of second is elapsed time from turn on the heater)

Pixel number of high

temperature cluster 10000

7500

5000

2500

0

0 60 120 180 240

Time (sec.)

Fig. 4 The time dependent pixel number of electric radiant heater

(a) 10 sec. (b) 60 sec. (c) 120 sec. (d) 180 sec. (e) 240 sec. (f) 360 sec. Fig.5 Growth of high temperature cluster of smoldering cushion

(Number of second is elapsed time from ignition)

Pixel number of high

temperature cluster 1000

750

500

250

0 60 120 180 240 300 360

Time (sec.)

Fig. 6 The time dependent pixel number of smoldering cushion

78
Dieter Wieser^ and Thomas Brupbacher

Siemens Building Technologies AG, Cerberus Products/Alarmcom Division

8708 Mannedorf, Switzerland

dieter.wieser@cerberus.ch

Smoke detection in tunnels using video images

Abstract

In the aftermath of the two disastrous fires in the Mont Blanc and the Hohentauern

road tunnels, there is a strong trend in Europe to require additional safety installations in tunnels for the detection of fires. Smoke detection based on processing

of video images promises fast detection of fires and can complement more traditional techniques such as temperature sensing. Because it uses already required

installations (the video system), this video smoke detection system can be deployed

at a reasonable cost and, furthermore, it can be retrofitted into already existing

installations.

In this paper a new video smoke detection system is described and a report on

full scale fire tests in tunnels is given. Furthermore, it is demonstrated that the

video smoke detection system correctly processes video sequences prone to give false alarms; no false alarms were given.

1. Introduction

There is a fast-growing infrastructure of video surveillance and monitoring equipment in European tunnels. A standard technique is to connect cameras to a video

multiplexing device and to have a human operator judge selected images at a time.

This approach has several deficiencies; two of them being the fact that it is inefficient because only a small number of images are observed at a given point in time,

and that it is expensive because attentive watching of video images is tiring work

and requires the operator to be changed frequently. More and more traffic control

systems based on automatic video processing are, therefore, being installed to detect automatically critical conditions and to display to the operator only images of

^corresponding author

79
those situations which must be assessed quickly; it is then up to the operator to take

appropriate measures. There are now several products available to automatically measure traffic flow, detect traffic jams, detect wrong-way drivers, etc.

As consequence of several disastrous fires in road tunnels (and in other tunnels, e.g. the Kaprun funicular catastrophe), public tenders in Europe for automatic video

surveillance of road tunnels now often also require automatic video smoke detection even though there are other, more traditional techniques, such as the very reliable

temperature sensor cables (e.g. the Siemens Cerberus system FibroLaser II), to detect fires in tunnels. This is because video smoke detection promises to alert

operators very quickly and display to them images of endangered tunnel sections.

Using both systems would, therefore, allow one to combine a very fast but more false

alarm prone technique for smoke detection with a slower but more reliable technique

that could even be used to control extinguishing.

Previous eff"orts ([1, 2, 3, 4, 5, 6]) in video smoke detection concentrate on three

main techniques:

Histogram based techniques: These approaches calculate the histogram of an ac- quired image and then use the information in the histogram to detect the presence

of smoke/fire. Some approaches in this category compare the computed histogram

with pre-computed histograms of typical smoke/fire scenes to make the decision.

Other approaches use statistical measures such as the mean and standard deviation

to determine the likelihood of the existence of a fire in the scene.

Temporal based techniques: Approaches in this category use the difference (s) between frames to generate growth patterns that could be smoke/fire objects. The

number of frames used in calculating the difference image varies depending on the

approach.

Once the difference frames are calculated, different techniques are used to segment

and identify smoke objects. In some approaches, a number of statistical metrics

are calculated from the difference images, then that information is used to infer

the presence of smoke/fire. Other approaches use the colour information in the

generated patterns to classify and identify smoke objects in the image. Techniques

such as color matching filtering are used.

Rule based techniques: A third category of video smoke detection uses domain spe80
cific information coded as rules to infer the presence of fire from a sequence of images.

Approaches in this category are in some cases combined with approaches in the first two categories to help during the recognition phase.

In road tunnels, smoke detection algorithms must be able to overcome several difficulties. Moving objects (the vehicles) which may stand for a while (traffic jam) can

fill most of the image, making definitions of stable references difficult. In addition,

car lights may induce dramatic changes in lighting conditions, especially with on- coming traffic, and near the openings of the tunnel water spray from cars passing

by may make detection even more difficult.

Our system analyzes the contrast in the image and uses special procedures to over- come the above difficulties. The aim of this paper is not to explain the algorithm

being used (the system will soon be commercialized), but to illustrate for an audi- ence more oriented towards indoor fire detection the possibilities of and difficulties

in this tunnel application.

2. Estimation of the loss of contrast

As mentioned above, our approach to the detection of smoke in a tunnel is based on

a loss of contrast in the image caused by the presence of smoke. For simplicity, we

consider only the luminance contrast. The contrast C of an isolated object viewed

against a uniform, extended background is defined by [7] C = (Lo - L^)/L^ (1)

I/O and Lb, respecively, are the luminance of the object and of the background. If a

light extincting medium, such as smoke, is present, then the contrast after passing

for a distance R through the medium is reduced to

Cn = CeM-KR) (2)

The extinction of light caused by smoke can be expressed through the particle concentration in the smoke as

/r = 2.3D = 2.3D^^ (3)

with D being the optical density per metre. Dm the mass optical density, AAf the mass loss of the burning sample, and K the volume of the chamber containing the

81
fire. Values for are tabulated and can be found e.g. in Mulholland [7]. In tunnels, Equation (3) has to be modified to account for the wind

M K = 2.3Dm^ (4)

For an estimate of V, the volume flow of air in the tunnel, both the effects of the

fire and of the forced air flow (natural and forced convection) have to be taken into

account. By looking at the relevant combustion reactions, the volume of exhaust

gases can be put into relation with the mass loss in the fire.

In the early stage of a typical fire in a tunnel, e.g. a burning car, mostly organic

matter (in the chemical sense) is decomposing. This means that we can restrict the

discussion to the most common elements occurring in organic substances, namely

carbon (chemical symbol C), hydrogen (H), and oxygen (O), and to sulphur (S),

which is often contained in fossile fuels as an impurity. Because of the superposition

principle for chemical reactions, it also suffices to look at the combustion reactions

of the elements.

C + O2 = CO2 (5)

2H+i02 = H2O (6)

S + O2 = SO2 (7)

In the case of oxygen deficiency, carbon monoxide, CO, is additionally formed and

at the same time the reaction of CO with water has to be taken into account:

C+i02 = CO (8)

CO + H2O - CO2 + H2 (9)

This last reaction, the reduction of water to hydrogen, is endothermic and is thus

strongly dependent on the temperature in the reaction zone. If the substances

involved in the fire also contain halogens (fluorine F, chlorine CI, bromine Br, iodine

I), these react preferentially with hydrogen to form acids. Halogens are largely being

replaced in todays materials (e.g. PVC is being replaced by other compounds) and

are, therefore, neglected in the following discussion.

Assuming the above stoichiometric reactions (5-9), the mass of oxygen, mo, needed

for complete combustion of 1 kg of burning material can be calculated from

mo = 2.664 wc + 7.937 wh + 0.998 ws - wq (10)

82
Where wx denote the mass fraction of the elements in the burning substance. Using

the ideal gas equation, this mass can be transferred into an equivalent volume at any given pressure, temperature, and composition of the burning material.

A common and time-proven estimate of the development of a fire with time is given

by

^g = <7 = A6^^ (11)

with being the rate of heat release, A the area of the fire, b a constant, and t the

time since ignition. The rate of heat release increases until it reaches a maximum

Qmax that is dependent on the geometry of the fire and on the nature of the combustant. With the reaction enthalpies of the chemical reactions ((5 - 9), the rate

of heat release q can be linked to the rate of mass loss of the combustant M. The

combustion enthalpy can be caclulated as

h = (338 X 10^ wc + 1004 x 10^w;h + 95 x 10^ - wo);^, (12)

-kg

using reaction enthalpies from [8]. Using q = hM, the rate of mass loss in the

combustant can be calculated to M = ^. ,13,

As an example, consider the scenario of a simple model calculation for a burning car

in a tunnel with a cross-section Aunnei = 60 m^: The combustable mass is assumed

to have a composition of wq = 0.78, wu = 0.12, and wq = 0.1, typical for plastics

and hydrocarbons. Using equation (10), the mass of oxygen needed to combust 1 kg

of substance equals to 0.852 kg, corresponding to approximately 26.7 mol of atomic

oxygen, or a volume V — 0.33 m"^ at standard conditions. The relative composition of

oxygen in air is 21 volume percent, thus this corresponds to roughly 1.5 m^ air. This

is a relatively small volume compared to a convection of 120 m-^/s at a wind speed

of 2 m/s; it becomes clear that, at least in the early stages of the fire, convection

a) can feed the fire with enough oxygen, and b) dominates the mass flow. It is,

therefore, justified to set V to the wind speed times the tunnel cross-section area. From this simplification, the time needed to raise an alarm can be now estimated:

Using equations (4) and (13), the extinction factor can be calculated to K = 2.3 Dm'-^. (14) hV ^

After ingnition of the fire, there will be a certain time delay until the smoke enters

the detection zone over which the extinction KL will be integrated. During this

83
time nothing is detected. Then the extinction KL will increase until it reaches a maximum.

Example: Assume the geometry of the tunnel as above, a distance between the camera and the fire of 150 m, and a distance L over which we integrate of 10 m. The fire is given by ^max = 5 MW, A = 2 m^, 6 = 30 W/m^/s^, a composition as above and thus h=\2 MJ/kg, and Dm = 0.5 m^/g. The smoke of the fire enters the

detection path 70 s after ignition of the fire; at this point in time K approximately

equals 23%/m. So already a short optical path lenght suffices to detect the smoke.

3. Test results

3.1. Fires in the fire test laboratory

The development of the video smoke detection method was based on experiences

with test-fires, done according to the European norm EN-54, performed in the fire

test laboratory at Siemens Cerberus. The dynamic contrast analysis algorithm was

developed so far that smoke is detected within seconds in normal office rooms. Long

term tests in hallways were also performed; these showed that the system gave no

to false alarms when running for weeks at a time.

3.2. Tests at the test tunnel Hagerbach

Siemens Cerberus have a tunnel fire laboratory installed in the ventillation section

of the "Versuchsstollen Hagerbach" near Sargans (Switzerland) and have been performing weekly tests with fires for the last two years. Figure 1 shows a schematic

of the setup. In collaboration with "Vereinigung deutscher Schadensversicherer"

(VdS) in Koln, a simulation program to describe tunnel fires is developed to allow

for a scaling up or scaling down of arbitrary fires in tunnels. Test fires with heat

releases of up to 5 MW can be produced under controlled conditions using different

combustants; the wind speed can be varied between 0.5 and 5 m/s. All fires are monitored using several video cameras.

One insight gained from the experiments in the Hagerbach tunnel is that the wind

speed is critical to the constrast observed in the video image; reliable detection at wind speeds above 2.5 m/s, where the smoke is turbulently mixed with air and

84
Wind approx. 1 mis

5 mis 2.8m

4.5m 8.5m

Figure 1: Schematic setup in the Hagerbach tunnel

diluted by the tunnel wind, is very important.

3.3. Calibration in a smoke box

Based on the results and experience gained from the Hagerbach experiments, realistic smoke densities were able to be reproduced in a "smoke box" sized Im x Im x Im.

Figure 2 shows this box equipped with an aerosolgenerator (PALAS AGF2.0), ventilator (generates the turbulence), extinction mesurement device (MIREX, according

to EN54). video camera, and several light sources.

A fire with hydro carbons (gasoline, diesel, plastic) generates aerosols and soot particles with diameters of less than 1000 nm. For the simulation of smoke, aerosols with

a Sauter-diameter of 500-1000 nm were produced from Di(2-ethylhexyl)-Seabacte

(Merck 1263). Using the above mentioned experimental setup, the contrast-algorithm was calibrated to raise an alarm at a total extinction KL — 12% over a path-length

Tests with open gasoline fires conducted on the 22"^^ September 1999 in the road tunnel "Schonberg" near Salzburg were recorded using standard video cameras. These

recordings were afterwards used to test the calibrated smoke detection algorithm.

The series of images in figure 3 illustrate this test; the image labelled with "!! IFire!!!"

is taken as a screen shot from a computer running the video smoke detection algorithm.

Considering the transfer time needed for the smoke to reach the position of the

L.

3.4. Alarm tests with large scale tunnel fires

85
Figure 2: The smoke box used to develop and calibrate the algorithm.

camera (60 m distance, wind speed of 1 m/s), the algorithm triggered an alarm

four seconds after the smoke could have reached the position of the camera. The

smoke density at the position of the camera was at this point in time far below

that found in the case of a fully developed fire. The calibration to KL = 12% set from measurements in the laboratory was adequate to trigger the alarm in the early

stages of a real, fullscale fire. It should be noted that the visibility decreases rapidly

with time after the alarm was given and quickly approaches 0 m. At this point, it

is impossible to monitor the fire with the camera raising the alarm.

3.5. Long term tests

To test the long term stability of the contrast algorithm under realistic conditions,

several cameras in the Gubrist tunnel in the greater Ziirich area were monitored.

This allowed for a test of the algorithm under diflficult conditions, such as traffic line

ups, accidents and ensuing line ups, switching of the lights from day to night mode.

86
i

Figiiro 3: Screen shots of the large scale test in the Schonberg tunnel near Salzburg.

Width of tunnel = 10 m, height = 6 m. Wind speed 1.0 - 1.2 m/s. Area of fire

1 in^, 20 L gasoline with 5 L diesel added. Distance between camera and fire 60 m

87
and combinations of the above. Figure 4 shows a schematic of the system setup.

composite video

video in

video out remote control RS 2327

Slot PC 1xWB80x1 Videolnputs

.AN

-i

Slot PC ISDN interface • H.320

SMS Alarm via ISDN / ISDN

power

Pager Powerswitch

Alarm via SMS

Figure 4: Schematic of the setup for the long term tests in the Gubrist tunnel.

Four cameras were installed at the side-wall of the tunnel, and four cameras were

installed in the centre of the ceiling in the tunnel. Cameras on the side were installed

at a height of 2.75 m, ceiling cameras at 4.3 m. The video smoke detection algorithm was run on a computer, and video recorders were installed that would be controlled

by a second computer. The whole system can be controlled through an ISDN link

so as to allow for critical sequences to be recorded.

Sequences of three hours duration each were recorded and especially critical situations were analysed. These sequences are now being used to test modifications of

the video smoke detection algorithm for false-alarm proofness. So far, situations

with solid line-ups, beginning line-ups, changing lighting conditions at the entrance

of the tunnel, and of changing lighting due to the changes between night and day in

the tunnel have been analysed. The algorithm has not produced a false alarm and

88
because there was no fire during the test period, also no fire was detected.

4. Conclusions

Results from a contrast-analysing video smoke detection algorithm have been given.

Experience so far indicates that the algorithm is quick to detect smoke in video

sequences and at the same time immune to false alarms. In the near future, the

system will be installed in several road tunnels to gain experience with non-stop

operation.

To judge a video smoke detection system, one has not only to consider response

times, but also how the system deals with effects which could induce false alarms

such as car lights, motion, blooming, reflexes and other disturbing effects. Unfortunately customers are often not aware of the importance of the above effects.

In order to establish video smoke detection as a new technique, system providers

should, therefore, define a standard test procedure which takes into account the

above mentioned effects as well as real fire situations. At Siemens Cerberus we are working towards such a standard.

References

[1] B. Aird and A. Brown, "Detection and Alarming of the Early Appearance of

Fire Using CCTV Cameras" , Nuclear Engineering International Fire & Safety

conference, London, 24-26 Feb. 1997.

[2] T. Oikawa, M. Tomizawa and S. Degawa, "New Monitoring System for Thermal

Power Plants Using Digital Image Processing and Sound Analysis", pp. 221-

224, IFAC Control of Power Plants and Power Systems, Cancun, Mexico, 1995.

[3] V. Cappellini, L. Mattii and A. Mecocci, "An Intelligent System for Automatic

Fire Detection in Forests" , University of Florence, Italy.

[4] S. Noda and K. Ueda, "Fire Detection in Tunnels Using an Image Processing

Method", Vehicle Navigation & Information Systems Conference Proceedings,

pp. 57-62, Yokohama, Japan, 1994.

89
[5] G. Healy, D. Slater, T. Lin, D. Drda and D. Goedeke, 'A system for Real-Time

Fire detection", IEEE computer Vision and Pattern Recognition, pp. 605-606,

New York, N.Y., 1993.

[6] S. Y. Foo, "A Rule-based machine vision for fire detection in aircraft dry bays

and engine compartments", Knowledge-Based Systems, Vol. 9, No. 8, pp. 531-

540, 1996

[7] George W. Mulholland, "Smoke Production and properties" in DiNenno ct

al. SFPE Handbook of Fire Protection Engineering, 2"'' Edition, Chapter 15,

Section 2, pp 217-227

[8] H. D. Baehr, Thermodynamik, Springer, 1973.

90
Woo C. Kim, Yudaya R. Sivathanu, and Jay P. Gore

Mid Infrared Sensing Diagnostics and Control Consortium

School of Mechanical Engineering

Purdue University, West Lafayette, IN 47907-1003

Characterization of Spectral Radiation Intensities

from Standard Test Fires for Fire Detection

Abstract

Spectral radiation intensities at 160 mid infrared wavelengths leaving six

standard test fires specified in the guidelines of the European Committee

for Standardization (ECN Fires) were measured. A recently developed Fast

Infrared Array Spectrometer (FIAS) was utilized to acquire spectral

radiation intensities in the 1.8 - 4.9 "m wavelength range from the

transient as well as the steady burning fires. The mean and root mean

square, the Probability Density Function (PDF), the Cumulative Probability

Density Function (CPDF), and the Power Spectral Density (PSD) of the

spectral radiation intensities were studied to characterize the fires. The

spectral radiation intensity in the 4.3 'm micron carbon dioxide band is

larger than that at other wavelengths for the open fires. The continuum

radiation from soot particles dominates gas band radiation for the

smoldering fires. The statistical characteristics of the fires depend on the

fuel type, time after ignition and measurement wavelength and can be used

in fire detection algorithms.

1. Introduction

Current residential fire detectors include optical smoke sensors, ionization

smoke sensors, and temperature (heat) detectors [1]. These detectors have

response times of the order of 2 to 20 minutes depending on the location of

91
the fire relative to the detector because combustion products must travel to

the detector location for the operation.

More recently, there has been increased interest in the use of radiation

emission sensors (flame detectors) for faster fire detection compared to

smoke and heat sensors [2] . A combination of detectors may be necessary to

detect fires that do not have significant radiation signatures. Single or

multiple radiation-based fire detectors with ultraviolet, near infrared and

mid infrared filters have been used under various conditions to allow

detection with minimal false alarms. Ultraviolet signals are emitted by

indoor radiation sources such as incandescent lights, arc welding processes,

etc. Therefore, ultraviolet sensors are limited to outdoor usage where

interfering solar radiation is absorbed by the earth's atmosphere. Another

disadvantage of ultraviolet flame detectors is that any contamination of the

optical windows causes a significant loss of sensitivity. Sivathanu and

Tseng [3] showed that a two-wavelength near infrared fire detector is

capable of detecting fires from a wide variety of materials with low false

alarm rates and fast response time. However, the near infrared fire

detector can barely detect an indirect smoldering fire since the signals from

these fires are very low in the near infrared. In addition, non-luminous fires

[4], such as those burning alcohol were not detected.

Radiation emitted by a fire has two primary components: continuum

radiation from glowing soot particles in the flame and various emission

bands such as at 2.7 "m and 4.3 'm originating from carbon dioxide

molecules and at 2.7 and 6.3 •m originating from the water vapor in the

combustion products. The characteristic temperatures of most accidental

fires are such that the continuum radiation peaks in the mid infrared region

of the spectrum. These unique characteristics may allow development of

multi-wavelength fire detectors that recognize the spectral characteristics to

avoid false alarms.

92
Past studies of spectral radiation properties of flames have involved

luminous and non-luminous laboratory flames. These have included

turbulent jet diffusion flames and pool fires. Two-wavelength pyrometric

measurements conducted by Sivathanu and Faeth [5], Sivathanu et. al. [6],

and Sivathanu and Gore [7] in luminous pool and jet fires indicate that the

peak temperatures within these fires are in the range 1400 ± 300 K. Other

radiation properties of the fires include the Probability Density Functions

(PDFs) and the Power Spectral Densities (PSDs) of the turbulent

fluctuations.

These characteristics can be used to design fire detectors that can avoid

false alarms. However, other than the pool fires, which can occur in fire

accidents, the fire spectral radiation intensities have not been studied in the

literature. Based on the background information provided above,

measurements of the spectral radiation intensities in the mid infrared part

of the spectrum leaving standard test fires conforming to the European

Committee for Standardization - CEN-54 (ECN) [8] were measured. These

data can contribute to advanced fire detection algorithm.

2. Experimental apparatus and method

To obtain measurements of spectral radiation intensities from fires for fast

and reliable fire detection, the use of fast spectrometer is required, because

the instantaneous intensities at different wavelengths should be used to

analyze the frequency contents of six standard ECN fires. In particular,

emissions of radiation from soot fluctuate at a higher frequency and have

larger fluctuation intensity than the emission of gas band radiation. Hence,

measurements were made using Fast Infrared Array Spectrometer (FIAS)

to acquire spectral radiation intensities at 160 pixels in the 1.8 - 4.9 'm

wavelength ranges and this unit is able to capture instantaneous spectra

from the fires at a rate of 390 Hz. Findings by Ji, et al. [9] show that the

93
agreement between the FIAS measurements and the previous data is very

good.

FIAS was aimed at different angles and positions depending on whether the

test fire is developing with time or not, assuming that all test fires are

axisymmetric. For the transient test fire, - optxi cellulosic fire (TFl),

smoldering p5rrolysis fire (TF2), glowing smoldering fire (TF3), and open

plastics fire (TF4) - the FIAS view angle is set at 45 degrees from the

gravitational direction, where the angle is defined as shown in Figure 1.,

and a different radius from the flame axis, and time dependent data are

acquired in a fixed position. For the TFl, TF2, and TF3, the FIAS is located

at 300 mm in radial direction and 500 mm for TF4.

Figure 1. Schematic of defining the direction viewed by the FIAS

for the TFl, TF2, TF3, and TF4

94
The present work involves utilization of fire radiation for detection and

generally the need for early fire detection suggests that the fire is smaller

than the size of the enclosure at the time of detection. Therefore, in the case

of the steady state test fires - liquid fire (n-heptane, TF5), and liquid fire

(ethanol, TF2) -, intensities leaving the fire at four different heights were

collected as representative values of fire development, and schematic

diagram for experimental facility is shown in Figure 2.

Detailed descriptions of ECN fires are provided in Ref. [81. The dimensions

of TFl were scaled down to one half so as to be able to accommodate our

laboratories. The liquid fires stabilized on a water-cooled 15 cm diameter

stainless steel burner. The height of the burner is 10 cm and the flame was

stabilized with a lip height (depth of liquid surface below the pool edge) of

10 mm. The pool fires reached a steady state operating condition in

approximately 3 to 4 minutes.

Figure 2. Schematic of defining the direction viewed by the FIAS for TF5

andTF6

95
3. Results and Discussion

3.1. General descriptions about standard test fires

The TFl lasted around 13 minutes before going out automatically. During

the first 5 minutes, the infrared signature from the methylated spirit

dominated the spectral radiation intensities from the wooden crib, however

in 5 minutes, the instantaneous spectral radiation intensities from both gas

band and in-flame soot resulting from the fire grew and reached peak values

around 9 minutes after ignition.

The TF2 began smoldering in 5mins and flame was made automatically in

9mins. Because the FIAS was aimed at the beechwood sticks, the infrared

signatures from the TF2 were low compared with other standard test fires.

For the TF3, the igniter flames were immediately put out after ignition, and

the cotton wicks continued to glow for the duration of the test.

The TF4 lasted around 7 mins and reached its peak intensities in 4mins.

TF5 is luminous and emit radiation from both soot and combustion

products. The radiation heat flux directly contributes to the hazard posed

by the flame and the spread rate of the fire. Fuels of similar composition,

such as fuels classified as paraffin or alcohols generally have comparable

flame shapes. The alcohol fuels, TF6, generally necked in just above the

burner lip and the paraffin (n-heptane) fuels had a larger distance before

necking begins.

3.2. Mean Values of Spectral Radiation Intensities during 12.8sec

Figure 3 represents the mean spectral radiation intensities during 12.8 sec

for all six standard test fires. Care should be taken for the scales of

96
ordinate, because the radiation signature from TF2 was too small to keep

the same scale with the other fires. One more thing to keep in mind is that

the positions of FIAS are different between the transient TFs and steady

state ones as we discussed earlier.

The TFl and TF4 are very luminous and both gas band radiation and

continuous soot radiation are evident. The radiation intensity from carbon

dioxide and soot at 4.3 'm is more than two orders higher than that from

soot, water, and carbon dioxide at 2.7 'm, which is similar to the spectral

distributions observed in the open pool fires (TFS and TF6).

For the two smoldering fires, TF2 and TFS, the continuous radiation from

the smoldering surface is evident and the gas band radiation is hardly

found, furthermore, the absorption in the carbon dioxide band in the room

air at 4.3 'm is seen. Using Wien's displacement law to estimate

temperature of the TF3, the temperature of inflame soot is 1024 K.

97
l/l O.O

tr 0,02

o

I—

I

-ao

<D&.

00

TFl in 9mins

TF3 in Imins

TF4 in 4mins

h/H =0.0833 (TF5)

h/Hf = 0.1833(TF5) — h/Hf = 0.4271 (TFS) _ h/Hf = 0.6250 (TF5)

h/Hf=0.1250(TF6)

h/Hf =0.4028 (TF6)

h/Hf=0.5417(TF6)

h/Hf =0.8194 (TF6)

Wavelength [jLim]

Figure 3. Spectral radiation intensities in TFs during 12.8 sec

The flame height of the TFS and TF6 is 480 mm, 360 mm respectively,

measured by visual inspection and four different dimensionless flame

98
heights from the lip of the flame are shown. Radiation from the CO^ band

and the H.,0 band are evident in the spectra. However, the TF6 contains

less soot than the TF5, and hence the radiation spectra are less dominated

by soot. Radiation from H.,0 becomes less intense as the dimensionless

flame height increase. The continuum radiation for soot particles is also

evident. As the dimensionless height increases, the spectral radiation

intensities decrease. If we use Wien's displacement law to estimate the

flame temperature associated with the maximum intensity of the soot

radiation, a temperature of approximately 1630 K for the TF5 and 1476 K

for the TF6 respectively, is obtained.

3.3. PDFs and CPDFs of spectral radiation intensities

The time series of normalized measured radiation intensities from inflame

soot and the gas band at 1.45 "m, 2.47 'm, 2.68 "m, and 4.35 'm obtained

from the test flres were analyzed to evaluate their Probability Density

Function (PDF). The four wavelengths are selected to study the effects of

soot radiation separately from those of soot+COj+HgO radiation and

soot+CO^ radiation, and, to study the differences between the open Are, and

smoldering Are, the PDFs of TF5 at h/H, = 0.0833 and TF3 were chosen and

shown in Figure 4.

Figure 4 illustrates that the PDFs are clipped Gaussian in shape, especially

for the carbon dioxide and soot band at 4.3 "m, where carbon dioxide

radiation is dominant, is almost symmetric around the mean intensity

value, however the PDF at 1.45 'm for TF5, where in-flame soot dominates

the spectral radiation intensity, is not symmetric in shape, and shows a

lognormal behavior. Because of the large fluctuations in soot radiation, the

overall ranges are broad.

Comparing the PDFs in TF3 with TF5, we can conclude that the PDFs are

still Gaussian in shape, although there is some noise. However the TF3

99
continued to glow for the duration of the test and the fluctuation in

radiation is not so high compared with TF6, the range of spectral radiation

intensities is not so broad.

1

1 1 Soot at 1 .45jim

(TFS)

i

i

1 1 Soot at 1 .45fim

(TF5)

1

'

1 Soot at 2.47pm

(TF3)

1

1

1 Soot + H2O at 2.47^

(TF5)

' Mil

—lii—^M^— fc>>ni 1 — —. .—. . 1—

.

J

1=] Soot at 2.68|.im

CTFS)

1

1

1 Soot + H2O + CO2 at 2.68|xm

(TF5)

JilL

jmKKKKK^KIKKKKmm^.u^i,^—.—.—1 . ..—_h—

1

1 1 Soot at 4.35^111 (TPS)

1

1 1 Soot + CO2 at4.35Mni

(TF5)

A

0-X - Imean) / Irms

Figure 4. PDFs of spectral radiation intensities of TF3 and TF5 at h/Hf = 0.0833

100
The differences in the statistical behavior of intensities at different

wavelengths can be examined in even greater detail by studying the

Cumulative Probability Density Function (CPDF) shown in Figure. 5, which

shows CPDFs of TF3 and TF6 at h/Hf = 0.1250. The CPDF is an integral of

the PDF and therefore less noisy. The information it presents consists of

the intensity values at which the probability of occurrence of intensities

below that value builds rapidly.

-0.4 -0.2 0.0 0.2 -1 0 1 2 3 4

Figure 5. CPDFs of spectral radiation intensities of TF3 and TF6 at h/Hf = 0.1250

Figure 5 show^s that the CPDF for intensities originating from the gaseous

molecules rise very rapidly near a well-defined most probable value. In

addition, the rise is the sharpest for the CO.-, band at 4.35 'm. The gas band

CPDFs are steep because of the concentration fluctuations are strongly

related to the level of mixing through the state relationships. The soot

CPDF is flatter because various amounts of soot can exist at different levels

of mixing depending on chemical kinetic and differential diffusion effects.

101
Independent of the reasons for this, the characteristic that a flatter CPDF

exists at soot wavelengths and a steeper CPDF exists at the gas band

wavelengths can be used as an effective discrimination method in the sensor

logic.

All the CPDFs of TF3 exhibit similar trend in slope because of the

continuum emission from the same surface.

For the case of TF6, the PDFs of the soot+CO^ and H^O intensities at 2.47

•m and 2.68 "m respectively show a smaller weight in intensity near zero compared to the PDF of soot+COg at 4.35 'm. This may be related to the

self-absorption by cold CO^ in the outer reaches of the flame at many

instances. In addition, the soot+ CO2 emission at 4.35 •m shows a clip at

the highest intensity values. These results suggest caution regarding the

use of this wavelength for fire detection. In addition, the result is also of

relevance to the fundamentals of participating media heat transfer in fire

and combustion environments.

3.4. PSDs of spectral radiation intensities

The Power Spectral Densities (PSDs) of spectral radiation intensities from

the soot, C02 and Hfi bands for TF3 and TF4 were analyzed to obtain their

frequency content as shown in Figure 6. The PSD at the respective

wavelengths were normalized by the mean-square fluctuations to compare

with one another; hence the total area under each PSD is unity. The PSD is

characterized by the flicker frequency and is determined by the size of the

fire, the gravitational constant, and the turbulent flow established within

the fire. The area under the PSD curve for any frequency range is

proportional to the energy of the instantaneous fluctuations of the apparent

source temperatures from the mean value. Although there is some noise

around the Nyquist frequency of 195 Hz, this could be ignored considering

the SN ratio.

102
It is important to note that the integrated infrared intensities do not show

characteristic pulsation frequencies observed in planar imaging studies.

There are commercial detectors that rely on pulsation frequencies and the

present results do not support their sensor logic.

For the case of open fire, TF4, the PSD at the wavelengths (all wavelengths

other than the 4.35 'm) dominated by soot radiation show higher energy

content at higher frequencies compared to those dominated by the gas

radiation (4.35 ^m). This characteristic of the sooty fires can also be

utilized as a discriminator in fire sensor logic. Most of the energy of the

fluctuations of the spectral radiation intensities is below 10-20 Hz and

shows a decline to noise levels that are approximately five to six orders of

magnitude lower for frequencies higher than 20 Hz.

10^

IQ-S I , , , ^ • ^ 10° 101 10' 102

Frequency [Hz]

Figure 6. PSDs of spectral radiation intensities of TF3 and TF4

103
An interesting and most distinguishable feature of smoldering fire could be

deduced from the left panel of Figure 6. Most of the energy of the

fluctuations of the spectral radiation intensities is below 2 or 3 Hz and

declines very rapidly to the noise levels that are approximately five to six

orders of magnitude lower after 3 Hz. However, flicker frequency of the

open fires is around 10 - 20Hz and declines relatively smoothly to the noise

level. The fact that the radiation from a smoldering fire has characteristics

similar to those of a hot surface poses special challenges to the detection of

such fires especially in environments where hot surfaces are to be expected.

However, since smoldering fires are likely to be away from kitchens and

utility rooms, a fire detector could still be devised but caution will be needed

in addressing the false alarm issues.

4. Conclusions

(1) . The open test fires are very luminous and both gas band radiation and

continuous soot radiation are evident. The radiation intensity from carbon

dioxide and soot at 4.3 'm is more than two orders higher than that from

soot, water, and carbon dioxide at 2.7 'm. For the two smoldering fires, TF2

and TF3, the continuous radiation from the smoldering surface is evident

and the gas band radiation is hardly found, furthermore, the absorption in

the carbon dioxide band in the room air at 4.3 'm is seen.

(2) . The PDFs are clipped Gaussian in shape, especially for the carbon

dioxide and soot band at 4.3 'm, where carbon dioxide radiation is

dominant, is almost symmetric around the mean intensity value, however,

in the case of open test fires, the PDF at 1.45 "m, where in-flame soot

dominates the spectral radiation intensity, is not symmetric in shape, and

shows a lognormal behavior. Because of the large fluctuations in soot

radiation, the overall ranges are broad, which is not applied to the case of

smoldering fires.

104
(3) . The gas band CPDFs are steep because of the concentration

fluctuations are strongly related to the level of mixing through the state

relationships. On the other hand, the soot CPDF is flatter because various

amounts of soot can exist at different levels of mixing depending on

chemical kinetic and differential diffusion effects.

(4) . Most of the energy of the fluctuations of the spectral radiation

intensities in smoldering fires is below 2 or 3 Hz and declines very rapidly to

the noise levels. However, flicker frequency of the open fires is around 10 — 20Hz and declines relatively smoothly to the noise level.

(5) . TF3 was used as to study the mid infrared characteristics of smoldering

fire, because, although it has same characteristics with TF2, the IR

signature from TF2 is not so high, and as a result of this, there are some

noises in PDFs, CPDFs, and PSDs of TF2.

5. Acknowledgement

This work was performed under the sponsorship of the U. S. Department of

Commerce, National Institute of Standards and Technology under Grant

No. 60NANB5D0113 with Dr. William Grosshandler and Dr. George

Mullholand serving as the federal Program Officer.

References

[1] Grosshandler, W. L., An Assessment of Technologies for Advanced Fire

Detection. Heat and Mass Transfer in Fire and Combustion Systems, HTD

vol 223, ASME, New York, 1992:1-9.

[2] Middleton, J. F. Flame Detectors. Ninth International Conference on

Automatic Fire Detection, AUBE 89, Duinsburg, Germany, 1989:143-154.

[3] Sivathanu, Y. R., and Tseng, L. K, Characterization of Radiation

Properties of Fires Using Multi-Wavelength Measurements, Thirtieth

105
section anniversary technical meeting, Central states section, The

combustion institute, 1996:304-309.

[4] Lloyd, A. C, Development and Evaluation of Fire Detection Algorithm

with Source Temperature Discrimination and Frequency Content, MSME

Thesis, Purdue University, West Lafayette, IN, 1997.

[5] Sivathanu, Y. R. and Faeth, G. M., Temperature / Soot Volume Fraction

Correlations in the Fuel-Rich Region of Buoyant Turbulent Diffusion

Flames, Combustion and Flame, 1990;81:150-165.

[6] Sivathanu, Y. R., Gore, J. P., and Dolinar, J., Transient Scalar

Properties of Strongly Radiating Jet Flames, Combustion Science and

Technology, 1991;76:45-66.

[7] Sivathanu, Y. R., and Gore, J. P., Simultaneous Multiline Emission

Absorption Measurements in Optically Thick Turbulent Flames,

Combustion Science and Technology, 1991;80:1-21.

[8] CEN, Components of Automatic Fire Detection Systems: Fire Sensitivity

Test, Part 9, European Committee for Standardization, Brussels, 1982.

[9] Ji, J., Gore, J. P., Sivathanu, Y. R., and Lim, J., Fast Infrared Array

Spectrometer Used for Radiation Measurements of Lean Premixed Flames,

34*^ National Heat Transfer Conference, Pittsburgh, Pensylvania, 2000:1-6.

106
Thomas Kaiser and Thorsten Kempka

Gerhard-Mercator-Universitat, Duisburg, Germany

Is microwave radiation useful for fire detection ?

Abstract

This paper deals with the question of whether microwaves might be useful for

fire detection. Planck's law shows us that a black body emits electromagnetic radiation also in the micro- or millimeterwave region. Since such waves

exhibit a different attenuation behaviour than infrared radiation an investigation of this phenomenon is interesting for a fire researcher. In this paper we

will present the physical basics, explain how to measure and to use such radiation for estimation of the fire location and give some preliminary results obtained by first experiments.

1 Introduction

There are already high-tech products available on the market to locate the fire origin, or may be also persons in danger, through the smoke. Such image processing devices are based on the infrared radiation (IR) of a hot spot. In general, such devices are called

radiometers since they simply measure the intensity of radiation. In the following we

will distinguish between IR-radiometers and MW-radiometers where MW stands for mi- crowaves or also for millimeter-waves. Radiometers are most commonly used for remote

sensing of the earth from satellites and airplanes, but that topic is beyond the scope of this

paper. In the industry, radiometers can be used for remote measurement of temperatures

in ovens, converters, kilns, and other places where the use of conventional contacting temperature sensors or IR-radiometers is impossible because of high temperatures, smoke, or water vapour. However, MW-radiometers can often not compete with IR-radiometers or conventional sensors since the latters are usually cheaper and simpler to build. The main

advantages of MW-radiometers for the use in fire detection are fourfold:

1. The possibility to measure through optically thick smoke and vapor.

2. The insensitivity of MW-radiometers to environmental conditions, such as water

vapor and dust (contrary to infrared methods), and high temperatures (contrary to semiconductor sensors).

107
3. The fact that thermal microwave noise radiation comes from a thicker surface layer

than the IR radiation does.

4. The fact that MW penetrates all materials except of metals.

In contrast, their main disadvantages concerning fire detection are:

1 . The higher the center frequency, the more expensive are the electronic components.

2. Because of the relatively long wavelengths compared to IR, the achievable spatial

resolution might be limited.

Hence, the only thing which is almost sure is that much efforts are needed to investigate

whether MW-radiation can be useful for fire detection. Exactly this problem is the central

topic of this paper. In the following we will present the physical basics prepared for the

non-physicist, explain how to measure and to use such radiation for estimation of the fire

location and give some preliminary results obtained by first experiments.

2 The physical basics

To investigate whether MW-radiation can be useful for fire detection we can split this problem into three seperate questions:

1. How much MW-power /^e emits a fire ?

2. How much MW-power Pr receives an antenna ?

3. How much MW-power Pd is available for detection at the antenna output ?

Before we start to answer these three questions let us shortly comment them. Of course,

a theoretical analysis to predict the expected power at the antenna output is essential to judge the use of MW-radiation in fire detection. For this reason we have first to find

a formula for the emitted MW-power of a fire. Obviously, since we usually not know

the burned material we cannot expect to find a general fonnula being independent of the

material properties. Fortunately, we cannot only find an upper bound but also a rough

idea how much the deviation of the upper bound is. This will be explained by answering

the first question. Then we consider the case of ideal transmission where we have no

attenuation on the propagation of the electromagenetic radiation through the air. Later on we will discuss the influence of smoke or water vapor or solid materials. So we obtain an

estimation of the received power. By answering the third question we demonstrate that

an antenna is definitely not an ideal measurement device. We have to include its own

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temperature and its directivity pattern to calculate the output power. This output power

is the most interesting quantity, since we have to process the antenna output signal to

detect a fire. Moreover, by use of an antenna array instead of a single antenna we are

able to scan the whole scenario and. therefore, we are also able to locate the fire origin

within a certain resolution. To illustrate the whole problem - separated into three different

questions - consider Fig. 1.

Figure 1: The emitted power Pe. the received power Pr, and the detected power Po

2.1 How much MW-power emits a fire ?

Consider an electromagnetic wave of frequency / w hich hits an arbitran.' bod\- w ith temperature T. The power of the incident wave is denoted as P\{f). whereas PRet\{f- T ) is the

Figure 2: The incident power P\{f). the reflected power PRenif-T). and the absorbed

power Pa (/^r)

reflected power, and P\{f. T) is the absorbed power. Note that the reflected as well as

the absorbed power do not only depend on the frequency / of the incident w a\ e but also on the temperature T . In general, they are also dependent on the direction of the incident

radiation related to the surface of the bod}- - the so-called direction of arrival - as well as

to the polarization of the electromagnetic wave. Since here the latter two parameters are

-"^ V V

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more of random nature and thus we consider them as uniform distributed, we omit them

as independent variables.

Let us now define the degree of absorption (or absorptivity for short) as

. ^ absorbed power P^{f, T)

mcident power n(/)

Obviously, the range of the absorptivity is between zero and one (0 < a{ f,T) < 1). An

interesting body is such a one which absorbs all incident power irrespective of the fre- quency / and the temperature T. Such a body is called black body (remember how a black hole works) and is fully described by

«BB(/,r)) = i, v/,r,

where the index BB denotes the black body.

Let us leave the area of absorption and consider now the area of emission. If a body

has a temperature greater than T > OK, it will emit radiation caused e.g. by a jump of an

electron to a state of lower energy. In case of such a jump an electromagnetic wave occurs with energy /?/, where h — 6.626 • lO^-^'^Ws^ is Planck's constant. Consequently, each

body with T > OK radiates electromagnetic waves. Suppose that an arbitrary body emits

the power Pe(./, T) than the degree of emission (or emissivity for short) is defined as

elf T) =

emitted power ^ P^jfJ)

emitted power of a black body ^e^bCAT')

In contrast to the absorption we do not have an incident wave. Thus we need a refer- ence quantity, where the black body emission PE,yi\i{f,T) is a natural choice. Note that

the emissivity of a black body is obviously equal to one. Note also that at this point we

cannot state that the emissivity does not exceed the value 1. This will be shown later.

In the following we try to find a relation between the emissivity e{f,T) and the absorptivity a{f,T) of an arbitrary body. For this reason consider Fig. 3. Suppose that the

Mirrors

Plate 1 Plate 2

Figure 3: An experiment to find a relation between e{f, T) and «(/, T).

two plates are on the same temperature T. Each of them emit the power Pea if-.T) or

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^E,2(/- T). The plates are surrounded by two mirrors, so that no emitted radiation can be

lost. Consequently, PeA/^ ^) is the incident power for the left plate whereas Pe,i (/, T) is

the incident power for the right plate. In other words, a\ (/, r)PE,2(/, T) is the absorbed

power of plate 1 and a2{f,T)PE,\{f,T) of plate 2, where a\{f,T) and ajif.T) are the

absorptivity of plate 1 and plate 2, respectively. Since we assume a thermical equilibrium - both plates have the same temperature T - both absorbed powers must be the same for

each frequency and for each temperature

«, (/, r)PE,2(/, T) =: «2(/, n^E,l (/, 7^).

By use of eq. (2) we immediately obtain

fli(/,r) ^ a2(/,n

eiif.T) e2{fj)

where (/, T) and eiif-, T) are the emissivity of plate 1 and plate 2, respectively. Suppose

that plate 2 is a black body then we get

since for a black body the absorptivity and the emissivity are per definition equal to one.

It follows for an arbitray body - here represented by plate 1 -

a{fj) = e{fj) \ffj. (3)

This rule is called KlRCHHOFF's law and it means that an arbitrary body that absorbs

much power also emits much power in the same band of frequencies. Of course, it can be

a good absorber/emitter in one frequency band but a bad one in another.

We are now able to write the emitted power of an arbitrary body as

/t(/,n=«(/,n^,BB(/,7'). (4)

Since the absorbtivity has a range 0 < T) < 1 we are now able to say that the emitted

power of an arbitrary body is always equal to or less than the emitted power of a black

body. Thus, if we know /^,bb(/5 T) we can use it as an upper bound for T). Note

that such a result is not clear by only use of the definition (2) for the emissivity. At that

point we are not able to say that the emissivity is always less or equal to one. The radiation Pe,bb(/, 7^) of a black body was first derived by Planck, the founder of

the quantum theory. Since the derivation is complicated we will here only give the final

result, widely known as Planck's law

d/ c2 'II (5)

efT - 1

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where A is the bodies surface, c = 3 • lO^m/s is the velocity of light, k= 1.3S- 10~^^Ws/K

is the BOLTZMANN-constant, and an emission in a half sphere is assumed (this means a

solid angle of O = 2n). Observe that the unit of d/^ bb (/, T) /df is W/Hz, so that it can be

interpreted as the distribution of the power versus frequency. The following Fig. 4 shows

this quantity as a function of the frequency for different temperatures and a body surface

A — Im^. It can be clearly seen that on the considered range of micro- and millimeterFigure 4: Planck's Law for A = Im'

waves Planck's law can be approximated by a linear function in a logarithmic scale. The

mathematical reason is that for such small frequencies the power series for the exponential

function

can be broken off after the second term

hf hf<^kT hf

e^ ^ 1 + —

.

kT

We obtain

df ~ c2

(6)

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which is a linear function in a logarithmic scale. This result is known as the law of

Rayleigh-Jeans. The advantage of eq. (6) is the possibility for an integration in a closed form. Denoting /c as a center frequency and A/ as the considered bandwidth the

total emitted power can be calculated as

/'E,BB(/c,A/,r) w —:a— .-A//2 C^ = ^(3/cV-2(A//2)^^) (7)

fc>% 4nAkT .

We have now obtained an equation for the transmitted power of a black body depending on

the user-chooseable center frequency /c, the user-chooseable bandwidth A/, the surface

A and the temperature T of the black body. To get a feeling about the range of this power consider a practical scenario. Suppose a surface of /\ = 0.5m^, T = lOOOK, /c —

UGHz, A/ = IGHz, then the emitted power is equal to /'e.bbCI IGHz, IGHz, lOOOK) =

116.24nW which is easy to detect with conventional antennas. Of course, here we have

not only omitted the absorptivity T) but also the distance r between the antenna and

the fire. Before we explain how to include r we will finish the first question by a short

discussion about «(/, T).

From Planck's law it can be seen that only the surface A of the black body influences

the emitted power. It is also known that microwaves or also millimeter-waves can travel through solid material with sometimes only a small attenuation. This is in contrast to

IR-radiation which is clearly attenuated by solid materials. Thus, we might expect that in case of MW-radiation not only the surface but also the inner of a real body (e.g. the fire)

will contribute to the emitted power. Since the black body radiation does only depend

on A the absorptivity a(/, T) must exhibit a corresponding behaviour. In other words we

have to expect - as a tendency - that a{f, T) must be higher for MW-frequencies than for

IR-frequencies. Moreover, this tendency must be valid for all temperatures. Therefore,

it might be very interesting to measure the absorptivity a{f,T) of typical fire materials

(e.g. wood). This is planned for a future research project. To get a feeling about the

quantity of absorptivity and equivalently of the emissivity, consider the following tabular

(see [1]). Although these are not materials typically occuring in fire detection it shows that

the values for the absorptivity and emissivity are not very small. Of course, one exception

is metal which totally attenuates electromagnetic radiation due to the huge number of free

electrons. So, the emissivity as well as the absorptivity are nearly zero for metal.

After we have answered the question How much MW-power emits a fire ? we can

say that a detection of a fire by measurement of MW-radiation seems to be possible if

all radiation near /c can be collected. However, this would mean that around the fire an

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Material ^(30- 90GHz, 280K) Material e(30-90GHz,280K)

Sand 0.90 Smooth rock 0.75

Asphalt 0.83 Concrete 0.76

Coarse gravel 0.84 Heavy vegetation 0.93

Table 1 : Typical emissivity for normal incidence and 280K

half sphere have to be constructed which fully works as an antenna. Since this is a very

unpractical approach we have to ask the second question:

2.2 How much MW-power receives an antenna ?

To explain the principal problem consider Fig. 5. We assume that a fire omnidirectionally

Figure 5: A practical setup

emits its radiation in a half sphere so that each point on the half sphere receives the same

amount of radiation power. Since a half sphere has a surface of 27rr^ we immediately

obtain for the received power

= (9)

where Aq is the so-called effective area indicated in Fig. 5 directly beneath the antenna

as a patterned region. We will not discuss the details of the idea of the effective area. For a short explanation consider an antenna of reasonable size and regular shape, e.g. a

satellite dish. Then the effective area is nearly equivalent to the geometrical surface of the

parabolic mirror. It should be pointed out that the effective area does only depend on the

antennas shape and not on the received radiation. We emphasize this fact because in the

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literature the relation = XjCe/ {4k) can often be found. Here Xq — cq/fc is the center

wavelength and Ge is the so-called antenna gain. Since the antenna gain is often given

as a number (e.g. for a Xc/2-dipole Gg = 1.64). we might assume that the effective area becomes quadratically dependent on the carrier frequency. This might cancel the increase

of /'e,bb(/c,^/: T) on /(? in eq. (8) so that the received power Pr^/, T) would not longer

depend on the center frequency. This is not true, since the antenna gain is frequency

dependent and the effective area is not. Thus, it follows from eq. (9) that the received power decreases inversely proportional to

the square /" of the distance r and increases linearily with the antennas effective area. Suppose that a measurement of picowatts is possible v\ ith a satellite dish. Assume that

the parabolic mirror has a diameter of 80cm. Then we obtain for the maximum distance

between the antenna and the fire in our practical example

/Tt(0.4m)2pE(l lGHz,lGHz, lOOOK) ^ rmax = \/ PS = 96.43m.

V 271 10- 12 W

Such a maximum distance might be sufficient in numerous applications. However, note

that the maximum distance is linearly depending on the center frequency fc so that it can be increased if needed.

Up to now we do not have considered a possible attenuation during the propagation of

the electromagnetic wave from the fire to the antenna. In other words we have assumed

a free space between fire and antenna. So. let us fill this free space with some material,

e.g. air. fog, smoke or a solid. Of course, we will have some additional attenuation which

depends on the emitted frequency / as well as the temperature T and may be also on other

parameters, e.g. the humidity of the material. Hence, we have to extend eq. (9) as follows

Pr(/. T) = \H[f., r) p A_ T), ( 10)

27T/-

where //(/, r) is the transfer function from the fire to the antenna (or vice versa) for a given

frequency / and distance r. In general, suppose that a source emits a signal S(,?,m{2nft)

with a constant amplitude 5^,. This signal propagates through the considered material, e.g.

air, fog or smoke, over a distance r and a sink receives the signal Sr{f, r) sin(27i/r + (})(/))

with a frequency dependent amplitude Sr[f) and an arbitrary phase ^{f). Then the transfer

function is simply given by

where a G R includes the properties (particle size distribution, optical index of refraction)

of the considered material and the received amplitude Sr{f, r) can be separated as

^r(/,r)=5,(/)e-«'-.

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The next figure shows the attenuation 101og|//(/, lm)p in dB/ni in case of air and fog.

It can be seen that the attenuation in case of air is strongly fluctuating. This is due to the

Micro Millimeter Sub mi Hi meter Infrared

Figure 6: Attenuation of Air and Fog

Visible

molecules in the air, like e.g. O2 or H2O, which absorb energy in certain frequency bands

leading to a high attenuation. This must not be necessarily a disadvantage. For example

in future generations of wireless communication a carrier frequency of 60GHz might be

chosen to achieve small communication cells. Comparing the MW- to the IR-region it can be also seen that the IR-region exhibits numerous window frequencies whereas the MW-region shows a principal increasing behaviour

with increasing frequency. This means that for MW the considered center frequency /c

should not be too high which is in contradiction to the result we found in eq. (7) or (8),

where the center frequency should be as high as possible to increase the emitted power.

In other words, here a compromise has to be found in future.

The other curve -fog - shows not only a more smooth behaviour, but, much more interesting, it demonstrates that IR-radiation is clearly attenuated whereas the attenuation of

MW-radiation nearly remains unchanged. Therefore, the MW-radiation power of a real body might become to a similar order of magnitude as the IR-radiation power for fog or.

116
more interesting, smoke. Although this result is surprising, it is further confirmed by the

former discussion that for MW-radiation not only the surface of the burning material but

also the deeper layers are contributing to the total emitted power. Moreover, it can be seen

in Fig. 6 that according to the EN 54 (European Standard) all of the smoke generating

test tires are leading to an attenuation greater than fog in the IR-domain around 300THz

(corresponding to a wavelength of appro.ximately 900nm). We have indicated well-known

typical values of 0.6dB/m (open wood tire) to 2dB/m (smouldering woodfire (pyrolysis),

glow ing smouldering tire (cotton)) for these test tires. To our best knowledge only a few

things are know n on the attenuation of MW-radiation in case of smoke (see [2]). The

experiments were done b\ the military (fog oil, dust dispersed by detonating high explosives, white and red phosphorous packages). For the window frequencies 35GHz and

94GHz onl\ the dust experiments - not the smoke experiments - have shown an attenuation of OdB/m to 0.000375dB/m. At 35GHz the maximum attenuation is a little bit

increased to 0.0005dB/m. These results are also show n in Fig. 6. as three filled bars. Thus, in case of a smoke caused b\ a tire w e expect only a very small attenuation so that

the MW-radiation approach might be prefered in comparison with existing IR-Radiation

based techniques. In conclusion we can state that more research is needed about the attenuation of MW-radiation for fire detection.

After answering the second question w e ask the last question:

2.3 How much MW-power detects an antenna ?

The remaining task is now to construct an almost ideal antenna which converts the re- ceived electromagnetic radiation in an electric power Pd as optimal as possible. Here

we are faced with two principal problems:

1. The antenna will also receive other radiation not caused by the fire.

2. The antenna itself introduces additional noise due to their imperfectness.

Concerning the first problem, it is clear to suppress as much as possible of the undesired

radiation. This leads to the question - What is the cause of undesired radiation ? Of

course, each antenna has a directivity pattern which shows to which extent an incident wave under angle ({) is amplified. For example, a satellite dish exhibits a high directivity in one direction - the so-called main lobe - whereas other directions (side lobes) are mainly

suppressed. However, if a small fire is propagating and even if the main lobe is directed

to the fire, then the antenna will also receive additional radiation caused by other heat

sources. These other sources are simply some material, e.g. the wall of the closed room,

for which the temperature is higher than OK. Thus, in practice we have to expect a lot of

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distortions. For this reason it is very important to develop an antenna with a very narrow beam and very low side lobes. We will not further discuss this task since this is outside

the scope of this paper. However, even if we have developed such an antenna, how can we achieve that its main beam is always pointed directly to the Hre ? This problem can

be solved by the so-called smart antennas known from wireless communications or radar

processing. Smart antennas are usually consisting of an array of antennas where all the

antenna outputs are processed together to steer the main beam of the whole antenna array

ill a certain direction. In other words, the antenna array is able to scan the environment.

Of course, this scanning leads to a picture which shows the temperature profile of the

environment. This picture could be for example displayed on a screen so that a fireman

entering a smoked room could be able to locate the hre. Moreover, since the antenna anay

scans the whole environment, the resulting temperature profile might show - beside the

tire location - also people in danger. The following figure shows the principal setup for a non-mobile situation.

/

/ /

/

/ /

/ / antenna array / — / inhoniogeneous medium

/ electronically ^f --.

\ / /

steerable / . / /

/ antenna beam /' /

Figure 7: An antenna array to estimate the location of the Hre origin.

Now let us consider the second problem. Since the antenna will also exhibit a temperature

of higher than OK. it will introduce some additional thermal noise in our measurement.

In addition, all successive amplifiers will decrease the precision of our measurement due

to additional thermal noise. Fortunately, a very clever approach is known to suppress at

least the noise caused by the amplifiers. This principle is called DiCKK-radiometer and it works as follows. The antenna output signal and a signal of a reference object of variable

118
temperature 77 are the inputs of a switch - called the DiCKE-switch. The output of the

switch is connected with an amplifier circuit so that the output of this circuit is used to measure the incident radiation power. The switch will be periodically switched, e.g. with

a frequency of IkHz. During the measurement the temperature of the reference object will

be varied until the output power of the whole amplifier circuit remains constant. So the

output power will be independent of the sw itching cycles. In this case we can determine

the temperature of the received radiation - it is simply equal to T, in this case - and due to Planck"s law also proportional to the power of the received radiation. Consequently, the

thermal noise of the amplifiers w ill not affect our measurement if we successfully apply

the DiCKE-approach.

In this subsection we discussed the main problems and gave an approach how it could be

possible to solve them. Of course, much work is still needed to find an optimal antenna

configuration for the desired purpose. In the last section we will show the first results obtained by som.e experiments in the fire detection laboratory of Gerh.ard-MercatorUniversity Duisburg. Germany.

3 Some first experiments

The first experiments for fire detection in garbage bunkers with microwaves were done by

DASA (DaimlerChrysler Aerospace AG). Their results have motivated us to investigate

this idea more detailed. In contrast to IR-radiation the MW-radiation seems not only to be

technically unused in fire detection but also it seems not to be measured for standardized

test fires (TFs). For this reason some first experiments were done at the fire detection

laboratory of GERHARD-MERCATOR-University Duisburg.

We have used a commercial satellite dish and a low noise converter (LNC). The dish was adjustable in space so that either the MW-radiation of the room walls or of the fire was measured. The center frequency was /c = 11 GHz and the chosen bandwidth was A/=lMHz.

In the first experiment charcoal of an area of 0.15 m- was ignited with a distance to the

antenna of r = 4 m. The measured power difference was fluctuating between 0.4 - 0.6

dBm. To study the principal effect of smoke we have blown some water vapor between

the antenna and the fire. The maximum loss in power was 0.05 dBm compared to the

smokeless situation. To get a more detailed picture we have carried out most of all test

fires according to the European standard (EN 54), where now the distance between the

antenna and the fire origin was increased to nearly the maximum possible of r = 7 m. We

have obtained the following results.

All fires containing some glowing material (TFl. TF2. TF4) can be easily detected. In

119
Test fire typical behaviour power gain remarks

TF 1 - open

wood fire

flames and later glowing wood0.6 dBm embers were measureable,

flames were not measureable

TF2

pyrolysis

much smoke and

later glowing wood

0.15 dBm smoke has no influence,

embers were measureable

TF 4 - open

polyurethan fire

smoke, flames and

embers

0.15 dBm increased power level probably due to embers

TF 5 - liquid

n-Heptan fire

flames and high

temperature

OdBm no change

TF 6 - liquid

spirit fire

flames OdBm no change

TF7 -

dekalin fire

flames, dark smoke,

low temperature

OdBm

OdBm

neon tube behind the fire can be easily detected

Table 2: Results of the test fires

contrast, fires only consisting of flames (TF 5, TF 6, TF7) cannot be seen. This is not

surprising since a flame has spectral components mainly in the IR- or visible light-region.

However, flames are not in our focus since sophisticated flame detectors are already developed to reliably solve this detection problem [3]. To get a first impression of the influence

of smoke we have arranged behind the test fire TF 7 a neon tube. Despite the optical

dark smoke the neon tube can be easily detected by its MW-radiation. As a consequence,

the assumption that smoke only slightly attenuate the MW-radiation is confirmed by this example.

4 Conclusions

In this paper we presented the fundamentals of microwave radiation for fire detection.

MW-radiation offers some distinct advantages in comparison with conventional IR-radiation. For example, MW-radiation penetrates also optical thick smoke and vapor with only

a slight attenuation and it penetrates principally all materials except metals. Moreover,

MW-radiometers are very insensitive to environmental conditions, such as water vapor

and dust (contrary to infrared methods) and high temperatures (contrary to semiconductor

sensors). Last but not least, thermal microwave noise radiation comes from a thicker

surface layer than does IR radiation. In contrast, two main disadvantages concerning

fire detection can be given, first, with increased center frequency the costs are clearly

increased, and second, because of the relatively long wavelengths compared to IR, the

120
achievable spatial resolution might be limited.

We have also derived a closed formula for the received antenna power. This formula

shows the influence of chosen parameters, like the antenna bandwidth, the antenna effective area and the antenna center frequency and of non-chooseable parameters, like the

temperature of the fire, the distance between fire and antenna, and the surface of the fire. By some first experiments we have demonstrated that with a very simple setup - a commercial satellite dish - fires can be easily detected if the burning material is glowing. In our opinion much research is needed on this area, since many applications can be imagined where microwaves are very useful for fire detection.

References

[1] H. Hellsten, J. Kjellgren, G. Stenstrom, and A. Sume, "A Scanning Airborne

Radiometer for 30GHz and 90GHz", Infrared and Millimeter Waves. Vol. 15,

Academic Press 1986

[2] L. A. Klein, "Millimeter-Wave and Infrared Multisensor Design and Signal Processing", Artech House, Inc., Boston, London 1998

[3] R. Siebel: "Ein LOW-COST Flammenmelder mit guten Alarm- und

Falschalarmeigenschaften" (in German), Proceedings AUBE '99 zur 11. In- temationalen Konferenz uber Automatische Brandentdeckung, 16.-18.3.1999 in Duisburg, Heinz Luck (Ed.), Agst Veriag, Moers 1999, ISBN 3926875 31 3

II

121
Shu Yan, Shu Wang, Zheng Dou

Huazhong University of Science & Technology, Wuhan, China

Henry Systems Technology, Nanjing, China

An Energy Model in Fire Detection and Integrated Analysis on False Alarms

Abstract

Fires in a closed space are theoretically considered as a thermodynamic system in this

paper. Genuine fires and fire like factors cause entropy increment, which demonstrates

the release of kinds of energy including heat, light radiation and material loss. Genuine

fires release much more energy than deceptive fire like factors.

An energy model was constructed and applied to smoke detection. Theoretical analysis

and test result showed that this model and the algorithm of energy release prediction

were suitable to fire detection study, and multi-sensor technique and adaptive alarm

threshold method are effective way to reduce the rate of false alarm and the rate of

failure to alert.

1. Introduction

The causes of false alarm are very complicated in automatic fire detection. Major

aspects are:

a) Non-fire aerosols like cooking fumes, steam, dust, insecticide and cigarette

smoking,

b) Environmental factors including electromagnetic interferences, airflow and rapid .

change of environmental temperature caused by air-conditioning,

c) Aging of components,

d) Poor quality in product design and production, and

e) Poor engineering design including wrong selection of the model and location of

detectors, and poor quality in installation, commissioning and maintenance.

This paper only focuses attention to the false alarm problems that are related to signal of

sensors and signal processing. In order to quantitatively analyze the false alarm, an

energy model in fire detection was constructed. Theoretical relations of the rate of false

alarm and the rate of failure to alert were deduced from this model. Application of this

122
model in smoke detection showed evidence that this energy model was an effective

method to reduce false alarm related to signal of sensors and signal processing ''I

2. The energy model

Fires in a closed space are theoretically considered as a thermodynamic system in this

paper. Genuine fire and deceptive fire like phenomena in this system cause the

increments of entropy, which demonstrates the release of kinds of energy such as heat,

light radiation and material losses (smoke and gasses etc.). The increments of entropy

are different for genuine fires and for deceptive fire like phenomena. When a genuine

fire happens, the system releases energy for a fairly long time, and total energy released

reaches a higher scale. When a deceptive phenomenon happens, the system releases

energy only for a short time, and not so much energy are released^^^.

In the real world, theoretically closed space does not exist. All systems are affected by

environmental factors. We may possibly find the system's entropy increase or decrease.

This means that there are energy flows outside the system caused by environmental

factors and it should be considered in fire detection. In fact, some fire detectors have a

variable steady value tracing environmental changes. In most cases genuine fires and

deceptive fire like phenomena do not last very long, the system's steady values do not

change too much. Therefore the system can be considered as a closed thermodynamic

system during observation period. The energy release will depend on the system itself

rather than outside world (see figure 1).

Energy E A

Steady value RW^

Steady value RWi

Fig. 1 Energy release of a closed system

The energy releases AE for a closed thermodynamic system are kind of random variable

having statistical feature.

123
Suppose the functions of probability density of the energy release AE under non-fire

and fire are respectively P(AE,x = 0) and P(AE,x = 1) , where x are Boolean values

that stand for non-fire and fire. For a given alarm threshold AEj (see figure 2), the rate

of false alarm is

P. = r P(AE,x = 0)dAE,

while the detectivity rate of fire alarm is

P, = P PiAE,x = l)dAE

.

JAE,

The rate of failure to alert is = 1 - P^, . Since I P{AE,x = \)d/SE = 1 , we get

Pe = f

' P{AE,x = l)dAE . (3)

(1)

(2)

Probability density P

PiAE.x = 0) P(AE,x = l)

The rate of false alarm Fire detectivity Pj

Energy release AE

Fig. 2 Probability distribution of the energy released in a closed

thermodynamic system

For a system with adaptive threshold values, the function of probability density of a

threshold AEj is P(AEj) . Then the instantaneous rate of false alarm is

P, = P(AE,x = 0)dAE , and the general rate of false alarm is

J" JAE,

p = E[P. ] = pP(A£^ ) f^" P{AE,x = 0)dAEdAE^ .

In the same way, the general detectivity is

P =E[PA= PP(A£^ ) f" P(A£, X = l)dAEdAET , J-oo JaEi

and the general rate of failure to alert is

(4)

(5)

124
1'+°° ^ cAE, ^ P^=l-P^ = \ P(AE^ ) r P{AE, X = \)dAEdAEj . (6)

J—oo J—oo

It is clear, from above equations, that by using an adaptive alarm threshold AEj the rate

of false alarm can be considerably reduced while the detectivity keeps at a given value,

or the detectivity can be considerably improved while the rate of false alarm keeps at a

given value.

With the method of constant rate of false alarm used in the signal processing in radar,

we can reduce the rate of false alarm to a given value provided that the function of

probability density and statistical feature of energy release AE are available.

According to the equations of thermodynamics, temperature rise is proportional to

energy release. Smoke and gas release are mass losses in a closed thermodynamic

system. From the mass-energy relation £' = mC", mass loss is proportional to energy

release too. The total energy release of a system is the sum of heat energy release, mass

loss (such as smoke, gas and so on) and other energy release (such as light etc.).

Existent fire detectors, such as heat detectors, smoke detectors, gas detectors and light

detectors, all take indirect measurement of energy release.

For different type of fire, the percentages of each part of energy release are different.

That is the reason why the fire detectors with single sensor cannot respond to all type

fires. The success of multi-sensor detectors shows that the more accurate energy

releases we measure, the lower rate of false alarm and higher detectivity we gain. Multisensor and adaptive alarm threshold (multi-criteria) become major development trend of

fire detection ^^^^"^l

3. Application of thermodynamic model in smoke detection

Smoke is one of the major characters at incipient stage for most fires. Other energy

releases, such as temperature change, light radiation etc., are too weak to be measured

in the incipient stage. So the smoke amount can be regarded as the energy release AE.

Suppose that the smoke amount is S, the test starts at the moment of To and ends at Ti,

and the output analogue value of smoke detector is V(t). We then have S =

125
Optical smoke detectors with analogue value ranging from 0 to 255 were used in our

test program. Statistical analysis was carried out on the basis of response curves against

test fires of EN54-9 and some deceptive fire like sources. The result is given in table 1.

Table 1 Smoke amounts against test 'ires and non-fire sources

Fire source Smoke

mount nDigit*se

condLI

Fire source Smoke

imountDDigit*sec

ondD

Non-fire source Smoke

amountDDigit^se

condD

TFi 4095 TF4 (3) 2373 Steam 3910

TF2 37440 TF5 4992 Mosquito incense 3960

TF3 100880 TF6 0 Mosquito incense 2100

TF4(1) 2688 TFT (1) 18020 (ventilated)

TF4 (2) 2835 TF7 (2) 16492 Cigarette smoking 18170

From table 1, we know that the energy release AE of non-fire sources except cigarette

smoking is of the magnitude about 10\ And AE for fire sources with the main product

of smoke (TF2, TF3, TF7) are of the magnitude between 10"* to 10^. However, for other

sources, the smoke amount cannot be regarded as total energy AE because, for example,

the temperature change for fire sources of TFI, TF4, TF5 and TF6 is an important part

of the energy release and can not be ignored. As a special case there is only temperature

change but no smoke product in the experiment of TF6. Smoke detector cannot respond

to TF6 no matter what fire detection algorithm is adopted. The reason is just that the

energy release AE cannot be predicted by smoke sensing. For the non-fire source of

cigarette smoking, the energy release AE reaches the magnitude that many fire sources

reach as this experiment was carried out under a strict condition (smoking just in front

of smoke detector). This implies that over smoking may lead to false alarm.

Fire alarm must be initiated in a real time. It is not permitted to obtain the total amount

of smoke (total energy release) for making a fire alarm. Instead, the method of

prediction of energy release is utilized in fire detection algorithms. Obviously, the

amplitude and changing trend of smoke signal are the main factor of prediction. On the

other hand, the longer the observation period lasts, the more accurate the prediction is.

Therefore using adaptive alarm threshold and suitable observation period can

effectively reduce the false alarm

126
A new algorithm of smoke detection based on the energy release prediction is shown in

Fig. 3. The key point of the algorithm is that the detector makes the prediction of energy

release i.e. smoke amount from time to time. The result of the prediction is A£ . For a

given risk of alarm fnsk, the alarm threshold AEj can be obtained. With the equations of

(4) to (6), the rate of false alarm, the detectivity and the rate of failure to alert can be

calculated. Fire alarm decision can finally be made by the information of Pfa, AEj and

friskSmoke

si gnal S

Prediction

Energy

released 1^ Threshold

Alarm

thre shold AEt

AEt

Rate of false alarm calculation Rate of false —alarm Pi ,—

Risk of alarm Risk of alarm f,risk

Fire alarm

decision- making

Alarm

output

Fig. 3 Block diagram of the algorithm of energy release prediction

for smoke detection

Energy release AE is, in fact, the area coved by smoke curve. Future part of smoke

curve can be predicted on the basis of present part of the curve. A simplest method is

straight-line method with a little poor prediction accuracy. The risk of alarm fnsk can be

set in accordance with the environmental conditions and protection requirements. A

preset alarm threshold AEjo can be determined by experimental data and experiences.

The alarm threshold is then = ^Ej-q *

/^,,^

.

A BP neural network trained by the experimental data can be used to fit the probability

function P(AE, x). The output of the neural network can directly be defined as Pfa and

Pe.

For other detection algorithms of smoke detector, Pfa and Pe can also be deduced,

provided that their equivalent alarm thresholds AEj can be obtained and the equivalent

127
neural network can then be trained. In this way many detection algorithms can be

compared each other and examined objectively.

The algorithm related above is still under development. The non-smoke energy release

cannot be acquired by smoke detector. Smoke detector is difficult to avoid false alarm

by non-fire sources and failure to report the fires that are without smoke (such as TF6).

Therefore, other type sensors (such as temperature sensor) must be combined in fire

detector in order to reduce the rate of false alarm and the rate of failure to alert.

4. Conclusions

The application in smoke detector shows that the energy model is suitable for fire

detection. The model provides theoretical relation of the rate of false alarm and the rate

of failure to alert. Multi-sensor detection technique and multi-criteria data processing

can estimate the energy releases more accurately. Reducing the rate of false alarm, in

the view of sensor signal and signal processing, is possible by using this energy model

and the algorithm of energy release prediction.

References

[1] H. Luck and K.R. Hase, Signal Detection Aspects in Automatic Fire Detection, Fire

Safety Journal, 6 (1983), pp.233-240.

[2] Shu Wang and Zheng Dou, Fire Detection and Fire Signal Processing, Publishing

House of Huazhong University of Science & Technology, 1998 (in Chinese).

[3] Bukowski, R.W. and Reneke, P.A., New Approaches to the Interpretation of Signal

from Fire Sensors, in Proc. Of the 11th Int. Conf. on Fire Detection at the Duisburg

University 1999 (AUBE'99), H. Luck ed., pp.11-21.

[4] Gottuk, D.T., Peatross, M.J. etc., Advanced Fire Detection Using Multi-signature

Alarm Algorithms, in Proc. Of the Uth Int. Conf. on Fire Detection at the Duisburg

University 1999 (AUBE'99), H. Luck ed., pp.237-246.

128
Hans-Christian Muller

Duisburg Gerhard-Mercator-University "

Department of Communication Engineering

Bismarckstrasse 81, 47048 Duisburg

A New Approach to Fire Detection Algorithms based on the Hidden Markov Model

Abstract

In this paper the theory and some experimental results of a new approach to

fire detection algorithms are discussed. The algorithm is based on a signal

classification principle, which is widely used in the field of speech recognition. The sensor signals are transformed into appropriate finite time series. The transformation is done in a preprocessing step that extract a suitable se- quence from the signals to be classified. The time series are modeled by a Hidden Markov Model (HMM). The classification of a recognized sequence

is the final step with respect to the decision making process.

1 Introduction

Advances in semiconductor technology have influenced the development of fire detection

algorithms to a high degree in recent years. The use of microprocessors in fire detection

devices is nearly state-of-the-art. Improvement of detectivity has been achieved in modem

fire detection systems independently of the design scheme with respect to central or distributed intelligence. Implementation of improved detection algorithms leads to reduced

false alarm rates and good detection features in comparison with the classical threshold

detector.

Modem fire detection algorithms are often based on a fuzzy logic or a neural network

approach. They might include some sort of feature extraction methods applied to the

sensor signals.

In the following we will introduce the concept of statistical models of sequential data with

respect to the fire detection problem.

129
2 Hidden Markov Models

Let us consider an um-and-ball system [1], [2]. We assume that there are N (large) glass

urns in a room. Within each is a large quantity of colored balls. There are M distinct

colors of the balls. The physical process to obtain observations is as follows. A genius

is in the room, and. according to some random procedure, it chooses an initial urn. From

this urn, a ball is chosen at random, and its color is recorded as the observation. The

ball is then replaced in the urn from which it was selected. A new urn is the selected

according to the random selection procedure associated with the current urn, and the ball

selection process is repeated. The entire process generates a finite observation sequence

of colors, which we would like to model as the observable output of an Hidden Markov

Model (HMM).

Obviously, the simplest HMM that corresponds to the um-and-ball process is one in which each state corresponds to a specific urn, and for which a (ball) color probability is

defined for each state. The choice of urns is dictated by the state transition-matrix of the

HMM.

Furthermore, it should be noted that the ball colors in each urn may be the same, and

the distinction among various urns is in the way the collection of colored balls is composed. Therefore an isolated observation of a particular color ball does not immediately

tell which urn it is drawn from.

2. 1 Elements of an HMM

The above experiment consists of drawing balls from urns in some sequence. Only the

sequence of balls is shown to us. An HMM for discrete symbol observation such as the

um-and-ball model is characterized by the following:

1. N, the number of states in the model. Although the states are hidden, for many

practical applications there is often physical significance attached to the states of

the model. Thus, in the um-and-ball model, the states correspond to the urns.

Generally, the states are interconnected in such a way that any state can be reached

from any other state. However, other possible interconnections of states are often of

interest. In the following individual states are labeled as { 1, 2, . .

. , A'} and the state

at time t is denoted as q,.

2. M, the number of distinct observation symbols per state. The observation symbols

correspond to the physical output of the system being modeled. For the um-andball experiment the observation symbols are the colors of the balls selected from

130
the urns. We denote the individual symbols as V = {v\,v2, . . . ,vm}.

3. T, the length of an observation sequence.

4. O — [Oi O2 Ot) thQ observation sequence, where O, denotes the observation at

time t.

5. The state transition probability distribution A — [aij] where

aij = P{cjt+\ = j\q, = 0, 1 < j <^

defines the probability of being in state j at time t + 1 given that we were in state /

at time t. For the special case where any state can be reached from any other state

in a single step, we have a,j > 0 for all ij. For other types of HMMs, we would

have a,j = 0 for one or more (/, j) pairs.

6. The observation symbol probability distribution B = {bj{k)},

bj{k) = P{0,=v,,\q,^i), \<k<M

defines the probability of observing the symbol Ot — \\ at time t given that we are

in state j.

7. The initial state distribution n — {ti,}, in which

defines the probability of being in state / at the beginning of the experiment (i.e., at

It can be seen from the above that a complete specification of an HMM requires the

specification of two model parameters, N and M. specification of observation symbols,

and the specification of the three sets of probability measures A, B and n. For convenience,

X — {A.B.n) will be used as a compact notation to denote an HMM.

Using the model, an observation sequence O = {0\ O2 Ot) is generated as follows:

We start our experiment at time r 1 by choosing one of the urns, according to the

initial probability distribution 71, then we choose a ball, the observation symbol from

this urn. The state and the observation symbol at time / = 1 are denoted as q\ and 0\

respectively. After this we choose an urn (may be the same or a different from the urn at

r = 1 ) according to the transition probability distribution A and again select a ball (denoted

as O2) from this urn depending on the observation symbol probability bj{k) for that urn

(state). The continuation of this procedure up to time t — T generates the observation

sequence O = {0\ O2 Ot)-

131
2.2 The Three Problems for HMMs

Most applications of HMMs are finally reduced to solving three main problems. These

are:

Problem 1 : Given the observation sequence 0 — {0\02 Or) and a model X= {A,B,n),

how do we efficiently compute P{0\X), the probability of the observation sequence, for a given model?

Problem 2: Given the observation sequence O = {0[ Oj Oj) and a model X — {A,B,n),

how do we choose a corresponding state sequence Q = [cji qi ... qj) such

that P(0, Q\ X), the joint probability of the observation sequence and the state sequence is maximized?

Problem 3: How do we adjust the HMM model parameter X = {A,B,k) so that P{0\X)

or P(0 , Q\ X) is maximized?

Problem 1 is the evaluation problem, namely, given a model and a observation sequence,

how do we compute the probability that the observation was produced by the model?

Problem 1 can be viewed as one of scoring how well a given model matches a given

observation sequence. For example, if we consider the fire detection problem the solution

of problem 1 allows to make an alarm decision.

Problem 2 is the one in which we attempt to uncover the hidden part of the model. Typical

uses might be to learn about the structure of the model, to find the optimal state sequence

for a given observation sequence.

Problem 3 is the one in which we attempt to optimize the model parameters to best describe how a given observation sequence comes about. The observation sequences to

adjust the model parameters are called training sequences because they are used to "train"

the model. This problem is the crucial one for most applications of HMMs, because it

allows us to create best models for real phenomena.

2.3 Types of the HMMs

One way to classify types of HMMs is by the structure of the transition matrix A. In the

special case of ergodic or fully connected HMMs every state can be reached from every

other state of the model in a single step.

132
(a) (b)

Figure 1: Two types of HMMs

As shown in Figure 1(a). for an N = 4 state model, we have

A =

an ai2 ai3 «14

fl23 fl24

«31 «32 «33 «34

fl41 a42 a43 «44

with a,- , > 0 for all 1 < /. ; < 4

For some applications, particular!} the one discussed here, other types of HMMs have

been found to account for observ ed properties of the signal being modeled better than the

standart ergodic model. One such model is shown in Figure 1(b). This model is called

a left-right-model because the underlying state sequence associated with the model has

the property that the system states proceed from left to right as time increases. Clearly,

the left-right-type of HMM has the desirable property that it can model signals whose

properties change in time in a successive manner (e.g., increasing sensor signal values

obtained form a scattering light smoke sensor). The transition coefficients have the property

Uij = 0.

Hence, no transitions are allowed to states whose indices are lower than that of the current

one. Clearly, the initial state distribution 71 = {ti,} is given by

71, =

133
because the state sequence must begin in state 1. Often, with left-right-models, additional

constraints are placed on the transition coefficients. A constraint of the form

aij =0, j > i + A/

is often used to make sure that large changes in state indices do not occur. In particular,

for the example of Figure 1(b), the value of Ai is 2.

3 Realization

A discussion of the solution of the three problems of HMMs will exceed the score of the

paper. The solution of problem 3, the synthesis or training problem, is not necessarily

part of a detection algorithm. The parameters of an HMM can be viewed as parameters

of the detection algorithm which have to be adjusted by a preceding training procedure.

The solution of problem 1, the analysis problem, will be discussed later from the computational effort point of view which is of some interest as far as the practical realization is

concerned.

3.1 The parameters of the HMM

We are using a left-right-model with N = 10 states. The number of distinct observation

symbols per state is M = 15. Here, the observation symbols represent certain sensor signal

values. The length of an observation sequence equals 7 = 12. We use a sampling rate of

0.2 Hz. Hence, the observation length corresponds to a duration of 1 minute.

The initial state distribution n = {tu, } is given according to the chosen left-right-model by

a 10 X 1 vector with only one nonzero component, i.e. 7ii = 1. The remaining parameter,

the transition probability distribution, represented by a 10 x 10 matrix, as well as the

observation symbol probability distribution, represented by a 10 x 15 matrix, are adjusted

by a couple of training sequences, i.e, we used 30 training sequences taken from test fires

of type TFl, TF3, TF4, TF5 and TF7.

3.2 Solution of Problem 1 A straightforward way to determine P{0\X) is by enumerating every possible state se- quence of length T. There are of such state sequences.

The probability of the observation sequence O = {0\ O2 ... Oj) given the state sequence

134
Q= {q\q2 Qt) and the model X is

T P{0\Q,X)=WP{0,\qt.'k) = hq,{0x)-bq,{02)---bqr{0T)

The probability of such a state sequence Q can be written as

The probability that the O and Q occur simultaneously, is the product of the above two

terms

P{0,Q\X)^P{0\Q,X)P{Q\X)

The probability of O given the model X is obtained by summing this joint probability over

all possible state sequences Q. Hence, we have

P{0\X)= nO\Q.X)P{Q\X)

all Q

From the last equation we see that the summand involves 27—1 multiplications. Hence

summation over all possible state sequences requires (27— 1)A'^ multiplications and — I additions. Even for small values, N — 10 and T — [2, this means approximately

2.3 • lO'^ multiplications. Clearly, a more efficient procedure is required to solve problem 1. Such a procedure exists and is called the forward procedure.

Consider the forward variable a,(/) defined as

a,{i)=PiOu02....,Or,qr = i\X)

i.e., the probability of the partial observation sequence, Oi , O2, . .

. , O,, (until time /) and

the state i at time t given the model X. We can solve for a/(/) inductively, as follows:

1. Initialization

ai(/) =7uA(0/) , l<i<N

2. Induction

3. Termination

N

i=\

bj{Or+i) l<t<T-l, l<j<N

N

P{0\X) = J^aT{i)

135
Again, let us examine the number of multiplications involved with this procedure. The

initialization step involves multiplications. The induction step requires N multiplications plus one for the out of bracket bj{Oi+i) term. This has to be done for I < j < N

and for 1 < / < r, which amounts to (A^+ i)N{T — 1) multiplications in the induction

step. The termination step requires no further multiplications. Hence the total number

of multiplications \s N + N{N + 1){T - 1). For N = 10 and T = 12 we need about 1200

computations for the forward procedure as compared to 2.3 10 '-^ required by the direct

computation of P{0\X).

3.3 The Algorithm

The algorithm we used works as follows: At each discrete time instant t (e.g., multiple

integer of 5 seconds) an observation sequence O is taken from the sensor signal including

the current signal value as well as the 1 1 preceding signal values. Then the a posteriori

probability P{0\ X) is calculated according to the forward-procedure.

4 Experiments and Results

In this section we present some results taken from a computer simulation of the algorithm

described above. The following plots show the output signal s{t) of a scattering light

smoke sensor and the a posteriori probability P{0\X) plotted as

P* [O, X) ^ mm([50, -log[P{0\X)])

The performance of the system in case of a testfire TFl is in the following plot.

250

600

Figure 2: Sensor signal s{t) and the logarithm of the a posteriori probability P{0\X) in

case of testfire TFl

136
Obviously. P[0' X) is maximum (or equivalent: P' (O./.) is minimum) about 100 seconds

after the smoke sensor signal s{t) leaves the steady-state \alue.

Figure 3 show s the preformance of the s> stem in case of a smoldering fire. i.e. testfire

TF2. Here, the maximum of P\ 0 a) is reached 60 seconds after the smoke sensor signal

s{t) leaves the steady-state value.

250

200

150

100

0

1

S{t) [..-

0 50 100 150 200 250 300 350 400 450 500

t H

Figure 3: Sensor signal s{t) and the logarithm of the a posteriori probability P{0\X) in

case of testfire TF2

Figure 4 show s the preformance of the system in case of a slow ly developing fire. i.e.

testfire TFT.

250

600

Figure 4: Sensor signal s{t) and the logarithm of the a posteriori probability P{0\X) in

case of testfire TF7

137
Again, the maximum of P{0\X) is reached 60 seconds after the smoke sensor signal s{t)

leaves the steady-state value.

Finally, in the last simulation presented here we used an artificial burst signal. Signlas

like this are most unlikely to occur in real fire situtations, but may, for example, occur due

to electromagnetic influences.

250

200

150

100

50

0

I 1 —s{t)

1 100 200 300 400 500 600 700 800 900 1000

t H

Figure 5; An artificial burst sensor signal s{t) and the logarithm of the a posteriori probability P{0\X)

Obviously, the maximum value of P{0\ X) (the minimum value of — \og{P{0\ X))) is much

smaller (greater) as compared to the last three examples. Note, a classical threshold detector will reach its alarm condition in case of the shown artificial burst signal.

5 Conclusion

In this paper we have derived a new approach for fire detection algorithms. The key

element of our approach, which appears to be quite useful, is a Hidden-Markov-Model.

To be more precise, we used a left-right-model. Simulation studies has been presented

which show the ability to improve detection features in comparision with the classical

threshold detector.

References

[1] Dugad, Rakesh and Desai, U. B.; A tutorial on hidden markov models; Indian Institute

of Technology, Bombay Powai; 1996

[2] Rabiner, Lawrence R; Fundamentals of speech recognition; Prentice Hall; 1993

138
Thomas Cleary and Kathy Notarianni

Building and Fire Research Laboratory, National Institute of Standards and Technology

Gaithersburg, MD, 20899 U.S.A.

Distributed Sensor Fire Detection

1. Introduction

Most fire detection systems, even those with lOO's of individual detectors reporting to a fire

panel, are designed to alarm on a single detector, be it a single sensor or muhi-sensor

design. The stochastic nature of fire dictates that no single location for a detector is

preferred. In current design, detectors are sited such that each detector "protects" a given

fraction of the building space. While there may be some threshold alarm value adjustment

if adjacent detectors start to sense fire conditions for particular designs, the concept of

distributed sensor fire detection has been pursued no further. Distributed parameter

systems, i.e., systems where the state vectors depend on spatial position, are frequently

encountered in the field of process control and give rise to control theories dedicated to

those systems. The fire detection problem is similar to control of distributed parameter

systems in that both address a problem stated as: what are the number of measurements

required and where are their locations to guarantee either early fire detection or stable

control of the system? Also taking a cue from distributed parameter control where the

distributed sensors need not be, and frequently are not, measuring the same state values

(e.g., a mixture of temperature, concentration, pH, and pressure measurements, etc. for

process control), different types of sensors distributed in space may afford an economical,

optimized fire detection system. The concept put forth here is multi-sensor, multi-criteria

detection with distributed sensing elements.

There are three conceivable reasons to integrate distributed sensing elements for fire

detection. First, a sensor primarily used for another purpose may provide useful

information related to early fire detection. An example might be a carbon dioxide sensor

used for demand-controlled ventilation in a building, and located either in a return air duct

common to several rooms, or in a single room. Second, cost may limit a particular sensor

139
to either a single location or a limited number of locations; other types of sensors would be

needed to fill in the gap in space coverage. Third, a particular sensor may not be suitable

for a particular location due to naturally-high ambient levels it would sense there (i.e., a CO

sensor located next to a parking garage). This paper details a case study that utilized model

simulations to assess the relative performance benefits of distributed sensing over singlestation, single-sensor smoke detection and co-located multi-sensor detection.

2. Fire Model Runs

This study utilized 500 individual CFAST fire simulations performed by Notarianni [1].

CFAST is a multi-room zone fire model developed at NIST [2]. The space configuration

considered was a seven room arrangement representing a single-floor apartment (Figure 1 ).

Door Q2 • Room 7 - Corridor

Door 39.6 m X 2.0 m

Room 4 - Kitchen

3.4 m X 2.1 m

Room 6 ^ Entrance

H-1.5 m x2.6 m

•

CO, C3

•

S, C1-4

CO, C2

T, C2

Room 5 - Bath

3.8 m X 3.1 m

Door

Window Door Door Door

Room 1 - Living Room

5.5 m X 5.3 m

Room 3 - Bedroom

3.4 m X 5.6 m

• •

CO, C4 T, C3

Dftor Window Window

Room 2 - Balcony

4.9 m X 1.9 m

Window Window

Figure I. Schematic of apartment layout. The distribution of smoke (S), temperature (T),

and carbon monoxide (CO) sensors are shown for the various cases (C#).

140
The 500 simulations encompassed a realistic distribution of potential fire scenarios. The set

included a combination of scenarios that represent statistically, both the types of fires and

the frequency at which they occur in a given occupancy type. Design fires were made up of

fire events (heat release rate and location) and the characteristics of the material burning.

Building geometry, properties of construction materials, and seasonal weather conditions,

(outside ambient temperature) were used to fix initial and boundary conditions.

Information about the uncertainty, variability, and correlational structure of the input

parameters was used to define the fire scenarios. Fire growth rate inputs were one of the

2, 2 "t " fires: slow, medium, or fast. A "t " fire is a modeled fire where the heat release rate

increases fi'om zero as a function of time to the 2 power. Standard pre-exponential values

are defined as slow, medium, fast, etc. The fire location was moved to different rooms for

different simulations. Model output included smoke concentration, CO concentration, and

temperature in the upper layer as a function of time in each room. There are significant

limitations in zone-model simulations with relation to detector response (instantaneous

uniform mixing in the layers, no flow velocity information, etc.). Here it was assumed that

the detector instantaneously sees the computed upper-layer value of smoke, CO, or

temperature. Thus, the computed results were used as the sensor response for smoke, CO

and temperature sensors. Given the model limitations and the assumption of instantaneous

detector response, it was decided not to consider rate-of-rise of any of these computed

values in the alarm rules. It is noted however that rate-of-change and other real sensor

signal features contain a wealth of information that could be exploited in advanced pattern

recognition algorithms developed for distributed sensor detection systems.

Figures 2-4 show the computed upper-layer values of smoke optical density, CO

concentration and temperature as a function of time in each room for one simulated fire, a

slow t fire located in the bedroom.

3. Detection Rules and Sensor Distributions

Four sensor spatial configurations were examined along with 4 different rules governing

the alarm state. Each sensor configuration and rule set constitutes a CASE for dentification

purposes. The sensor configurations are shown in Figure 1. Configuration 1 is the base

case of a single smoke detector in the entrance (room 6) which represents the minimum

141
Time (s)

2 Figure 2. Smoke optical density for a slow t fire located in the bedroom.

0 100 200 300 400 500

Time (s)

Figure 3. CO volume fraction for a slow t^ fire located in the bedroom.

0 100 200 300 400 500

* Time (s)

Figure 4. Upper-layer temperature for a slow t fire located in the bedroom.

142
code requirements for existing residential dwellings. Configuration 2 is a detector

consisting of three sensors: smoke, CO, and temperature, co-located in the same space, the

entrance (and presumably in the same detector housing). Configuration 3 is a smoke

detector in the entrance, a heat detector in the bedroom, and a CO detector in the kitchen.

A detector in the bedroom is a logical addition based on U.S. code requirements for new

construction. The CO sensor in the kitchen is logical if gas appliances (water heater, dryer,

etc.) are located there. Configuration 4 moves the heat sensor to the corridor, and moves

the CO sensor to the living room.

The four rules consist of fixed thresholds or threshold adjustments based on other sensor

signals. They are:

Rule 1 - If smoke optical density > 0.06 m'^ ( 4 %/ft obscuration), then

alarm is on

Rule 2 - If CO volume fraction > 1.0 x 10"^ and smoke optical density >

0.015 m"' (1 %/ft obscuration), then alarm is on

Rule 3 - If AT > 5 °C and smoke > 0.015 m"', then alarm is on

Rule 4 - IF AT > 15 °C, then alarm is on. AT is the temperature difference between initial ambient room temperature and the upperlayer temperature. At each time step the rules were checked to see if an alarm condition

was indicated. If so, the time to alarm was noted along with the rule that yielded the alarm.

The following Cases were examined:

CASE 1 : Configuration 1 and Rule 1 , (Base Case)

CASE 2: Configuration 2 and Rules 1-4

CASE 3: Configuration 3 and Rules 1-4

CASE 4: Configuration 4 and Rules 1-4.

4. Results and Analysis

Figure 5 shows the alarm time versus simulation run for CASE 1. The mean alarm time

was 88 s. It is more illustrative to compare other CASES to their improvement from the

CASE 1 . Figures 6-8 show the difference between the base case alarm time and the alarm

time for CASES 2, 3, and 4 respectively. The mean alarm time for CASES 2, 3, and 4 were

50 s, 81 s, and 67 s respectively. The distributed sensor configuration with the temperature

143
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144
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Figure 8. Decrease in time to alarm between CASE 4 (smoke, CO, and temperature sensors

in the entrance, living room, and corridor respectively) and CASE 1 (base case).

sensor located in the bedroom, (CASE 3) performed only slightly better than the single

smoke sensor CASE 1 . The co-located sensor configuration, CASE 2, performed the best,

on average, over all simulations.

Figures 9- 1 1 show the decrease in alarm time from the base case (CASE 1 ) versus the rule

that >ielded the alarm in each simulation for CASES 2, 3 and 4 respectively. In Figure 9,

notice that rule 2 (CO and smoke) was never first to yield an alarm in the co-located sensors

configuration, and tiiat significant reductions in alarm time were obtained with the

temperature criterion (rule 4). Figures 10 and 1 1 show that rule 2 was first to yield an

alarm a number of times in these distributed sensor configurations, though the decrease in

time to alarm was less than 50 s for each time that rule was first

The reason rule 2 was never the first to indicate alarm in Configuration 2, co-located

sensors, is due to the selection of the CO and smoke yields used in the simulations. Smoke

was always present in sufficient quantity to invoke rule 1 prior to CO volume fiction

reaching the threshold in rule 2.

The preceding analysis considered only the reduction in alarm time for each fire scenario.

A better metric would be to consider the life safety impact of the detection schemes. Here,

a simple tenability criterion, a threshold upper-layer temperature in the room of (fire)

origin, was used to compare the relative performance ofthe detection schemes. Two

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smoke, CO and temperature in the entrance, living room, and corridor respectively.

146
threshold upper-layer temperatures, 65 °C and 150 °C were specified as distinct limits

indicating untenable conditions, with the lower value representing a more conservative

criterion. A more complete hazard analysis would include multiple tenability criteria

evaluated along egress paths.

Figures 12-15 show the difference between the time to reach the upper-layer threshold in

the room of origin and the time to alarm for each simulation. A positive time difference

represents the length of time after alarm but before a hazardous condition is reached in the

room of origin, and conversely a negative time difference represents the length of time a

hazardous condition exists in the room of origin before alarm. For both threshold

temperatures all cases show positive and negative differences. The average difference over

all simulation runs for each threshold and CASE is given in Table 1

.

Threshold

Temperature

Criterion ( °C)

Average of Tuntenabilm- - Talarm (s)

CASE 1 CASE 2 CASE 3 CASE 4

65 -2 35 5 19

150 61 98 67 81

Table 1. Average difference between time to reach threshold temperature in the room of

origin and the time to alarm for all simulations.

For the 65 °C threshold, the base case average time difference was less than zero, meaning

on average the room of origin reached hazardous conditions before alarm. For a 150 °C

threshold, the base case average time difference increased to over a minute. For both

threshold temperatures CASE 2, the co-located sensor configuration yielded the longest

average time difference, 37 s longer on average than the base case. The next best was

CASE 4, 20 s to 21 s longer on average than the base case. The distributed sensor

configuration CASE 3 performed only slightly better than the base case. Maximizing the

average time to escape hazardous conditions would be a goal of an optimized sensor

configuration. Another constraint would be to reduce or eliminate negative time

differences and increase small positive time differences between hazardous conditions and

time to alarm (i.e., maximize the number of fire scenarios where an alarm allows for

adequate escape from the hazard).

147
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and the time to alarm for CASE 1 (base case).

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and the time to alarm for CASE 3 (distributed sensors).

148
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Figure 15. The difference between the time to untenable temperature in the room of origin

and the time to alarm for CASE 4 (distributed sensors).

While the distributed sensor configurations did not perform as well as the co-located

sensors configuration, the results were still encouraging. The rule base was not optimized

for the distributed sensor configuration, nor were all three sensor/room combinations tried.

Also, this set of simulations was skewed toward rapidly growing fires compared to fire

sensitivity test fires developed for detectors (even a "slow" t^ fire reaches 29 kW in 100 s).

Slower growing fires or smoldering fires would not have the rapid smoke and temperature

rise associated with these simulated fires, and could produce higher CO yields. The fact

that the apartment entrance was centrally located was particularly beneficial to the colocated sensor configuration.

5. Conclusions

From this modeling exercise and analysis, it is concluded the distributed sensing may

improve time to alarm over single-station detectors, however, the location of sensors, their

type, and the rules for processing multiple sensor signals need to be tailored for each

specific application. More work is needed to develop simulation data that includes many

more very slow growing fires with properties reflecting both smoldering and flaming

conditions. Sensor environments obtained fi'om simulations need to more closely follow

actual temporal and spatial variations expected in real cases. With such data realistic sensor

response models can be employed. Nuisance source simulation data is needed to assess the

ability of sensor combinations and algorithms to reject nuisance alarms. HVAC flow needs

to be included since it affects transport of combustion products. The most appropriate

149
models need to be selected and tested to assess their ability to produce "good" simulation

data sets. If this research evolves to point where a rich set of statistically valid fire and nonfire simulations can be generated for given building configurations and occupancies, such

data could be exploited by using it to develop and train distributed-sensor, pattern

recognition algorithms for early fire detection.

6. References

[ 1 ] Notarianni, K. A., "The Role of Uncertainty in Improving Fire Protection

Regulation," Ph.D. Thesis, Carnegie Mellon University, Pittsburgh, PA, May, 2000.

[2] Jones, W. W., Forney, G.P., Peacock, R.D., and Reneke, P.A., "A Technical Reference

for CFAST: An Engineering Tool for Estimating Fire and Smoke Transport," Tech. Note

143 1 , National Institute of Standards and Technology, Gaithersburg, MD, January, 2000.

150
Daniel T. Gottuk, Ph.D., P.E. and Lawrence A. McKenna, Jr.

Hughes Associates, Inc., Baltimore, Maryland USA

Spot And Aspirated Laser Smoke Detection In Teiecommunications Facilities

ABSTRACT

Full-scale, in-situ comparison tests of spot and aspirated laser detection systems in an

active telephone exchange were conducted to compare the response times of spot and

aspirated smoke detection systems when exposed to a variety of smoke sources and

ventilation conditions. The paper discusses the performance of multiple systems

varying in detector type, detector spacing and detector locations (i.e., mounted on the

ceiling versus on return air grills).

1.0 Introduction

With the continuing advances in technology and the use of high value electrical

equipment, the need for very early warning fire detection (VEWFD) has increased [1].

The increased use of VEWFD systems has resulted in more system options being

available to the user. Currently, the two primary types of VEWFD systems are aspirated

(air sampling) smoke detectors and laser-based spot-type smoke detectors. Also

available are the standard photoelectric and ionization detectors set to very high

sensitivities. Besides having several technologies to choose from, system designers

must address the varying complexities of using these systems in diverse applications,

such as semiconductor and telecommunication facilities, with little validation or test

data.

Full-scale, in-situ comparison tests of spot and aspirated laser detection systems in an

active telephone exchange were conducted to compare the response times of spot and

aspirated laser smoke detection systems when exposed to a variety of smoke sources

and ventilation conditions. The paper discusses the performance of multiple systems

varying in detector type, detector spacing and detector locations (i.e., mounted on the

151
ceiling versus on return air grills). The work provides data intended to guide system

designers in the selection and use of very early warning smoke detection systems.

2.0 Experiments

Reference 2 provides details of the experimental setup and procedure. The test space

was one entire floor of a three-story building, 30.5 m (100 ft) wide, 54.9 m (180 ft) long

(1672 m' (18,000 ft^)) 4.7 m (15.5 ft) high (Figure 1). The exchange was an active

facility, which continued to operate throughout the course of the test program. The

facility housed a DMS switching system, which covered approximately 223 m^ (2400

ft^) of the south end of the building (designated the DMS equipment area), and a main

frame and transmission equipment that covers approximately 30 percent of the

remaining open floor space (designated the toll/frame area). Except for three small

rooms on the periphery (total ~ 67.8 m" (730 ft") the test area was not subdivided,

allowing free transport of gas/smoke throughout the entire space.

The test facility was maintained at normal conditions between 22.2 °C and 22.7 °C (72

and 73 °-F) and 45% relative humidity. Conditions in the test area were maintained via

two independent ventilation systems: 1) a recirculation system with three pre-packaged

fan/coil cooling units which served the DMS equipment area and 2) the general building

HVAC which primarily served the larger toll/frame area. The layout of both systems is shown in Figure 2. The recirculation system serving the DMS equipment area consisted

of three air conditioning units sharing a common discharge ductwork (ACl, AC-2, and

AC-3 in Figure 2). This system provided the bulk of the airflow through the switch

equipment. Compared to the toll/frame area, the airflow velocity in the DMS equipment

area was high. Two ventilation conditions were investigated. The first condition

consisted of all three recirculation units and the general HVAC unit operating (i.e.,

normal operation at 12.2 and 5.9 mVs (25844 and 12618 cfm), respectively). The

second condition consisted of shutting down two of the three recirculation AC units in

the DMS area yielding a supply flow rate of 2.9 mVs (6,224 cfm) from the remaining

152

154
AC unit (ACl) and the normal 5.9 m^/s (12618 cfm) from the general HVAC supply.

Two different types of commercially available laser based smoke detectors were

evaluated in this test program: 1) spot type and 2) aspirated. Both types utilized the

light scattering principal for smoke detection. The detector layouts were typical of that

used in the telecommunication industry [3] and was driven by the 6.1 m x 6.1 m (20 ft x

20 ft) column spacing (bay) in the facility, which is typical of North American telecom

buildings. Figure 2 shows the locations of the detection systems. All systems were

designed and installed according to standard practices and commissioned by

manufacturer authorized personnel.

The spot detection systems consisted of VIEW detectors (LPX-751) installed at ceiling

level at both 18.6 m' (200 sq ft) (hereinafter referred to as "200 SF') and 37.2 m" (400

sq ft) (hereinafter referred to as "400 SF") spacing. Spot detectors were also mounted

over the return grills of the ventilation systems (designated as Spot Returns). The 200

SF detectors covered regions 3 m x 6 m (10 ft by 20 ft). The 400 SF detectors covered

areas that measured 6 x 6 m (20 by 20 ft), corresponding to the coverage areas of the

two closest 200 SF detectors. All of the detectors (including the aspiration system) were

installed using four circuits and were monitored by two Notifier panels. The Notifier

AFP-400 and AFP-300 control panels were programmed using the Veri«Fire 400TM

Programming Utility, revision 3.51. VIEW detector sensitivity levels were fixed at the

beginning of the test program to Pre-Alarm setting #4 (0.05 %/ft) and Alarm setting #3

(0.10 %/ft). The Cooperating Multi-Detector Sensing feature was enabled

independently for the 200 SF and the 400 SF View systems using condition C, which

involves two adjacent detectors. Alarm verification was not used.

The layout and installation of the air aspirated smoke detection systems were performed

in conformance with current mdustry practice, as embodied in Reference 3. The

aspirated systems consisted of three VESDA LaserPlus detectors installed to cover three

regions of the test space: 1) the 929 m" (10,000 ft^) of space at the north end of the space

(toll/frame area); 2) the remaining 743 m" (8,000 ft^) at the south end of the space (DMS

equipment area); and 3) the return air inlets. The VESDA systems (1,2 and 3) were

numbered according to the designations described. The detector settings corresponded

155
to typical sensitivity levels used in telecommunication facilities: Alert was 0.0125 %/ft.

Action was 0.022 %/ft, and Firel was 0.03 %/ft. No delay settings were used.

Five smoke sources were evaluated in this study, based on an understanding of past fire

incidents in the targeted occupancies (telecommunications and data processing) as well

as an understanding of the equipment and materials used in these occupancies that may

become either the source of ignition energy, or first fuels for a fire [4-8]. The smoke

sources consisted of the following:

1. Overheat of BSI 6266 wire samples,

2. Overheat of Bell Canada wire samples,

3. Mixture of potassium chlorate and lactose (BSI 6266 test [7]),

4. Intemal printed wire board (PWB) failure [8], and

5. Conductive heating test of an EPDM insulated cable [8].

The smoke sources were evaluated at ten different locations throughout the test facility,

as well as at various heights (e.g., floor level, 2 m (6.6 ft) and 3.8 m (12.5 ft) high).

Figure 1 shows the smoke source locations. Some source locations were selected to

specifically evaluate the effect of various ventilation conditions (e.g., high airflow in the

DMS and relatively still air zones) and geometric considerations (e.g., obstructions due

to duct work or cable trays). Three locations (7, 9 and 10) were randomly selected.

3.0 Results And Analysis

In total, fifty-six (56) tests were conducted, encompassing variations in source type,

source location, and ventilation conditions. Nineteen (19) BSI wire tests were

conducted, ten (10) Bell wire tests, twenty (20) chlorate/lactose tests, five (5) PWB

tests, and two (2) conductive heating tests. In thirteen of the tests, the smoke sources

were either of such small size and duration or in a hard to detect location that no system

reached a detection threshold. Of the remaining 43 tests in which at least one system

reached a detection threshold, various analyses were conducted to compare the response

perfoitnance of the different detection systems evaluated.

156
The analyses of response times was conducted based on two critena. The first cnterion is

the time of first response. This time refers to the first time at which any detector within a

system reaches any prealarm or alarm criteria. For the aspirated detectors, alarm condition

"Alert" was always first even if "Firel" was attained during the test. Spot detection systems

sometimes transitioned directly from normal conditions to "'Alarm"" without signaling a

"Prealarm"" condition first. The time of first response critenon pro\ ides data from w hich one

can assess the earliest possible notification provided by a detection s\stem in response to a

smoke source.

The second criterion used for evaluating system performance w as the time to full alaiTn,

either "Alarm" for the spot or "Firel" for the aspirated systems. Depending on the end

user and the particular facility application, an alarm condition ma\- be the significant

criteria for evaluating a detection system. As the results show, comparison of detection

systems based on the two evaluation criteria can van,- with respect to which system

provides faster or more complete detection capabilities.

Results were evaluated for five different systems, defined as follows: (a) 200 SF Spot,

(b) 400 SF Spot, (c) Spot Returns, (d) Aspirated Ceiling (1 and 2). and (e) Aspirated

Returns (3). Using these five systems allowed results of key components of both the

spot-type and the aspirated detection systems to be evaluated. Current practices would

dictate that a ceiling mounted spot-type detection system (200 SF or 400 SF) would be

the primary alternative to the AT&T specification of an aspirated system installed at the

ceiling and at air return gnlls (i.e., full aspirated system 1. 2 and 3). The above system

categories allow for such comparisons to be made while also evaluating other

combinations, such as the benefits of including spot or aspirated detection at the air

returns.

An example of results for selected system comparisons have been summarized below.

Table 1 shows a summary of the comparisons between the 200 SF Spot system and the

full Aspiration (1,2, and 3) system based on time to first response and time to alarm

results. The "Total Number of Tests" reported in the table represents the number of

tests in which at least one of the systems being compared responded according to the

157
criterion being evaluated. As can be seen in Table 1, the 200 SF Spot system responded

to more sources when evaluated according to either the time to first response or time to

alarm criterion. The table also shows the number of tests in which each system

responded faster than the other. The Aspirated (1,2, and 3) system was first to respond

in more tests (26 v. 17) than the 200 SF Spot system when compared according to the

time to first response, but the 200 SF Spot system alarmed first in more tests (19 v. 13)

when evaluated according to the time to alarm. For most system comparisons, excluding

the cases in which one system did not respond, the difference in response times were

generally within one to two minutes. Table 2 presents a similar summary of the

comparisons between the 400 SF Spot system and the full Aspiration (1,2, and 3)

Table 1 - Comparison of First Response and Alarm Results for the 200 SF Spot and the

Aspirated (Zones 1, 2, & 3) Detection Systems.

FIRST RESPONSE

Total Number of Tests Where System Achieved Either Pre-Alarm or Alarm

43

Number of times Aspirated system responded 34

Number of times 200 SF Spot system responded 40

Number of times Aspirated system responded first 26

Number of times 200 SF Spot system responded first 17 ALARM

Total Number of Tests Where System Reached Alarm 32

Number of times Aspirated system alarmed 19

Number of times 200 SF Spot system alarmed 29

Number of times Aspirated system alarmed first 13

Number of times 200 SF Spot system alarmed first 19

Table 2 - Comparison of First Response and Alarm Results for the 400 SF Spot and the

Aspirated (Zones 1, 2, & 3) Detection Systems.

FIRST RESPONSE

Total Number of Tests Where System Achieved Either Pre-Alarm or Alarm

39

Number of times Aspirated system responded 34

Nimiber of times 400 SF Spot system responded 31

Number of times Aspirated system responded first 28

Number of times 400 SF Spot system responded first 11 ALARM

Total Number of Tests Where System Reached Alarm 24

Number of times Aspirated system alarmed 19

Number of times 400 SF Spot system alarmed 15

Number of times Aspirated system alarmed first 17

Number of times 400 SF Spot system alarmed first 7

system. Several observations regarding system response relative to smoke source type

158
and location can be made based on the test results. The aspirated detection systems

(both ceiling and return detectors) were unable to detect most of the BSI wire tests.

Considering all three aspirated detectors, only 6 of 19 BSI wire tests were detected. In

comparison, the 200 SF Spot system responded in 13 tests and the 400 SF Spot system

responded in 8 tests. All detection systems were unable to detect a majority of the Bell

wire tests. Evaluating the detector responses when sorted per source location revealed

that smoke from source Location 4 (the frame, which consists of horizontal racks of

wire) was not detectable by either the spot or the aspirated return detectors. Except

primarily for one PWB test, smoke from Location 9 was not detected by any of the

systems. Location 9 was in the toll/Frame area on top of a cable tray and subject to high

airflows.

Based on the criteria of time to full alarm, few alarm conditions were reached by any

detection system for the Bell wire tests (4 of 10 tests). The Spot Return detectors

reached alarm conditions in only 5 of all 53 tests reported. The Aspirated system (1,2

and 3) and the 400 SF Spot system only reached alarm level in 3 of the 19 BSI wire

tests. The 200 SF Spot system alarmed in 10 BSI wire tests.

A review of the response times showed that there is considerable variability in the alarm

times from a given detection system due to changes in source type and location. Even

for tests in which conditions were nominally the same, response times varied by up to a

minute. For example, four lactose/chlorate tests were conducted in the same location

with no changes in ventilation. The 200 SF Spot system and the ceiling and return

aspirated systems had times to first response that varied by up to 48 to 72 seconds

between tests. On a percentage basis with respect to the times to respond, these time

differences represent roughly 30 to 70 percent variability. These differences may be

partially attributable to the way in which the lactose/chlorate burned, but they also

illustrate the variability that can be obtained due to subtle and imperceptible differences

in events, such as ventilation conditions.

159
4.0 Conclusions

Based on the analyses of the detection system response times, the following conclusions

were reached:

1. The 200 SF Spot detection system (ceiling mounted detectors) and the full

Aspiration (1, 2 and 3) system (ceiling and return air grills) performed

comparably. Each system demonstrated relative performance advantages

depending on the criteria of performance. Considering the time to first response,

the Aspiration system responded sooner in a majority of tests (26 v. 17);

however, the 200 SF Spot system detected a greater number of sources (40 v.

34). Based on the time to alarm results, the 200 SF Spot system provided more

responses (29 v. 19) than the aspiration system. The spot detection system also

alarmed first in more tests (19 v. 13). Even though neither system was clearly

superior in response time compared to the other, due to the high sensitivity of

these systems, it is expected that they respond faster and to more incipient fires

than conventional, standard sensitivity smoke detection systems.

2. The 400 SF Spot system provided a decreased level of performance (i.e., ability

to detect the smoke sources and reach alarm points) compared to the 200 SF

Spot system. Consequently, the performance of the 400 SF Spot system was

also not as good as that of the full Aspirated (1,2 and 3) system. However, the

performance of the 400 SF Spot and the Ceiling Aspirated (1 and 2) systems

were more comparable. Comparing the 400 SF Spot versus the full Aspiration

(1,2 and 3) system, the Aspirated system was able to respond (34 v. 31) and

alarm (19 v. 15) to more fires than the 400 SF Spot system. Similarly, the full

Aspirated system responded faster than the 400 SF Spot system when evaluated

according to either the time to first response or time to alarm criterion (28 v. 1

1

and 17 v. 7, respectively).

The Ceiling Aspirated and the 400 SF Spot systems detected approximately the

same number of sources when evaluated according to either the time to first

response (31 v. 31) or time to alarm criterion (14 v. 15). The Ceiling Aspirated

system typically responded and alarmed first (26 v. 11 and 13 v. 8, respectively),

160 .
typically ranging from 2 to 80 seconds faster than the 400 SF Spot system. The

time difference was less than 60 seconds for the majority of the tests. Depending

on the facility and the end-user requirements, these time differences may not be

considered significant for the incipient-type sources evaluated.

There was no clear performance difference between the spot detectors and the

aspirated detectors used at the return grills in these tests. The Aspirated Returns

system and the Spot Retum detectors provide approximately the same capability

to respond and alarm to a fire (25 v. 29 and 10 v. 7, respectively). Depending on

the criterion used for evaluation (time to first response or time to alarm), both

systems were shown to respond faster than the other.

The inclusion of the spot detectors at the retum air grills with the 200 SF Spot

ceiling system provided little improvement in detection performance compared

to the 200 SF Spot ceiling system used alone. Considering the time to first

response, the Spot Returns detected fewer tests (25 v. 40 out of 43) and were

slower to respond in most tests (4 v. 39) than the 200 SF Spot system. Based on

the time to alarm results, the Spot Returns system also provided fewer alarms (7

V. 29 out of 31) and alarmed slower in most tests (2 v. 29) than the 200 SF Spot

system. The biggest contribution provided by the Retum detectors was the

detection of 3 additional fires that were not detected by the 200 SF system.

The Aspirated 3 detector installed at the air handler retum grills provided a small

increase in detection capability for the full Aspirated system (1, 2 and 3)

compared to the Ceiling Aspirated (1 and 2) system used alone. Considering the

time to first response, the Aspirated Retums system detected fewer tests (25 v.

31 out of 34) and were slower to respond in most tests (7 v. 27) than the Ceiling

Aspirated system. Based on the time to alarm results, the Aspirated system at

the air retums also provided fewer alarms (10 v. 14 out of 19) and alarmed

slower in the majority of tests (7 v. 12) than the Ceiling Aspirated system. All of

the five tests, in which the ceiling system did not alarm and the retum system

did, were at a single source location close to the air retums.

161
6. The results reported in this test series, with respect to the value of return air

intake detection, are counter to general practices and recommendations in the

telecommunications industry [1,3]. HVAC practices in the telecommunications

industry vary with different airflow patterns and volumetric flow rates depending

on the heat loads imposed by the telecommunications equipment present in the

room and the design concept used. The differences observed in this study may

be attributed to the fact that the general practices were based on studies in which

the HVAC systems consisted of a single return point and higher airflow rates.

5.0 References

[1] "Fire Prevention in Telecommunications Facilities," in Network Reliability: A

Report to the Nation, Compendium of Technical Papers, Federal

Communications Commission Network Reliability Council, published by

National Engineering Consortium, Inc., Chicago, IL, June 1993.

[2] Gottuk, D.T. and McKenna, L.A., "Spot and Aspirated Laser Smoke Detection

in Telecommunication Facilities," submitted to Fire Technology, 2000.

[3] AT&T Fire Safety Practice 760-630-307, Engineering, Design, and Installation

of Air Sampling Smoke Detection (ASSD) Systems for Telecommunications

Equipment and Data Processing Applications, Issue 1, AT&T Corporation,

Basking Ridge, New Jersey, September 1995.

[4] Hinsdale Central Office Fire Final Report, Joint Report of the State Fire

Marshal and Illinois Commerce Commission Staff, Springfield, IL, March 1989.

[5] Taylor, Kenneth T., "Temporarily Disconnected," Fire Journal, Vol. 83, No. 3

(May/June, 1989).

[6] McKenna, Lawrence A. Jr., "Reconstruction of the Power Plant Fire at the

Pacific Bell Central Office at 420 South Grand Avenue, Los Angeles, CA,

March 15, 1994," AT&T Environmental & Safety Engineering Center, Basking

Ridge, NJ, November 1994.

[7] British Standards Institute standard BS 6266, "Code of Practice for Fire

Protection for Electronic Data Processing Installations, " 1992.

[8] McKenna, Jr., Lawrence A., Daniel T. Gottuk, and Phillip J. DiNenno,

"Extinguishment tests of Continuously Energized Class C Fires," Halon Options

Technical Working Conference, 12-14 May 1998.

162
Dipl.-Ing. RainerSiebel

Gerhard-Mercator-Universitat, Duisburg, Germany

Strategies for the development of detection algorithms

1. Introduction

Multi-sensor/Multi-criteria based fire detectors require far more complicated detection

algorithms as compared to simple threshold detectors, which are still used as single sensor based fire detectors. Cheap and powerful microprocessors are available to carry out this task. They may be implemented in each detector or alternatively as a central processing

unit to carry out the fire or no-fire decision for all measured values being transmitted from

various sensor-groups of a fire detection system.

A combination of smoke- and heat-sensors is at present frequently used in fire detection

systems. The discussions not only on this congress show that a threefold sensor combination using smoke, heat and a gas sensors (for example CO) might be used in the

foreseeable future. The advantage of such a threefold combination is obvious for the simple reason that a more reliable alarm decision is possible if it is based on the observation of different physical phenomena associated with a genuine fire. The combination of smoke- and heat-sensors is advantageous in case of open flame fires but the heat sensor does not help if the important class of smoldering fires is considered.

A combination of smoke- and CO-sensor, however, would be beneficial in the latter case. At present there are still some important restrictions to be taken into consideration for the

design of new detection algorithms of higher complexity.

1. They possibly require much more computational power for each sensor combination. For hundreds of sensors per line this may quickly exceed the available computational power of a central processing unit since part of it is reserved for other

administrative tasks (line protocols, handling of displays etc.).

2. Manufacturers of fire detection systems are for good reasons not too eager to re- place their tried, tested and reliable central processing units by completely new

devices with more powerful processors because a complete redesign is extremely

expensive! For this reason the available processing power as well as the "Random

Access Memory" (RAM) resources are usually rather limited.

3. The test authorities in Europe insist to understand at least the principle design rules

of a detection algorithm. This is important as far as the selection of the detection

method is concerned to avoid problems which may occur later in the certification

process.

For these reasons it is recommendable to pursue a certain strategy and to keep in mind

the given restrictions during the early design phase of a new detection algorithm.

163
2. Binary signal detection.

2.1 Principle solution of the detection problem.

Figure 1 shows a block diagram of a multi-sensor based detector and figure 2 illustrates the mode of operation of the decision-block from figure 1. Hq denotes the so called

Slit) o52 (t) ^

S3W ^

Sampling

Sampling

Sampling

5i

52

Decision

5

S\n

I 53„ )

*-oD(S)

D e {0,1}

Figure 1: Block scheme of a multi-sensor based detector

Observation space F

(multi-dimensional)

Figure 2: Observation space.

"zero-hypothesis", i.e. the signals Si{t) or signal vectors S\Hq originate from no-fire

situations. Correspondingly, hypothesis Hi denotes all signals Si{t) or signal vectors S\Hi originating from fire situations.

Each signal vector points to one specific location inside the multidimensional observation

space, which is here schematically plotted as a two dimensional space.

164
In the aim to draw an unambiguous decision it is necessary to subdivide the observation

space into two disjoint subspaces. This means:

if the signal vector points to the subspace Fi the decision is: alarm,

if the signal vector points to the subspace Fq =^ the decision is: no-alarm.

Unfortunately, signal vectors S\Ho and S\Hi under either hypotheses may point to both

subspaces Fo and F]. Hence, we have not only two types of correct decisions but moreover two possible types of wrong decisions - a false alarm or a missed alarm.

The most simple case: threshold detector for one sensor. (Here the observation space is a one dimensional straight line.)

Nearest more complicated case: Two subsequently measured values from one sensor

or one measured value from each of two sensors. (Here the observation space is a two

dimensional plane.)

and so forth: one measured value from each of three sensors (with a three dimensional

observation space.).

n measured values from each of m sensors (requires a representation in a m • n dimensional hyper-space).

The well known decision theory clearly states, how to divide the observation space into

two disjoint subspaces taking into account certain optimization criteria. The "Bayes"-

decision rule is as follows (see for example ( I ], [2]):

-no From the formula above follows that we need to know the conditional multivariate probability densities pg|jj.(5|ifi) V?l € {0,1}. Unfortunately, these conditional probability

densities are neither known nor determinable. Particularly, our knowledge concerning

the signals of different sensors in no fire situations is very poor.

Intuitively, engineers would apply something similar to the above decision rule even if they have never heard of the "Bayes decision rule". Consider for example a decision, which is based on one measured value from each of

two sensors for smoke and CO-concentration. A reasonable subdivision of the total observation space could be chosen as shown in figure 3 where we have plotted a sequence

of measured sample values Si(tf), 52(^7) V?' G {1, (1), n} from a scattering light smoke

sensor and a CO-measuring device which has been recorded during a TF2-smolderingtest-fire. Each pair of measured values is represented by one point in the .Si, S2-plane.

Intuitively, an engineer would fix for example two threshold values swi, sw2 in such a way that all points in the plane which are above the thresholds indicate more likely a genuine fire than a no-fire situation and vice versa - according to the assumption of the

engineer. This reasoning corresponds to the decision rule in eqn.(l) but there however,

the conditional probability densities are assumed to be known and thus, the borderline

between the subspaces would look quite different.

The "Bayes decision rule" moreover takes into account the probabilities of occurrence

165
S2

40

35

CO- 30

concentr.

in ppm

25

51^2 20

15

10

5 r

0

0

oo

oOo

o To

o o

^ ooo J> o ^ i

- o o ^ <» ?

o oo o o

o

o <^

oo o

50 100 150 200

SWi

measured smoke magnitude S]

250

Figure 3: Intuitive selection of the subspaces Fq and Fi.

for fire- and no-fire situations (go and qi) and cost- or risk factors {Ka and K^) assigned

to correct or incorrect decisions.

Thus, the observation space is subdivided into two disjoint subspaces Fq, Fi as indicated

by the solid line in the plot of figure 3. The corresponding decision rule in mathematical

terms is:

if ((si(ti) > swi) or (S2(ii) > 51^2)) alarm, (2)

which is a simple twofold threshold detector.

Slightly more complicated is a subdivision of the observation space as indicated by the

dashed line in figure 3. This approach reflects the fact, that simultaneously existing smoke

and CO-concentration indicates more likely a fire than solely the presence of smoke or CO-concentration. In this case it makes sense to trigger an alarm at lower magnitudes of

both smoke and CO-concentration.

The corresponding decision rule in this case is as follows:

if((si(ti) > swi) or (S2{ti) > SW2) or (siiU) + S2{ti) > sw;3)) =4> alarm (3)

The joint evaluation of one sample from each of three sensors (like for example smoke,

heat and CO-concentration) is more complicated because in this case it is necessary to

fix a border-plane in the 3-dimensional space.

If we intend to include not only actual samples but also the last m sampled values of each

of the n sensor signals then the confusing task is to find an adequate hyper-plane in an m • n dimensional hyper-space. Thus, the task becomes more and more complicated with an increasing dimension of the observation vector and the question arises how to simplify

the solution of the task.

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2.2 Feature extraction methods

Feature extraction is a suitable method to simplify the decision problem considerably if we want to include the history of signals in our decision.

Feature extraction means to extract from each signal vector Sj, consisting of n subsequently sampled values of the sensor signal, one (or a few) features rrij^ k G {1..^^} as shown schematically in figure 4.

Thus, we obtain a feature vector M of dimension XlJLi ' which is considerably smaller

than the dimension m • n of the combined signal vectors. We can use the same decision

method as previously described using the feature vector M.

S2it) <^

S3{t) Osi{t) o— Sampling

Sampling

Si

Sampling

J2

Feature 1,

Feature 1

2

Feature2i

Feature22

Feature2,,3i

Features,

Feature32

M =

Decision

m2i

V J

D{M) —»—

o

D 6 {0,1}

Figure 4: Schematic sketch of a detector with feature extraction.

Feature extraction is an important engineering task which requires to consider previously

known individual characteristics of the sensors and their specific response in fire- and

no-fire situations. Only a few examples are mentioned in the following:

• Determination of the average rate of rise.

• Suppression of the signal fluctuation which is considered to be irrelevant in no fire situations.

• Rate of rise limitation for sensor signals in those cases where step-like changes are unlikely to occur either physically or due to the features of the sensor (for example

heat) but where step-like changes may occur due to data transmission errors or noise influence.

• Accumulation of signal amplitudes.

• Determination of significant spectral coefficients, if the signal fluctuation of sensors

differs in fire- or no-fire situations.

• Joint evaluation of signal features from different sensors (correlation). e.t.c.

167
The few examples mentioned above show that feature extraction methods usually eval- uate not only actual signal samples but include moreover the temporal development of

signals. As an additional advantage we usually have a smaller variance of the features in comparison with the variance of the signals itself because features are calculated as some

sort of average from several signal samples.

Feature extraction is applicable with more or less computational effort and storagecapacity efficient methods. One method to evaluate the temporal behaviour of signals is the so called "windowing"-method, i.e. feature extraction is based at each time instance

with a new sample .s,;(/c) on the last n-samples (where k denotes a discrete time variable): - s.ik - 1), .s,(A; - 2), . . . , s,{k - n + 1)}

If the next sample Si{k + 1) is drawn the eldest previously used sample Si{k — n 4- 1) is skipped and the new sample vector consists of the following components:

Si^{k + 1) = {si{k + 1), s^{k), Si{k - 1), ... , s^{k -n + 2)}

Usually, we can find a recursive calculation from this vector for whatever feature to be

calculated in the following form:

common components

rrii^ (^Si^ik + 1)) = rrii^ (S^(A:)) + f[s^{k + 1), s^(A;), . . . ,s^i{k-n + 2)] +

-f[s^{k), ... , s^{k -n + 2),s,{k-n + 1)] (4)

common components

Recursive calculations reduce the computational effort significantly but not the required

storage capacity.

Since features are usually calculated as some sort of average on account of the random nature of the signals, the following alternative method is not only computationally efficient

but saves moreover storage capacity.

m^.{k) = a-m^^{k-l) + {l-a)- f[s,{k), s^{k - 1); {sj{k)}] and 0.9<a<l (5)

In this formula /() denotes a so called "partial feature" which is calculated either from

the actual sample Si{k), or from two subsequent samples of one sensor, or from one

sample each of two sensors. The updated feature mij{k) is calculated from its previous value mij{k — 1) and the

actually calculated partial feature /(•). This type of recursion is called "exponential

windowing" and operates like a digital filter with i?C-low-pass transfer-characteristic

where the partial feature is the input signal and the output corresponds to the smoothed

partial feature values. The denotation "exponential windowing" indicates, that the filter output corresponds to the sum of an exponentially weighted input sequence of the partial

features with highest weight for the actual input and decreasing weights for previous

partial features.

The constant a determines the 3dB-cut off frequency ujg of the low-pass characteristic or

the "memory-depth" respectively.

ln(a) . . A . u>g = —-— with At, the samplmg period.

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Apart from that, the use of integer or byte arithmetic saves storage capacity for random

access variables as well as computation time because it is often possible to substitute

multiplication- and division-operations by simple shift operations and integer or byte

variables require less storage capacity by definition.

Although our electronic technology offers enormous processing power and giant storage

capacity on one chip it seems to be important at present to regard the above mentioned

aspects. The development of new control and indicating equipment with powerful central

processors and large storage capacity requires time and enormous expenditures. But the use of tried and tested existing equipment with newly implemented detection algorithms

sets hard limits with respect to storage capacity and computation time per detector.

A central processing unit in a fire detection system must carry out for example one decision/sec. based on feature extraction and preprocessing methods for hundredth of sensors

or sensor combinations apart from various administrative tasks. For this reason only a few

fractions of milliseconds remain as computation time for each sensor combination of a multi-sensor detector and the total amount of required RAM-storage capacity is proportional to the total number of detectors.

2.3 Automatic decision

The mode of operation of the decision-block in figure 1 or figure 4 we have already

discussed in the previous section - i.e. the division of the observation- or feature-space

into two disjoint subspaces. This task can be solved by two different approaches:

by a rule based decision or by a trained classifier.

For both of these methods there are various different realizations. Equations (2) and (3)

represent without doubt simple rule based decisions. Clearly, the rule base becomes more

complicated if more features are involved.

The advantage of a trained classifier is, that the designer does not need to think about

various complicated rules for plenty of features because these rules are automatically

determined during the training phase in a neural net for example. In other words the

neural net automatically subdivides the multidimensional observation- or feature-space

into the subspaces Fq and Fi.

Some proposals using neural nets as fire detectors are already to be found in the literature. For this reason it seems to be necessary to investigate the chances and shortcomings of

this method - particularly, because there are software tools available which configure

and train neural nets without any requirement for the user to understand much about the

background.

3. Detectors with neural nets.

3.1 Realization of a neural net. Each neuron has n + I continuous valued inputs where n denotes either the total number

of vector components of the input feature vector M (or the signal vector S) which is applied to the input nodes or the number of neurons in the previous layer. Each neuron

169
Figure 5: Sketch of a neural net.

computes a weighted sum of its input elements, subtracts a variable threshold and passes

the result through a hard-limiting nonlinearity such that the output is between 0 and 1

.

In the above formula k denotes the neural layer and the indices i,j the neurons in the

k — I'st and /c'th layer, respectively. The inputs of each neuron are weighted outputs

(with weight Wij) of the neurons of the previous layer (or the input node values). The

nonlinearity cr(. •

• ) is of a "sigmoid type"; two representative examples of which are shown in figure 6.

with Oo{k — I) -- 1 and 0^(0) = m, (6)

0.2

0.4

0.6

0.8

0

-10 -5 0 5 10

X

Figure 6: Hard-limiting nonlinearity of sigmoid type a{x)

*

170
A net as shown in figure 5 transforms the set of all input vectors to a set of output values

in the range 0..1 with a more or less steep transition between 0 and 1. This corresponds

nearly with the task to subdivide the observation space into two subspaces, i.e. to assign a value of 0 =^ no-fire to all elements of a subset of the input vectors and a value of 1 alarm to the complementary subset of the input vectors.

If a binary decision (fire or no-fire) is required one neuron is sufficient in the output layer

with its output values in the range 0..1. If we interpret the output values such, that for

output values >0.5 the probability for a fire is higher than for output values <0.5, we

only need one additional threshold comparison to achieve an unambiguous decision.

1=^ alarm

0=» no alarm

smokevalue

200 0

Figure 7: Decision function plotted above the observation space which corre- sponds to figure 3 or equation (3), respectively.

Thus, the output of a neural net defines an almost binary function above the observation

space. For the simple example according to equation (3) and figure 3 the corresponding

neural net output function is shown in figure 7. The precise modeling of this simple

decision rule (equation (3)) requires 2 input nodes, 3 neurons in the first layer and 1 neuron in the second layer, which is the output layer in this case. Moreover, 9-1-4 weight

factors are necessary which indicates the considerable effort. For this reason the rule- based solution is preferable in such simple cases.

3.2 Advantages and shortcomings of neural nets. At first glance a neural net is attractive from the point of view that even in the case of

observation- or feature-vectors of high dimension and perhaps confusing rules the neural

net learns the underlying rules during a training phase. This requires to assign in ad- vance the correct (or required) decision to any of the input vectors of the training set by

a teacher.

All weights Wij are initialized by small random values. During the training phase the in- put vectors are randomly selected from the complete set and each corresponding output is calculated. From the difference between the calculated output and the required output all

171
weights in the net are updated (with the so called "error back-propagation algorithm" for

example) such that the mean square error for all input vectors tends toward a minimum.

It has been shown that a trained neural net for certain assumptions classifies precisely

according to the decision rule in equation (1) (see for example [3], p. 14).

The disadvantages are:

1. the computational effort is enormous in comparison with other classifiers, not only

during the training phase (which can be carried out off-line) but also during the

classification phase,

2. the training procedure does not guarantee an optimal solution, i.e. the training

phase must perhaps be repeated several times with different weight initializations,

3. only vague statements about the necessary number of neurons in different layers are possible,

4. the final classification performance depends very much on the correct selection of

the observation vectors and even on the particular selection sequences during the

training phase,

5. high partial errors may occur (while classifying single input vectors) although the

mean square error indicates a good overall classification result. This might lead to false or missed alarms,

6. and last but not least - it is almost impossible to comprehend this classifying method

in detail even if it works properly, because we do not know the classification rules

after the training phase.

Some additional remarks concerning item 3 and the following:

Item 3.: 3.1: r? -neurons in the first layer and 1 neuron in the output layer

(Two-Layer-Perceptron):

Each neuron in the first layer subdivides the (2D)-observation space by a straight line,

the (3D)-observation space by a plane and the (mD)-observation space by a hyper-plane,

the inclination of which is determined by the corresponding weights Wij. With such a

structure it is possible to separate open or simple enclosed regions in the observation

space. The number of neurons in the first layer determine the details of the separated

regions which are separated by polygons (see for example figure. 7 with 3 neurons in the first layer). The output neuron carries out some sort of logical OR operation which

results in a final decision region that is the inclusion of all partial regions formed in the

first layer.

This statement is true for sigmoid-nonlinearities with almost hard-limiting characteristic.

For soft-limiting characteristics, the edges of the bounding polygons of the separated

regions are smoothed.

172
3.2: Three neural layers with one neuron in the output layer

(Three-Layer-Perceptron):

With a 3-layer structure it is possible to form arbitrary complex decision regions even

with separated enclosed regions belonging to the same class. Hence, more than 3 neuron layers are not necessary to solve an arbitrary complicated

classification problem but it is not prohibited to use more than 3 layers.

Item 4.: The aggregate of observation vectors during the training phase.

One fundamental requirement to achieve a safe classification is, that the observationvectors in the training set are representative for all possibly occuring fire situations and

scattered over the whole observation-space during the learning phase, i.e. many different

observation-vectors and their assigned class (fire or no-fire) must form the aggregate of

the training set. Special attention must be paid to the subset of training vectors which

cannot be assigned definitely to one or the other class or those vectors which point to approximately the same region in the observation space with contradictory meanings

assigned by the teacher. The final classification of the trained network for contradictory

training vectors is with higher probability in favour to those vectors, which appear more

often in the training set with the same assigned meaning.

Contradictory training vectors can be avoided if the teacher observes the following rule

in assigning the required class, which was already mentioned as part of the '"Bayes"-

decision rule in equation (1).

Is qo Ka < q\ h's ? Here qq denotes the probability of occurrence for such a vector during a no-fire situation

and qi = 1 — Qq the probability of occurrence for a similar vector during a fire situation.

Ka and are the corresponding cost-factors for wrong decisions; i.e. Ka the cost-factor

for a false alarm and /\',3 the cost-factor for a missed alarm.

The required class must be assigned to the hypothesis with the higher factor. Moreover it is of importance to select the training-vectors at random and uniformly distributed from the complete training-vector aggregate.

Item 5.: Average classification-error and partial-errors.

The learning-progress during the training-phase is indicated by a decreasing mean square

classification-error which is calculated according to the following sum:

1 ^

or, more simply, by a low-pass filtered sequence of the squared partial errors 0(i) — ^(i). Here, N denotes the total number of training-vectors. 0(j) the classification value of the

output neuron for the i-th training vector and the corresponding correct or required

output value assigned by the teacher.

F = 0 can only b^ achieved after a sufficiently long training-phase if no conflicting vectors exist in the training-vector aggregate. Unfortunately, this does not hold for automatic

173
fire-detection problems otherwise false-alarms or missed alarms would not occur in practice. Consequently, the average classification error reduces during the training-phase and

finally fluctuates around a minimum value >0 and the learning phase is stopped if no more learning progress can be achieved.

Even if the average classification-error is sufficiently small, some partial errors may be

high and thus lead to wrong classifications of the trained network in certain situations.

Particularly, if the requirements mentioned in item 4 are neglected this may lead to missed

alarms or unnecessary false alarms for 3-layer perceptrons with many neurons in the first and second layer.

For this reason a small mean square classification-error is not at all an adequate criterion.

It is moreover urgently recommended to inspect closely all training-vectors with high

partial-errors and their real classification after the training phase!

An adequate configuration of the neural net (number of layers and number of neurons

per layer) may help to avoid such errors, though there is no other rule than: the least number of neurons and layers being sufficient and necessary to achieve a low mean square

classification error.

Generally, neural nets are beyond doubt, powerful classifiers. Their use in automatic fire detection systems requires some caution, however, as shown by the remarks above - not

to mention the problem of explaining with some plausibility their mode of operation to the authorities of a test institute for example.

4. Test of new designed detection-algorithms.

A performance test is an important part in the design process of a newly developed detection algorithm independent of the chosen type of detector. Personal computers in these

days can be used as a powerful and rigorous test equipment. If the detection algorithm

is software-implemented on the PC, it can be tested in time-lapse mode, using recorded

data from fire-tests or, with artificially generated and randomly varied signal sequences

for fire- and no-fire situations.

Though all manufacturers know the specific test procedures carried out by test institutes

for certification, it is not recommendable to restrict the performance test to EN54-fires,

for example, because in practice it is very unlikely that a fire develops according to EN54-

conditions.

In our Institute, the sensor signals are recorded during the same test-fire from various

sensor-heads, which are placed in different locations on the ceiling of the fire laboratory.

Some of these locations are chosen arbitrarily unfavourable for detection purposes. Thus,

the reproducibility conditions according to EN54 are usually not fulfilled for all sensor combinations but the recorded signals still represent fires to be detected.

Moreover, typical false alarm situations are either simulated or applied as recorded signals measured in more or less false alarm relevant environments.

We have already reported about a special test method which uses signal-models with

stochastic variation of recorded signals (see for example: [4]). This method generates

from a comparatively small set of recorded signals in fire- and no-fire situations arbitrarily

many randomly varied test signals with similar characteristics (correlation features) than

those of the recorded signals.

174
As a measure of quality, we compare the detection features of the newly designed al- gorithm either with a simple threshold detector or with the previously used detection

algorithm of the manufacturer.

Summary

After some introductory remarks concerning the chances and still existing restrictions in the development of improved multi-sensor-/multi-criteria detectors, the principle solution

of a detection problem has been discussed.

The task becomes more and more complicated and confusing as more sensors and/or cri- teria are to be taken into consideration. It was shown that feature extraction simplifies the

problem and the use of recursive methods reduces the necessary computational effort. Special attention was paid to neural nets as classifiers with the principal advantage of

learning even complicated rules automatically during a training phase with the aid of a

teacher. But also the shortcomings and risks associated with neural nets as classifiers

have been discussed.

The complexity of multi-sensor-/multi-criteria detectors requires careful performance

tests. A few experimental tests with EN54-test fires are insufficient for a rigorous test

in the opinion of the author. Test methods using computer simulations with randomly varied test sequences for fire- and no-fire situations are proposed as an alternative.

References

[1] Bayes, Th.; An Essay Towards Solving a Problem in the Doctrine of Chances; Phil. Trans. 53, 370-418 (1764)'

[2] van Trees, H.L.; Detection, Estimation and Modulation Theory; Part 1; J. Wiley &

Sons, New York 1968

[3] Lippmann, R. P.; An Introduction to Computing with Neural Nets; IEEE ASSP

Magazine, April 1987

[4] Fischer, A.. Miiller, C; A simulation technique for the design ofmulti sensor based

fire detection algorithms ; Proceedings of the 10th International Conference on Automatic Fire Detection AUBE'95 in Duisburg, Germany; April 1995;

ISBN 3-930911-46-9

'This 236 years old work of an english clergyman, who pondered about minimizing the gambling risk

is until today the basis of decision theory!

175
Susan L. Rose-Pehrsson, Sean J. Hart, Thomas T. Street, Patricia A. Tatem,

and Frederick W. Williams

Naval Research Laboratory, Chemistry Division, Washington DC

Mark H. Hammond

NOVA Research, Inc., Alexandria, Virginia

Daniel T. Gottuk, Mark T. Wright, Jennifer T. Wong

Hughes Associates, Inc., Baltimore, MD

Real-Time Probabilistic Neural Network Performance and

Optimization for Fire Detection and Nuisance Alarm Rejection

The U.S. Navy program. Damage Control-Automation for Reduced Manning

is focused on enhancing automation of shipboard fire and damage control

systems. A key element to this objective is the improvement of current fire

detection systems. As in many applications, it is desirable to increase

detection sensitivity, decrease the detection time and increase the reliability

of the detection system through improved nuisance alarm immunity. The

use of multi-criteria based detection technology offers the most promising

means to achieve both improved sensitivity to real fires and reduced

susceptibility to nuisance alarm sources. An early warning fire detection

system is being developed by properly processing the output from sensors

that measure different parameters of a developing fire or from analyzing

multiple aspects of a given sensor output (e.g., rate of change as well as

absolute value). The classification performance and speed of the

probabilistic neural network deployed in real-time during recent field tests

have been evaluated aboard the ex-USS SHADWELL, the Advanced

Damage Control fire research platform of the Naval Research Laboratory.

176
The real-time performance is documented, and, as a result of optimization

efforts, improvements in performance have been recognized. Early fire

detection, while maintaining nuisance source immunity, has been

demonstrated. A detailed examination of the PNN during fire testing has

been undertaken. Using real data and simulated data, a variety of scenarios

(taken from recent field experiences) have been used or recreated for the

purpose of understanding potential failure modes of the PNN in this

application.

1. Introduction

The U.S. Navy program, Damage Control-Automation for Reduced Manning

(DC-ARM) is focused on enhancing automation of shipboard fire and

damage control systems. Improvement of current fire detection systems is a

key element of the program. Increased detection sensitivity, decreased

detection time and increased reliability of the detection system through

improved nuisance alarm immunity are desired. The use of multi-criteria

based detection technology [1] offers the most promising means to achieve

both improved sensitivity to real fires and reduced susceptibility to nuisance

alarm sources. A multi-year effort to develop an early warning fire detection

system is currently underway. The system being developed uses the output

from sensors that measure different parameters of a developing fire and a

neural network for fire recognition. Two test series were conducted on the

ex-USS SHADWELL [2] to evaluate and optimize candidate protot3^es of

the early warning fire detection system (EWFD). A third test series was

used to validate the optimized system.

Improved fire recognition and low false alarm rates were observed using

data from full-scale laboratory tests [3,4,5]. Several different sensor

combinations were identified for use with a probabilistic neural network

(PNN). Full-scale shipboard tests were conducted on the ex-USS

SHADWELL to further develop detection algorithms and to expand the

177
fire/nuisance source database [6,7]. Using these two data sets, two

candidate suites of sensors were identified for prototype development. Test

Series 1 tested the real-time responses of the prototypes [8,9]. Two months

later, under different environmental conditions, the optimized prototypes

were tested with more fire and nuisance sources in Test Series 2 [10]. Test

Series 1 and 2 were used to optimize the prototype, which was then

evaluated in Test Series 3 [11,12].

Two data sets (laboratory and shipboard tests) served as the basis for a

comprehensive PNN training data set used for the subsequent real-time

applications. The classification of fire and nuisance events and the alarm

speed were used to determine the performance of the multi-criteria fire

detection system in the test series. The EWFD system with the PNN

developed for real-time detection demonstrated improved classification

performance as well as faster response times to fires compared to

commercial smoke detectors. Some problems with the real-time

implementation of the algorithm were identified and have been addressed.

Using a variety of methods for speed and classification improvements, the

PNN has been extensively tested and modified accordingly. As a result of

these optimization efforts, significant improvements in performance have

been recognized. The best classification performance observed for Test

Series 1 and 2, while maintaining a fast detection speed, was observed with

a sensor array consisting of ionization (ION), photoelectric (Photo), carbon

monoxide (CO), and carbon dioxide (COg) [11].

This paper describes the results of Test Series 3 for the ION, Photo, CO, and

CO2 sensors using magnitude and slope information, and background

subtraction. A fire classification probability of 0.75 was used in Test Series

1 and 2 as the alarm threshold. The use of higher probability alarm

thresholds has resulted in improved nuisance source rejection while still

maintaining fast fire detection. The best probability cut off to maximize

178
detection and minimize false alarms was investigated. Finally, detailed

examination of potential PNN failures during fire testing has been

undertaken. Using real data and simulated data, several scenarios have

been used or recreated for the purpose of understanding the potential

failure modes of the PNN in this application.

2. Experimental

The tests consisted of different fire scenarios and several different nuisance

sources. In general, incipient size sources were used to challenge the

detection limits of the commercial smoke detectors and to establish the

minimum detection capability of new multi-criteria detection algorithms.

The shipboard tests presented in this study were conducted on the second

deck of the ex-USS SHADWELL in a space with a volume of approximately

144 TCI. The sensors were mounted on the ceiling, 2.9 m above the deck. All

of the sensors operated via gas diffusion to the unit. The fires were typically

located about 4.3 m fi:om the sensors and the nuisance sources were 1.5 m

fi:'om the sensors. The heights of each source above the floor were selected to

be representative of actual shipboard conditions.

All sensors were hard-wired to a National Instruments/LabVIEW data

acquisition system that was used to acquire data and execute the PNN

alarm algorithm in real time, save the data, and display the data. During

tests, the data acquisition/processing system was synchronized in time with

the commercial-off-the-shelf (COTS) Simplex smoke detection system

currently installed on the ship. The shipboard system consisted of

ionization detectors (Model 4098-9717) and photoelectric detectors (Model

4098-9714) monitored with a single alarm panel (Model 4020). The alarm

sensitivity of these detectors was 4.2% obs/m for ionization and 8% obs/m for

photoelectric. A more detailed explanation of the data acquisition system

can be found in other reports [8,10,12].

179
The PNN analysis of sensor data included pre-processing, pattern

calculation and scaling. The vector of input sensor responses, one number

for each sensor in the array, comprises the set of data that is passed to the

algorithm for pre-processing and PNN analysis during real-time

deployment. All sensor measurements were background subtracted (i.e.,

values were evaluated as a change from an ambient baseline value). Data

were collected every 2 seconds. The pattern magnitudes and slopes were

computed and then autoscaled (mean zero and unit variance) using the

means and standard deviations derived from the training set. The resulting

scaled pattern was then submitted to the PNN algorithm for event

classification (fire and nuisance) and determination of the probability of a

fire event. The training set for the PNN consisted of 140 background

subtracted response patterns generated in previous laboratory and

shipboard tests. The training set data were compared at discrete times

corresponding to the 1.63% obs/ih alarm setting of a commercial

photoelectric detector. This method of comparing signatures at particular

alarm times provides a means of identifying parameters with respect to a

practical benchmark. However, it also has the drawback of identifying

signatures at a singular point in time, therefore, potentially missing key

signature patterns.

Algorithm development and optimization was executed on a PC using

routines written in MATLAB, version 5.2 (Mathworks, Inc., Natick, MA).

Many of the routines were implemented using the PLS_toolbox, version 2.0c

(Eigenvector Technologies, Inc., Manson, WA). The classifier used in this

study is a Probabilistic Neural Network (PNN) [13] that was developed at

the Naval Research Laboratory for chemical sensor arrays [14]. Originally

introduced to the neural network literature by Donald Specht in the 1980's,

the PNN is a nonlinear, nonparametric classification algorithm that has

been described as the neural network implementation of kernel

discriminant analysis [15,16,17]. The PNN operates by defining a

180
probability density function (PDF) for each data class based on the training

set data and the optimized kernel width parameter. The PDF defines the

boundaries for each data class. For classifying new events, the PDF is used

to estimate the probability that the new pattern belongs to each data class.

The output probability can be used as a level of confidence in the

classification decision, which can serve as a guide to reducing nuisance

alarms. An improved PNN algorithm for chemical sensor array

classification has also been developed at the NRL for use in real-time sensor

systems [18].

3. Results and Discussion

Test Series 3 was used to test a revised prototype that utilized four sensors:

ION, Photo, CO, and CO, as described in Table 1. Thirty-nine tests were

conducted that consisted of fire/nuisance sources used during previous tests

as well as several new sources.

The detection rate of the PNN verses the false alarm rate is shown in Figure

1 for each probability cut off. If the probability of fire is required to reach

100%, then the false alarm rate is zero, but only 60% of the incipient fires

generated in this test series are detected. As the probability cut off is

reduced, the false alarm rate increases. At a probability cut off of 85%, 78%

of the fires are detected and less than 20% of the nuisance sources are

improperly classified. Both replicates of cutting steel with a torch were the

only nuisance sources incorrectly classified. An alarm threshold of 85%

provides a fire detection rate similar to both the COTS Photo and the

combination of Photo and ION. When the COTS Photo and ION sensors are

combined with an "OR" logic statement, the fire detection rate is better than

either sensor used individually, however, the false alarm rate is also

increased. The multi-criteria approach used here with the PNN classifier

has a detection rate similar to the Photo+ION combination and a lower false

alarm rate.

181
Table 1. Details of Prototype Fire Detectors.

riO. L/OuiponeiiL lIlSCruIIieilL iVlOQCl i^O* jMaiiuia.CLuTer

1 Ionization smoke detector 1251 with base no. B501 System Sensor

2

Photoelectric smoke

detector

2251 with base no. B501 System Sensor

3

Carbon monoxide (CO)

0-100 ppm

TB7F-1A City Technology

6

Carbon dioxide (CO^)

0-5000 ppm

2001V (EWFDl and 2

only), 8002W Ventostat

Telaire/Engelha

rd

The classification performance of the three EWFD prototypes is presented

in Table 2. Each detector is presented as the number of tests correctly

classified with respect to each of five categories; Overall, Total Fires,

Flaming Fires, Smoldering Fires, and Nuisances. It should be noted that

the overall and fire classifications are not 100 % for any detector because

some of the fires were of such small size and duration (e.g., smoldering wire

samples) that the detectors did not alarm. The EWFD system performed

better than the COTS smoke detectors. The EWFD correctly classified more

events than the COTS detectors in both flaming fires and nuisance source

rejection. The EWFD performance to smoldering fires was not as good as

the COTS Photo responses. The Photo-i-ION combination provides

equivalent fire detection to the EWFD, but the nuisance alarm rate is poor.

These results indicate that the PNN algorithm with the CO and C02

signatures provide improved detection performance than can be achieved by

co-locating ionization and photoelectric smoke detectors. The EWFD system

is faster than the COTS detectors for smoldering fires and is similar to the

COTS ION for flaming fires.

182
B

CE

c

o

0Q

1

0.9

0.8

0.7

0.6

0.5

0.4

0.3

0.2

0.1

0

A

EWFD 85% I Photo

j ION I Photo+ION

0.1 0.2 0.3 0.4 0.5 0.6 False Alarm Rate0.7 0.8 0.9

Figure 1. A Receiver Operator Curve (ROC Plot) showing the detection rate

and false alarm rate for each PNN probability cut off. The 85% cut off level

for the EWFD (*) is compared to the detection rate and false alarm rate of

the COTS Photo (•), COTS ION (), and the combination Photo + ION (i^).

183
Table 2. Summary of Classification Performance of Test Series 3.

overall

Total,

% Correct

(39)

1 OLol

Fires,

% Correct

(27)

r icmimg

Fires,

% Correct

(13)

onioiQenii

Fires.

% Correct

(14)

i^iiiisajice

Sources.

% Correct

(12)

EWFD 1 79.5(31) 77.8(21) 100.0(13) 57.1(8) 83.3(10)

EWFD 3 79.5 (31) 77.8 (21) 100.0 (13) 57.1 (8) 83.3 (10)

EWFD 5 76.9 (30) 74.1 (20) 100.0(13) 50.0(7) 83.3(10)

COTS Ion 56.4 (22) 66.7 (18) 92.3 (12) 42.9 (6) 33.3 (4)

COTS

Photo 69.2 (27) 74.1 (20) 84.6(11) 64.3 (9) 58.3 (7)

Photo+ION 61.5(24) 77.8 (21) 92.3 (12) 64.3 (9) 25.0(3)

4. Failure Mode Simulations

A variety of simulations and tests were performed to evaluate several

scenarios that could occur with a multi-sensor system and to elucidate PNN

function, performance, and failure. Scenarios include increased noise on 1,

2, 3 channels while maintaining the integrity of the remaining channels,

increasing the noise on all channels simultaneously, single sensor dropout

(sensor value = 0), and single sensor erroneous values (values set to high or low values). The data used for testing were a heptane fire experiment

performed in Test Series 1.

4.1 Noise simulations

The effect of noise added to the data was investigated by adding noise to

each sensor alone, while maintaining the integrity of the others. The noise

was added to the background data (data prior to ignition) in 13 steps from

typical S/N levels to a S/N level of 3. The test simulates a false alarm when

exposed to typical background air. Additionally, noise was added to two,

three, and finally all sensor channels to probe the ability of the PNN to

function under extremely adverse conditions. If the S/N is reduced to low

levels, between approximately 3 and 5, then the PNN false alarms. When

184
the PNN predicts a fire at any point before the actual ignition time, this is

considered to be a failure state, which cannot be tolerated.

To understand the effects of noise in the sensor arrays, the number of

alarms before ignition as a function of S/N was investigated for noise added

to 1, 2, 3, and finally all channels simultaneously. In general, the number of

false alarms in background air increases as noise is added to more sensors

simultaneously. The point immediately before the PNN fails is more

important in terms of predicting and preventing future failures in the field.

The pre-failure point is defined as the S/N level immediately before the

PNN first fails. A summary of the pre-failure points is given in Table 3, in

terms of the raw input data S/N. The pre-failure noise level is shown in

bold. The S/N level for ION can drop to 4.8 if all the other sensors have

typical noise levels. However, a S/N level below 5.8 will produce a false

alarm if the S/N decreases on the CO2 to 6.6. These pre-failure points may

be used to estimate the raw data S/N levels that are tolerable in each sensor

when one or more sensors may have excessive noise present.

4.2 Extreme Sensor Failure

Other sensor events that could occur in the field include sensor dropout

(zero reading) and erroneous sensor values. There could be a case in the

field where activities in the compartment cause temporary sensor responses

of this kind, such as the use of hand radios for communication. Table 4

shows the results for each of these conditions on classification results. For

sensor dropouts, the PNN will classify all but the most extreme cases. One

or two sensors failing did not cause the EWFD system to fail. When sensor

values are above the t5^ical maximum values (constant high value), the

PNN almost always produces an erroneous alarm condition. The

erroneously high values used for each sensor were 120, 5.5, 50, and 2500 for

Ion, Photo, CO, and CO^, respectively. When sensor values are below typical

baseline values, the PNN results were variable. The below baseline values

185
used for each sensor were -60, -2.5, -30, and -1000 for ION, Photo, CO, and

COg, respectively. The PNN produced an alarm for half of the various

situations tested. The response times under these conditions have not been

investigated.

Table 3. Summary of PNN Pre-Failure Points Due to Added Noise.

Sensors Ion S / N Photo S / N CO S / N CO, S / N

1 channel noise addition:

Ion 4.8 14.4 98.3 441.0

Photo 175.0 4.2 98.3 441.0

CO 175.0 14.4 3.1 441.0

CO, 175.0 14.4 98.3 5.1

2 channel noise addition:

Ion and CO, 5.8 14.4 98.3 6.6

Ion and CO 17.8 14.4 5.2 441.0

Ion and Photo 17.3 4.0 98.3 441.0

Photo and CO 175.0 6.0 7.4 441.0

Photo and CO, 175.0 4.3 98.3 68.8

CO and CO, 175.0 14.4 4.7 71.4

3 channel noise addition:

Ion, Photo, and CO 47.5 5.4 5.7 441.0

Ion, Photo, and CO, 21.5 4.3 98.3 233.5

Photo, CO, and CO, 175.0 3.9 3.4 258.3

Ion, CO, and CO, 11.3 14.4 3.7 52.2

4 channel noise addition:

Ion, Photo, CO, CO, 44.7 5.7 5.3 406.4

186
Table 4. Summary of PNN alarm states for sensor dropout (zero-reading),

sensor responses erroneously high and low.

uropouL

Alarm

ociisor xugn

IVIiss^H Alarm

ion Nn X CO

X noLO "Mn 1 tJb Ypt; X t5o MnIN U Ypc: Mn

Mn 1 fcJb MnIN U

ion, X nobO Mniy O 1 CO Ypc X CO

ion, Kjkj I eb 1 fcJb MnIN O

ion, V/ MnIN O Ypc: Mn

XPVinfn IlUtU, POv^W Mn X CO Ypt; X CD

Photo, C02 No Yes Yes

CO, C02 No No No

Ion, Photo, CO No Yes Yes

Ion, Photo, C02 Yes Yes Yes

Ion, CO, C02 No No No

Photo, CO, C02 Yes Yes • Yes

5. Conclusions

The results of these studies have demonstrated the advantage of a PNN

classifier with the multi-sensor, multi-criteria approach for fire detection.

The current alarm algorithm resulted in better overall performance than

the commercial smoke detectors by providing both improved nuisance

source immunity with generally equivalent or faster response times.

Additional studies were performed that examined the effects of noise and

erroneous sensor responses (sensor dropout, and high or low sensor values).

The results of these experiments indicate that the PNN is robust to sensor

noise and dropout. Unacceptable performance was not reached until the S/N

level on a single sensor was reduced to 3-5. Noise introduced in multiple

sensors produced mixed results with higher S/N levels producing poor PNN

performance. The PNN classification results were not effected by single

sensor dropout (flat-line zero). The remaining sensors and the PNN are

capable of detecting the fires in the absence of one sensor. These effects are

187
entirely acceptable for a continuous early warning fire detection system

until such time that the problem can be corrected.

6. Acknowledgements

The Office of Naval Research under the U.S. Navy Damage ControlAutomation supported this work for Reduced Manning Program, PE603608N.

7. References

1. Gottuk DT, WilHams FW. Multi-Criteria Fire Detection: A Review of the

State-of-the-Art. NRL Ltr Rpt Ser 6180/0472, September 10, 1998.

2. Carhart HW, Toomey TA, Williams FW. The ex-USS SHADWELL FullScale Fire Research and Test Ship. NRL Memorandum Report 6074,

October 1987, Reissue, September 1992.

3. Gottuk DT, Hill SA, Schemel CF, Strehlen BD, Shaffer RE, RosePehrsson SL, Tatem PA, Williams FW. Identification of Fire Signatures for

Shipboard Multi-criteria Fire Detection Systems. NRL Memorandum

Report, NRL/MRy6180-99-8386, June 18, 1999.

4. Shaffer RE, Rose-Pehrsson SL, Barry C, Gottuk DT, Wilhams FW.

Development of an Early Warning Multi-Criteria Fire Detection System:

Analysis of Transient Fire Signatures Using a Probabilistic Neural

Network. NRL Memorandum Report, NRL/MR/6110-00-8429, February 16,

2000.

5. Rose-Pehrsson SL, Shaffer RE, Hart SJ, Williams FW, Gottuk DT, Hill

SA, Strehlen BD. Multi-Criteria Fire Detection Systems using a

Probabihstic Neural Network. Sensors and Actuators, B, 2000; 69(3): 325-

335.

6. Wong JT, Gottuk DT, Shaffer RE, Rose-Pehrsson SL, Hart SJ, Tatem PA,

Williams FW. Results of Multi-Criteria Fire Detection System Tests. NRL

Memorandum Report, NRL/MRy6180-00-8452, May 22, 2000.

7. Rose-Pehrsson SL, Hart SJ, Shaffer RE, Wong JT, Gottuk JT, Tatem PA,

Williams FW. Analysis of Multi-Criteria Fire Detection Systems Results for

Test Series 1. NRL Memorandum Report, NRL/MR/6110-00-8484,

September 18, 2000.

188
8. Wright MT, Gottuk DT, Wong JT, Rose-Pehrsson SL, Hart SJ, Tatem PA,

Williams FW. Prototype Early Warning Fire Detection System: Test Series

1 Results. NRL Memorandum Report, NRL/MR/G 180-00-8486, September

18, 2000.

9. Hart SJ, Hammond MH, Rose-Pehrsson SL, Shaffer RE, Wong JT, Gottuk

DT, Wright MT, Street TT, Tatem PA, Williams FW. Real-Time

Probabilistic Neural Network Performance and Optimization for Fire

Detection and Nuisance Alarm Rejection: Test Series 1 Results. NRL

Memorandum Report, NLR/MR/6 110-00-8480, August 31, 2000.

10. Wright MT, Gottuk, DT, Wong JT, Rose-Pehrsson SL, Hart SJ,

Hammond MH, Tatem PA, Street TT, Williams FW. Prototype Early

Warning Fire Detection System: Test Series 2 Results. NRL Memorandum

Report, NRL/MRy6180-00-8506, October 23, 2000.

11. Rose-Pehrsson SL, Hart SJ, Hammond MH, Wong JT, Gottuk DT,

Wright MT, Street TT, Tatem PA, Williams FW. Real-Time Probabilistic

Neural Network Performance and Optimization for Fire Detection and

Nuisance Alarm Rejection: Test Series 2 Results. NRL Memorandum

Report, NRL/MRy6110-00-8499, October 10, 2000.

12. Wright MT, Gottuk DT, Wong JT, Pham H, Rose-Pehrsson SL, Hart SJ,

Hammond MH, Tatem PA, Street TT, Williams FW. Prototype Early

Warning Fire Detection System: Test Series 3 Results. NRL Memorandum

Report, (In Preparation).

13. Shaffer RE, Rose-Pehrsson SL, McGill RA. Probabilistic Neural

Networks for Chemical Sensor Array Pattern Recognition: Comparison

Studies, Improvements and Automated Outlier Rejection. NRL Formal

Report 6110-98-9879, 1998.

14. Shaffer RE, Rose-Pehrsson SL, McGill RA. A Comparison Study of

Chemical Sensor Array Pattern Recognition Algorithms. Anal Chim Acta

1999; 384: 305-317.

15. Specht DF. Neural Networks 1990; 3: 109-118.

16. Masters T. Practical Neural Network Recipes in C++. Academic Press

Inc, Boston, MA, 1993.

17. Masters T. Advanced Algorithms for Neural Networks. John Wiley,

New York, 1995.

189
18. Shaffer RE, Rose-Pehrsson SL. Improved Probabilistic Neural Network

Algorithm for Chemical Sensor Array Pattern Recognition. Anal Chem

1999; 71(9): 4263-4271.

190
Shu Wang, Shu Yan, Zheng Dou

Huazhong University of Science & Technology, Wuhan, China

A New Type of Neural Fuzzv System and its Application in Automatic Fire

Detection

Abstract

A new type of neural fuzzy system is proposed in this paper. With the advantages of

network learning, this system solved the problem of defining training data set by

human's experiential knowledge, which was impossible to be solved with conventional

fuzzy principle. The construction and learning algorithm of this system, and extraction

of inferential rules are discussed. Theoretical analysis and the application in automatic

fire detection demonstrated that the new neural fuzzy system could be effectively used

in the areas where inferential rules are changeable and difficult to be extracted.

1. Introduction

Fuzzy system based on fuzzy logic simulates human's comprehensive inference and

judgement and solve the problems of fuzzy information processing which is hard to be

solved by normal methods. Fuzzy system has been widely used in industrial and

domestic intelligent products. But the further development of fuzzy technique has been

puzzled by the problem of extraction of inferential rules. The over changed and

innumerable applications of inferential rules cannot be worked out through expert's

experiences and knowledge based inductive and trial -and-error methods.

Artificial neural network which features nonlinear and large scale parallel processing,

has recently made attractive progress. Simulating biological neural network makes its

strong ability of adaptive learning, tolerance and robustness, and it is good in

association, synthesis and dissemination. Combined with neural network, the fuzzy

system can overcome the shortcomings of normal fuzzy system and improve the ability

of learning and expression. The research work has made great progress since the middle

of the 1980s and many combinations of fuzzy system with neural network have been

introduced t''^^^^-^^. These combinations can be divided into two types.

191
One is the type of structure equivalence ^ ' where all the nodes and parameters have

specified meanings corresponding to membership function and inference procedure of

fuzzy system, and they can even transform each other. The structure equivalence system

has therefore been widely used. The problem of this type of fuzzy system is that the

number of inferential rules depends on the structure of network. It becomes unsuitable

when the number of inferential rule is changeable.

Another form is the combination of network learning where all or part of the fuzzy

system's fuzzification, linguistical rules, inference and defuzzification are realized with

neural network. The physical meanings of neural network are not clear. Therefore the

extraction of linguistical rules and the inference procedure are opaque and hard to

understand. Particularly when linguistical rules and inference procedure are realized

with neural network, and the inputs and outputs of the system are all fuzzy values, it is

impossible to define the training data set by experiential knowledge. This has been the

main problem hindering this type of fuzzy system from practical application.

A new combination system featuring network learning is proposed in this paper. The

system, with the advantages of network learning system, solved the task of defining the

training data set by human's experiential knowledge therefore could be used in those

fuzzy systems where the number of inferential rules is of infinity. The extraction of

inferential rules is also discussed in this paper.

2. The system's construction

A typical fuzzy system mainly includes three parts: fuzzification, fuzzy logic inference

and linguistical rules, and defuzzification. If linguistical rules are hard to extract or the

number of the rules is large or even infinite, the system cannot be realized by normal

fuzzy method. A multi-layer feed forward neural network can be used to store the

linguistical rules and the inference of fuzzy logic. The network is a typical BP neural

network with three layers (see figure 1), where the inputs and outputs are fuzzy values

192
Inputs X], Xi. ... x„

Output

Weight

Output

fuzzy

value y X

layer

leacher

signal t

Realize fuzzy logic

Defuzzifi

-cation

Output y

Teacher

signal d

Fig. 1 A fuzzy system with inferential rules realized by a threelayer BP network

(respectively u and v), the immanent layer of the network is used to realize the inference

of fuzzy logic, and the weight between each layer are used to store the linguistical rules.

The network has, in fact, the form of network learning

The kernel operation of realization of BP network is the procedure of learning. The

output of the three-layer BP network is fuzzy value v rather than the final output y of

the whole fuzzy system. So the teacher signal t is difficult to define. In general,

experiential knowledge can be used to define the teacher signal d for final output y. It

seems that deducing the teacher signal t from the teacher signal d would solve the

problem. Unfortunately this cannot be realized. A typical procedure of defuzzification,

for example the gravity center method is not a reversible procedure. The teacher signal

d can be deduced from the teacher signal t, while the teacher signal t cannot be deduced

from d. This is why the combination of network learning has difficulty in application.

193
The neural network with five layers (NET1+NET2)

i 6

Vl /( Z JL 1.0

1 Output y Input X Fuzzification 10 A^^^^vx Teacher

V io\(z jM-o signal d -|—Lb^^^\\ 8/

V2 BP netvVbrk with Defuz^ication

1 three layers (NETi) network (NETi)

Fig. 2 A typical and simple neural fuzzy system

The system structure can be improved. According to the principle of structure

equivalence, the part of defuzzification can be realized by a special neural network (see

NET2 in Figure 2). And the network (NET2) can also be combined with a BP network of

three layers (NETi) to form a larger neural network, which can be trained by the data set

(u, d). The fuzzy value u can be transformed from input values Xj through fuzzification.

The fuzzification procedure can be done by directly training the BP network or by

extracting the membership function through trial-and-error method. The whole system

consists of two parts (see figure 2). One is the part of fuzzification. Another one is the

five-layer neural network used to realize fuzzy logical rule inference and

defuzzification. The system can be named as neural fuzzy system, and can also be

regarded as a fuzzy neural network.

3. Algorithm of leaning

Suppose that the neural fuzzy system (see figure 2) has a single input x and a single

output y. The fuzzy values Ui and U2 are the fuzzification results of input signal x. The

system output y is the defuzzification result of fuzzy values vi and V2 that are the result

194
of fuzzy logical inference. And the immanent layer of BP neural network has three

neurons.

The fuzzification procedure has no relationship with the learning and application of the

follow up network whatever fuzzification method is used.

For the algorithm, only the section from fuzzy values ul and u2 to the system output y

needs to be considered. Inside this section is the five layer neural network including

three-layer BP network and two layers of structure equivalence network (see the dotted

part of figure 2). For this combined neural network, the conventional error backpropagation algorithm should be effective. The problem is that the errors of neuron 4

and 5 are difficult to be estimated. With reference to the inferential procedure of BP

algorithm, the learning algorithm of the five layers neural network can be inducted from

the principle of maximum gradient reducing'"'^

Suppose the output of neuron i is Oj (i=l,2...10), the weighted sum of inputs is netj

(i=l,2...10), and Wjj is the connecting weight of neuron i and j, among which the value

of W21, W31, W43, W53 is constantly 1.0. The neurons "2^" and 'V are different from the

normal in that: the "X ' are add units, their outputs are the sum of all inputs, i.e. their

excitation function is f(net)=net; the 'V is a division unit with the output of quotient for

two inputs. The mean-square error of the system is

E = ^{d-oJ , (1)

where d - teacher signal.

Two weights W42 and W52 of the neural network need to be adjusted. And others are

unchangeable:

O, = , - w,,0, + , O3 - + O5 . (2)

We have Aw,. =-77-^ = 77.(^-0,)--^, = -r]^ = rj {d - O,)-^ . 0)

With generalized 5 rule^ a 5k can be defined as = - O, )~. (4)

In fact the above result can be worked out by using the procedure of generalized 5 rule.

195
dE

That means S2 = . Because neuron 2 is an add unit, the result of the procedure is onet^

the same as formula 4. Although the weight of W43, W53 are fixed, the 5 of neuron 3 can

still be calculated:

(5)

The 5 values of neuron 4 and 5 are:

^4 = i^^Az^i + )• f^inet^ )^ ^5= {^52^2 + ) /sX^^^s )• (6)

If f(-) is a sigmoid function, then

S,=0,-{[-0,)-{w,J,+6,), S,=0,-{i-0,y{w,J,+S,), (7)

and Aw - 77^,0^ , Aw^, = r]S,0^ (j=6, 7, 8). (8)

These are formal resuhs because 83 has no actual significance, but the results are the

same form as the ones of using the method of maximum gradient reducing, the

derivation procedure of which is omitted in this paper. The purpose of using the similar

deducing procedure as the general 6 rule uses is to make calculation and remembrance

easy. The meaning of 83 is clearly different from other 5 values.

Since 84 and 85 have been obtained, the five layers neural network can now be trained

directly by using the generalized § rule. And the conventional BP algorithm can be

applied to learning procedure of the new network. The kernel procedure of learning

algorithm based on the algorithm of maximum gradient reducing is still the back

propagation of error. The algorithm has the same form as the BP algorithm has, except

that the deducing method of 5 values of special neuron is different.

4. Application in automatic fire detection

The neural fuzzy system was applied to signal processing of an optical smoke and heat

combined detector. The block diagram of the experimental system is similar to figure 2.

There is an operation of characteristic extraction before fuzzification. The system input

signals are smoke and temperature. Two characteristics are extracted from each signal:

signal amplitude and signal rising trend f^^^^^^f^^. Let S and T be the signals of smoke and

temperature, Sb and Tbbe the base values of smoke and temperature. The characteristics

of amplitude are S^ = S-S^ for smoke and T^ -T-T^ for temperature. The signal

196
trends are deduced with the algorithm of exclusive trend expressed in documents ^'^^ ^^"^l

The function of sgnl and sgn2 are redefined as follows:

sgnl(A)= ,sgn2(x)= ^ (9)

[0 .v<.v [0 x<l

Where x - input variables for the fuzzification (Sa, Ta and their trends), and s = 0.25Tb

or 0.25Sb. The window length of exclusive trend algorithm is 10 seconds. The

inferential procedure is realized by the fixed membership function determined by the

method of trial and error. Each input x, has two fuzzy values: big and small. The three

layers of BP neural network have 8, 10 and 4 neurons for the first, second and third

layer. The outputs of the system are the probability of fire and the probability of

smoulder.

Based on the data analysis of test fires of European standard EN54-9, 12 sets of training

data sample corresponding to inferential rules were delivered. Some of the training data

are given in table 1. By using the above learning algorithm, the system error was less

than 0.0001 after 8180 times of iterative operation.

Table 1 Some training data samples

NO. Ta T. Sa s, F HP

1 0.70 0.45 0 0 0.80 0.05

3 0 0 0.60 0.50 0.60 0.65

5 0.55 0.45 0.05 0.10 0.60 0.20

11 0.70 0.45 0.95 0 0.80 0.45

Notes:

Ta - Amplitude characteristic of temperature Sa - Amplitude characteristic of smoke

Tt - Trend of Ta S^ - Trend of Sa

F - Probability of fire HF - Probability of smouldering fire

We used the data of seven test fires of EN54-9 collected in a standard fire laboratory to

simulate fire, and the data of no-fire and smoking collected in the standard fire

laboratory to simulate no-fire and smoking situations. Experimental results are shown in

table 2. The system responded to fires effectively and did not respond to deceptive

197
phenomena caused by, for example, smoking, while the response time was shorter. The

neural fuzzy system was of benefit to respond to incipient fire and to reduce the rate of

false alarm effectively.

Table 2 Responses to fire, no fire and smoking situations

TFl TF2 TF3 TF4 TF5 TF6 TF7 No-fire Cigarette

smoke

T r\\\/

threshold

3Q7<i R5<; 03<s 1in7<: Vy / o O lliliCd \Ji

false alarm alarm

High

threshold

308s 436s 624s 94s 120s 254s 175s 1 time of

false alarm

No false

alarm

Neural

fuzzy

system

252s 387s 365s 67s 79s 157s 99s No false

alarm

No false

alarm

Note: The response time in this table is the time from the start point of test fire to the

alarm point, in terms of second (s). "No-fire" is the data collected in a standard

laboratory for about 24 hours, for reference only. "Low threshold" and "High threshold"

are the data given by conventional detector working at high and low sensitivity

respectively.

5. Conclusions

A new type of neural fuzzy system is constructed in this paper. Linguistical rules, fuzzy

logic inference and defuzzification of the system are realized by using a neural network

of five layers. And a learning algorithm similar to the BP algorithm is provided for

training the network. The system not only has the advantages of network learning, but

also overcomes the shortcomings that its training data model of inferential rules cannot

be obtained by using experiences and knowledge. The new system has strong

adaptability and can effectively solve the problem of extraction of inferential rules in

many applications.

198
There was no effective method of fire signal characteristic extraction used for

discrimination between fire and non-fire. So it has been difficult to find all inferential

rules of automatic fire detection.

The new type of neural fuzzy system was applied to an experimental system of

automatic fire detection. With this new neural fuzzy system, effective inferential rules

can be obtained from a small amount of training data model. These rules, which are

suitable for various types of fire and more effective than those obtained by conventional

method, are implied into the weights of the neural network. Experimental result shows

that the system can reduce false alarm effectively while does not fail to alert, and the

alarm time becomes shorter. With this new system, the inferential rules can be

automatically extracted for individual applications. The shortcoming that the inferential

rules of existed fuzzy system are unchangeable can be overcome.

The system has stronger adaptive learning ability. In real field condition, the probability

of real fire would be about zero during long time of observation. If the system reports

an alarm during this period, it must be a false alarm and the data can be used as training

data model for the system's learning. Then the system could obtain the inferential rules

of reducing false alarm after learning. Theoretical analysis and experiments demonstrate

that this new neural fuzzy system discussed in this paper can be effectively used in the

applications where inferential rules are changeable and difficult to be extracted.

References

[1] Zhijian Zhou, Zongyuan Mao, Cross Study on Fuzzy Neural Network, Circuitry and

System Journal, Vol. 3(3) 1998, pp. 81-85 (in Chinese).

[2] Liangjie Zhang, Yanda Li, New Development in Fuzzy Neural Network Technology,

Vol. 24(1) 1995, Information and Control Journal, pp. 39-45 (in Chinese).

[3] Zhenyu Zhao, Yongmao Xu-, The Basis and Application of Fuzzy Theory and Neural

Network, Tsinghua University Publishing House, 1996 (in Chinese).

[4] Shu Wang, Combined Exclusive Trend Algorithm of Fire Automatic Detection. Fire

Safety Science, Vol. 5(1) 1996, pp. 8-13 (in Chinese).

[5] Shu Wang, A Trend Duration and Gradient Detector for Automatic Fire Detection,

Fire Safety Journal, Vol. 27(3) 1996,pp.239-248.

199
[6] Qing Zhang, Shu Wang, Zongkai Yang, AppMcations of Fuzzy Neural Network in

Fire Detection, Circuitry and System Journal, Vol. 2(3) 1997, pp.5 1-55 (in Chinese).

[7] Yoshiaki Okayama, A Primitive Study of a Fire Detection Method Controlled by

Artificial Neural Net, Fire Safety Journal, Vol. 17 1991, pp.535-553.

[8] Shu Wang and Zheng Dou, Exclusive Trend Detector with Variable Observation

Windows for Signal Detection, Electronics Letters, Vol.33 No. 17, August 1997,

ppl433-1435.

200
Milan Dj. Blagojevich, Ph.D. Candidate, Dejan M. Petkovich, Ph.D., prof.,

Djordje Simich, Ph.D.. prof.

Faculty of Occupational Safety, Department of Fire Protection, Nish, Yugoslavia

e-mail: milan@znrfak.znrfak.ni.ac.yu

A new algorithm for adaptive alarm threshold in fire detection system

ABSTRACT

A basic approach in adaptive modeling of any data acquisition is based on the

comparison of real time data with the data previously predicted from the adequate

numencal model. In this paper we suggest the time sliding window principle, the length

of which is variable in real time and depends on the calculated error. Due to equal time

distance in data acquisition in fire detection systems, the number of acquired data and

the length time sliding window are linearly interdepended. This approach demands that

the borders of time sliding window' vary in real time simultaneously with the window.

This approach also leads to the best fit between real time and the predicted data when

the difference between those two time series of data is used as a feedback.

Generally, the source of false alarms is the application of the fixed alarm threshold

decision. This can be overcome using the method of the adaptive threshold. The

decision method based on an adaptive threshold employs a threshold changing in time

according to the values of the input data and a fixed threshold. The aim of this paper is

to introduce the method for controlling fire detection systems in adaptive sense, based

on the approximation of the signal with a suitable function.

INTRODUCTION AND BACKGROUND

The major characteristic of most known algorithms for treatment of alarm is based on a

fixed value of alarm threshold. The decision that was made in such a way means that

this decision is based on default (defined by the standard) value. However, selection of

201
adequate alarm threshold is not easy, because of the noise present during data

acquisition, other disturbances or uncertainness. The selection of alarm threshold is

subject to permanent compromise between the low rate of false alarming with small

sensitivity lo failure on the one hand, and the high rate of false alarming with high

sensitivity on the other hand. This conventional logic practically leads to a discrete

function which from the given set of data gives one predicted output value. he adaptive

alarm threshold is based on the "history" of acquired data, usually known as time series.

Unfortunately, w hen all of data are known, the fire is fully developed, and no prediction

has any sense. The prediction of fire alarm has to be stated on the arbitrary set of

previous, acquired data, the fewer of them the better, if we choose to view the set of

data in the time window the length of which is constant, consequently we define time

sliding window with constant length. Because of the nature of the process this window

must have variable length.

fx

Pi evioiis timie : :CiiiTent time : sliding vviriidcivv: sliding vviridow: A*

...L..*..

...... J...

t f r ; r • •

1 2 3 4 5 6 7

Fig. I. Time sliding window

The length of time sliding window is variable in time and depends on the error, i.e. the

difference between the predicted data and the data that are acquired at every time

instant. Measuring of time in every sliding window begins from relative time zero. The

method of variable length time sliding window includes feedback that corrects the

errors in prediction and consequently enables:

1. System fault detection

2. Detection of an accident in the early stage

3. Adaptation of the alarm level

4. Prediction of a system behavior

202
THE TIME VARIABLE SLIDING WINDOW ALGORITHM

In every time instant two values are significant: The first is the value of acquired data,

and the second is the \ alue of the velocity of change of data in time. In that case, the

first is the temperature and the second is the gradient of temperature (rate of nse).

Because of that, it is important to supervise both \ alues. \\ hich means that the algonthm

must have two major branches, m other words, there are two models. The first model

includes approximation of the front edge of fire alarm signal \\ hen the adaptation of the

alarm threshold is not necessary. The second model includes threshold adaptation based

on the method we propose here. In both cases the false alarm can be detected.

Nfew data (t=t-^AFr

approximation and prediction

in window of standard lenath

comparation new data with previous predicted data No . 'difference greater then permitted?^

-%Yes

<^max. length of window reached'^>^^^

Not

^ expand window backwardj

approximation and prediction in curr^it window i

Yes

C difference greater then permitted?^

Noj»-

difference areater than max. No

Yes

\ permitted increase of threshold'^ T Yes

jign of diffgence negative or varies^ ^No

1 detector fault alarm: approximation establish new threshold

of fi"ont edse of sisnal (adaprive threshold)

.,end of time routine >

Fig 2. Ti??ie sliding window algorithm

203
The adaptation of the alarm threshold can be applied to the domain in which the two

basic criteria are not reached: maximum permitted value of adaptation or critical rate of

rise of the signal from the detector.

12 n

Fig. 3. Domains for approximation

The decision method based on an adaptive threshold employs a threshold changing in

time according to the values of the input data and a fixed threshold (see Fig. 4.).

JIMESUDING mNDOW

TIME SLIDING UlNDOW

Predicted d(Aa.

/rti-Ai/J : :

U}reshotd ; i i i ^

T i T ; -* T i Pi'eiMch dixiid.''

: : /

f T

t ^

-Pixid

4di q>^v^

4-

pFe(Scieddate

Pivdmte'ddcdq.

Fig. 4. Adaptive threshold in time sliding window

204
In fire. detect! on systems where the collecting of data is stationary (polling in equal time

distance), the simplest method, which is satisfactorily accurate, is the least squares

method with the polynomial approximation of the detected signal. The various types of

functions f(t) can be used to approximate the collected fire data. Functions that

approximate the time dependent occurrence have m unknown parameters and can be

constructed using the weighted residuals method, WRM. Some of the procedures are the

least squares method, point matching method, etc. Also, spline polynomials are possible

approximation for collected data and approximation of front edge of fire signal with

conveniently chosen function.

The first step is to choose the order of polynomial, m, that is equal or less than the

number of data in the current window, the time length of which is r= (m-l)Ar, and can

be increased as window increase. Discrete time is K = m at the observed moment. From

the numerical point of view, the length of the window is L> m. The left border of

window can be calculated as P=K-L+i.

The second step is to choose the appropriate method of fitting the curve (from a variety

of WRM or spline) and determine m unknown parameters which, in turn, determine the

function for approximation.

The third step is to calculate the value of the function in the next time step.

The fourth step is to estimate the difference between the real and predicted data

followed by making a decision.

These steps will be repeated until the mathematical model that describes the real state of

the system gives results is not satisfactory accurate. Finally, the only two states of the

system are possible: accident or not accident.

205
THE SMOOTHING SPLINE APPROXIMATION IN TIME SLIDING WINDOW

Spline functions are the category of very useful non analytic functions which consist of

different polynomials on segments (and thereby segmental-analytic) linked in specific

points which are called nodes. For prediction in time sliding window, in adaptable

region, we have used a smooth cubic spline approximation of Schoenberg and Reinch

with modification discussed in de Boor [4]. It is a natural cubic spline with knots at all

the data abscissas, but it does not interpolate the data (.Vj,/]) . The smoothing spline is

unique C2 function, which minimizes

]s\xfdx (1)

a

Minimization of (1) establishes a compromise between two conflicting goals:

1. to stay close to the given data, and

2. to obtain a smooth function.

These two conflicting goals may be expressed as

;=I <CT (2)

where w - weights, - smoothing parameter, and L - length of time sliding window

(number of obtained data in window) in every time instant.

The parameter has to be chosen somehow and depends on the weights. In [4]

proposes to choose somewhere within -JlL of L in caseV2Z is a good estimate for

the standard deviation of the data. That is,

L-42L<o<L + 42L (3)

Simply, represent a knob which one may set or turn to achieve a satisfactory

approximation to the data. More sophisticated choice for based on an estimate of the

noise in the data obtained by a process called "cross validation".

206
In our approach, because of quasistationary nature of fire phenomena, we have defined

weights of /, in window uniformly from 0 to 1, where the data on the right border of

the window has vv-1. On the figure 5 the results of this approach on fictitious ("real"

data) are shown.

( i

-

)

1

/ \

1 \

f j

<

n

\\n hI

< »

\\\\\\

> ;

•

approximation - smoofJi splirie # "real" data

uUerpolation - cubic spli/ie O prediction

, . cg?proxi7nation - potynomicd

Fig. 5. Smooth spline approximation in time sliding window

The most advantage of spline polynomials usage is that this method of prediction is

totally independent of time sliding window length. Application of functional analysis

described in [6] shows that all comparisons in sense of vector calculus with various

lengths of time sliding window, lead to almost identical values of parameters like

relative difference, cosine etc.

Spline polynomials as possible approximation for collected data and approximation of

front edge of signal with conveniently chosen function built in a simulation software.

This software is being developed at the Faculty of Occupational Safety, Department of

Fire Detection - Nish. The figure 6. shows a example of experimental data and related

approximations in time sliding window.

207
m

Adaptive Thieshold SIMULATION X point No. Y

58.000

PoinlsY Ypiedict

22.000 144 448

21 000 ~i ' , 22.000 —' Eitoi 1

21 000 r3n§5

34 000 I 42 000 Ettoi 2 52 000

64.000 ^

Method

Diaw

Next i

Oegiee Obset. ExtraX

[3 |10 [1 000 ci?at|

t'3

Save

Points I

Giaph

Exit

L2 polynomial p point matching

; 17 smooth cuhiic spline approx P cubic spSne inteipol

; Colot

I r Mag. C Hed C White

i C Yell, r Blue T Gteer

i

: . , • / "A

\ •

•

/

.

/

\ : ; : M : ;

/ V

/: ^

Id

1^

IRunning

Fig. 6. (/^^r interface of simulation software

CONCLUSIONS

The time sliding window method, which is appHed for prediction of the development of

fire, consists of two basic models. The first model uses a new function for

approximation of front edge of fire signal, and the second, which uses smooth spline

polynomials for adaptation of the alarm threshold. The method is built in software

package for simulation and the first results are promising.

REFERENCES

1. Blagojevich M., Petkovich D.: Adaptive control and sliding window principle.

International Conference "System Identification and Control Problems - SICPRO

2000", Moscow, 2000.

208
J Blagojcvich M.. Petkcn ich D.: An approach to complex safety, Problemi upravlenija

bc/opasnosijLi slo/nih sistem. Moscow. 1999.

3. Biikou ski R.. Rcncke P. : New approaches to the interpretation of signals from fire

sensors. Natl. Insi. Stand.. MD USA. 1999.

4 dc Boov C: .\ practical i^uide to splines. Springer- Verlag New York, 1978.. 235-

243.

Forsvlhc. G.E.: (Icncraiion and nse of orthoi^onal polynomials for fitting data with

a digital computer, SIA.M .lournal on Applied Mathematics, 5, 74-78., 1957.

6. Peacock R.. Rcncke P.. l)a\ is W.. Jones W.: Quantifying fire model evaluating

using functional annlvsis. f ire Safety JounaL No. 33. 1999.

7. Rcncke P.. Peatross M.. Jones W.. Beyler C, Richards R.: A comparison of CFAST

predictions to LfSCG real-scale fre tests, Natl. Inst. Stand., MD USA, 2000.

8. Shanipine, L.F.: Discrete least-squares polynomial fits. Communications of ACM.

18. 179-180.. 1975.

9. Blagojcvich M.. Petkox ich D.: New model for approximation of front edge of fire

signal, (will be published).

209
Pascal DUBOIS

CNPP, Saint Marcel, France

Electromagnetic compatibility and radio-linked systems

Abstract

In April 2000, the new European directive R-TTE (Radio and

Telecommunications Terminal Equipment) came into effect. The

manufacturers are now able to use this new directive to demonstrate by

themselves, their product compliance with the relevant protection criteria.

We explain this new approach by analysing the requirements which are

given in the relevant ETSI standards for the security systems, which used

radio-frequency links. Then we'll make the comparison between the EMC

requirements for a dedicated wired system, for a radio-frequency linked

system and for a system using these two kinds of communication paths.

Finally, we'll compare the new R-TTE Directive EMC requirements with

security and reliability requirements given in the standard draft for alarm

system using radio-frequency links (PrEN 50 131-5-3 standard draft)

Introduction

Since 1996, all the apparatus which have been placed on the European

Community market, shall bear with the CE conformity marking. This

marking presumed of the conformity of this device to the essential

requirements of the relevant European directives. This CE marking allows,

in a regulatory framework, the placing on the market, free movement and

putting service in the European Community.

For electronic security systems, we can distinguish mainly, three European

directives:

r- The Electromagnetic Compatibility Directive (mainly: 89/336/EEC)

K The Low Voltage Directive (mainly 73/23/EEC)

210
r- The Radio and Telecommunication Terminal Equipment Directive

(1999/5/EEC)

1) Application field and essential requirements of these relevant

directives

1.1) The electromagnetic compatibility directive (89/336/EEC)

The electromagnetic compatibility Directive applies to electrical

and/or electronic apparatus, where apparatus means all electrical and

electronic appliances together with equipment and mstallations containing

electrical and/or electronic.

The electromagnetic compatibility means the ability of a device, unit or equipment or system to function satisfactorily in its electromagnetic

environment without introducing intolerable electromagnetic disturbances

to an\"thing in that environment.

There are two essential requirements for this European directive:

The disturbance, which is generatmg by the apparatus does

not exceed a level allowing the radio and

telecommunications equipments and other apparatus to

operate as intended, and

The apparatus has an adequate level of intrinsic nnmunity

of electromagnetic disturbance to enable it to operate as intended

1.2) The low voltage directive (73/23/EEC)

The Low Voltage Directive shall only apply if voltage between 50V and

l.OOOV for alternating current and/or between 75V and 1.500V for direct

current is involved in the apparatus. This is the case for apparatus plugged

on the main power network.

There are 11 essential requirements in this directive which can be resumed

as follows:

The manufacturers, or brand name . or trade mark should be clearly

marked on the equipment or, if it is not possible, on it's packaging.

211
The essential characteristics, the recognition and observance of which

will ensure that electrical equipment will be used safely and in

applications for which it was made, shall be marked on the

equipment, or, if this is not possible, on an accompanying notice.

The electrical equipment should be so designed and manufactured as

to ensure that it can be safely and properly assembled and connected,

and to ensure that protection against internal and external hazards

are provided in such a way that persons and domestic animals are

adequately protected against danger. These objectives should be met

just as well, when the equipment is used in applications for which it

was made, as in foreseeable conditions of overload.

1.3) The Radio and telecommunication terminal equipment

directive(1999/5/EEC)

This directive applies to apparatus which, in this background, has to be

defined as followed:

"apparatus" means any equipment that is either radio equipment or telecommunication terminal equipment or both.

"telecommunications terminal equipment" means a product enabling

communication or a relevant component thereof which is intended to be

connected directly or indirectly by any means whatsoever to interfaces of

public telecommunications networks (that is to say, telecommunications

networks used wholly or partly for the provision of publicly available

telecommunications services);

"Radio equipment" means a product, or relevant component thereof,

capable of communication by means of the emission and/or reception of

radio waves utilising the spectrum allocated to terrestrial/space radiocommunication.

212
The essential requirements for all the equipments which are concerned by

this directive are; - to meet the requirements of the electromagnetic

compatibility directive

(89/336/EEC)and,

to meet the low voltage directive (73/23/EEC), but with no

low voltage limit appl3dng.

In addition, there is another essential requirement which applies only to

radio equipment, subscribing that radio equipment shall be so constructed

that it effectively uses the spectrum allocated to terrestrial/space radio

communication and orbital resources so as to avoid harmful interference.

2) The harmonized standards to meet these three European

directives:

The essential requirements to meet European directives are described in

harmonized standards, which contain technical specifications and which are

adopted by a recognised standards body under a mandate from the

commission.

There are many harmonised standards for these European directives in the

field of electronic security systems. If we consider the most complete case (in

the sense of these three directives) for electronic security equipment, this

latter may be made of

:

- An enclosure,

- Alternative current inputs and/or outputs (generally: the main pov.^er

input),

- Direct current inputs and/or outputs,

- A functional earth port,

- Signal and/or control inputs and/or outputs,

- Connections to telecommunications networks,

- Radio-frequency transmitting subsets, and

- Radio-frequency receiving subsets.

An illustration of this kind of equipment us given in illustration 1.

213
Transmitting part

Enclosure

DC Power AC Power

TELECOMMUNICATIO

N

Signal/control

Functional earth port

Illustration n°l: most complete case of an electronic security

equipment

At the opposite, the most simple case for an electronic security equipment is

an equipment without any external connection and powered with low

voltage batteries (less than 75 V), for example: a domestic smoke detector

(shown in illustration n°2).

Illustration n°2: most simple case of an electronic security

equipment

At first, we can list the relevant standards which have to be applied to meet

the three European directives for electronic security equipments:

214
2.1) For the Low voltage directive:

DIRECTIV

E

KIND OF

EQUIPMENT

SUBSE

T

HARMONISED

STANDARDS

Date of

publication

Any radio

equipmeiiL

ALL

Any

telecommunicatio

n terminal

equipment

ALL

EN 60 950

+Amendments January

LOW

VOLTAGE

DIRECTIV

E

(73/23/EE

C)

Other equipments

involving Voltage

in the following

ranges:

between 50 and

l.OOOVfor

altematmg

current and/or

between 75 and

1.500V for direct

current

ALL

Al, A2, A3 &

A4

1993

Any other

equipment which

is not part of

those

aforementioned

Low voltage directive does not apply

Table n°l: harmonised standards to meet the essential

requirements of the low voltage directive in the case of an

electronic security equipment

215
2.2) For the Electromagnetic compatibility directive:

DIRECTIVE

KIND OF

EQUIPMENT SUBSE

T

HARMONISED

STANDARDS

DATE OF

PUBLICATTION

EMC

DIRECTIVE

(89/336/EE

C)

EMISSION

ASPECTS

Any radio

equipment ALL no harmonised standard at

this day

Any

telecommmiicat

ion leiiiiinai

equipment

ALL EN 50 081-1 1992

Any other

equipment ALL EN 50 081-1 1992

Table n''2: harmonised standards to meet the essential

requirements for the emission aspects of the electromagnetic

compatibility directive in the case of an electronic security

equipment

DIRECTIVE KIND OF

EQUIPMENT

SUBSE

T

HARMONISED

STANDARDS

DATE OF

PUBLICATTION

EMC

DIRECTIVE

(89/336/EE

C)

IMMUNITY

ASPECTS

Any radio

equipment ALL no harmonised standard at

this day

Any

telecommtmicat

ion terminal

equipment

ALL EN 50 130-4 1995

Any other

equipment ALL EN 50 130-4 1995

Table n°3: harmonised standards to meet the essential

requirements for the immunity aspects of the

electromagnetic compatibility directive in the case of an

electronic security equipment

216
2.3) The Radio and telecommunication terminal equipment

directive

In the case of a radio equipment, the harmonised standards are not yet

defined. But we already know that they will be defined in function of the

transmitted power.

For the communication inside an alarm system, only short range devices are

involved in the system and in this case we have good presumptions to say

that the harmonised standards will be: - ETS 300 220 series for the

requirements related to the

spectrum utilisation

And - ETS 300 683 standard for the EMC aspects for the

short range devices operating on frequencies between 9

kHz and 25 GHz.

For the radio-telecommunication equipments (like GSM, DCS 1800, UMTS,

TETRA, etc..) the standards are not yet harmonised in all over Europe, and

in this case, national regulations may still be in force or, it may happen that

there are restrictions on the use of the equipment in certain European

countries.

For that reason, at this date, we can see different kinds of temporary CE

marking as shown in illustration n°3.

Illustration n°3: temporary CE marking in the case of a radio

equipment

These temporary CE marking may be split up into three parts:

- At first, the CE marking in itself ( ^ ^ ), as defined in the main

European directive

217
- then: the number of the notified body (|^° XXXX ^' ^^^^^ intervened

in the conformity evaluation according to European standards which are not

yet harmonised

- and, at least: the warning sign ( ), which means that the frequency

range which is used is not yet harmonised in Europe

In conclusion, for a radio device we have four different CE marking:

At this date, we have: ^ ^ : in the case of frequency range which is used is already

harmonised in Europe and standards which has been used to

assess the conformity to the R-TTE directive are already

harmonised

( ^ in the case of frequency range which is used is not yet

harmonised in Europe and standards which has been used to

assess the conformity to the R-TTE directive are already

harmonised

(( N°XXXX

in the case of frequency range which is used is already

harmonised in Europe and standards which has been used to

assess the conformity to the R-TTE directive are not yet

harmonised

N°XXXX©

in the case of frequency range which is used is not yet

harmonised in Europe and standards which has been used to

assess the conformity to the R-TTE directive are not yet

harmonised

218
When the relevant frequency ranges and standards to assess the conformity

to the essential requirements of the R-TTE directive will be harmonised in

Europe, it will only remain the simple ( ^ marking.

2.3.1) Requirements to meet the BMC directive and the BMC

aspects of the R-TTB directive in a case of a short range device:

In the field of electronic security systems, the short range devices are

mainly the equipments which make up the radio-linked alarm systems such

as: Radio-frequency linked intrusion and/or fire alarm systems, and the

social alarm systems. There are two frequency bands which are allowed in

Europe:

- 433,05 MHz - 434,79 MHz and

- 868 MHz - 870 MHz

These two frequency ranges are allocated for short range devices, which

have an allowed maximum radiated power lying between 10 mW and 25

mW for the application of alarm systems.

For this kind of device, there are some restrictions for the use of the

frequencies in certain European countries, that means that there is no

harmonisation for the frequency bands. But, we know that the relevant

standard which provides the requirements for a correct use of these

frequency bands, is BTS 300 220-1 [Radio Bquipment and Systems

(RBS), Short range devices Technical characteristics and test

method for radio equipment to be used in the 25 MHz to 1.000

MHz frequency range with power level ranging up to 500 mW; Part

1: Requirements related to spectnmi utilisation]. This latter mainly

defines the maximum power level in function of the frequencies inside and

outside the used frequency channel.

For the EMC requirements, the relevant European standard is EiTS 300

683 [Radio Bquipment and Systems (RE^) BlectroMagnetic

219
Compatibility (BMC) standard for Short Range Devices (SRD)

operating on frequencies between 9 kHz and 25 GHz].

This standard defines four kinds of EMC requirements:

The Continuous phenomena appHed to Receivers (CR)

- The Continuous phenomena apphed to Transmitters (CT)

- The Transient phenomena appHed to Receivers (TR)

- The Transient phenomena apphed to Transmitters (TT)

It defines also 3 classes of equipment (1,2 & 3) in function of the impact on

persons and/or goods in case the equipment does not operate above the

specified minimum performance level under EMC stress. For the radiofrequency linked alarm systems, the corresponding class of equipment is the

class 1 (Alarms application; Domestic security).

And at least, the standard defines 3 equipment t3rpes (I, II &III) in function

of the technical nature of the primary function of the equipment, for the

radio-frequency linked alarm system, the appropriate equipment tyipe is I

[transfer of messages (digital or analogue signals)].

The tests which are required to meet the ETS 300 683 for the radio linked

alarm systems are described in the table n°4.

For the Radio-frequency electromagnetic field (80 MHz - 1.000 MHz), the

standard specifies an exclusion band for the receiving equipments in which

this test does not need to be performed. For the frequency bands which are

allocated for the alarm systems, this exclusion band is defined as follows:

The lower frequency of the exclusion band is the lower frequency of the

intended operating frequency band minus 5% of the operating frequency

and the upper frequency of the exclusion band is the upper frequency of the

intended operating band plus 5% of the operating frequency.

220
The other tests have to be performed with the same procedure as for the

none radio equipments.

The performance criteria are defined in function of the kind of test

(continuous or transient) and the class of equipment (for the alarm systems:

class 1). For each test there are performance criteria during the test and

after the test.

221
Phenomena Application

Equipment test

requirement Reference

Fixed document

equipment

s

Remote

control

EMISSION REQUIREMENTS

Emission

measurement

s

Enclosure

Applicable

to ancillary

equipments

Applicable to

ancillary

equipments

EN 55 022

DC power

in/out

Applicable Not

applicable

EN 55 022

CISPR 16-1

AC mains Applicable Not

applicable

EN 55 022

IMMUNITY REQUIREMENTS

RF

electromagnet

ic field (SO

MHz - 1.000

MHz)

Enclosure Applicable Applicable EN 61000-4-

3

Electrostatic

discharge

Enclosure Applicable Applicable EN 61000-4-

2

Fast

transients

common

mode

Signal and

control ports,

DC and AC

power ports

Applicable Not

applicable

EN 61000-4-

4

RF common

mode (current

clamp

injection)

0,15 - 80 MHz

Signal and

control ports,

DC and AC

power ports

Not

applicable

Not

applicable

EN 61000-4-

6

Transients

and surges

DC power input

ports

Applicable Not

applicable

ISO 7637

Parts 1 & 2

Voltage dips

and

interruptions

AC mains

power input

ports

Applicable Not

applicable

EN 61000-4-

11

Surges,

common and

differential

mode

AC mains

power input

ports

Applicable Not

applicable

EN 61000-4-

5

Table n''4: EMC tests for short range devices according to ETS 300

683

222
3) The European standard draft for radio linked alarm systems:

For the radio-frequency linked alarm systems there are, on top of the

essential requirements to meet the three relevant European directives and

of the requirements related to spectrum utilisation, an European product

standard draft in preparation at the CENELEC/TC79 level: Pr EN 50 131-

5-3 (Alarm systems - Requirements for interconnections

equipment using radio frequency techniques).

This standard specifies reliability and security requirements for the radio

frequency link in the field of alarm systems. It specifies four grades (1 to 4)

based on different level of reliability and security, for which the higher level

is obtained for the grade 4.

For the reliability aspects this draft proposes requirements about:

the throughput rate, which requires that a minimum

number of alarm messages that generate an alarm at the

receiving equipment shall be 999 out of 1.000 for grades 1 &

2 and 9.999 out of 10.000 for grades 3 & 4.

Immunity to unintentional message and component

substitution

Immunity to attenuation, in order to prevent any

modification in the surround which may affect the RF link

budget.

Immunity to outband interferences

Immunity to inband interferences (saturation)

RF links monitoring

For the security aspects, there are mainly requirements about tampering:

Immunity to intentional component substitution (for grades

3 & 4)

Immunity to intentional message substitution (probability

to discover an identification code in less than one hour (less

223
than 5% for grades 1 & 2, less than 0,5% for grade 3 and

less than 0,05% for grade 4)

Detection of interference (saturation of the receiver with an

intentional signal at the same frequency as the operating

frequency of the system)

Among all the requirements provided by the Pr EN 50 131-5-3, there are

only two requirements which are comparable to those provided by the

European directives.

There are about: o the RF electromagnetic field immunity test required

by the

ETS 300 683 and,

o the outband and the inband immunity test required

by the Pr EN 50 131-5-3

If we compare these two tests, we can see that they are complementary. We

have seen before that for the RF electromagnetic immunity test of the ETS

30 683, there are an exclusion band, inside which the test is not performed.

In Pr EN 50 131-5-3, these tests have to be performed at the limit

frequencies and/or inside this exclusion band in function of the grade as

described in the table 5. The signal which is applied is different from the

two standards, such as for the product standard (Pr En 50 131-5-3), the

interference signal is very closed to the original one (at the difference of the

frequency) and the performance criteria in the case of the product standard

ask for 80% of good transmissions in the presence of the interference signal.

224
F* o F2 KjKASjej 1 J v/m Lo - 12

dB

3 V/m

GRADE 2 3 V/m Lo - 12

dB

3 V/m

GRADE 3 10 V/m 10 V/m 10 V/m

GRADE 4 10 V/m 10 V/m 10 V/m

Table n°5: Outband & inband immunity test according to PrEN 50

131-5-3

In the table 5; - F^ means the lower frequency limit of the exclusion

band

- Fq means the operating frequency of the considered

equipment

- F2 means the higher frequency limit of the exclusion band

Lq means the level of the operating signal

These requirements show that higher is the grade, higher is the reliability

level for the equipment which meets the PrEN 50 131-5-3 standard.

Conclusion

So, we can conclude that there is a good complementarity between the

essential requirements in the sense of CE marking and those from the

product standard draft for the radio frequency links used in the wire free

alarm systems.

Indeed the main aim of the essential requirements of the European

directives is to assure that the equipment can be used safely and that this

equipment would not cause any trouble to the other devices which are in its

environment, whereas the aim of the CENELEC product standards is to

225
ensure that the devices have a good level of reUabiUty, security and tamper

resistance.

226
Karlheinz Schreyer

Siemens Gebaudesicherheit GmbH & Co. oHG, Munich, Germany

Radio Module Characteristics and Their Relevance to Fire

Detection Systems

1. Introduction

Integrating wireless fire detectors into a fire detection system offers the

user many advantages (see 'Technical State of 868-870 MHz Radio Modules

with SRD Band" in the same conference volume). Since this new technology

must cover the same risks as conventional fire detection systems, the same

requirements naturally apply to the quality of the radio link. With modern

transmission processes and high-quality hardware, it has recently become

possible to achieve the same quality as that of tried and trusted wired

systems.

While the committed security expert is well able to discuss the advantages

and disadvantages of any transmission process with the relevant supplier,

the details which determine the hardw^are functionality of radio systems are

almost certainly a mystery to him/her. This presentation attempts to shed

some light on the most important technical terms and to explain their

relevance to fire detection systems.

2. Propagation attenuation

One of the first questions always asked is "What is the range of your radio

link in the building? " In order to impress, various manufacturers therefore

give the outdoor range. This value has very little meaning because there is

no concrete reference basis. In a building, the waves are attenuated by more

than a power often compared with unhindered propagation.

227
The attenuation of radio waves in a building depends entirely on the

physical design properties and the distance between the transmitter and

receiver. The manufacturer of a fire detection system cannot generally

influence these matters and can only control the transmitted power and,

above all, the sensitivity of the receiver. Instead of specifying distances, it

would therefore be better to specify the attenuation budget, the attenuation

reserve and the carrier frequency:

The attenuation budget is a measure of the

range in the building.

2.1 Attenuation budget

The attenuation budget is the difference between the radiated transmitter

power and the minimum received power required for good reception. The

higher the value, the greater the range of the radio link, although it is

important to remember that the attenuation in the masonry depends

heavily on the frequency. A direct comparison can only be made between

two values if the frequency is the same (see 2.3).

o

5

oa

radiated transmitter power

minimum received

power when

commissioning

sensitivity limit of receiver

c

_o

fS3

C

a> a>O)

•Dnq

ab c

.9 a.

0) re u 3

u C fS 0>n

c

.2 V

« £

C w

a 0) t:

*- n

noise

Figure 1 The attenuation budget

228
Some brochures highlight the transmitted power even though doubhng it

only means an increase of 3 dB in the attenuation budget while doubling the

20 dB more received sensitivity = 100 times greater transmitted

current drawn from the battery! Investing in receiver sensitivity makes

greater sense (figure 1). For example, SIGMASPACE has an attenuation

budget of approx. 115 dB.

2.2 Attenuation reserve

For various reasons which could have a negative (or positive) effect on wave

reflection, e.g. moving cupboards or plant pots, or even wet window panes,

the propagation conditions in a building are constantly changing, so that a

path which has a barely adequate power level should not be commissioned.

A comparatively large attenuation reserve is essential in order to ensure a

reliable link for a period of several years. In the case of SIGMASPACE, an

attenuation reserve of more than 25 dB was chosen. This produces an

Effective attenuation = attenuation budget - attenuation

effective attenuation of approx. 90 dB.

2.3 Frequency-dependant attenuation

If waves penetrate dense materials such as wood, concrete or masonry, then

both electrical and magnetic field components interact with their atoms and

molecules so that there is a linear increase in attenuation which is roughly

in line with the increase in frequency.

229
Generally speaking, the higher the frequency, the smaller the aerials.

Smaller ones draw less energy from the electromagnetic field than large

ones.

Rule of thumb: If the frequency is doubled, it is at the

expense of approx. 10 dB of the received signal in

3. Types of modulation

In addition to conventional amplitude (AM) and phase or frequency

modulation (FM) (figure 2), a buzz word frequently used these days is

"spread spectrum modulation". It is explained in greater detail in section 6.

Amplitude modulation Frequency modulation

Figure 2 Amplitude modulation und Frequency modulation

3.1 AM and FM

For noise and distortion-free demodulation,

the overall amplification in AM receivers must be precisely regulated

to the demodulator operating point and

the mixing frequency in FM receivers must be tuned precisely to the

received signal.

Since interference and changes in the transmission path affect the received

amplitude but not the frequency, FM is thought to be more robust.

FM less sensitive to interference

230
Note:

Narrow-band direct conversion receivers must also regulate their amplitude

and can therefore respond to fluctuations in amplitude with distortion.

3.2 Spread spectnun modulation

Within the comparatively narrow bandwidths in the ISM band at 433 MHz

and in the SRD band at 870 MHz which have been approved for Europe,

there is probably little sense in using this wide band transmission method

for fire detection systems.

For further information regarding spread spectrum see section 6.

In Europe's narrow SRD band, spread spectrum modulation

seems to make little sense for fire detection systems.

4. Transmitters

In all likelihood, a '"bad" transmitter", i.e. one which produces several

spurious emissions will not have a negative impact on its own transmission

path. It is more likely to interfere with other services or even other

transmission paths for the same fire detection system, so that it results in

inefficient utilisation of the frequency spectrum which, by its nature, is

limited.

5. Receivers

Receivers must filter out and amplify signals of the order of pW from the

spectrum and in the process must not allow interference either from

processes in neighbouring channels or electromagnetic influences. A truly

enormous task. In order to tackle it, double detection receivers (heterodyne)

are almost always used these days. Regenerative detectors are no longer in

231
general use and ultra wide band receivers which use correlators, have

barely gone beyond the development stage.

5.1 Heterodyne with one or more intermediate frequencies

Figure 3 shows the major function blocks of a heterodyne receiver with an

intermediate frequency (IF). In principle, the variants with two

intermediate frequencies (double super) do not offer any advantage or disadvantage, they are simply design variants. They tend to provide better

image frequency suppression and filtering.

Antenna

Preselection LNA Mixer

Baseband

Filter and

IF Filter IF Amplifier Discriminator Amplifier

. 0

(g)

Local

Oscillator

Figure 3 Circuit diagram of a heterodyne receiver

5.1.1 Preselection

In general, only high-quality radio modules have effective preselection

which is fitted with effective components (e.g. surface wave resonators).

While they attenuate the received signal by a few dB - which can be easily

compensated by carrying out the next steps - they do improve:

electromagnetic compatibility and therefore

suppression of distant interference signals:

An oscillating antenna circuit consisting of inductances and capacitances is

a rather poor compromise. Even with the antenna's selectivity, it only

improves distance selection a little.

232
Effective preselection improves EMC

and suppresses the image frequency.

5.1.2 Preamplifier and mixer

Preamplifiers are mainly used in places where very low signals need to be

received. In doing so, they must not be overloaded with large signals missed

by the preselection operation.

Along with the mixer and IF filter function blocks which follow it, the

preamplifier determines the receiver dynamic range, i.e. the ability to

register a small wanted signal in addition to a strong signal in one of the

neighbouring channels (figure 4).

A

inband

outband

wanted signal in signal neighbouring

channel

frequency

Figure 4 Dynamic range

In fire detection systems, the receiver

dynamic range should be at least 60 dB.

233
Generally speaking, high-quality mixers and preamplifiers require

comparatively high operating current. An acceptable level of energy

consumption for the detector can only be guaranteed if the circuit concept is

carefully chosen.

5.1.3 Local oscillator

The local oscillator must deliver a very clean signal, i.e. one which is free of

spurious emissions or noise. For the mixer, every spurious emission, and

even the oscillator signal noise, is seen as an additional mixer frequency, i.e.

one with which it mixes unwanted signals from the spectrum into the IF

(figure 5).

•o outband

0

>u

0)

spurious sidebands

of LO

inband

outband

signal in neighbouring

channel

c

.

g>

35

u. J

f - f - f

'IF ~ 'antenna ' mixer

stopband

of IF filter

, , passband of

IF filter

It

wanted disturbance

signal

Figure 5 Unwanted mixed frequencies

frequency

stopband

of IF filter

frequency

234
Narrow-band receivers such as those required for the 25 kHz wide alarm

channels in Europe's SRD band, demand very high quality in relation to the

cleanness of the oscillator signal. This applies in particular to the

immediate vicinity of the actual mixed frequency.

In high-quality receivers of approx. 900 MHz, the mixer frequency cannot be

directly produced with the required precision in the local oscillator. A

voltage controlled oscillator (VCO) supplies the output signal. It is linked to

a lower oscillating quartz via a phase locked loop (PLL) (figure 6).

1:n VCO

PLL

T

/ Refeftmce

crystaloscillator

synthesizer

with

crystal

Figure 6 Basic structure of a local oscillator

Based on a single stable frequency, in transceivers, a s5Tithesiser between

the quartz oscillator and the PLL allows the creation of the transmission

signal and the LO signal for the receiver mixer. For receivers which can

operate in several channels, it supplies the variable reference frequency.

Unfortunately, S5mthesisers produce unavoidable spurious emissions.

Precautions must therefore be taken to ensure that these do not couple to

the mixer signal through the PLL. Otherwise other channels would mix and

cause interference at the IF.

LO noise and spurious emissions should be suppressed 10

dB more than the required receiver dynamic range.

235
5.1.4 The discriminator

At this point, the only type of demodulator worth considering is the

discriminator because amplitude modulation is not used in high-quality fire

detection systems.

In principle, the discriminator is a fi'equency-dependent resistor. It converts

the IF fi'equency variation into a variation in amplitude (figure 7).

output voltage

range

Figure 7 Discriminator curve

In order to achieve the greatest possible discriminator efficiency, the

received signal must be mixed quite precisely at the middle of its linearity

range. Broad-band receivers are somewhat less critical. Their quartz

precision is generally sufficient to pinpoint the middle with sufficient

accuracy. With narrow-band receivers, however, costly control mechanisms

are required to tune the receiver to the transmitter.

Low priced broad-band solutions often also require frequency regulation

because their reference oscillator is too imprecise. Simple versions of these

regulation circuits are unable to transmit several "ones" or "zeros" in

succession without becoming deregulated and verging on the limits of the

discriminator's linearity range. Direct current-free basic channel coding

236
(Manchester code, return to zero code) solves the problem, although it takes

up double the bandwidth. Owing to the greater noise levels in broad-band

receivers, they are somewhat less sensitive.

The maximum sensitivity and optimum utilisation of the

spectrum can only be achieved with discriminators which

can demodulate data which has a direct current

5.1.5 Base-band filter and base-band amplifiers

Owing to the current state of the art, base-band filters and amplifiers no

longer have a negative effect on transmission quality, so there is no need for

any further discussion.

5.2 Single chip double detection receivers

In conventional double detection receivers, IF filtering cannot yet be

integrated on silicon. At present, active filters can only be used up to

approx. 100 kHz. Recently, therefore, two different receiver principles which

can be satisfactorily integrated have appeared on the market, or will do so

in the near future:

Receiver with low IF

- Direct conversion

5.2.1 Direct conversion

In receivers with direct conversion (DC - not to be confused with the

abbreviation for direct current!), the IF is at 0 Hz. With conventional

discriminators, the resultant signal cannot be demodulated because both

positive and negative frequency deflection of an FM signal is reproduced as

a positive frequency in the "IF" (figure 8).

237
Figure 9 shows the ampUtude-time curve of a 0/1 transition. The clearly

recognisable irregularity originates from the phase change at the RF level.

Hov^^ever, demodulation is still possible because the phase between the

mixer signal coming from the LO and the received signal changes when the

received signal changes from fm+»f to fm-»f. DC receivers therefore have

two mixers and two *1F filters" in the so-called I and Q branches. The I and

238
Q mixers are driven with a signal offset by 90 degrees (sine and cosine) in

the process (figure 10).

AF-Amplifier

Antenna A Preselection

Local

Oscillator AF-Amplifier

A Phase

Comparator

Figure 10 Circuit diagram of a direct conversion receiver

Figure 11 shows how the ampUtude-time relationship changes between

channels I and Q when the received signal switches from + Af to - Af. It is

easy to see that this phase difference can be evaluated in principle.

Figure 11 Amplitude-time diagram in the I and Q channel

Q channel

I channel

DC receivers produce comparatively satisfactory results at low

cost although they require a broader bandwidth for the same

data rate. This results in somewhat lower sensitivity.

Fast-moving objects in the vicinity of the receiver could present a problem

since mixing takes place in the receiver at precisely the received frequency.

Needless to say, this mixing frequency cannot be completely attenuated and

is radiated backwards, so to speak, through the aerial. If this signal meets a

nearby object which is moving (ventilator) it is reflected. The resultant

Doppler frequency (radar) superimposes itself on the receiving signal and

causes interference.

239
DC receivers may react sensitively to moving objects in the

5.2.2 Receivers with low IF

In receivers with low IF, the received signal is mixed close to the base-band

or even at half the RF bandwidth. In this range of just a few tens of kHz,

good active filters can simply be realised on a chip.

With a very small intermediate frequency, the image frequencies come so

close to the basic channel that they can no longer be suppressed with

conventional filters. Figure 12 shows a suitable process which at first glance

is similar to direct conversion. In one of the two channels, the AF phase is

shifted by 90°. Both channels (I and Q) are then linked together in an

addition stage. The base-band data become available following low pass

filtering.

Mixer AF-Amplifier

90" Phase

Shift

Antenna

Preselection

I - Channel

AF-Amplifier

Q - Channel

1

Data Out >

Low Pass

Figure 12 Circuit diagram of a receiver with low IF

Receivers with low IF make better use of the available

spectrum than DC receivers.

240
6. Spread spectrum transmission

Originally spread spectrum transmission came from the military field. It

was based on the idea of having the carrier frequency transmitted through a

broad frequency range so quickly that it could no longer be detected by

(conventional) enemy receivers.

If the carrier frequency does not change constantly but follows a specified

digital algorithm, this is described as a "direct sequence spread spectrum"

(DSSS).

When expanding the transmission band to a large spectral range, the level

which can be measured by a non-synchronous receiver is reduced in

proportion to the band spread (figure 13).

0) i original energy

outband inband density without

spread spectrum

outband

distribution of energy in the

transmission band with

spread spectrum

frequency

Figure 13 Energy distribution in the spread spectrum transmission channel

This factor is described as system gain G

G = Bandwidth RF

Bandwidth baseband

[1]

241
It is the main parameter for spread spectrum transmission and

characterises:

the extent to which the average ampHtude is reduced

the extent to which transmission interferes with conventional receivers

the extent to which a conventional transmission is mixed in with the

spread spectrum transmission

the extent to which spread spectrum transmissions interfere with one

another

the maximum dynamic range compared with other signals in the

received band

Given the above conditions, 60 dB system gain would seem to be a

reasonable minimum. At a data rate of 1 kBit/s this process would require a

bandwidth of 1 GHz. At present, however, this is not realistic.

Another drawback of the system should also be mentioned. It is not easy to

synchronise a receiver to a transmitter. Correlators which synchronise with

a preamble are used for this.

The receiver ICs currently available still require too much energy to fit

them in a fire detector. Apart from this, they only offer a comparatively

modest system gain of approx. 30 dB.

At present, spread spectrum technology does not

appear to be developed enough for use in fire detection

242
Georg Schmitz. Dr. Peter Witschital

Siemens Gebaudesicherheit GmbH & Co. OHG, Munich, Germany

Internet Technology: New Perspectives for Alarm Systems

Internet technology is making rapid progress with a high pace of innovation and very

short innovation cycles. Today, an , internet year' is to be considered less than 90 days'.

Prices of standard hardware and software components are dropping constantly and fast.

This is the reason why it becomes more and more attractive to benefit from this

technological progress and to make use of standard Internet technology for products and

applications which had to rely on , traditional' branch-specific technological solutions by

now.

1. Learning from other business segments

A good example is the telecommunications industry. Communication systems based on

traditional switching technology are now facing strong competition by new systems

using new standardized protocols like Voice-over-IP (e.g., the ITU H.323 standard

defines how to send voice packages via the Internet using the Internet Protocol (IP)) and

Internet technology in general. The new systems reduce infrastructure costs by using the

same technology for voice and data communication.

Furthermore they take advantage from Internet technology's steep innovation curve and

resulting price/performance advantages. For example. Fast Ethernet boards declined in

price by as much as 50 percent in the past year, while the price of legacy PBXs (private

branch exchanges) declined by only 7 percent approximately (according to Cisco

Systems Inc.; Internet Communications Software Group - Driving the Market for

Converged Applications and Services (White Paper); July 2000).

As a consequence, established manufacturers of communication systems lost market

share to new manufacturers coming from the IT business segment.

^ In fact various numbers are mentioned with respect to how long an 'internet year' is. However, the innovation cycles are very short in any case.

243
2. What does this mean for building automation and alarm systems?

The crude fact is: There is a challenge coming from IT business and if we do not face it, we will get in trouble. But let's look at it positively: It's not merely a challenge, it offers

new chances. Internet technology opens new perspectives not only for building

automation (HVAC) systems but for alarm systems with higher demand on reliability as

well.

There are mainly two aspects of using Internet technology for alarm systems: Internet

technology for system communication and Internet technology for providing new

services.

3. Internet technology for system communication

First, the technology can be used for internal communication between system

components. This will become attractive for two reasons. On the one hand, the cost of

Internet technology hardware components will drop dramatically because of mass scale

production, e.g., a processor chip with an Internet Protocol stack on board will probably

cost less than five dollars in the near future. On the other hand, standard software

implementations of all the relevant protocols are available on the market and,

furthermore, numerous software packages are offered, based on the Internet Protocol

suite and implementing interesting services of all kinds, e.g. encryption, authentication

etc. There is no need to make this software, one can buy it off-the-shelf instead, saving

lots of development effort.

In order to understand the situation properly it will help to have a look at the OSI 7-layer

communication model (see Table 1).

If two devices want to communicate with each other a common protocol is needed in

order to make sure that they understand each other, i.e. 'they have to speak the same

language'. The messages to be exchanged have usually application specific content and

the protocol to be used is a protocol belonging to the application layer of the layered

OSI communication model. In order to transport the message frpm one device to the

other, services provided by lower level protocols are used.

244
Layer Function of the layer

^

Application Layer User level applications

(e.g. WWW (HTTP), FileTransfer (FTP))

^ Presentation Layer

o

Adaptation of formats

(empty for Internet technology)

^

Session Layer Synchronization, administration

(almost empty for Internet technology) ^

Transport Layer Peer-to-peer communication

(e.g. TCP (Transport Control Protocol),

UDP (Universal Datagram Protocol)) ^ Network Layer Routing and switching

(e.g. IP (Internet Protocol))

Data Link Layer 2Access method to the physical media

(e.g. Ethernet, Token Ring)

-j^ Physical Layer Physical media and transport of bits

(e.g. Coax, Twisted Pair)

Table 1: OS! 7-layer communication model

What does it mean if we say we want to use Internet technology for system

communication? First of all, it means that the Internet Protocol (IP, Layer 3) and

probably TCP or UDP (Layer 4) shall be used. This decision allows us to use a lot of

software components based on TCP/IP or UDP/IP available off-the-shelf on the market,

as it was already described above.

3.1 TCP/IP is not enough

However, this decision alone does not guarantee that two devices understand each other.

Therefore, a common protocol on the application layer is needed. There are many

possibilities to choose from. As there is the general tendency away from proprietary

protocols to open standardized protocols (for obvious reasons), BACnet is one of the

candidates for the common language among the devices. A little bit deviating from the

OSI model, BACnet defines its own network layer protocol and uses UDP/IP as one of

its possible data link layers.

When a common application layer protocol has been found, the other open question is what to use below IP as data link and physical layer. In order to profit as much as

245
possible from widespread technology, Ethernet is the best choice. Ethernet is an

established, worldwide standard supported by IEEE and ISO. It has been broadly used in

office environments and is penetrating the industrial environment as well. The high

number of users ensures the downward price of its components. The long-term exposure

to Ethernet technology has produced an expansive knowledge base and unparalleled

resources. However, for industrial usage Ethernet has some drawbacks which are

lously stressed by its critics. But these drawbacks can be avoided or overcome

luuay.

3.2 Ethernet and real-time behavior

Ethernet as used in an office environment uses a media access method known as CSMA/CD (carrier sense multiple 'iccess with collision detection) that cannot guarantee

a deterministic behavior of the network. This can be overcome by using an appropriate

network topology with so-called Switched Ethernet technology. The switches make sure

that every device connected to the network can operate full-duplex with a guaranteed

bandwidth. Thus, the network supports real-time communication with guaranteed

response times. In addition, there are several options how to assign priority levels to

messages. The fact, that it is possible to build networks with guaranteed response times

based on Ethernet technology has been proved many times in industrial automation

applications today. One should keep in mind that the requirements with regard to realtime behavior are usually much higher in industrial automation than in alarm systems.

Furthermore, it is for sure that the real-time behavior of the Internet (including Ethernet

technology) will be further improved because there is high demand for transporting realtime video information via the Internet.

The bandwidth of standard Ethernet is 10 Mbit/s which is quite high compared with

other bus systems used for alarm systems today. If that should not be enough. Fast

Ethernet is available today with a bandwidth of 100 Mbit/s and Gigabit Ethernet

(1 Gigabit/s) is emerging. The IEEE standardization group has even started work to

define 10 Gigabit/s Ethernet.

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3.3 Ethernet and reliability

Reliability is not only an issue for alarm systems but for industrial automation as well. It

is not acceptable that single faults may cause a complete stand-still of a production line

easily resulting in million dollar losses. For that reason the issue of making an Ethernetbased control network reliable has also been solved already in industrial automation.

There are several solutions for making an industrial Ethernet redundant and faulttolerant, some of them can even cope with double faults in the system.

More and more often customers ask whether it is possible that existing company-owned

TCP/IP network infra-structure can be used for the alarm system as well, in order to

save efforts for installation of a separate network. The answer is: yes and no.

As mentioned before, a standard office LAN (local area network) based on Ethernet is

not suited for time-critical applications. The network topology would have to be

adjusted. For several reasons, it may be the better solution to install a separate security

network for safety-critical and time-critical applications which may be connected to the

office network via a TCP/IP-Gateway (the terms router or bridge may also be used,

although all of them have different functions). The big advantage of using Internet

technology for both the security network and the office network is that information

which is not time-critical can easily travel across the border between the sub-networks.

From a PC in the office network it is then possible to get always up-to-date information

about the status of the alarm system. In automation technology this concept is know as

the sensor-to-boardroom integration. All plant devices act as servers providing information

to anyone anywhere.

3.4 Ethernet and robustness

Some critics of Ethernet for industrial use argue that the connectors (in an office

environment cheap RJ45 connectors are used) are not suited for an industrial

environment. But this problem has been solved already, too. Several alternative Ethernet

connectors are available for rugged environments.

247
3.5 Internet technology has an influence on the system architecture

As prices drop, the level on which Internet technology can be used profitably drops.

While Ethernet and IP communication is used on the management system level

(management layer) already today, it starts to be profitable on the control panel level

(control layer) now. Even some IP-based sensors are available today, but currently this

not yet profitable for low-cost sensors. However, we see no reason why Internet

technology should not move down to the sensor level (device layer) in some years time.

Using Internet technology will of course also have an impact on the overall system

architecture of an alarm system. Today a simple alarm system installation usually

consists of a control panel with hundreds or even thousands of sensors connected to it. If

you thmk of a multi-functional alarm system for fire detection, intrusion detection, and

maybe access control, it is necessary to install lots of cables in the building for the

different sensors to be connected with the control panel(s). From our point of view an

architecture for a new system that makes use of Internet technology would be a more

decentralized architecture with the sensors connected to smaller system units spread

over the building and connected via Internet technology. This could probably help to

reduce installation and maintenance costs of the system.

The progress in wireless technology may be another influence on future system

architecture. Again, using Internet technology has the advantage that e.g. Wireless LAN

technology or next generation mobile communication technology, like UMTS

(Universal Mobile Telecommunication System), can easily be integrated into the future

alarm system architecture because it is for sure that these new technologies will support

the transport of IP packages and Internet technology in general.

4. Internet technology for providing new services

Besides system communication, the other important aspect of Internet technology is the

provision of opportunities to add value for the customer by offering really new services.

In the following, we can just try to give a few examples.

248
4.1 Remote control and remote service via web technology

First of all, web technology (using the standardized application layer protocol HTTP

(HyperText Transfer Protocol)) allows to access the alarm system anytime from

anywhere. Of course, access shall be restricted to authorized personnel. No problem,

just take an authentication software package, perhaps some encryption package or what

ever you need off the Internet technology shelf. In the alarm system an embedded web

server is needed. Again, hard- and software are available on the market in various sizes

at various prices. On the client side the big advantage is that every device supporting

standard web browser technology can be used for access, whether a PC, a PDA

(Personal Digital Assistant), a wireless 'web pad' or - soon - a UMTS mobile phone.

Remote Access by web technology enables you to offer remote control and remote

services to your customers, perhaps the service being your USP (unique selling point)

giving you an advantage over your competitor. For your service organization remote

access offers a huge cost reduction potential. Of course, remote service can also be

implemented without web technology. But with web technology you can buy most of

the components needed on the market and there is no need to implement and maintain a

proprietary solution.

By using Internet technology it is also quite easy to integrate audio- and videoinformation into an alarm system. There are standards, e.g. the H.323 standard we

already mentioned before, which define how to transmit multimedia information over

IP-based networks. Not to forget the messaging services which come for free with

Internet technology, like Electronic Mail. Alarm or system fault messages can be

forwarded by E-Mail, Fax or SMS (Short Message Service for sending and receiving

short test messages with a mobile phone). All this offers many opportunities how to

offer added value to the customers.

4.2 Software download and application service providing

Another simple application layer protocol is FTP, the File Transfer Protocol. Based on

this protocol, it will easily be possible to download software or data from remote into

the system in order to update the system software. If the alarm system is connected with

249
the Iniemel (whether the public Internet or a VPN (Virtual Private Network) of the

system vendor or any service provider) it becomes possible to offer services to the

customer which do not have to be installed on the alarm system. Instead, they are

located on a so-called application server owned by the alarm system vendor or a service

provider. Such ASPs (application server providers) are just emerging rapidly in the

office world. An ASP offers software which can be used via the Internet, but does not

have to be installed on the customers computer. The customer can choose to pay a

monthly flat-rale for the license to use the software or to pay per use. For an alarm

system the customer may want to run a diagnostic program from time to time providing

.some information about the status of the sensors and detectors. Because he runs the

program only if needed, he may prefer to run it from the application server and to pay

for it by use. An alarm system installer may prefer not to install a planning and

provisioning tool on his own computer but to use the tool from the application server, where he always gets the most recent version supporting all new system features. For

the system vendor or service provider it is much more convenient to update the version

of the application program on his own application server than to update hundreds of

application programs which are installed on the systems of the customers.

4.3 New possibilities for improved risk calculation and priority analysis

Due to the closer integration of the alarm system's control network with the office

network and the transparency between those networks it will be much easier than before

to combine information related to the alarm system with information available from the

customer's office network. In case of fire alarm, for example, information about the

material stored in a warehouse could be taken into account, in order to perform a more

detailed risk calculation and priority analysis. This information could help in the

decision whether a building has to be evacuated immediately, or in the decision about

the best strategy for fire fighting.

Of course, it would be great if the fire fighters could get more detailed information

about the situation on-site, before they arrive there. Our vision is, that the fire fighters

have access to detailed information from the alarm system via a mobile Internet terminal

in their trucks (e.g. using mobile Internet access via UMTS). They can get every

250
information they need to fight the fire effectively, whether there are specific risks, how

to get most quickly to the center of the fire etc. Even transmission of life video images

from the site is possible. This vision is not far away.

5. Summary

We have to be aware that if the alarm system manufacturers will not take the opportunities

offered by Internet technology for offering new services to the customers, some vendors of IT

technology will start to offer more and more safety and security related services. There is a

clear trend from delivering only technology to delivering applications and services.

The customers, of course, like systems which are based on open standards and standardized

interfaces because this helps to protect the customers' investments, to manage complexity and

to reduce risks of change. The use of standard hardware and software components and the new

system architecture of multifunctional integrated alarm systems supported by Internet

technology will significantly reduce installation and maintenance costs of the systems.

The experiences made with using Internet technology in industrial automation have produced

reliable, robust solutions with real-time behavior which are equally suited for use in alarm

system applications.

Internet technology opens new perspectives for alarm system manufacturers.

251
Bjoern Silberberg

Siemens Gebaudesicherheit GmbH & Co. oHG, Munich, Germany

Technical State of 868-870 MHz Radio Modules in the SRD Band

1 Introduction

Wireless detection systems are becoming more and more the norm in modem

building technologies. Their competitive advantages are their cost-efficiency and

flexibility with which the system parameters can be set and the detector zones be

configured. Due to the fact that they have to compete with wired (conventional)

systems they need the same standard of safety and reliability. This and other

economical aspects are challenging the development of today's wireless detection

systems.

2 Attenuation of RF-transmission calculation

The attenuation factors inside a building are an important factor for safe planning

and running of the system because radio transmission is limited in range. Mistakes

which are made in early project stages lead, without fail, to much customer

dissatisfaction.

The attenuation is a measure of how much the radio wave is weakened . It is

needed to determine the placement and number of the wireless cell components in

advance to guarantee a safe radio transmission. Beside this it is very important for

the sales to make an accurate offer.

Which factors have to be taken into account for safe planning?

2.1 Theoretical

Field attenuation

The spreading of electromagnetic at frequencies above 30 MHz is like light

beams. If a light beam hits an object's surface, it is illuminated with the strength

E. With increasing (doubling) of the distance the light beam has to lighten a

252
surface which is four times as big as the original surface. Therefore the illuminate

strength decreases proportional to the surface increase i.e. to a quarter of the

original strength E.

Liaht source

Figure 2.1: Relation light strength to distance

The behaviour of radio waves is quite similar.

One of a radio signals features which differs from sun light is its coherence

(coherent = waves of one wavelength in phase). Sunlight (incoherent) runs

without coupling in all directions form it's source.

253
s SUNLIGHT

incoherent

coherent

Figure 2.2: Coherent and incoherent Hght.

Reflection

When Hke in the algebra, several vectors meet together one point the resulting

vector is the sum of all. Depending on the vector orientation vectors are added or

subtracted (sun light beams always amplify each other, e.g. switching on of two

lamps). In the worst case the vectors subtract from each other and the resulting

vector is zero (which is called fading).

In this case no signal can be received on the radio wave.

254
Superimposition of radio waves at 870 MHz

AWA

16,5 cm

33 cm

wavelength

The sending of radio

waves is always

spherical around the

sender (Tz).

At every spherical

obstacle we get again

spherical reflections

(D).

Therefore the amount

of radiowaves which

hit the receiver (Rz)

have a variety of

strength and

orientation.

The resulting signal is the sunn of them.

Figure 2.3: Wave reflection inside buildings

It's hard to give a prediction inside buildings with its large amount of vector

reflections.

There are further ways to calculate the range using computers. These models take

account of other influences which have an impact on the transmission of radio

waves. Some computer programs use a combination of the beam analysis method

and the reflection method.

Putting empirical collected data in a data sheet together we get:

255
s

Relation of field strength and distance

• CXitdoors:

• F ield strength <f U2

• Doube dstcnce: Reduction of ^ 6cB

• lnclcx>rs:

• Field strength <P U5

• DouOe dstcnce: Reduction of ^ 1 6cB

Attenuation = f (dstcnoe) indoor exarples

Distcnce(m) 40 30 25 20 15 10 5

Attenuction (dB) 90 83 79 74 67 57 40

Figure 2.4: Relation of field strengtli and distance

By the rule of thumb we can calculate that in an outdoor environment with every

doubling of the distance between transmitter and receiver we get an attenuation

increase of 6 dB. In an indoor environment it is 16 dB.

Obstacles

When radio beams hit an obstacle which prevent the direct way to the receiver

two things can happen. Depending on the thickness and material the beam gets

either reflected or gets through the material or both.

Unfortunately the relative permitivity is in most cases unknown. Therefore the

best way to insert the right values is to take empirically developed attenuation

figures.

SIEMENS empirically developed attenuation values for building materials can be

found in the next table:

256
Relation of attenuation and material

• Room separator • very low < 1 dB

• Brick, concrete dry • low <P 6dB

• Sandstone • moderate lOdB

• Wood wall • moderate <f lOdB

• Brick wet • moderate <f lOdB

• Plaster plates coated • moderate / high <f 15dB

• Reinforced concrete • high <P 30dB

• Heavy wet brick wall • very high >40dB

Figure 2.5: Relation of attenuation and materials

S

Spread of radio waves within a building

Figure 2.6: Spread of radio waves within a building

257
2.2 Practical

As we can see in the above picture the direct way has mostly the highest field

strength. In areas where reflection of the radio wave is needed the resulting field

strength is different in every room depending on addition and subtraction of the

field strength vectors.

Combination method by using the computer

A common way to do the calculation is by using a computer.

The method which is used is called combination method, because it combines the

beam analysis method and the reflection method. Both methods have advantages

and disadvantages, the combination of both delivers an acceptable result.

Unfortunately the calculation with a computer is currently very time intensive and

only possible with high performing computers. The procedures allows the

definition of the obstacle material through which the beam has to go through. All

insertions of information into the computer need much time.

Attenuation addition method

A way to calculate the attenuation inside of buildings quite simply is the addition

of empirical developed single attenuation factors (just distances and obstacles and

neglecting the reflection).

This is quite an easy method for every layman to calculate the maximum distance

between transmitter and receiver where just a few environment factors have to be

taken into account.

Principle:

The difficulties for the proper methology are the right definition of the material

which the radio wave has to go through when the direct way is disturbed, and the

calculation of the distance between transmitter and receiver or the reflection path

between both.

As seen in the previous chapter table 2.4 and 2.5 give the empirical developed

overview.

Finally all numbers are added up to the total amount of attenuation.

258
Example:

Distance: 20m 74 dB

Obstacle: concrete dry 6 dB

Amount: 80 dB

The disadvantage of this method is that the reflections are not included but it

gives a suitable start for making a sales offer and doing the project.

In cases of uncertainty a measurement of the range of a radio cell is imperative.

This has also to be done at new installations to guarantee customer satisfaction.

3 Handling of radio frequency fading

As described in the previous chapters the reason for fading is that the resulting

vector is zero therefore no signal can be received.

When watching the radio reception over a whole day we find strong deviations

because of changing reflection circumstances inside the rooms.

259
DBM

-75

-90

-105

-120

11 17 20:55 6 34 16 12 150 11:28 21 06 6:44 16 22 2:00 1138 2116 6 54 16 32 2 10 '01 99 07.01 99 Ce.Ol, 99 08 01 99 09 01.99 09 01 99 09 01.99 1001.99 10.01 99 11 01 99 11 01 99 11 01 99 1201 99 1201.99 1301 99

Figure 3.1: Transmission quality of a radio cell watched over a whole week.

The reasons for the differences in reception quality are the movements inside the

building and therefore changes of the reflections. ( tests with just a single hand

covering of the radio module show a disturbance of the signal).

How can a radio system avoid this variation in reception?

3.1 Aerial diversity

One solution to avoid this is to equip the radio module with two aerial which are

orthogonally oriented to each other. Due to the different vector orientation the

module can take the antenna which has the best reception of the signal.

260
Figure 3.2: Transmission difference with two antennas

(e.g. We can see similarities in our daily life, with our femte-rod aerial radio. To

get the best reception we rotate the ferrite-rod (orientation) until the radio receives

best.)

3.2 Channel switching

S

Alternate frequency channels .

IHI 16 Basic channels

Figure 3.3: Channel switching changing the wave length through changing the

frequency.

261
Another way to avoid short-term reception loss is to change the frequency. This

has an impact on the wavelength and the sum of vectors between transmitter and

receiver.

*This can be achieved with SIGMASPACE/ TeieRex in a maximum of four

times at fixed preprogrammed channels.

3.3 Bypass of information

SIEMENS

Telegram bypass for radio disturbances

Figure 3.4: Bypass of information

In cases where the transmission from transmitter to receiver is disturbed a technique is to

bypass the message via a neighbour radio module and use this as a repeater. The above

picture explains how this can work.

*With all these procedures installed in our wireless system SIGMASPACE/TeleRex we

have achieved a very high transmission quality. Beside that, disturbances are detected

within 100 seconds!

262
4 Conclusion

Many challenges are testing the progress of modem wireless systems in the

building technology infrastructure. Only when the technique dominates totally can

it replace wired systems in the future.

This presentation has shown how at SIEMENS these challenges are accepted. We

are looking to a sound future of wireless building technology systems.

APPENDIX

Application SIGMASPACE/TeleRex

All building danger alarms as defined under DIN VDE 0833 and a range of

additional technical information can be displayed and processed with one system,

the SIEMENS SIGMASYS/AlgoRex danger alarm system.

To enhance the flexibility and suitability characteristics of SIGMASYS/AlgoRex,

SIEMENS developed SIGMASPACE/TeleRex, a wireless detector system which

is integrated into SIGMASYS/AlgoRex. The products allow

SIGMASYS/AlgoRex to protect each individual customer's building and keeps

pace with the customer's changing environment and requirements.

SIGMASYS hazard detection equipment is able now to keep pace with every

customer requirement through the simplest and quickest of system extensions

with SIGMASPACE/TeleRex detectors.

The advantages of SIEMENS' wireless system in a nutshell:

installation neatness - through wireless connection

flexibility- through use of the most modem radio technology

functional security- through two patented disturbance handling

procedures

- detection reliability - through SIEMENS' 1^' class detector

experience

profitability- through simple installation and long battery life

automation - through automatic radio system configuration

263
As an example how a typical configuration of SIGAMSYS/AlgoRex with

SIGMASPACE/TeleRex can look, see below picture.

DOW1171

D0W1171

Figure 5.1: Typical configuration SIGAMSYS/AlgoRex with

SIGMASPACE/TeleRex

As can be seen in figure 5.1. the SIGMASYS/AlgoRex control unit is wired connected to

the detector and the call point. In the middle we see the SIGMASPACE/TeleRex, which

is the interface between wired and wireless technology.. The gateway is the master of

every radio cell. SIGMASPACE/TeleRex uses wired and wireless technology it is called

a hybrid system.

Features

Only approved (VdS EN 54-7) hybrid system in the SRD band with text alarm

location message.

Transmission power: 5mW

Frequency: 870MHz

Attenuation budget: I15dB

Wireless distance: approx. 40m in buildings

264
Oliver Linden, Hans Holemann

University of Wuppertal - FB 14 - Section: Fire and Explosion Protection

Gauss Str. 20, 42097 Wuppertal, Germany

Testing Methods for Gas Sensor Based Fire Detectors

VdS Testing Methods - Advantages and Disadvantages -

Suggestions for Improvement

Abstract

Witliin tlie past years fire detection technology has developed at a high speed

of innovation. Computerization and the use of sophisticated fire detection

algorithms gained more and more importance at taking advantage of the wide

range of opportunities multisensor detectors show. Despite this development

testing methods still remain to be the same. European testing standards and

testing equipment that have been created for fire detectors decades ago are undergoing adoption to state of the art of fire detection technology step by step.

Against this background the new VdS testing methods for gas sensor based fire

detectors have been created basing on the European standard EN 54.

1 Introduction

Nowadays, fire detection technology is characterized by an increasing use of

multisensor-detectors. In particular optical smoke sensors, ionization chambers

and heat sensors are combined. By the continuous development of gas

sensors, which are based on different physical and chemical principles, there is

- in the meantime - the possibility to further improve multisensor-detectors by

the additional use of gaseous fire criteria. Using suitable detection algorithms

gas sensors can contribute to reduce the rate of deceptive alarms.

265
Furthermore, gas sensors seem to be suitable for the use in some special

applications, in which conventional fire detectors are - due to the predominant

environmental factors - not to be used or in which they cannot react sufficiently

fast.

Gas sensor based fire detectors as well as conventional smoke- and heat

detectors have to be subjected to a testing procedure in accordance with EN 54

to receive VdS approval. In this context, the University of Wuppertal, section fire and explosion protection, supports VdS Schadenverhutung at the development

of a new testing procedure in design, realization, and testing out.

This paper gives an outline of VdS testing methods [1] for gas sensor based fire

detectors. The advantages and disadvantages of the testing procedure will be

illustrated and possible future solutions will be derived.

2 VdS Testing Methods for Gas Sensor Based Fire Detectors

In co-operation with industry and the University of Wuppertal and supported by

the Institute of Applied Physics in Giessen, VdS Schadenverhutung has

developed test guidelines for the testing of gas sensor based fire detectors.

266
As presented below the VdS-test procedure can be broken down into various

modules:

1/1

CO 00

Test Procedure for

Gas Sensor Based Detectors

Basic Tests

General Requirements

Environmental Testing

Mechanical Tests

Climate Tests

EMC Tests

Cross Sensitivity Tests

Sensor Poisoning Tests

Long-Term Stability Test

Fire Sensitivity Test

-D

^5

Figure 1 : VdS test procedure for gas sensor based fire detectors

267
2.1 Environmental Testing

The requirements of the test methods, which is to assure the efficiency and the

reliability of the individual components of fire detection systems, are described

in the DIN EN 54 series of standards [3]. This series of standards also includes

the functional testing of fire detectors with gas sensors (gas sensor based fire

detectors). Section 1.3 of DIN EN 54, Part 1 stipulates:

"The fire detection system must not only worl< satisfactorily under tlie

conditions of the fire event but also under environmental conditions which

will normally occur in practice. The stipulated tests have the purpose to provide evidence for the performance of the system and its components

under such conditions.

The definition of this objective allows us to conclude that it is not necessarily

sufficient to test fire detectors based on a new technology using a selection of

existing tests in accordance with EN 54. Rather, it may be considered as an

appeal to find out whether - and if so, which - additional tests may be necessary

for the application of new technologies in fire detection in order to make sure

that a detector will operate satisfactorily "also under environmental conditions,

which will normally occur in practice". Against this background, new tests have

been included in the test schedule for gas sensor based fire detectors and

these have been chosen with respect to the well-known problems of sensor

poisoning and cross-sensitivities found in many gas sensors.

Cross Sensitivity Tests

The occasionally insufficient selectivity of gas sensors includes the risk that gas

sensor based fire detectors might be put in the alarm status by a large number

of non-fire specific parameters [2]. For this reason, two deceptive alarm tests

have so far been defined, where the detector must not emit an alarm signal. For

the time being, the following parameters apply to the existing tests:

268
Deceptive parameter Concentration Test period

Ethanol (similar to [4]) 500 ppm 1 h

Ammonia (similar to [5]) 50 ppm 1 h

Sensor Poisoning Tests

In order to largely exclude "poisoning phenomena" of gas sensors in practical

use, additional tests are carried out to determine the effect of sensor poisons. It

should thus be determined, whether certain substances will irreversibly damage

the sensors or negatively impact on the function of the test specimen or even

prevent the detector's use altogether. For the time being, two potential sensor

poisons have been chosen:

Sensor poisons Concentration Test period

Ozone 500 ppb 1 h

Hexamethyldisiloxane (HMDS) 10 ppm 1 h

(similar to [4])

In future it will probably be necessary to subject gas sensor based fire detectors

to further tests for deceptive alarms and poisoning - depending on the sensors'

principle. The ozone test concentration determined so far corresponds

approximately to the maximum value which can be measured in the ambient air

in summer. There are considerations to increase the requirements for this test.

2.2 Stability Test

According to the VdS testing methods and similar to the smoke (heat) channel

for testing smoke (heat) detectors, a gas mixing apparatus is used for testing

the signal reproducibility. This way of performing the stability test is - nowadays -

the only one and logical means to establish defined testing methods for gas

sensor based fire detectors within a short period of time.

269
Wolfgang Kriill

Gerhard-Mercator Universitat Duisburg, Germany,

Department of Comunication Engineering

A modular data acquisition system for the measurement of fire characteristics

1. Introduction

This article describes the development of a computer-aided data acquisition system as a component of an installation in a fire detection laboratory. With this laboratory

standardized test fires (EN54) are possible as well as fundamental experiments. Besides

the required measuring techniques (measurement of smoke density according to the

ionization current principle and the extinction principle, temperature and weight loss)

the system offers the possibility of implementing new measuring instruments and

sensors relatively simply due to its modular structure. Thus this system can accomodate

new measuring instruments of interest in the future. The motivation for developing this new data acquisition system was to submit a proposal for a standardized data acquisition

in other laboratories which can accomodate various existing and possible new standards.

The goal was to launch a flexible hardware and a user friendly expandable software.

Thus the source code of the program has to be understandable not only for experts such

as e.g. C++ programs. On the other hand we wanted to replace the existing system with

a much more comfortable system.

2. The fire lab

The existing fire lab consists of the main fire room with a base of 10.5 m * 9.0 m and

two observation rooms, situated over the other. The ceiling in the main fire room is vertically adjustable (2.87 m ... 6.57 m). Therefore, the fire lab provides the opportunity to

perform standardized test fires as well as basic experiments with different room heights

and volumes. The technical equipment is placed in the upper observation room as well

as on top of the adjustable ceiling. The top of the ceiling can be walked on if the ceiling

is in the maintenance position. Non-inflammable plates (0.6 m * 0.6 m) on the under270
side of the ceiling can be removed. This enables easy equipment assembly.

Requirements for the data acquisition system to be developed:

1 . Computer-based data acquisition system

The data acquisition hardware should be placed on top of the vertically adjustable

ceiling. That has the advantage that new devices can be installed easily and

quickly. Further computing facilities should be placed in the observation room.

2. The existing devices should be connected directly and trouble-free. Figure 1 shows the structure of the data acquisition system to be realized.

Terminal box

"T

CO/CO. JLl

mi

DAQ

MIC MIREX

RreLab

—I 1

bar. pressure max. 16 •

rel. humidity temperature

mm

Control

Panel

Figure 1 : Structure of the data acquisition system to be realized

• Basic measuring equipment meeting the requirements of the European standard

EN 54:

Measuring ionization chamber (MIC), smoke density measuring instrument according to the extinction principle (MIREX), temperature sensor (PtlOO), balance,

controller for the heater and measurement of the heater temperature

• Further devices for basic experiments:

The reduction of false alarms is only possible if (as much as possible) information

about normal surrounding conditions of fire detectors is known (e.g. relative air

271
humidity, barometric pressure, temperature distribution under the ceiling). The

inclusion of gas detection within multi-sensor detectors requires a new reference

measuring technique. Therefore measurement of the CO and C02-concentration is recommended. Appropriate measuring instruments are installed.

3. The hardware should be produced and tested by a well-known manufacturer. A

trouble-free installation and a world-wide support had to be guaranteed.

4. The software to be developed should be written in a programming language which

is adapted to the chosen hardware. The software should have the ability to monitor

the measured data online.

3. Modular hardware design

The implemented system consists of the following components:

• Data Acquisition System (hardware and software)

• Computing Facilities

• Measuring Devices

• Control Panel

To develop a high quality data acquisition system for measurement and control it is

important to understand the function of all components. In this field signal conditioning

is especially important because existing sensors provide multiple electrical signals in

various forms. The data acquisition system has to be adapted to measure all these

different signals. The type and number of sensors (thermocouple, RTD, voltage or

current output of a device) define the components of the system. Interesting is for

example the number of channels (single-ended or differential), the resolution of the

analog signals and the sample rate.

The chosen modular SCXI-system is produced by National Instruments Corporation.

The data acquisition system consists of a module housing (SCXI-2000), a data acquisition card (SCXI-1200) with 24 additional digital inputs and outputs for the adaptive

control of devices (e.g. controlling the air conditioning system) during a test fire, a 16-

channel-multiplexer (SCXI-1122) for temperature sensors (Pt 100) with a terminal box

and a 32-channel-multiplexer (SCXI-1100) for voltage channels with a 32-channel272
terminal-box (BNC-2095) with BNC-connectors (for various measuring instruments).

Figure 2 show s the structure.

Terminal box

12-B it-Da ta -A cq u isi ti 0 n

SCXI 2000

1 6-C ha nnel-Multip lexer

32-0 ha n ne]-M u Iti p texer

oooooooo oooooooo

oooooooo oooooooo BNC-2095

PC 1

RS 232

Figure 2: Structure of the chosen data acquisition s\ stem

The realized system concerns a modular structuredsystem which is expandable at any

time and is connected to the central computer via RS-485 serial communication. The

ad\ antages of data transmission via RS-485 over the conventional RS-232 are:

• longer distance (max. 1200 m)

• faster data transmission (max. 1 Ivlbps), data throughput v\ ill usually be limited

by serial communication rates

• lower error probability during data transmission (by symmetric signals)

The appointed computer is a standard-PC (Pentium 3 / 500 Mhz) with 256 MB RAM

and an additional RS-485 serial interface, the operating system is Windows-NT. The

presented flexible data acquisition s\'stem is an integral component of a computer net- work (see figure 3). The archived data can be accessed by all installed computers.

The control panel in the observation room contains the display unit for CO and CO2, the

com.ponents for controlling the heater, a module \\\ih the power switch, fuses and switches to drive the connected measuring instruments with the option to install further devices in the housing. It is connected to the central computer via a RS-232 serial communication.

273
DAQ-PC Archive-PC Office-PC Office-PC Office-PC

WIN

NT

LINUX

PC1

RS4a5

RS232

SQCi

VSCXI

PC 2 CD Writer

Printer

T Balance

Heater-Controller

WIN

NT

LINUX UNUX

PCS PC 4 PCn

50 ft BNC-RGSa

Figure 3: Structure of the computer network

4. Data acquisition software

The software integrates the sensors, signal conditioning, and data acquisition hardware

into a complete and functional system. The developed software enables controlling of

test fires with the ability to monitor measured data online. Filesaving and documentation

are possible as well as the following representation and analysis of the measured values.

The software is designed with the graphic programming language „LabView" and

afterwards compiled into a not editable, only executable program by the „Application

builder". This executable application runs at compiled execution speeds and does not

require a separate run-time system. A non-changeable user frienly software is provided

to the user, so that the source code is protected against undesignedly modifications.

Modifications can be made only by an authorized person.

4.1 Review of use of the graphic programming language „LabView"

The graphic programming language ,J^abView" was developed by „National

Instruments Corporation" for controlling data acquisition hardware and has broad in the

application field of automation and instrumentation. The use of „LabView" as

274
programming language offers the advantage that a clear, easily understandable surface is

pro\ ided. which can be served without detailed knowledge.

rhe programs are titled ..Virtual Instruments (VI)" and consist of two parts:

• the front panel user interface with indicators and operative functions for the

interactive control of the software system

• the diagram with the wiring of the applied functions

(iraphic programming with ..l.abView" is accomplished in two steps: Vis are built instead of written programs:

• component placement (indicators and controls) in the front panel window:

Objects are chosen from the ..Controls palette", e.g. numeric displays, meters,

thermometers, charts, and graphs. Parallel to the component placement in the

panel window conesponding symbols are generated in the diagram. When the

VI is complete the front panel is used to control the system while the VI is

running. Figure 4 and 5 show

• component placement and

wiring of the components in

the diagram window:

Component placement and

wiring is similar to constructing a graphical block

diagram. Objects are chosen

from the ,.Functions palette"

e.g. arithmetic or logic

functions and file I/O operations. These interconnections

are used to transport data

from one component to the

next as well as controlling

the program sequence at the same time. A component does not start processing until valid data are available

at all inputs. This type of programming is called dataflow programming because the interfaces transmit data as well as controlling the program sequence.

an example.

Y

Picture 4:

m {dB/mJ

Wz 5-

8-: 4:

3^

- (

4-: 2:i

nif

0-

f2~6oo

1

Quit

TfC}

50:

40:

30:

20:

10-

0-

26.3

Panel with three indicators.

Checking start conditions

275
Figure 5: Diagram for the front parted shown in figure 4

A running VI is controlled by the flow of data between blocks, and not by the linear architecture of sequential lines of a text. Any VI can run independent or as part of another

VI. It is possible to create diagrams that have simultaneous operations. „LabView" has

the ability to create Vis in a modular design. Created Vis can have their own icons, so

that a hierarchy of Vis and SubVis can be designed.

An example for a diagram with custom made SubVIs (with the icons „V2m.vi" and

„V2Y.vi") and „LabView"-functions is shown in figures 5, 6 and 7. The diagram shown

in figure 5 belongs to the front panel shown in figure 4.

--i5M.J;

Figure 6: „SubVr' converting MIC-voltage

to „Y

Figure 7: „SubVI" converting

MIREX-voltage to m [dB/m]

276
Smoke density measured with the .pleasuring Ionization Chamber (MIC)":

Y\ X : relative chamber current change yJ-±-^.x [1]

(1-^) Y : smoke density

Tj- U: measured .Y^-value X= -^

lOV

Smoke density measured with the .Extinction measuring equipment (MIREX)":

r , , , 10 . 1 w : extinction module

m\dBlm] = [^y\og[i-) [2] " ^ d : active light path in smoke

J J T : transmission

T=—

Uq U : received light intensity with smoke

Uo : received light intensity with smoke

It is possible to modify, interchange, and combine created Vis with other Vis to meet

application needs. Only the programmer can use these two levels. Operators may only

use the front panels. Thus operators cannot modify programs.

4.2 Review of the developed data acquisition program

Beginning with the selection of the test fire to be recorded, the installation and selection

of sensors, the representation of the measured values up to archiving and editing all

functions are controlled by the system. The program is arranged in such a way that

operating errors are largely excluded. Before a measurement can be started, the operator

has to load a , .profile" (see figure 8) to provide the program with all necessary

information:

• selection of the test fire to be recorded

• additional information:

• operator, name of the project, additional remarks, etc.

• sampling rate

• data path

• channel configuration:

• status (inactive, to be measured, to be measured and displayed)

• label (name of the sensor)

• data range (minimum and maximum voltage level) of the sensor

277
After provide the program with these parameters it is possible to start the measurement.

Measurement includes the following functions:

• checking start conditions according to EN 54 (see figure 4)

• offset adjustment of the input amplifiers

• data acquisition, scaling and online monitoring of selected measured signals

(see figure 9)

• controlled fixing of the ..end of test" condition te and check of the reproduction

requirements according to EN 54 part 9 as well as EN 54 part 7

• hand controlled setting of the ..end of test" te is possible

• filesaving

Following the measurement and filesaving it is possible to load and print prepared data

(see figure 10) . Recorded data consist of two parts:

• the header with detailed information of the measurement

• measured values as a table

Its advantage is the representation of the measured values with a „LabView" program as

well as with a conventional spread sheet program (e.g. EXCEL).

5. Conclusion

The application of the chosen hardware, the programming language .,LabView" and the

developed data acquisition software offers a high-performance data acquisition system,

which is readily expandable. A further advantage of ,XabView" as programming

language is the compatibility with the installed hardware which bith come from the same supplier. ..LabView" was designed by ..National Instruments Corporation" to

control data acquisition hardware. It is applied in various industry branches very

successfully. World-wide support is guaranteed.

In „LabView" the interaction between a user and the application proceeds in panels. The

topics of these panels are specified by the respective application. Panels can generate

other, overlapping panels. But only one panel is active at a time. The source code of a

278
,i<abView"-program is not a linear program in a top-down design. Thus it is not

sequentially readable as with other text-based program codes (e.g. C++). The program

consists of numerous partly serial sequences, loops or other child panels (SubVIs).

,J.^bView" is a multitasking system. Thus it is possible to create diagrams that have

simultaneous operations with the result that large and complex programs require a high

degree of discipline from the developer during programming.

Even though the designed data acquisition program is very large, it is possible to expand

or modify the program to adapt it to different standards which create additional

requirements. The source code of the introduced software is understandable. Created

Vis can be combined with other Vis to meet new application needs.

The modular structure of the introduced hardware and the developed software offer the

possibility of also adapting this system for use in other laboratories as well as for special

requests.

[1] Delta Electronics : Instruction Manual for Smoke Measuring Equipment,

MICTypeEC-9i2

[2] Delta Electronics : Instruction Manual for Smoke Measuring Equipment,

MIREX TypeEC-910

279
* - f

Figure 8: Load a „profile" to feed the program with all necessary information

280
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Figure 10: Representation of filesaved and prepared data

282
J. R. Qualey III, L. Desmarais, J. Pratt

Simplex Time Recorder Co., 100 Simplex Drive, Westminster, MA 01441

Response-Time Comparisons of Ionization and Photoelectric/Heat

Detectors

1. Introduction

Despite the recent introduction of new technologies, the vast majority of

smoke detectors sold and in service today are based on either the

photoelectric or the ionization principle. In the twenty-five years since

smoke detectors began to attain widespread acceptance as essential

life/safety fire protection devices [1], it has become generally accepted that

"ionization smoke detection is more responsive to invisible particles (smaller

than 1 micron in size) produced by most flaming fires" [2]. It is also

generally accepted that photoelectric detection is "more responsive to visible

particles (larger than 1 micron in size) produced by most smoldering fires",

"somewhat less responsive to smaller particles typical of most flaming fires",

and "less responsive to black smoke than lighter colored smoke" [2].

However, the relative merits of the two detector t5rpes continue to be a

subject of discussion [3].

We recently reported the results of fire tests comparing the response time

performance of three models of ionization smoke detector (from three

different manufacturers) to a photoelectric smoke detector model [4]. As an

extension of that work, we conducted an additional series of fire tests

comparing the performance of two ionization detector models used in the

earlier study to the performance of a single model of photoelectric/heat

detector. The photoelectric/heat detector combines a thermistor-based heat

detector with a photoelectric smoke detector which is otherwise identical to

the model used in the earlier study. These series of fire tests are the latest

in an ongoing investigation which Simplex is conducting to help develop

283
objective criteria for which smoke detector technologies are most

appropriate for different applications.

Consistent with the results of the earlier investigation comparing ionization

smoke detectors to photoelectric detectors, the results reported here show

that in UL 268 Smoldering Smoke tests, photoelectric detection occurred

many minutes earlier than ionization detection. The results also show that

in UL 268 Flammable Liquid Fire tests and TF-5 type liquid heptane fire

tests, photoelectric and ionization detection occurred at about the same

time. The three heat detector modes evaluated (15 °F/min ROR, 20 °F/min

ROR, and 135 °F fixed temperature) generally did not exceed their alarm

thresholds in either the TF-5 type fire tests or the UL 268 Smoldering

Smoke and Flammable Liquid Fire tests. However, the maximum rate-ofrise measured for the heat detectors in the TF-5 type tests suggest that the

heat detection component would be useful for fires with a heat release rate

(HRR) somewhat larger than that generated in the test.

2. Test Procedures

Two commercially-available ion smoke detectors were compared to a

commercially-available photoelectric/heat detector which incorporates a

thermistor-based heat detector. For each test, the basis of comparison was

the response-time-difference between the ion detector under test and an

adjacent photoelectric/heat detector. The comparisons were conducted using

standardized test fires in Simplex's UL 268 Fire Test Room.

For each test series, six samples of the ion detector under test were surface

mounted on the fire room ceiling. Four of the ion detectors were arranged in

a 15-foot square array and two were placed in the right and left positions of

the ceiling "Test Panel" specified by UL 268 [5] (ceiling positions Ion 5 and

Ion 6 in Fig. 1). A photoelectric/heat detector was placed adjacent

(approximately 6-in. separation) to each of the ion detectors in the square

284
array. A fifth photoelectric/heat detector was mounted midway between the

Ion 5 and Ion 6 ceiUng positions. For each test, the distances from the

detector locations to the test fire are given in the results table for that test

(Tables 1-8). For all tests, each photoelectric, and ion detector was placed

so that its "least favorable position for smoke entry" was oriented towards

the test fire location.

Three different fire types were used to evaluate each ion detector model.

The first test-type was the UL 268 "Smoldering Smoke Test" found in

section 40 of UL 268. This test used 10 sticks of ponderosa pine (3" x 1" x

on a laboratory hotplate to produce a slow, smoldering fire. The second

test-type was the UL 268 'Tlammable Liquid Fire - Test C" found in section

39.4 of UL 268. This test used 38 milliliters of a mixture of 65% heptane

and 35% toluene by volume to produce a hot, flaming fire. Both of these UL

268 fire tests were performed according to the test method outlined in UL

268, 4'*' Ed. paragraph 39.6. As specified in UL 268, photo beam and

measuring ionization chamber (MIC) data were collected during all tests

and analyzed to ensure that the buildup rate and the light-transmission vs. MIC curves conformed to the requirements of UL 268.

285
7 ft

Ion 3

UL26B Fire Room (L 36 ft; W 22 ft; H lOfQ

•i.^.?".^!^.....-. «PhDto/He^4

Photo/Heats

(2.13 m) ; UL268 I Tast Fires.

TF-5Type

Test Fires

e^ Ion2

Photo/Heat 1 4

Photo/Heat 2 fon 1 All six Ions were the same model of Ionization detector for each test and all i five PhotoH were the same model Photo/Heat detector for each test

Figure 1. Detector Positions for Fire Tests

The third test type was similar to the "TF-5 - Liquid (Heptane) Fire"

described in Appendix K of pr EN 54-7 (Draft A3). The ceiling of the UL

268 fire room is 1 meter less than that specified by prEN 54-7 so the amount

of the heptane (97%)/toluene (3%) mixture was reduced to 463 ml to prevent

heat and fire damage to our test facility. The fuel was burned in a round

receptacle 33 cm in diameter and 7.5 cm deep to attain the required smoke

density build-up rate. The test fire location was selected so that ceiling

positions Ion 5, Ion 6, and Photo 5 were contained in the prEN-54-specified

volume.

The fire room was instrumented with an NIR obscuration meter and MIC

which met the prEN 54-7 criteria. Optical density (m-value in dB/m) and

MIC data (y-value) were collected and analyzed for each test to determine if

the "m against y" and "m against time" tolerance limits of prEN 54-7 were

met. The TF-5 type fires typically came close to or met the tolerance limits

as shown in Fig. 2.

286
3. Description of Devices Tested

Two ion sensor models from different manufacturers were tested. Ion

detector Type A is currently sold by Simplex for use in Simplex fire alarm

systems. Ion detector Type B was previously sold by Simplex for use in

Simplex fire alarm systems. Each type of ion detector consists of a

detector/base combination which sends a digital representation of smoke

density to a Simplex control panel. The alarm activation time of each ion

detector was evaluated at 0.5 %/ft (the most sensitive of its four standard

settings) and at its default installation sensitivity of 1.3 %/ft. All six

detectors of each ion type were selected at random from stock. Each ion

detector was used as calibrated by its manufacturer.

The five photoelectric/heat detectors used were standard Simplex UL-listed

units taken at random from stock. This detector type consists of a

sensor/base combination which sends digital representations of smoke

density and temperature to a Simplex control panel. The alarm activation

time of the photoelectric component was evaluated at 0.5 %/ft (the most

sensitive of its eight standard settings) and its default installation

sensitivity of 2.5 %/ft. Each of the five photoelectric/heat detectors was

calibrated in a Simplex UL 268 Sensitivity Test Box using Simplex's

standard manufacturing calibration procedure.

287
The thermistor-based heat sensor element of the photoelectric/heat detector

is a fixed temperature/rate-of-rise type whose sensitivity can be selected at

the control panel. In the present investigation, the alarm activation time of

the heat detector component was evaluated using two rate-of-rise (ROR)

sensitivities (15 °F/min and 20 °F/min) and one fixed-temperature (FT)

sensitivity (135 °F). In addition, maximum ROR data for each heat detector

component was collected for each TF-5 type test

4. Data Collection

In each test, a digital representation of each ion and photoelectric/heat

detector's output voltage was transmitted to a PC-based data acquisition

system. Ion, photoelectric, and heat detector response times were calculated

by post-processing the data. The algorithms used, together with a 4 second

polling interval and alarm thresholds based on the device calibration,

simulated the performance of the Simplex Model 4010 fire panel operating

with no pre-alarm and no alarm verification delay. A chief advantage of

using this method for obtaining detector response times was that it enabled

the determination of detector response times at multiple sensitivities during

a single test fire, thus decreasing the total number of test fires. For each

combination of ion detector type and test fire type, four trials were

conducted.

The ion and photoelectric detectors were compared at two combinations of

sensitivity levels. The comparison at the same sensitivity of 0.5 %/ft was

selected because these are the most sensitive standard Simplex sensitivities

of these two detector types. The comparison of the ion detector at 1.3 %/ft to

the photoelectric detector at 2.5 %/ft was selected because these are the

default sensitivities of these two detector types and therefore represent a

typical Simplex installation.

288
5. Fire Test Results

The results for the comparison of ion detector Type A and the

photoelectric/heat detector are summarized in Tables 1 - 4. Unless

otherwise noted, each alarm time entry is the average of 4 trials. The

detector positions are indicated in parentheses beneath the distance from

the fire. Note that, for each test type, only one average photoelectric alarm

time is listed for ceiling positions 5 and 6. This is because the ion detectors

at ceiling positions 5 and 6 were compared to a single photoelectric detector

midway between them at ceiling position 5. For the smoldering smoke tests,

the individual Type A ion and photoelectric detector response times

measured for the four trials generally varied over a range of a few hundred

seconds at each ceiling position. For the UL 268 Flammable Liquid Fire

and the TF-5 type tests, the range of variation was on the order of ten

seconds. Each table includes the difference between the average response

times of the ion and photoelectric detectors for each ceiling position.

The average response-times recorded for the Type A ion detector at 1.3 %/ft

and the photoelectric/heat detector at 2.5 %/ft are listed in Table 1 for the

UL 268 Smoldering Smoke and Flammable Liquid Fire tests. Note that not

all ion detectors alarmed in each smoldering smoke test. For each ion

detector position, the number of tests for which no alarm (N/A) was

observed is indicated in parentheses beneath the average response time

value. Table 2 lists the average response-times of the ion and the

photoelectric detectors in the UL tests when both were set to a sensitivity of

0.5 %/ft.

289
UL 268 Tests

Ionization Type A: 1.3

O/n/ft:

Distance from Test Fire

(CeiliniS Position #)

8.0 ft 17.7 ft 19.2 ft

Test Devi (2) (3) (5) (6) (1) (4)

UL 268 Smold.

Smoke

Averages of 4 Trials

Ion A

(N/A)

3459 3317 3843

(3)

3614 3864

(9)

3591

Photo 2421 2253 2916 2726 2823

Diff. of Avg. Time (Ion - Photo) 1038 1064 927 698 1138 768

UL 268 Flamm.

Liquid

Ion A 31 36 61 56 65 65

Photo 26 29 55 57 57

Diff. of Avg. Time (Ion - Photo) 5 7 6 1 8 8

Table 1. Ion Detector A, UL 268 Tests: Default Sensitivity Alarm Times

(in sec.)

The data for the smoldering smoke tests show that typically the

photoelectric detectors set to 2.5 %/ft responded 12 - 18 minutes earlier than

the Type A ion detectors set to 1.3 %/ft. Table 2 shows that when both were

evaluated at 0.5%/ft, the photoelectric detectors typically responded 25 - 30

minutes faster than the Type A ion detectors. As Tables 1 and 2 show, in

the UL 268 Flammable Liquid Fire tests, there was no significant difference

in response time between the photoelectric and Type A ion detectors

whether compared at their default sensitivities (2.5 %/ft and 1.3 %/ft) or the

same, higher sensitivity (0.5 %/ft).

290
UL 268 Tests

Ionization Type A: 0.5 %/ft

PhntnelertHrt O S o/n/ft

Distance &om Test Fire

(Ceiling Position #)

8.0 ft 17.7 ft 19.2 ft

Test Devi (2) (3) (5) (6) (1) (4)

UL 268 Smold.

Smoke

Ion A 3318 3236 3691 3471 3677 3474

Photo 1556 1577 2008 1854 2002

Diff. of Avg. Time (Ion - Photo) 1762 1659 1683 1463 1823 1472

UL 268 Flamm.

Liquid

Ion A 29 31 60 56 65 63

Photo 18 20 45 53 52

Diff. of Avg. Time (Ion - Photo) 11 11 15 11 12 11

Table 2. Ion Detector A, UL 268 Tests: 0.5 %/ft Sensitivity Alarm Times

(in sec.)

TF-5 Type Tests

Ionization Type A; 1.3 %/ft

Distance from Test Fire

(CeiUng Position #)

Photoelectric: i 9.1 ft 9.8 ft 12.3 ft

Test Devi (2) (3) (5) (6)* (1) (4)

Modified TF-5 Fire

Averages of 4 Trials

Ion A 19 20 26 22 38 32

Photo 55 58 69 76 67

Diff. of Avg. Time (Ion - Photo) -36 -38 -43 -47 -38 -35

Max ROR result

(°F/Tr,in Aver of 4

ROR 14 7 14 12 10

Max. Temperature

r°F Ave. nf4t.rial.O

FT 115 89 114 101 99

Table 3. Ion Detector A, TF-5 Type Tests: Default Sensitivity Alarm

Times (in sec.)

Maximum ROR and Fixed Temperature Values

Table 4 lists the average response times in the TF-5 type tests of the Type A

ion detectors evaluated at 1.3 %/ft and the photoelectric detectors evaluated

at 2.5 %/ft. Table 4 lists the average response times in the TF-5 type tests

of the ion and the photoelectric detectors when both were set to a sensitivity

of 0.5 %/ft. In the TF-5 type tests, Type A ion detectors evaluated at 1.3 %/ft

» 291
responded in 19 to 38 seconds; about 40 seconds faster than the

photoelectric detectors set at 2.5 %/ft. When the sensitivity levels were set

to 0.5 %/ft for both types, there was no significant difference in TF-5 test

response time between the photoelectric and Type A ion detectors. It is

interesting to note that in both the UL 268 Flammable Liquid Fire tests and

the TF-5 type tests, there were no significant differences in the T3^e A ion

detector alarm times whether set at 0.5% or 1.3 %/ft.

TF-5 Type Tests Distance from Test Fire

Ionization Type A: 0.5 %/ft (CeilinjS Position #)

Photoelectric: 0.5

9.1 ft 9.8 ft 12.3 ft

Test Devi (2) (3) (5) (6)* (1) (4)

Modified TF-5 Fire Ion A 16 19 24 22 35 30

Averages of 4 Trials Photo 15 17 19 23 23

Diff. ofAvg. Time (IonPhoto) 1 2 5 3 12 7

Table 4. Ion Detector A, TF-5 Type Tests: 0.5 %/ft Sensitivity Alarm Times

(in sec.)

The results for the comparison of ion detector Type B and the

photoelectric/heat detector are summarized in Tables 5-8. Each alarm

time entry is the average of 4 trials. For the four smoldering smoke tests,

the response times measured for the individual Type B ion and photoelectric

detectors generally varied over a range of a few hundred seconds at each

ceiling position. For the UL 268 Flammable Liquid Fire and the TF-5 type

tests, the range of variation was on the order of ten seconds.

The average response-times recorded in the UL 268 Smoldering Smoke and

Flammable Liquid Fire tests for the Type B ion detector at 1.3 %/ft and the

photoelectric/heat detector at 2.5 %/ft are listed in Table 5. Table 6 lists the

average response times of the ion and the photoelectric detectors in the UL

tests when both were set to a sensitivity of 0.5 %/ft. The data for the

292
smoldering smoke tests show that the photoelectric detectors set to 2.5 %/ft

responded 8-14 minutes earlier than the Type B ion detectors set to 1.3

%/ft. When both were evaluated at 0.5%/ft, the photoelectric detectors

typically responded 17 - 25 minutes faster than the Type B ion detectors. In

the UL 268 Flammable Liquid Fire tests, there was no significant difference

in response time between the photoelectric and Type B ion detectors

whether compared at their default sensitivities (2.5 %/ft and 1.3 %/ft) or the

same, higher sensitivity (0.5 %/ft).

UL 268 Tests

Ionization Type B: 1.3

O/n/ft

Distance from Test Fire

(Ceiling Position #)

8.0 ft 17.7 ft 19.2 ft

Test Devi (2) (3) (5) (6) (1) (4)

UL 268 Smold.

Smoke

lonB 3350 3368 3470 3518 3602 3553

Photo 2566 2534 3008 2871 2970

Diff. of Avg. Time (Ion - Photo) 784 834 462 510 731 583

UL 268 Flamm.

Liquid

lonB 25 22 50 50 56 55

Photo 29 32 56 58 58

Diff. of Avg. Time (Ion - Photo) -4 -10 -6 -6 -2 -3

Table 5. Ion Detector B, UL 268 Tests: Default Sensitivity Alarm Times (in

sec.)

Table 7 lists the average response times in the TF-5 type tests of the Type B

ion detectors evaluated at 1.3 %/ft and the photoelectric detectors evaluated

at 2.5 %/ft. Table 8 lists the average response-times of the ionization and

the photoelectric detectors in the UL tests when both were set to a

sensitivity of 0.5 %/ft. The Type B ion detectors evaluated at 1.3 %/ft

responded in 19 to 27 seconds in these tests; about 45 seconds faster than

the photoelectric detectors set at 2.5 %/ft. When the sensitivity levels were

evaluated at 0.5 %/ft for both t3rpes, there was no significant response-timedifference between the photoelectric and Type A ion detectors. For both the

293
UL Flammable Liquid Fire tests and the TF-5 type tests, there were no

significant differences in the Type B ion detector alarm times whether set at

0.5% or 1.3 %/ft

.

UL 268 Tests

Ionization Type B: 0.5 %/ft

Photoelectric: O.S

Distance from Test Fire

(Ceiling Position #)

8.0 ft 17.7 ft 19.2 ft

Test Devi (2) (3) (5) (6) (1) (4)

UL 268 Smold.

Smoke

Ion B 3159 3211 3340 3343 3450 3395

Photo 1676 1697 2331 1929 2198

Diff. of Avg. Time (Ion - Photo) 1483 1514 1009 1012 1521 1197

UL 268 Flamm.

Liquid

lonB 21 21 49 48 55 54

Photo 19 18 49 55 49

Diff. of Avg. Time (Ion - Photo) 2 3 0 -1 0 5

Table 6. Ion Detector B, UL 268 Tests: 0.5 %/ft Sensitivity Alarm Times (in

sec.)

The heat detector fixed temperature and ROR functions generally did not

exhibit a significant response in the UL 268 Smoldering Smoke and UL 268

Flammable Liquid fire tests. In the TF-5 type fire tests, the fixed

temperature and ROR functions did not generally exceed their alarm

thresholds, but, these quantities reached significant levels which are

tabulated in Tables 3 and 7 for the Type A ion tests and Type B ion tests,

respectively.

294
TF-5 Type Tests

Ionization Type B: 1.3 %/ft

Distance from Test Fire

(Ceilinjg Position #)

9.1 ft 9.8 ft 12.3 ft

Test Devi (2) (3) (5) (6) (1) (4)

Modified TF-5 Fire

Averages of 4 Trials

lonB 19 21 20 20 27 26

Photo 67 58 72 71 65

Diff. of Avg. Time (Ion - Photo) -48 -37 -52 -52 -44 -39

Max ROR restdt

r°F/min Aver nf4

ROR 9 7 13 13 10

Max. Temperature

(°V Avp- of4t,Hal.O

FT 100 93 115 106 104

Table 7. Ion Detector B, TF-5 Type Tests: Default Sensitivity Alarm Times

(in sec);

Maximum ROR and Fixed Temperature Values

TF-5 Type Tests

Ionization Type B: 0.5 %/ft

Photoelectrir: (\.fi

Distance from Test Fire

(Ceilinig Position #)

9.1 ft 9.8 ft 12.3 ft

Test Devi (2) (3) (5) (6)* (1) (4)

Modified TF-5 Fire

Averages of 4 Trials

lonB 16 17 18 17 23 23

Photo 16 18 16 25 23

Diff. of Avg. Time (Ion - Photo) 0 -1 2 1 -2 0

Table 8. Ion Detector B, TF-5 Type Tests: 0.5 %/ft Sensitivity Alarm Times

(in sec.)

6. Discussion: Photoelectric/Heat vs. Ion Response

A series of UL 268 Smoldering Smoke (gray smoke), UL 268 Flammable

Liquid Fire (black smoke), and TF-5 type Liquid (Heptane) Fire (black

smoke) tests were conducted to compare the performance of two models of

commercially available ion detectors (designated Type A and Type B) to the

performance of a commercially available photoelectric/heat detector. The

295
basis of comparison was the length of time required for each detector to

exceed its alarm threshold.

In the smoldering smoke tests, at both combinations of sensitivity tested,

both models of ion detector took considerably longer to respond than the

photoelectric component of the photoelectric/heat detectors. This result

strongly supports the generally accepted view [2] that photoelectric detector

technology possesses an advantage over ion detector technology with

regards to smoldering smoke response.

In the UL 268 Flammable Liquid Fire tests, the performance of the

photoelectric component of the photoelectric/heat detector was fully

equivalent to that of both ion detector models at both combinations of

sensitivity. The detectors of all three types t3^ically alarmed in about a

minute or less.

In the TF-5 type tests, the ion detectors (both types) responded about 40 - 45

seconds earlier than the photoelectric detectors when both technologies were

evaluated at their default sensitivities (2.5 %/ft for the photoelectric and 1.3

%/ft for the ions). In Figure 3, the 8-bit digital representation of smoke

density is plotted versus time for an ion detector and an adjacent

photoelectric detector for typical UL 268 Flammable Liquid Fire and TF-5

type tests. The 0.5 and 1.3 %/ft alarm thresholds of the ionization detectors

are indicated by dashed lines. Solid lines indicate the 0.5 and 2.5 %/ft

photoelectric alarm thresholds of the photoelectric/heat detectors. In the

UL 268 Flammable Liquid Fire tests, ion and photoelectric technologies

responded approximately at the same time. In the TF-5 type fires, the

photoelectric detectors generally responded more slowly than they did in the

UL 268 Flammable Liquid Fire tests. The ion detectors took about the same

length of time in both types of fire. The slower performance of the

photoelectric detectors in the UL 268 Flammable Liquid Fire tests is

296
possibly due to the fuel mixtures used. In the TF-5 fire, the heptane fuel

(smoke yield .037 g/g) contains only 3% toluene (smoke yield .178 g/g) [6]. It

seems likely, therefore, that the 65% heptane/35% toluene fuel mixture used

in the UL 268 Flammable Liquid Fire test will produce a greater proportion

of visible smoke than the TF-5 fire. Since ion detectors have a greater

sensitivity to the invisible particles produced by a hot flaming fire, the

conditions of the TF-5 tj^De fire would therefore be more favorable for ion

detectors than the UL 268 Flammable Liquid Fire test.

250

150 -

lOO -

50 -

Tvpt B Ionization and Photo/lleat

8.0 ft. From I'L 268 Flammable Liquid Fire

Ionization

250

=. 200 -

Type B Ionization and Photo/IIeat

9.1 ft. From TF-5 Type Fire

Ionization

Photo: 0.5''c/ri

200

Figure 3. Detector Outputs for TF-5 and UL 268 Flammable Liquid Fire Tests

With the 4 second sample time used, the two technologies yield equivalent

response times of about 20 seconds at the higher sensitivity of 0.5 %/ft. At

the lower default sensitivities (1.3 %/ft for the ion, 2.5 %/ft for the photo),

the faster response of the ion detector to the TF-5 fire products becomes

more obvious. This interesting result illustrates the importance of fully

specifying the experimental conditions when performing technology

comparisons.

The three heat detector modes evaluated (15 °F/min ROR, 20°F/min ROR,

and 135 '^F fixed temperature) generally did not exceed their alarm

thresholds in the fire tests performed. Furthermore, the response of the

fixed temperature and ROR functions, though negligible in the UL 268

Smoldering Smoke and Flammable Liquid Fire tests, was significant in the

297
TF-5 type tests. For example, the ROR heat detection function came

relatively close to alarming at the 15 °F/niin setting in the TF-5 fire tests.

Two factors probably contribute most to this performance differential.

First, in the TF-5 type tests, all detectors were closer to the fire than in the

two UL 268 tests. Second, the heat release rate (HRR) of the TF-5 type test

was much greater than for either UL 268 test. Using the heat release rate

(HRR) calculation described in the SFPE Handbook [7], the HRR of the TF-5

type fire was estimated to be 123 kW and the HRR of the UL 268

Flammable Liquid Fire test was estimated to be 12.7 kW. The HRR of the

UL 268 smoldering fire was estimated to be about 1.5 kW, based on the

hotplate characteristics.

7. Conclusions

In this investigation, the response of the photoelectric smoke detection

technology to smoldering smoke was much faster than the response of the

ion technology. The photoelectric response to the black smoke produced by

the UL 268 Flammable Liquid Fire was generally as fast as (or faster than)

the ion response. In the TF-5 type fire tests, the photoelectric response

lagged the ion response by only about 45 seconds when both were evaluated

at their default sensitivities (1.3 %/ft for ion, 2.5 %/ft for photoelectric).

However, the photoelectric response to the TF-5 type fire was typically as

fast as the ion response when both were evaluated at the same sensitivity

(0.5 %/ft). Furthermore, the TF-5 type fire test results also indicate that,

even in the absence of visible smoke, the photoelectric/heat detector would

be effective for the detection of fires with a heat release rate or duration

slightly greater than that of the TF-5 type test fire used. Therefore, these

results strongly support the conclusion that photoelectric and

photoelectric/heat technologies possess a clear overall performance

advantage over ion technology if the most likely sources of fire danger are

smoldering fires (as some believe [3]) or flaming hydrocarbons.

298
References

[1] Bukowski, R.W. and Mulholland, G.W.; Smoke Detector Design and

Smoke Properties; NBS Technical Note 973 U.S. Department of Commerce,

National Bureau of Standards, Nov. 1978.

[2] Section A- 1-4, Appendix A Explanatory Material; NFPA 72 National

Fire Alarm Code 1996; p. 196.

[3] Fleming, J; Photoelectric vs. Ionization Detectors - A Review of the

Literature; Proceedings Fire Suppression and Detection Research

Application Symposium, February 25-27, 1998, Natl. Fire Protection

Association, Quincy, MA, 1998, pp. 18-59

[4] Qualey, J, Desmarais, L, and Pratt, J.; Fire Test Comparisons of Ion and

Photoelectric Smoke Detector Response Times; Fire Suppression and

Detection Research Application Symposium, Orlando, FL, February 7-9,

2001.

[5] UL268: Standard for Smoke Detectors for Fire Protective Signaling Systems; 4th

Ed., 12/30/96, (Rev. 1/4/99), Underwriters Laboratories, Inc., Noithbrook, IL, 1999

[6] Babrauskas, V.; Burning Rates, Section 3, Chapter 1, SFPE Handbook

of Fire Protection Engineering, Second Edition, 1995, pp. 3-1 to 3-15.

[7] Tewarson, A.; Generation of Heat and Chemical Compounds in Fires,

Section 3, Chapter 4, SFPE Handbook of Fire Protection Engineering,

Second Edition, 1995, pp. 3-53 to 3-124.

299
J N Smithies

FRS, Building Research Estabhshment Ltd., Garston, Watford, U.K.

M J Spearpoint

Department of Civil Engineering, University of Canterbury, Christchurch,

New Zealand.

The Performance of Mains-Powered Residential Smoke Alarms with a Backup

Energy Source

Abstract

Mains-powered residential smoke alarms are considered more reliable than equivalent

battery-powered devices since it is not necessary to have an annual battery replacement

and there less chance that the home occupier will remove the battery for some reason. However, the reliability of residential smoke detection systems comprising of mainspowered devices can be further improved by the use of a backup energy source. This

allows a smoke alarm to continue operating if the mains supply becomes unavailable.

This paper examines the long-term performance of a range of energy sources that are

used as backup for mains-powered smoke alarms. The paper reports on the results from

a research programme that has be running for over four years in which energy sources

have been tested in both standby and alarm modes.

300
1 Introduction

1.1 Background

The benefit of using smoke alarms is recognised in the 1992 revisions to the UK's

Approved Document B of the Building Regulations [1] which recommends that all

newly constructed dwellings should have a linked mains-powered domestic smoke

alarm on each floor. As noted in BS 5839 Part 6 [2], the reliability of systems

comprising of mains-powered smoke alarms can be improved by the use of a backup

energy source. This allows a smoke alarm to continue operating when the mains supply

is unavailable.

The majority of homes in the UK now have at least one domestic smoke alarm installed

[3, 4]. These devices are only of benefit to the occupants in a fire when they are

operating correctly. A US study of domestic smoke alarms [5] found that around one

fifth were not operational, mainly because of non-flinctioning power sources. Nearly all

of these non-operating devices had missing, flat or disconnected batteries. The use of

smoke alarms powered by the mains electricity supply eliminates the problems of

battery removal and flat batteries.

In the UK, the mains AC electricity supply can be considered a reliable source. However, there are cases where the supply may become accidentally or deliberately

disconnected. The frequency and duration of any disconnections will depend on several

factors such as the location of the property and the type of tenant. The use of a backup

energy source would allow a smoke alarm to continue operating until the mains supply

is re-established. A backup power supply will also maintain the operation of a smoke

alarm should the mains power be lost during a fire (early on, before the alarm is heard)

or as a result of the fire being due to an electrical fault that interrupts the supply.

Should a mains-powered smoke alarm with a backup energy source become

disconnected fi"om the AC power it will continue to ftinction using the backup energy

source. The capacity of the energy source determines how long the smoke alarm can

operate and raise an alarm in case of fire. If the backup supply voltage drops below a

301
particular threshold, an audible warning is given. If the voltage continues to drop, it will

reach a certain point where the sounder will no longer be able to function but the low

power warning will continue to operate. A fiirther drop in the voltage will result in

failure of the low power warning. The particular voltage levels at which these events

occur vary between devices but typical values are 7.5 V for the low power warning to commence, 4.5 V for the sounder to fail and 3.5 V for the low power warning to fail.

A range of mains-powered smoke alarms is available in which various types of backup

energy source are used. Each type of backup source has particular advantages and

disadvantages. Backup supplies can be considered to consist of two broad categories;

primary and rechargeable as described below. This paper considers the different types

available and compares their performance. It is not the intention, nor is it possible, to

declare that any one type of backup source is 'better' than any other as the selection of

backup energy sources depends on many factors:

1. Cost - the type of backup source has an influence on the initial purchase cost of

the smoke alarm and also subsequent maintenance where the backup supply may

need servicing or replacing,

2. Shelf life - the life of the source while not in use and any storage requirements,

3. Operating power capacity - the available power when no mains power is being

supplied,

4. Alarm power capacity - the available capacity during an alarm,

5. Recharge time - the length of time for the backup supply to recharge after fijll or

partial usage where appropriate,

6. Safety - any risks to the user or maintenance personnel during normal operation

or if the backup energy source is misused,

7. Environmental considerations - the cost and requirements for the eventual

disposal of the smoke alarm that includes its accompanying backup power

source.

1.2 Primary cells

Cells with irreversible reactions are commonly known as primary cells and are available

in a number of forms: zinc carbon, zinc chloride, alkaline manganese and lithium. The

302
standard 9V PP3 primary cells, in particular zinc carbon and alkaline have been used in smoke alarms for many years and have been shown to operate effectively, powering

smoke alarms for extended lengths of time. However the user can remove primary cells

and although several manufacturers have designed their mains-powered smoke alarms

to limit removal of this form of backup energy source, it is not possible to eliminate

such abuse.

Zinc carbon cells (or 'Leclanche' cells) were the first primary batteries made available

for household use. The zinc chloride battery is a development of the zinc carbon battery

and it is used where the higher energy of alkaline is not required. Currently alkaline

manganese is the most commonly used primary cell and although it is suited to high

current drain applications, the very low self-discharge current also allows for low drain

uses such as smoke alarms. Lithium cells are available in a range of chemistries that are

suited to different current drain applications. The cells have a very long shelf life due to

extremely low self-discharge rates. Standard size PP3 lithium manganese dioxide

batteries are available which can be fitted to smoke alarms that accept primary cells. It

is reported that these cells should be able to power a smoke alarm for between five [6]

and ten years [7].

1.3 Rechargeable cells

There are several types of rechargeable technology available as a backup power sources

for smoke alarms; Nickel cadmium (NiCd), super-capacitor and rechargeable lithium.

NiCd batteries are used extensively in a range of applications such as rechargeable

power tools and portable electronic equipment. The type of NiCd cell used in many

smoke alarms cannot be removed by the user although PP3 size NiCd cells are available

that could be used as a rechargeable substitute for primary cells. The recharge time of

NiCd cells is relatively long compared with other technologies and they can exhibit a 'memory effect' where continual partial discharging and recharging can reduce their

capacity. Cadmium is a 'heavy' metal and should not be allowed to contaminate the

environment and the disposal of devices containing NiCd batteries may require special

procedures particularly where large numbers are involved.

303
The super-capacitor (or memory capacitor) technology was introduced into the smoke

alarm market in around 1991. The performance of the super-capacitor may be

influenced by temperature although in normal domestic environments this should not be

a problem. Compared with other rechargeable technologies the recharge time of the

super-capacitor is relatively quick however their power capacity is less. The cells are

environmentally friendly, as they do not contain any toxic material.

Rechargeable lithium cells are the latest technology to be used as a backup power

source and they reportedly do not exhibit the memory effect. They also do not contain

heavy metals, however, lithium should not be put in a persons mouth or swallowed.

Lithium is potentially explosive which can cause particular problems for recycling and

in landfill sites.

1.4 Testing arrangements

This study was carried out in collaboration with the Building Research Housing Group

(BRHG), a group of local authorities and housing associations with interests in public

housing needs. The BRHG members supplied a representative selection of mainspowered domestic smoke alarms to FRS for the study. These devices were tested both

when connected to the mains AC supply and in standby mode with the backup supply

voltage measured either daily or weekly depending on the type of source and the testing

procedures. In addition, some stand-alone smoke alarms (i.e. devices that use primary

cells as their only source of power) were also tested as part of the study.

2 Backup supply performance

2.1 Primary cells

Figure J shows typical voltage discharge profiles for three types of primary cell

installed in mains-powered smoke alarms operating in standby mode. After about

8 months the device containing zinc chloride cell began to give the low power warning.

However, the battery continued to supply a sufficiently high enough voltage to power

the device for at least another 1 000 days while continuing to give a low power warning.

304
The early battery warning and the extent of the standby capacity of the zinc chloride cell

were somewhat unexpected. It appears that this particular device had its low power

warning threshold set too high.

Three test modes are shown for the alkaline cells; (1) optical sensor smoke alarm

without weekly test, (2) ionisation sensor with a sounder tested for five seconds once

per week, (3) stand-alone dual optical/ionisation sensor smoke alarm without weekly

test. The smoke alarm containing the optical sensor gave a low power warning after 855

days however the other two devices have yet to reach their low power warning

thresholds.

The results for the lithium primary cell exhibits an almost flat profile with no

appreciable reduction in the voltage demonstrating that it can continue to fully power a smoke alarm for at least four years.

2.2 Rechargeable NiCd

Figure 2 shows the performance of a NiCd backup energy source in an ionisation smoke

alarm over a series of discharge cycles. The first discharge cycle gave a standby time of

around 75 days. After the third discharge the cell shows a degraded performance with

the low power warning given at an earlier time than previously.

305
o

oOOsONOOOOtN in o in o ir> o .&o iN Ct,

[a] aSuHOA

306
By the eighth discharge cycle the voltage soon falls to a level at which the low power

warning is given however this period extends further than the earlier discharge cycles.

Similar behaviour was recorded in several smoke alarms having a NiCd backup supply.

0 20 40 60 80 100 120

Elapsed time [days]

Figure 2. Voltage discharge curves for a NiCd backup source.

From these results it is clear that the NiCd cells will exhibit a memory effect. The

repeated discharging and recharging the cells leads to a reduced capacity. However

these tests present a demanding requirement on the performance of the cells that may

not be encountered in the field. Where smoke alarms using a NiCd backup source were

continuously connected to the mains supply it was found that the capacity of the cell was not affected.

2.3 Super-capacitors

Figure 3 shows the voltage discharge curves for optical sensor and ionisation sensor smoke alarms with super-capacitor backup energy source. Both devices show similar

profiles and on average give eight days backup until the low power warning is given.

307
0 5 10 15 20 25

Elapsed time [days]

Figure 3. Voltage discharge curvesfor super-capacitor backup sources.

During the testing it was noted that several devices containing super-capacitors did not

fully charge during installation and one device failed during the test procedures. These

faults were identified with early models and the manufacturer has since resolved these

problems. The majority of the smoke alarms using the super-capacitor backup supply

(including devices that were subjected to repeated discharge cycles) functioned well and

users in the field report acceptable performance from their installations.

2.4 Rechargeable lithium

The performance of the rechargeable lithium cell is shown in Figure 4. The first

discharge of the cell gave approximately 265 days of backup capacity. However by the

sixth discharge cycle the capacity had fallen to only 6 days. It is clear from these tests

that the capacity of the rechargeable lithium cell deteriorates significantly when it is

repeatedly discharged and recharged. As with the NiCd cells, these tests present a demanding requirement on the performance of the cell.

308
Elapsed time [days]

Figure 4. Voltage discharge curx'es for a rechargeable lithium backup source.

2.5 Summary of results

The results of the standby mode tests are summarised in Table 1. What is somewhat

surprising is the length of time that primary cells are able to supply a smoke alarm in

standby mode. These extended periods of operation have meant that complete discharge

times of the sources cannot be reported here. The times quoted for the NiCd and

rechargeable lithium cells are typical values since their performance was found to vary

with each discharge cycle.

3 Conclusions

Primary cell batteries provide an effective backup supply to mains-powered smoke

alarms. The standby capacity of a cell has the potential to extend for several years

depending on the particular technology. The use of primary cells may not eliminate the

problem of battery removal and they may need to be replaced if they become

sufficiently discharged.

309
Backup power Specifications Time to Time to Time between low

source low power sounaer power warning and

warning power failure sounder failure

[Udybj [Udysj [aaysj

Zinc chloride

cell

Optical sensor, no weekly test 245 > 1250 > 1005

Optical sensor, no weekly test 855 > 1295 >440

Alkaline

primary cell

lonisation sensor, 5 second weekly sounder

test Optical/ionisation

sensor, no weekly test

> 1040

>725 - -

Lithium lonisation sensor, no >985

primary cell weekly test

Nickel

cadmium"*

:

Optical sensor, no weekly test lonisation sensor, 5 second weekly

sounder test

148

68

160

78

12

10

ouperOptical sensor, no wecKiy lesi 7 '/2 12 4 '/2

capacitor lonisation sensor, no weekly test 8 '/2 16 7 '/2

Rechargeable lonisation sensor, no

253 265 12

lithium weekly test No data available

(1) Typical values before memory effect

(2) Initial value before degradation

Table 1. Summary ofbackup power source standby mode performance.

The potential memory effect and the disposal difficulties of NiCd cells place some

limitations for their use as backup power sources for smoke alarms. They are more

suitable in applications where disconnections from the AC supply are expected to be

infrequent. The super-capacitor backup power source is suited to situations where

expected periods of disconnection from the mains supply will be relatively short. They

can be repeatedly discharged and recharged without any memory effect. Rechargeable

lithium cells have a standby capacity somewhere between a typical NiCd cell and an

alkaline cell. Although they reportedly do not suffer from the memory effect, the results

presented in this paper show that their performance does degrade with repeated

310
discharge cycles. Compared to the primary cells, all of the rechargeable backup sources

have a much shorter time between the onset of the low power warning and the voltage

falling below the sounder operation threshold.

New backup power source technologies are being developed and current technologies

are being advanced. Some of these developments may find their way into future designs

of smoke alarms with potential improvements in their backup energy source

performance.

4 Acknowledgements

The authors would like to thank those members of the BRHG and those smoke alarm

manufacturers who supplied test devices and supporting information as part of this

project. The authors would also like to thank Roger Harrison for his continued

assistance in monitoring the smoke alarms.

References

1. Department of the Environment and The Welsh Office. The Building

Regulations, Approved Document B, 1992 edition. HMSO, 1991.

2. Fire detection and alarm systems for buildings. Part 6; Code of practice for the

design and installation of fire detection and alarm systems in dwellings.

BS 5839: Part 6: 1995. British Standards Institution.

3. Marriott M D. Reliability and effectiveness of domestic smoke alarms. Fire

Engineers Journal, June 1995.

4. Arvidsson T & Gale M. Homing in on smoke alarms. Fire Engineers Journal,

November 2000, pp.53-55.

5. Ahrens M. Batteries not included. NFPA Journal, May/June 1998 pp.98- 106.

6. Walsh J. Mains powered smoke alarms. Fire Surveyor, February 1990,

pp.9-12.

7. Wolf A. The 10-year battery. NFPA Journal, January/February 1999,

pp.49-52.

311
Thomas Cleary, Michelle Donnelly, and William Grosshandler

Building and Fire Research Laboratory, National Institute of Standards and Technology

Gaithersburg, MD, 20899 U.S.A.

The Fire Emulator/Detector Evaluator: Design, Operation, and Performance

1. Introduction

Grosshandler introduced the concept of a "universal fire emulator/detector evaluator"

(FE/DE) at AUBE '95, and development of such an apparatus began at NIST soon

thereafter [1]. The FE/DE has proven to be a very flexible design. The main function of

the device is to reproduce the environment (temperature, air velocity, aerosol and gas

species concentrations) a detector would be exposed to during fire and non-fire events.

It has been used primarily for fire detection, but may prove useful in testing other types

of sensors such as those used for indoor air quality assessment, building HVAC control,

or hazardous gas monitoring. The FE/DE is a single-pass "wind tunnel" that allows for

the control of the flow velocity, air temperature, gas species, and aerosol concentrations

at a test section wherein detectors and sensors are exposed to these environmental

conditions.

While others have developed tunnels to test specific aspects of fire detector

performance, the FE/DE is the only apparatus designed to reproduce all relevant

conditions needed to assess the performance of spot-type particulate, thermal and gas

sensor detectors or combination detectors. It has been used in a study of the smoke entry

lag of commercial analog-output photoelectric and ionization detectors, where a twoparameter model was developed that allows for the prediction of the analog detector

response given smoke concentration and velocity at the detector opening as a function of

time [2]. It was used to emulate the smoke temperature and flow velocity conditions

developed in a modeled detector sensitivity room fire test [3]. Experimental results of

analog-output detector response to test smoke from a propene soot generator, oil aerosol

from a smoke detector testing device, and aerosolized Arizona test dust were presented

at AUBE '99 [4]. Recent work at NIST that utilized the FE/DE is described in several

312
papers presented at this conference: A UBE '01 (see other papers in this proceedings).

Below is a description of the FE/DE hardware, performance range, and selected

experimental results.

2. Experimental Apparatus

A schematic of the FE/DE is shown in Figure 1 . Room air is drawn into the opening,

and exhausted to a hood at the end of the duct. The air velocity at the test section is

controlled over a range of flows between 0.02 m/s to over 2 m/s by means of the

computer-controlled axial blower. Air is first propelled through the annular finned

heating elements, then travels along the duct to the test section. The flow is conditioned

before it reaches the test section by passing through a 10 cm long aluminum honeycomb

with 5 mm rectangular openings. The goal is to provide a nominally flat flow profile

indicative of what would be experienced by a detector in a ceiling jet flow. The flow is

monitored at the test section by a thermo-anemometer capable of recording flows as low

as 0.05 m/s with a stated uncertainty of 4 % of the reading. For lower velocities, or nonisothermal flows, other means must be employed such as pitot probes, hot-v/ire

anemometry, or laser doppler velocimetry. Figure 2 shows the mean center! ine axial

velocity as a function of distance from the top of the duct for a range of fan speeds. The

flow profile is nearly top-hat, and at each stationary position the velocity fluctuates

indicating turbulent flow. The horizontal bars indicate a two standard deviation range in

the mean velocity measured by the thermo-anemometer. Transition from laminar to

turbulent flow is expected at velocities above 0.08 m/s based on the duct Reynolds

number. Heat is added to the flow by a series of 9 annular finned heating elements.

Each element is rated at 5 kW for a total maximum heat input of 45 kW. Power to the

heating elements is controlled by a feedback controller that receives set-point values

from a computer file and compares them to the air temperature exiting the heaters. (An

air temperature difference between the heater exit and test section location is due to heat

losses to the duct section between those two points. Therefore, it is not practical to use

the test section temperature for feedback control.) A rate of temperature rise in air flow

of 0.5 °C/s is achievable at the test section, up to maximum of about 80 °C. Air

temperatures and duct wall temperatures are recorded at the test section with type-K

thermocouples. Figure 3 shows the air temperature response to a programmed sequence

J) 1

3
Exhaust

Test Section Honeycomb

Variable

Speed

Fan

Ceiling IMounted

Detector

Water mietfor Dust Spray oases Feeder

I I t-Li

Propene

Smoke

Burner

Velocity

probe

Gas

Sampling

30 cm

Electrical Heaters — -yr

Vertical Riser

Laser Beams

Flow

Fire Emulator

Detector Evaluator

Test Section

Side View

Figure 1 . Schematic of the fire emulator/detector evaluator.

E

cHi

'35 o

E

o

o

c

(0CO

%mmm O

0)

4Q

O

0

5

10

15

20

25

30

1 1 1 1 H - » _ _ 1—

~ ~ -

f-^

' 1

'

"M,

" W

h

Hjw

~ "—1

l-H H-l

1 1 M

H

1 M i-Jh

1 \ lA

I—

I

4

1 Fan Speed

7Hz

10 Hz

15 Hz — 20 Hz

J I I. 0.2 0.4 0.6 0.8

Axial Velocity (m/s)

Figure 2. Axial velocity at the duct centerline as a function of distance from the ceiling.

314
of temperature set point values sent to the heater controller, and fan speed settings. This

sequence was designed to reproduce velocity and air temperature rise at a detector

location predicted from a modeled test fire (the particular example is for location 1

1

described in reference [5] found in these proceedings). The graph shows the air

temperature for four repeated runs, along with the heater set point values. Figure 4

shows the flow velocity at the duct center at the test section for these repeated runs along

with the fan speed settings.

CO, CO2, or other gas blends may be metered into the flow via electronic mass flow

controllers. Superheated water may be sprayed into the flow after the heater section to

fix humidity between ambient room and saturation conditions depending on the spray

flow. Water, CO, CO2, and hydrocarbon gas concentrations at the test section are

monitored by non-dispersive infrared (NDIR) analyzers. The ability to control gas

concentrations independently benefits both fire and nuisance alarm scenario emulation.

For example, both CO and CO2 may be normally present in ever-changing

concentrations in a building due to the external environmental sources such as attached

parking garages, or internal sources such as the diurnal CO2 variation due to occupancy

and ventilation levels.

Various types of smokes and non-combustion aerosols may be introduced into the flow,

including flaming soot, smolder smokes, dust, nebulized liquid mists, and cooking

aerosols. Laser light transmission measurements across the duct at the test section are

used to calculate the light extinction coefficient or optical density of the aerosol.

Extinction coefficient or optical density is the typical "concentration" measurement of

smoke or other non-fire aerosol. A HeNe laser at 632.8 nm wavelength is the light

source, and a stabilizer utilizing a liquid crystal polarizer maintains a nearly constant

laser intensity. The beam is split and introduced at two heights: the center of the duct,

and 5 cm below the ceiling. Each light beam is reflected off two mirrors inside the duct

and directed at a photodetector placed on the opposite side of where the beam enters the

duct. The total light transmission path length inside the duct is 1.5 m. The

photodetector output voltage is linear with respect to the transmitted light intensity. The

standard relative uncertainty due to random fluctuations in the output is 0,06 % of the

315
0 50 100 150 200 250 300 350

Time (s)

Figure 3. Temperature at a detector location for repeated emulations of a modeled fire.

0 50 100 150 200 250 300 350

Time (s)

Figure 4. Velocity at a detector location for repeated emulations of a modeled fire.

316
measured light transmittance (light intensity divided by smoke-free initial light

intensity). The extinction coefficient is computed by dividing the natural logarithm of

the transmittance by the path length through the smoke and multiplying by (-1). A

measuring ionization chamber (MIC) can be located in the test section to provide a

reference chamber current measurement more appropriate for ionization detectors than

light extinction.

3. Smoke Aerosols

The flaming and smoldering smokes produced cover a wide range of physical properties

and concentrations. The propene smoke generator provides black soot typical of flaming

hydrocarbon or plastics fire smoke. The generator is directly attached to the FE/DE duct

at the vertical riser section. The concentration of smoke in the flow is varied by

changing the fuel flow of the burner, and opening or closing dampers allowing more or

less flow from the burner to enter the duct. Examples of emulated flaming fire

conditions are given in references [5,6] in these proceedings. Propene smoke generated

in the FE/DE was collected for the light scattering study in reference [7]. A steady

concentration of smoke was provided at the test section (fan speed set at 7 Hz), then

collected for light scattering experiments. Figure 5 shows the light transmittance of the

upper laser beam traversing the test section and the MIC current output. Here, data

gathering began after steady conditions were achieved, as indicated by the transmittance

and MIC output. The smoke collection time for the smoke used in the light scattering

experiment was from 30 s to 390 s.

Pyrolyzing wood smoke was generated by heating 8 beech wood blocks, 3.5 cm x 2.0 cm

X 1.0 cm on an electric hotplate placed inside the duct at the bottom of the vertical riser

(Figure 6). This scenario is similar to the fire sensitivity test fire 2 in EN 54 part 9 [8].

The hotplate was operated at full power for this test (750 kW), and the fan speed was set

to 7 Hz. For this test the hotplate was turned on at 30 s and turned off at 1300 s. Smoke

generated from the heated wood blocks was also collected and studied in the light

scattering and size distribution experiments described in [7]. Figure 7 shows the light

transmittance of the upper laser beam traversing the test section and the MIC current

output for this scenario. The MIC output started to drop at approximately 550 s while the

317
^ 1 o

^ 0.9

o

5 0.8

E

c

(0

O)

0

(0

(0

0.7

0.6

0.5

0.4

0.3

"T 1 r -1 1 1 r "I 1 rMIC

Transmittance

_l I L_ _1 I L_

100

80

60

40

20

100 200 300

Time (s)

400

Uia

E

COo

o

3

a

3

O

o

Figure 5. Transmittance and MIC output for steady propene smoke concentration.

Figure 6. Electric hotplate with arranged wood blocks prior to testing.

318
0 400 800 1200 1600

Time (s)

Figure 7. Transmittance and MIC output for pyrolyzing wood blocks source.

transmittance began to drop around 700 s, and both were continuously changing

throughout the test. Smoke for the light scattering and size distribution measurements

was collected over a time period from 800 s to 1 1 60 s. Over this collection period, the

transmittance dropped from 0.9 to 0.4.

Smoldering cotton smoke is generated by a staged-wick-ignition device (Figure 8) that

ignites wicks by applying power to electrical heating wires in a prescribed computercontrolled sequence to affect a specific rate of smoke build-up at the test section. Eight

groups of up to four individual wicks can be ignited in sequence to provide the

controlled rate-of-rise in smoke concentration at the test section. The cotton smolder

source is similar to the cotton smolder test fire 3 in EN 54 part 9 [8]. Smoke generated

from the smoldering wicks was also collected and studied in the light scattering and size

distribution experiments described in [7]. Successive application of power to each of

the eight sets of four wick igniters was performed at 12 s intervals to achieve the smoke

buildup; data collection began 30 s prior to the start of the ignition sequence. Figure 9

319
Figure 8. Staged-wick ignition device with close-up of an igniter.

Figure 9. Transmittance and MIC output for cotton smolder smoke.

320
shows the shows the light transmittance of the upper laser beam traversing the test

section and the MIC current output for this scenario. The transmittance and MIC current

initially drop at the same rate. It then appears that the MIC output looses sensitivity at a

light transmittance below 0.6. At about 190 s, the transmittance reached its lowest

value. All 32 wicics have ignited and each are approaching the steady burning rate

period. Between 350 s to 750 s the wicks bum at a steady rate as evidenced by the

transmittance and MIC output values. After 750 s the first set of four wicks start to bum

out, followed by successive groups of four later. Smoke for the light scattering and size

distribution measurements in [7] were collected over a time period from 360 s to 720 s.

An illustration of the relative difference in the properties of these test smokes as related

to the response of light scattering and ionization detectors is shown in Figure 10 where

the ratio of the normalized MIC output ( (Io-I)/Io where lo is the initial chamber current

and I is the present value of the chamber current) to extinction coefficient over the

collection times for each of the smokes is plotted. The ratio is steady for cotton smolder

a

o

o

= 4

i i

O fl) o O O

N O ^

75 *- c

E .2 2

o o

z .E

«*.o

(0

0^ 0

—Wood Pyrolysis — Cotton Smolde — Propene Flame

200 400 600 800 1000 1200

Collection Time Period (s)

Figure 10. Ratio of normalized MIC output to extinction coefficient for FFE/DE test smokes collected for light scattering and size distribution measurements.

321
and propene smoke with means of 4.2 and 2.5 respectively, following a collection time

period when the extinction coefficient was a constant value of 0.2 m'' in both cases. The ratio computed for the wood smoke varied from about 2 to 1 over the collection

time period. The ratio for wood smoke during the brief collection time when the

extinction coefficient was 0.2 m"' was 1.3. Comparing the two non-flaming smokes, the

ratio is 3.2 times higher for the cotton smoke at that fixed extinction coefficient. This

implies that at a fixed concentration, an ionization sensor will be more sensitive to

cotton smoke compared to the wood smoke. The size distribution measurements in

reference [7] offer an explanation for this effect based on the observation that the cotton

smoke size distribution is shifted to smaller particle size compared to wood smoke.

More work is planned to characterize other fire smokes and nuisance aerosols produced

in the FE/DE

4. Conclusions

The FE/DE is capable of emulating a wide range of fire and non-fire environments to which a spot-type detector could be exposed. Air flows, temperatures, smoke and

combustion gas concentrations from growing fires can be emulated accurately in the

FE/DE up to levels where detectors should alarm. The flaming and non-flaming fire

smokes generated cover a range of concentrations and physical properties that impact

smoke detector response.

5. References

[1] Grosshandler, W.L., "Toward the Development of a Universal Fire Emulator/

Detector Evaluator," Fire Safety Journal 29, 1 13-128, (1997).; also in AUBE '95

Proceedings, pp. 368-380.

[2] Cleary, T., Chemovsky, A., Grosshandler, W., and Anderson, M., "Particulate

Entry Lag in Spot-Type Smoke Detectors," Sixth International Symposium on

Fire Safety Science, University of Poitiers, France, July, 1999.

[3] Cleary, T., Anderson, M., Averill, J., and Grosshandler, W,, "Evaluating Multisensor Fire Detectors in the Fire Emulator/Detector Evaluator," Proceedings of

the 8th Inter. Conf. on Fire Science and Eng., Interflarn '99, Edinburg, Scotland,

June 1999.

322
[4] Cleary, T., Grosshandler, W., and Chemovsky, A., "Smoke Detector Response to

Nuisance Aerosols," Proceeding of the 11th International Conference on

Automatic Fire Detection "AUBE '99", March 16-18, 1999, Gerhard Mercator

University, Duisburg, Germany, Luck, H., Ed., pp 32-41, 1999.

[5] Cleary, T., Donnelly, M., Mulholland, G., and Farouk, B., "Fire Detector

Performance Predictions in a Simulated Multi-room Configuration," Proceeding

of the 12th International Conference on Automatic Fire Detection "AUBE '01",

March 26-28, 2001, National Institute of Standards and Technology,

Gaithersburg, MD, USA, Grosshandler, W., Ed., 2001.

[6] Cleary, T., and Donnelly, M., "Aircraft Cargo Compartment Fire and Nuisance

Source Tests in the FE/DE," Proceeding of the 12th International Conference on

Automatic Fire Detection "AUBE '01", March 26-28, 2001, National Institute of

Standards and Technology, Gaithersburg, MD, USA, Grosshandler, W., Ed.,

2001.

[7] Weinert, D., Cleary, T., and Mulholland, G., "Size Distribution and Light

Scattering Properties of Test Smokes," Proceeding of the 12th International

Conference on Automatic Fire Detection "AUBE '01", March 26-28, 2001,

National Institute of Standards and Technology, Gaithersburg, MD, USA,

Grosshandler, W., Ed., 2001.

[8] EN 54: Components of Automatic Fire Detection Systems, European Committee

for Standardization, Parts 1-9, 1988
Rudolf Maegerle

Siemens Building Technologies Ltd., Cerberus Division

Fire Protection Systems for Traffic Tunnels Under Test

1. Introduction

Underground transport facilities are sensitive links in the economic chain

that carry thousands of people and tons of goods every day and they are

growing in importance. Therefore a breakdown in operations can have catastrophic consequences. That is why safety precautions are vitally important

and the work involved is commensurate with the high potential risk. By far

the greatest risk is a fire out of control. There is great danger to life from

toxic combustion gases, exceedingly high temperatures, total loss of visibility, limited means of escape and the panic reactions of drivers and passengers. The emergency services are usually hindered by vehicles and by the

smoke and gas generated by the fire which at the same time causes serious

damage to the infrastructure of the transport facility.

The dramatic events in the Mont Blanc and Tauern tunnels triggered a wide

public debate on the safety of road and rail tunnels leading to demands for

comprehensive safety precautions to safeguard the survival of travellers, to

keep damage to the facility to a minimum and to maintain its availability.

Structural and organizational measures such as refuges, escape tunnels,

effective smoke-venting and rescue concepts are an important first step.

Equally important for the prevention of a catastrophe are

• fire detection which is fast, reliable and indicates the precise fire location

without being influenced by high-speed air currents

324
• automatic activation of traffic control systems, the alerting of the emergency services, a fire spread assessment at the scene of the accident, the

activation of ventilation systems etc.

• the activation of extinguishing systems to keep the fire in check until the

fire department arrives at the scene of the accident.

As with all high-risk installations, those underground call for systems offering the highest reliability. Furthermore, early and certain detection and

immunity to false alarms is of the greatest importance. What previously

was a very basic form of fire detection for tunnels has, in recent years, developed into a special discipline which has had a powerful influence on the

entire security concept for underground transport facilities.

2. Aim and purpose

Concepts for electronic security systems have existed for years and have

been consistently upgraded. However, the question is, how the effectiveness,

the reliability and immunity to interference of the systems proposed can be

practically tested using the previously made physical calculations. Similarly, the test environment, i.e. the location or the premises as well as the

technical installations play a decisive role in such tests. The following description deals with these tests with emphasis on fire detection and extinguishing.

3. Test Infrastructure

The Hagerbach tunnels near Sargans in Switzerland offer almost ideal conditions for the tests. Hagerbach comprises approx. 4500 m of underground

325
tunnels of various lengths equipped in different ways. It is mainly used for

research into new methods of tunnel construction as well as demonstrations

(see "The Siemens Magazine - New World No. 3", August 1999).

Entrance to the Hagerbach (Switzerland) The plan of the test tunnels.

The tunnel

test tunnel complex consists of approx.

4500 m tunnel sections of

varying lengths and equipped

in different ways

The extract air tunnels with wind speeds of up to 5 m/s, are particularly

good for genuine fire and extinguishing tests. For this purpose the floor of

the used air tunnel was concreted and fitted with a sump to collect the

waste water resulting from extinguishing tests. Various temperature sensors, which can be located wherever required, measure convection and radi326
ant heat. The air speed is also measured. The early warning flame detectors

signal the first open fire in a series of tests. The entire test area for fire and

extinguishing tests is monitored by video cameras. The control centre was

set up some distance away and monitors the test equipment and measuring

devices and records all test, measuring and graphics data.

In addition to pool fires for the simulation of rail and road vehicle accidents,

a specially-designed dummy vehicle is used to re-create various fire se- quences as well as the assessment of the effect of extinguishing in different

extinguishing configurations.

Test set-up in tunnel with dummy vehicle. The control center with its monitors.

As the test tunnel with a height of 3m and a width of 3m is smaller than a standard tunnel, the measurements were scaled down to provide a mathematically correct basis for proper tests. Thus the procedure could be used for

the application of test results for tunnels with different dimensions, lane

directions and ventilation conditions. The "Scaling Up / Down" calculation is

part of the VdS test for the FibroLaser II calculation program (see also section 4.2).

327
4. Testing systems

4.1 Fire detection with FibroLaser II

The FibroLaser II fire detection system is used for fire detection in tunnels.

The system is an intelUgent heat detector connected to a fire detection control unit and as required can provide detailed alarm information for further

processing. FibroLaser II has a simple system architecture:

• Fibreoptic sensor cable up to 4 kilometers long

• Controller with laser light generation and software-supported

evaluation

- Volt-free outputs for alarm and fault messages

- Communication interface for the setting of the optimized operating parameters for the tunnel concerned during commissioning

and for a link to the control and visualization units with no retroactive effect

- Power supply 24V DC or 220V AC (lOOW)

2 Fibreoptic cables Insulation

Sensor cable

Controller

Communication

Supply

Volt-free outputs

System architecture FibroLaser II Design of FibroLaser II

sensor cable

328
The sensor cable consists of a steel capillary tube with an outer diameter of

1.65 mm. The capillary tube contains 2 separate quartz fibers each with an

outer diameter of 0,25 mm. The remaining space in the capillary tube is

filled with an anhydrous, heat-conductive material.

The steel capillary tube is encased in a plastic coating with a diameter of up

to 8 mm. This plastic coating makes handling easier during installation and

provides mechanical stability as well as sensitivity to strictly radiated heat.

The FibroLaser II fire detection system is VdS listed. FM and UL listings

are pending.

The FibroLaser II sensor cable was installed at different locations along the

roof and walls of the tunnel in the test set-up in order to be able to record all

reactions to convection and radiant heat. In addition, by placing the sensor

cable of FibroLaser II in cable raceways in the vicinity of the tunnel test section, it is possible to determine heat detection capability in bunches of cables, cable ducts etc.

4.2 Calculation program for FibroLaser II

The planning of tunnel fire detection systems is very demanding as wind

speeds have to be taken into account in addition to traffic conditions and

geometric dimensions.

A calculation program (patent applied for) has been developed to check the

effectiveness of the fire detection system as planned and works out the re- sponse time of the FibroLaser II on the basis of the tunnel dimensions, the

fire load and wind speeds etc. Known or assumed parameters are entered in

the input template of the calculation program. The result is presented in

table form or as graphics.

329
The series of fire tests in the Hagerbach tunnels has provided valuable basic

data which, supplemented with results fi'om fire tests in real tunnels has

yielded the basis for the mathematical model of the calculation program.

The calculation program is VdS listed. As already mentioned, this review

includes the "Scaling Up / Down" process.

4.3 Fire detection with video

Fire detection with video or so-called "cold fire detection" is frequently mentioned in publications at the moment. However, so far no video system has

been produced for application in tunnels which can independently guarantee reliable and false alarm free, fire detection and which has international

approval. Nevertheless, video techno- logy is making great strides.

For this reason provision has been made in the Hagerbach installation to

enable new developments in video technology to be tested.

4.4 Extinguishing

Extinguishing systems for tunnels are specially designed water spray systems with nozzles modified to the type of tunnel in order to guarantee the

best extinguishing effect. The extinguishing system is automatically actuated at the fire location by the FibroLaser II system. This means that extinguishing is only triggered at the fire location and that no other vehicles in

the tunnel are involved. This procedure also allows us to keep extinguishing

water supplies and the amount of waste water to a minimum.

330
Extinguishing systems in tunnels must be able to withstand the special ambient conditions such as below-zero temperatures or corrosive exhaust

gases. Furthermore, the extinguishing system must be able to bring the fire

under control in spite of powerful air currents.

Water pipes for servo-mechanical nozzles are mounted along the roof and

walls of the tunnel section of the test installation. This simplifies the

evaluation of the various extinguishing configurations. The pressurized water is supplied via an arrangement of pumps which enables not only the water pressure to be varied, but also different rates of flow. It is also possible

to introduce additives into the extinguishing water. The waste water from

the extinguishing system is collected in sumps and correctly disposed of. Gas detectors monitor the water collecting points for impermissible gas concer

The extinguishing water pump station Tanks for the disposal of waste water

VdS approval of the extinguishing system described is pending.

331
4.5 Systems engineering

In the field of systems engineering emphasis is on practical tests for operating sequences.

System specialists work out the interfaces, interactions, data communication and control system technology in accordance with customers' specifications.

5. First findings from the test series

The series of tests at the Hagerbach test tunnels have the following objectives:

• Validation of the calculation program for the optimum application of FibroLaser II

• Examination of the "Scaling Up / Down" method, which enables configuration of the detection system irrespective of the size of the tunnel

• Optimization of tunnel extinguishing in the form of a water spray deluge

system with minimum water consumption

When validating the calculation program, (PC software) particular attention

is paid to examining the following thermodjuamic phenomena:

• Combustion behavior according to the type of combustible and the fire

surface

332
• The influence of the wind on the rate of combustion and on the mixing of

combustion gases

• The radiated power of the fire on the FibroLaser sensor cable

• The convection heat exchange with the FibroLaser sensor cable

Previous tests have shown that the tunnel wind, especially that from the

emergency ventilation, which in the event of a fire is switched on to disperse

the smoke, has a decisive influence on the course of the fire and detection.

Furthermore, it has been proved that the linear detection of the radiated

heat of a fire is the only relevant alarm criterion at higher wind speeds. As

the radiated heat is not influenced by the tunnel wind, the fire location can

also be precisely determined.

333
Influence of wind speed

Fire with area 0,5 and 4 litres of petrol FibroLaser II sensor cable on tunnel roof 45

0

I

^ ^ ^ ^ ^ '

1 2 3 4 5 6 7

Measuring cycles FibroLaser II

Diagram of fire test results

The diagram above shows the results of fire tests in the Hagerbach test

tunnels with a petrol fire (4 liters) covering 0,5m^ and various wind speeds.

This size of fire is equivalent to that involving one car.

The diagram clearly shows the influence of wind on detection and the combustion time. In this particular case only the field of radiation is detected.

The combustion gas temperatures beneath the roof of the tunnel lie below

the measured values in the

FibroLaser sensor cable.

334
Fire test with dummy vehicle

without tunnel wind at fire source

Fire test with dummy vehicle and a tunnel wind of ~ 5m/s at fire source

An important aspect for the interpretation of the Hagerbach tests is the

"ScaHng Up / Down" procedure. Tests in small or large tunnels must be

comparable and it must be possible for the calculation program to process

them irrespective of tunnel geometry. Experience shows that empirical values are the decisive parameters for the conversion of the specific radiation

volume and the specific calorific output of a fire.

Tunnel fire extinguishing is based on an optimized water spray deluge system with a nozzle concept that achieves its protection aim with minimum

water consumption. Fire tests with a dummy vehicle and realistic combustibles (plastics, petrol, rubber etc.) show that the protection aim must be to

bring the fire under control in the vehicle where it originated and to prevent

fire spreading to the surrounding area. It has proved to be essential to ex- tend the coverage area of flooding to zones of approx. 30m, whereby basically the extinguishing system in the zones adjacent to the zone in which

extinguishing has been actuated should also be actuated. With FibroLaser

II it is possible to locate the fire to within about 3-4m and to register the

spread of hot gases.

335
Extinguishing test with fire in the dummy

vehicle and a tunnel wind of - 3 m/s

at fire source

Extinguishing test with fire in the dummy

vehicle and a tunnel wind of ~ Sm/s

at fire source

6. International activities in tunnel protection

Following the tragic accidents in spring 1999, groups of experts were formed

in a number of countries to check existing tunnels and to work out plans for

the realization of comprehensive concepts for tunnel safety. It is hoped that

some of these projects will be financially supported by national authorities.

The scope of these concepts stretches from structural measures such as es- cape tunnels, the design of ventilation systems, electronic security systems

and extinguishing systems, to training centres for the emergency services.

Within the framework of such schemes Siemens Building Technologies Ltd.,

Cerberus Division can make a solid contribution towards the protection of

tunnels.

336
The author of this paper, Rudolf Maegerle, is Head of Application Support

at Siemens Building Technologies Ltd., Cerberus Division and is also responsible for the development and application of new products.

337
Ingolf Willms

Gerhard-Mercator-Universitat Duisburg, Germany

Department of Communication Engineering

Concepts for the test of volumetric fire detectors

Abstract

This paper covers the question, if it is possible to apply to volumetric fire detectors a

testing concept, which was introduced in intrusion detector testing. The concept follows

a so-called 'solid-state' approach, leading to the stimulation of detectors by means of

electronically controllable signals in such a way that the detector cannot distinguish

between an artificial stimulation and a situation with a real fire. The application of this

technique for the test of video fire detectors and other fire detectors, possibly coming up

in the future is discussed.

1 Introduction

Since many years for traditional types of fire detectors test procedures and suitable test

apparatus exist, which are specified in national and international standards. These

standards rely on evaluating the detectors performance in a) a set of basic tests, which

determine the response threshold of detectors before and after environmental tests and

b) check the detector in a set of full scale tests [1]. The corresponding test equipment

and fire test rooms are available in all major test laboratories.

In a project sponsored by the European Commission, for the basic test of intrusion

detectors, testing concepts were worked out and prototypes were set-up, which aim at an

apparatus for the determination of the response threshold of such detectors. Although

intrusion detectors are designed for the detection of the motion of a human within the

detection range, the stimulation of such detectors within the test is carried out without

any motion inside the test chamber [2].

338
The apparatus is designed to be the counterpart of tiie smoke and heat tunnel, which

enables to determine the response threshold for smoke and heat detectors.

One key feature of this equipment is the fact that all physical stimulation signals can be

controlled by electronic signals. Thus in a certain range any arbitrary stimulation of the

detector is produced. So a stimulation following very closely to a normal motion of

humans can be realised.

After the introduction of such a "solid state" testing concept for intrusion detector tests,

the question came up to what extent such a method could be applied to the test of fire

detectors. In detail: Is it possible (and reasonable) to consider a test equipment, which

determ.ines the relative or absolute response threshold of a smoke detector e.g. without

producing any smoke or flames. An investigation into these questions led to some first

answers and the following considerations.

2 The stimulation concept

In a full scale test fire detectors have to prove their ability to respond to a set of artificial

fires in a more or less well defined environment. To some degree such tests do not rely

on the special operating principle of detectors, so a kind of black-box testing is given.

The concept of "solid state" detector stimulation can be considered as a white-box

testing method. Such test methods must perfectly match the required detector type and

its design parameters. Different detector types need to be stimulated by different test setups. The test apparatus therefore might require a sophisticated construction. Also some

essential design details of detectors have to be known in order to select the proper

stimulation method and to efficiently carry out such tests. But a clear benefit of such

tests is that the detector stimulation is to a very high extent easily adaptable to different

test scenarios and superior repeatability is given. Electronic control of the detector

stimulation provides also a means of down-scaling the tests with respect to detection

range and test chamber dimensions. So the test equipment might be reduced in size to a minimum.
3 Applicability to point-type detectors

This paper essentially covers test methods on volumetric detectors. For point-type

detectors test methods are in use for many years, so significant improvements cannot be

expected. For newer detector types such as gas detectors the situation is a bit different,

improvements of test methods are under discussion.

An important example of such improvements in the test methods for point-type

detectors is a test facility, developed at NIST, called in short FE/DE [3]. The apparatus

is essentially based on the smoke & heat tunnel and is able to stimulate fire detectors in

a very flexible and controlled way. Also the evaluation of gas detectors and multi-sensor

detectors is implemented. The stimulation of fire detectors with this system covers the

detector response in fire alarm and in some false-alarm situations.

Another reason for the restriction of the following considerations only on volumetric

detectors is given by practical limitations for the stimulation of point-type detectors. If

an optical point smoke detector for instance is considered, the detector principle requires

that inside of the detector housing and inside its measurement chamber the stimulation

has to be produced. This chamber is of course always optically shielded very well

against environmental light so that an optical stimulation from the outside cannot work.

One might take into consideration that a scattering effect might be realised by inserting

a fibre, which carries an optical receiver and transmitter, producing a defined radiation

intensity as a function of the received radiation level whilst keeping the signal form.

Today's technology allows for such intelligent fibres, but without precise information of

the design of the tiny measurement chamber, without a suitable opening and without

micro manipulation techniques such a stimulation cannot repeatably be produced.

On the other hand it has to be expected that advances in sensor and processor

technology and improvements in signal processing will in the future lead to new costeffective products with volumetric operating principles.

340
Volumetric principles open up the possibility of including much more infonnation about

the spread of a fire than point type detectors due to the additional spatial information.

Also the location estimation of a fire can be enhanced using a volumetric detection.

4 Some testing aspects concerning early volumetric detectors - flame detectors

For flame detectors a standardised test method is in use since many years. The concept

of 'solid state' stimulation could be realised using an appropriate IR /UV source and a

modulator for producing the flickering effect of flames. Various options exist for the

light modulator. A solid-state solution would implement a micro-mirror spatial light

modulator, which offers full digital control, wide spectral response from DR. to UV and

cut-off frequencies up to 500 Hz [4].

But the limited advantages of easier control, more flexibility of the detectors stimulation

and the expected costs do not predict lots of success for such a concept over the existing

test method. For other volumetric detectors a possible introduction of an alternative

testing concept is much more promising.

5 Volumetric ultrasonic fire detector and a corresponding test concept

By Appleby and Ell wood [5] in 1995 a number of volumetric fire detection techniques

were described. One very interesting technique makes use of the Doppler effect of

ultrasonic waves produced by the convection of hot air above a fire. Although such

ultrasonic fire detectors don't play any role in fire detection up to now, the potential

advantages of the described testing concepts are highlighted very well.

The Doppler signal of such a detector is dependent on the type of the heat source, so a

stimulation of an ultrasonic fire detector has to produce a signal form very close to that

gained in a real open fire. For an ultrasonic detector the concept of 'solid stale'

stimulation signals can be applied without problems. Scaling down the scenario with a

scale down factor of 5 to 10 leads a compact test chamber.

341
The stimulation can be produced by means of an IQ modulator, an ultrasonic transducer

and a suitable microphone, which have to be located in front of the detector.

If appropriate generation of the I- and Q-signals is provided, the ultrasonic transducer is

able to generate any arbitrary low-frequency Doppler signal in the specified frequency

range. Typical ultrasonic frequency bands for long range operation and low-cost cover

the range of about 25 kHz to 40 kHz Doppler. As the ultrasonic Doppler signals exhibit

dominant spectral components of about 40 Hz, it is sufficient to produce Doppler

signals with a cut-off frequency of less than 300 Hz. Main cost driving components for

such a test apparatus will be the microphone, alignment components for the detector and

a test enclosure covered with absorbers.

6 Testing of state-of-the-art video fire detectors

The application areas for video fire detection systems are developing fast. Detecting

smoke plumes in forests, fire in aircraft hangars have been the first applications. Video

surveillance in manufacturing plants and tunnels are additional areas [6], [7]. Video fire

detection systems usually consist of a video control unit to which a number of video

cameras are connected and which perform the detection algorithms. Such a system

structure opens up the possibility of repeatable test procedures, which follow the same

principles as for video motion detection systems [8]. The signals of a predefined set of

digital video sequences are fed into the control unit instead of the video camera outputs.

By this means perfectly repeatable stimulation signals are fed into the system. Such sets

include fire and non-fire situations.

Also the determination of response threshold values can be carried out. Different

methods for its determination might be applied. One example of such a method makes

use of the response time of the video detection system to a typical fire test scenario as

the key parameter for the response threshold determination. A more sophisticated, but

also more time consuming onset would use a set of test fire videos produced with

different amounts of burning material.

342
The response threshold can then be defined by that test fire video sequence with the

minimum of burning material leading to a detector response.

7 Self contained video fire detectors

Self contained video fire detectors, which integrate the video signal processing

components in the detector housing are no product for the mass market of the year 2001.

But in the far future the situation might change. Self contained video motion detectors

are already on the way. They can make use of CMOS image sensors offering superior

performance in several aspects on CCD chips, e.g. in terms of power consumption of

less than 35 mW. For the moment the higher price level is the one exception of the long

list of benefits. But it is expected that the price level will follow the same development

as for CCD chips. It can also be expected that fire detection algorithm might then be

offered as on add-on to such components or perhaps as their main function.

In such a situation the presently used techniques for video fire detector tests will not

always work because CMOS image sensor and signal processing circuitry will due to

cost reasons be implemented on the same chip. So in general an access to internal input

signals to the signal processing components is not given. Advanced locally adaptive

CMOS image sensor designs already combine local pixel signal processing features,

where the local image processing circuits are located inside the pixel cells.

So for such detectors an external stimulation has to be provided using a real or an

artificial image. It is proposed to provide such an image by a screen placed in front of

the video detector. Onto such a screen video sequences should be played back using a

suitable display. The response threshold determination method then need no changes

compared to normal video detectors. Because of the usual small focal length of the

cameras the screen might be placed quite close to the detector without the risk of a

blurred image, so scale-down factors of 10 to 20 might be applied given a rather small

size of the corresponding test chamber.

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8 Microwave detector testing

Let us assume that also microwave fire detectors find their way in applications with the

need for hot spot detection. How could a testing concept look like for such detectors? A

more repeatable stimulation than by means of a real fire with enough embers might be

carried out using a hot plate of defined temperature placed in front of the detector at a

fixed distance and orientation. The problem is to find a material with high emissivity

and easy, electrically controllable temperature.

These problems can be overcome in stimulating the detector by means of a microwave

signal generator and an antenna at the location of the hot plate. Such tests could be

carried out in a small chamber, equipped with microwave absorbing material or in a fire

test room. The main requirement is, that few details on the evaluated microwave band

have to be known in order to select the proper antenna and signal generator settings.

9 Conclusion

For point smoke detectors a solid-state test concept seems to be far from applicable. For

volumetric fire detectors the situation is different. For IR and UV flame detectors the

application is possible, but leads to no significant improvements. For future fire

detectors, such as self contained video fire detectors and microwave detectors the 'solid

state' approach is quite promising.

References

[1] EN54 Components of automatic fire detection systems, European Committee

for Standardisation, Parts 1-9, 1988

[2] Willms, I., Berentsen, M., Rexfort, C. Solid state simulation of movements for

the test of volumetric intrusion detectors. International Camahan Conference on

Security Technology, Madrid, 1999

[3] Cleary, Th., Grosshandler, W., Chemovski, A. Smoke detection to nuisance

aerosols, AUBE ^99, Duisburg 1999

344
[4] DiiiT,?., Gehner, A.. Dauderstadt, U. Micromirror spatial light modulators,

International Conference on Micro Optical Mechanical Systems, Mainz, 1999

[5] Ellwood, S.H., Appleby, D. Volumetric fire detection using imaging of fire

products and transport phenomena, AUBE'95, Duisburg, 1995

[6] Lloyd. D. Video Smoke Detection. Fire Safety Engineering 2000, 7

[7] Lopfe, M. Status of video and smoke detection. EUSAS Workshop on Fire

Protection in Tunnels, Mannedorf, 2001

[8] Pritchard, D.A., Virgil, J.T. On the development of a digital video motion

detection test set, IEEE International Camahan Conference on Security

Technology, Madrid, 1999

345
Thomas Cleary and Takashi Ono*

Building and Fire Research Laboratory, National Institute of Standards and Technology

Gaithersburg, MD, 20899 U.S.A.

Department of Electrical Engineering, College of Science and Technology

Nihon University, Tokyo Japan

Enhanced Residential Fire Detection by Combining Smoke and CO Sensors

1. Introduction

The advantages of multi-sensor detection in general, and gas sensing in particular, suggest

that a combined carbon monoxide-smoke fire detector could benefit the consumer by

providing better fire detection through discrimination of some common nuisance sources,

while warning of hazardous CO concentration in the living space. Heskestad and Newman

[1] reported that the cross-correlation of CO concentration and measuring ionization

chamber (MIC) measurements taken during room fire experiments was capable of detecting

fires similar to EN 54 test fires with high sensitivity. Gottuk et al. [2] performed a number

of fire and nuisance source room experiments, and showed that by combining CO and

ionization signals, many nuisance sources were discriminated, while most fire source were

detected. Ishii et al. [3] developed a detection algorithm using CO concentration, smoke

concentration, and temperature measures in an artificial neural network. Here, three fire

sources and four nuisance sources were emulated in the fire emulator/detector evaluator

(FE/DE). Analog output photoelectric and ionization detector signals, along with an

electrochemical CO cell's signal were gathered during the tests from sensors located in the

FE/DE test section. The sensor signals from these tests were used to test a multi-parameter,

rule-based frizzy logic detection algorithm.

2. Fuzzy Logic Detection Algorithm

Residential smoke detectors are typically ionization type or photoelectric type. There are

dual sensor designs that contain both ionization and photoelectric sensors in the same

housing, however, they frmction separately. The benefit of monitoring both photoelectric

and ionization sensor outputs lies in their differing relative response to both fire smokes

346
and nuisance aerosols. In combination with a CO measurement, potentially better fire

detection and nuisance source discrimination is possible compared to a CO and ionization

detector combination. Here, photoelectric, ionization and CO sensor signals were

combined in a rule-based, fuzzy logic algorithm designed to discriminate between the

emulated fire and nuisance source test results. The signal feature considered was the

instantaneous value of the measured output Hence, this algorithm is considered a static

classification algorithm.

An overview of the fuzzy logic methodology and computations are described below. A

more detailed treatment for the application of this methodology related to fire detection is

given by Mueller [4]. Here, photoelectric, ionization, and CO signals were the input

variables, and assigned to classes low, medium and high depending on their values. The

state of alarm (fire) was the output variable, and assigned to class true or false. A rule base

consisting of 27 rules of the form below was specified.

IF Photoelectric = (low, medium, or high) and Ionization = (low, medium, or high) and

CO = (low, medium, or high) THEN fire = (TRUE or FALSE).

Each variable has a degree of membership in each class ranging from 0 to 1, given by

defined membership functions. The membership functions for the variables photoelectric,

ionization, and CO are shown in Figures 1-3. The final shape was obtained by an iterative

process of testing the algorithm against the FE/DE test signals then adjusting membership

fiinctions. A commercial analog output photoelectric, ionization, and heat combination

detector was used to obtain the photoelectric, and ionization sensor signals. The

photoelectric and ionization sensor signals represent integer values from 0-255 spanning

the sensor range; the membership function spans a portion of this range, where a zerooffset has been subtracted from the values. Figures 4-6 show the response of these sensors

to steady concentrations of flaming soot, dust, and nebulized oil aerosol [5]. A linear fit

through the data for each sensor is given along with the correlation coefficient. Error bars

represent ± 1 standard deviation about the mean for each data point (an indication of

aerosol source stability). The CO sensor signal was obtained from the voltage drop across a

resistor attached to the terminals of an electrochemical CO cell. The voltage drop may be

converted to CO volume fraction by multiplying it by 0. 1 3 (volume fraction/volt).

347
0 30 60 90 120 150 180

Sensor Signal

Figure 1 . Membership functions for photoelectric sensor ou^ut.

0 30 60 90 120 150 180

Sensor Signal

Figure 2, Membership functions for ionization sensor output

CO Cell Output (volts)

Figure 3. Membership functions for CO cell output.

348
• % % a.

<o o

100

80

60

40

T~—I— I "/• 1 r T T

. 1' To 20

o

-I—1

1 r- -1 1 1 1 T"

^PE =3520x R=0.99 ^ Ion =2040x RsO.94

-I I I L. -I 1 1 1 1 1_ 0.05 0.1 0.15

-1, Optical Density (m )

Figure 5. Photoelectric and ionization detector response to nebulized oil.

0 0.05 0.1 0.15

Optical Density (m'^)

Figure 6. Photoelectric and ionization detector response to Arizona test dust

349
The degree of membership in each class for the input variables is used to evaluate the

output variable's degree of membership. A weighted average of this set is used to

determine if an alarm is reached by comparing it to a threshold value.

By computing the results over the entire range of photoelectric, ionization and CO sensor

outputs, a surface plot reflecting the boundary between no alarm, and alarm conditions was

constructed. In Figure 7, all space on and above the surface reflects computed alarm

conditions, while the space below reflects the computed no alarm conditions. At an

ionization signal of about 70 and higher and a photoelectric signal of 80 and higher, no CO

concentration was required for alarm. At lower ionization and photoelectric signal values,

CO cell voltage must be above zero to indicate alarm. No alarm was indicated when

ionization signal fell below 34. No single sensor value was sufficient to cause alarm by

itself over the range considered.

Figure 7. Boundary surface between alarm and no-alarm conditions.

350
3. Fire and Nuisance Source Signals and Algorithm Output

Three fire smokes (flaming fire, smoldering cotton, and pyrolyzing wood blocks) and 4

nuisance sources (Arizona test dust, toasting bread, heated cooking oil, and cigarette

smoke) were produced in the FE/DE, and sensor data for the analog output detector and

CO electrochemical cell were gathered (see other papers by Cleary et al. in this proceedings

for details on the FE/DE). Residential photoelectric and ionization detectors were placed

on the ceiling of the FE/DE test section just down stream from the analog output detector

and CO cell. Alarm times for these detectors were recorded during the tests. Each source

is described below, along with the photoelectric, ionization, and CO sensor outputs and the

alarm algorithm computations over the length ofthe test.

The flaming fire source was produced by heating the air, ramping the fan speed, and

increasing soot fi-om the propene smoke generator to emulate conditions fi-om a burgeoning

pool fire. Figure 8 shows the photoelectric, ionization, and CO sensor signals along with

residential detector alarm points and the computed fiizzy alarm algorithm. The

photoelectric, ionization, and CO signals all begin to rise at about 280 s. The residential

ionization detector alarms first, followed by the algorithm computation (indicated by the

transition fi"om an arbitrarily low computed value to a high value), then the residential

photoelectric detector.

Cotton smolder smoke was generated by igniting 10 cotton wicks inside the FE/DE at the

bottom of the vertical riser with the fan speed set at 5 Hz (corresponding to a mean flow

velocity of 0.05 m/s). Figure 9 shows the sensor signals and alarm algorithm computation.

The photoelectric signal climbs more steeply than the ionization signal and peaks at a

higher value. For this source, the algorithm computation alarms first, followed by the

residential photoelectric detector, then the residential ionization detector less than 10 s later.

Smoldering wood smoke was generated by heating 8 blocks of wood on an electric ho^late

placed in the FE/DE at the bottom of the vertical riser (this source was a scaled down

version of EN 54 test fire 2) with a fan speed of 7 Hz. Figure 1 0 show the sensor signals

and alarm algorithm computation. While all signals begin to rise between 600 s to 700 s,

the photoelectric signal rises much more steeply and reaches its maximum value. The

351
Figure 8. Sensor signals and algorithm computation for flaming fire scenario.

Figure 9. Sensor signals and algorithm computation for smoldering cotton source.

352
Time (s)

Figure 10. Sensor signals and algorithm computation for pyrolyzing wood source.

residential photoelectric detector was the first to alarm, followed about 200 s later by the

residential ionization detector and the algorithm computation.

The dust exposure was produced by introducing Arizona test dust into the FE/DE duct by a

powder-delivery feeder at a constant rate, with a fan speed setting of 10 Hz. Figure 1

1

shows the sensor signals and the alarm computation. The photoelectric and ionization

signal began to rise at 80 s with the photoelectric signal reaching its maximum value less

than 10 s later. The ionization signal reached a much lower steady maximum 30 s to 40 s

after begiiming to rise. No appreciable rise in CO cell voltage was observed. The

residential photoelectric detector was the only one to alarm. The algorithm computation

stayed low throughout the test

Cigarette smoke was produced by igniting a single unfiltered cigarette that had its unlit end

attached to an air ejector (designed for this test to provide a continuous draw on the

cigarette ) placed at the bottom of the vertical riser. Smoke emitted directly from the

burning coal, and drawn through the cigarette by the air ejector was blown to the test

353
section by a duct flow from a fan speed setting of 5 Hz. Figure 1 2 shows the sensor signals

and alarm computation. Smoke from the cigarette reached the test section and the sensors began to rise at about 70 s. Although all signals rise, the alarm computation stayed low and

neither residential detector alarmed. (In one repeated test, the residential photoelectric

detector did alarm at 500 s)

Cooking oil placed in a glass dish and heated on an electric hotplate (which was placed at

the bottom of the vertical riser) produced a nuisance cooking aerosol. For this test

condition the fan speed was set to 5 Hz. Figure 1 3 shows the sensor signals and alarm

computation for this test. The ionization signal was the first to rise, starting at about 400 s. The photoelectric signal began to rise at 700 s and reached its maximum before 1 000 s. At

about 1000 s, the CO cell voltage began to rise, signifying that the cooking oil was

beginning to pyrolyze. The residential photoelectric detector alarmed at 920 s, while the

residential ionization detector alarmed at 1360 s, and the algorithm computation alarmed at

1435 s. Given the CO production and heavy smoke later in the test, this "nuisance"

cooking source, in reality, transitioned to a hazardous condition that a detector should sense

and alarm.

Smoke from toasting bread was produced by placing two slices of white bread into a twoslice toaster placed at the bottom of the vertical riser and setting the fan speed at 5 Hz. This

toaster was modified by wiring the pop-up mechanism down so that as long as electrical

power was applied, the bread continued to toast. Figure 14 shows the sensor signals and

alarm computation for this test. The ionization signal was the first to rise, starting at about

60 s, followed by the photoelectric signal around 160 s, and the CO cell voltage at 220 s. The residential ionization detector was the first to alarm at 1 3 1 s followed by the algorithm

computation and residential photoelectric detector at 235 s and 238 s respectively. Before

200 s there was no appreciable visible smoke emitted from the toaster, but soon thereafter,

heavy smoke was produced followed by significant increase in CO production between 300

s and 400 s. Similar to the cooking oil test above, the power was left on long enough such

that this nuisance source transitioned to a hazardous condition that should be detected.

354
75 160

c

at ^1

.2 Sl20 '-1 " 2 N Q- c E

o o

80 c E

(0 >-

(0 40

o

Q.

PE on PE off

CO Cell

-*— Ion -B— PE

Alarm

Algorithm

50 100 150

Time (s)

1 10

8 10

-4

(0o

>

6 10 -

3

Q.

4 10 * =

0) U

2 10

O

O

0 10

200 250

Figure 1 1 . Sensor signals and algorithm computation for dust exposure.

Figure 12. Sensor signals and algorithm computation for cigarette smoke exposure.

355
Time (s)

Figure 13. Sensor signals and algorithm computation for heated cooking oil source.

0 100 200 300 400 500 600 700 800

Time (s)

Figure 14. Sensor signals and algorithm computation for toasting bread source.

356
4. Conclusions

By combining photoelectric, ionization, and CO cell sensor signals in a rule-based, flizzy

logic alarm algorithm, a robust, multi-sensor detection scheme suitable for residential

applications was achieved. The alarm algorithm was able to identify emulated fire

conditions earlier than both residential detectors for the smoldering cotton source, earlier

i than the residential photoelectric detector for the flaming fire source, and approximately the

I same time as the residential ionization detector for the pyrolyzing wood source. The alarm

algorithm did not indicate alarm during dust or cigarette smoke exposure, and did indicate

alarm during the transition to hazardous conditions for the pyrolyzing cooking oil and

burning toast sources. The fact that the algorithm employed signals from existing sensor

: technologies already in use in homes today suggests that this approach is viable in the near

^ future.

I 5. Acknowledgements

The authors would like to thank Edwards Systems Technology for the use of their detection

I equipment Part of this work was funded by a contract from the U.S. Dept. of Housing and

Urban Development as part oftheir Healthy Homes Initiative project.

I| 6. References

[1] Heskestad, G., and Newman, J. S., "Fire Detection Using Cross-Correlations of

Sensor Signals," Fire Safety Journal, Vol. 18, No. 4, 355-374, 1992.

[2] Gottuk, D.T., Peatross, M.J., Roby, R.J., and Beyler, C.L., "Advanced Fire

Detection Using Multi-signature Alarm Algorithms," International Conference on

Automatic Fire Detection "AUBE '99", 1 1th, March 16-18, 1999, Gerhard Mercator

University, Duisburg, Germany, Luck, H., Editor, pp. 237-246, 1999.

[3] Ishii, H., Ono, T., Yamauchi, Y., and Ohtani, S., "Fire Detection System by Multij Layered Neural Network with Delay Circuit," 4^*^ International Symposium, Intl. Assoc. for Fire Safety Science, Boston, MA, Kashiwagi, T., Editor, pp. 761-772,

1994.

j [4] Mueller, H., "Rule Based Detection Algorithms," Proceedings of VdS Conf. on

I Gas Sensors for Fire Detection, November 15-16, Cologne, Germany, 2000.

[5] Cleary, T., Grosshandler, W., and Chemovsky, A., "Smoke Detector Response to

Nuisance Aerosols," Proceeding of the 11th International Conference on

I Automatic Fire Detection "AUBE '99", March 16-18, 1999, Gerhard Mercator

University, Duisburg, Germany, Luck, H., Ed., pp 32-41, 1999.

357
David Bomse

Southwest Sciences, Inc., Santa Fe, NM, U.S.A.

A Diode Laser Multigas Analyzer for Advanced Detection of Fires

1. Introduction

An ideal instrument for early fire detection would combine high sensitivity, rapid response,

automatic self-testing and a low false alarm rate while generating information specific to the

location and nature of the fire. Current fire detection strategies in large buildings call for

many point sensors such as standard smoke detectors. In these systems, the false alarm rate becomes unacceptably high because the overall rate is the sum of the false alarm rate from

each sensor. Even if sensors are inexpensive, routine maintenance and testing can be very

costly when hundreds of detectors are in use. Some new systems use multiple point air

sampling and a centralized, highly sensitive smoke detector; this approach reduces false

alarm rates but is expensive to install, particularly in existing structures.

Near-infrared diode lasers allow detection oftrace gases by optical absorption spectroscopy.

It is possible to provide rapid, part per million (or better!) quantitation of combustion

products including CO, HCN, HCl, CO,, CH4, acetylene. HF, and perhaps formaldehyde.

Detection of incipient fires is possible. The approach being studied shows high selectivity

for each gas detected; the concentration of each species is measured separately. False alarm

rates can be reduced dramatically because a positive indication of a fire requires observing

higher than normal concentrations of more than one target gas.

The type of diode lasers useful for fire sensing was originally developed for long haul fiber

optic communications. These miniature devices are rugged, reliable, operate at low power,

and require absolutely no maintenance. Near-infrared diode lasers are compatible with

conventional, inexpensive fiber optics. Thus, we envision a fire sensing system in which the

light from several lasers housed in a central location is distributed via a fiber optic system

to hundreds of remote measurement locations. Feasibility calculations imply that light from

one laser can be distributed among 800 such measurement points.

358
Most implementations of our diode-laser-based fire sensing technique will rely on

simultaneous quantitation of three or four key gases selected from carbon dioxide, carbon

monoxide, hydrogen cyanide, hydrogen chloride, and acetylene. Carbon dioxide is included

because it provides a known non-zero background level that can be used as a continuous

check of instrument operation. This solves one of the problems found with instruments

designed to report rare events: how to provide frequent verification of proper operation?

Carbon dioxide levels will fluctuate, but it is unreasonable to expect the concentration to

drop significantly below 350 ppm. Such a loss of signal would be indicative of system error.

Carbon monoxide and hydrogen cyanide are particularly important because these gases are

implicated in most smoke inhalation deaths. [1-4] Hydrogen chloride is a key signature

compound produced during pyrolysis and combustion of PVC-containing plastics. [5]

Acetylene is a soot precursor. In addition, detection of HCN, HCl, and HCCH is useful for

fire sensing because all of these compounds have extremely low background levels in nearly

all environments. Finally, measuring CO and CO^ levels may also prove useful for indoor

air quality monitoring and can be incorporated into energy conservation schemes for

building-wide HVAC systems.

Initial experiments demonstrated that modulation frequency multiplexing is the optimum

method for simultaneous (or near simultaneous) detection of multiple gases. In this

approach, one laser is used for each gas detected. The output from each laser is combined

into a single optical fiber. It is possible, however, to distinguish the signals due to each

laser-hence the concentration of each gas-by modulating each laser at a slightly different

frequency. Selective demodulation of the photodetector output allows discrimination

among the absorbance signals. This approach is similar to radio where a tuner picks out one

station among a myriad of broadcast signals.

Our current research is building on these results by constructing and testing a prototype

system for simultaneous detection of CO, CO^, and HCN. It should be possible to build

rugged, reliable, multigas sensors for the detection of incipient fires. Output from each laser

can be distributed to hundreds of measurement locations using a fiber optic distribution

network.

359
2. Gas Detection Using Near-Infrared Diode Lasers

For diode- laser-based fire detection, the sensitivity toward each compound is determined,

in large part, by the smallest optical absorbance that can be measured. Absorbance, a, is a

dimensionless quantity defined by Beer's Law, — = where a = o-N^, (I)

where I is the laser intensity reaching the detector, Iq is the laser intensity in the absence of

absorption, o is the absorption cross section, N is the number density of the absorbing

species, and C is the absorption path length. Since absorption cross sections and optical path

lengths are known, measurement of absorbance allows determination of concentration.

Conventional spectrometers such as FTIR instruments or diode array devices can measure

absorbances down to a ~ 10 \ whereas using diode lasers we and other groups[6-19] have

demonstrated minimum detectable absorbances as small as 10'', For the deployment scheme

planned for fire detection, where the laser output is distributed among many measurement

points, the theoretical lower bound is defined by laser/detector shot noise and equals a minimum detectable absorbance better than 10'^ (1 Hz bandwidth). We estimate that each

of the optical measurement locations in our fire detection scheme will achieve minimum

detectable absorbances of 10'^

The detection method we employ with diode lasers is a high frequency version of what is

described in the literature by the terms "wavelength modulation spectroscopy," "derivative

spectroscopy," or "harmonic detection." [6-14] The absorbance measurement is shifted to

high frequency to avoid laser excess (1//) noise. A small sinusoidal modulation is

superimposed on the diode laser injection current. This current modulation produces a

modulation of the laser wavelength, since the laser wavelength is tuned by changing the

current. As the laser beam passes through the sample gas, absorption converts the

wavelength modulation into amplitude modulation. The AM components of the laser

intensity induce synchronous modulation of the photodiode current at the modulation

frequency, /, and higher harmonics, n/. Phase-sensitive electronics isolate and quantify

(demodulate) the photocurrent component at a selected harmonic. The demodulated signal

360
lineshape looks like the n'*' derivative with respect to wavelength of the direct transmission

spectrum. Absolute absorbances are obtained by dividing the demodulated AC signal by the

detector DC output; high sensitivity absorbance measurements are possible using a single

optical beam.

Another important advantage of wavelength modulation spectroscopy is that the technique

responds only to the type of narrow, isolated absorbance features characteristic of gas phase

compounds. Attenuation of the laser beam by particulates, or losses due to scattering do

not generate false positive signals. Since the actual absorbance is proportional to the ratio

of the AC to DC components of the detector output, measurements can tolerate a ten- to

hundred-fold loss in overall light intensity (the DC signal), yet provide accurate gas

concentrations. If the DC level falls below some predetermined minimum, then the

instrument will report an error.

3. Characteristics of Near-Infrared Diode Lasers

Diode lasers are ideally suited to spectroscopic detection of gases because the lasers are

wavelength tunable and are highly monochromatic. Coarse tuning can be accomplished by

varying the laser temperature using an inexpensive thermoelectric cooler, which can be an

integral part of the laser packaging. High resolution wavelength tuning is obtained by

varying the laser injection current. AC modulation of the laser current is a simple method

to implement wavelength modulation.

Gases important for fire detection can be detected using InGaAsP lasers which were

originally developed for long haul, fiber optic communications. These lasers can be

fabricated at any wavelength between 1.2 and 2.0 /xm, and each device has a tuning range

of about ±2 nm. This relatively limited tuning range requires a separate laser for each gas

detected in most cases.

InGaAsP lasers operate at room temperature and are compatible with standard silica fiber

optics and with high efficiency InGaAs photodiode detectors that also operate at room

temperature. The lasers are hermetically sealed in miniature (approx. 1" x V2" x V2") DIP

361
or butterfly IC packages and consume less than 1 00 mWofelectrical power while producing

from 2 to 5 mW of near-infrared light.

Spectral purity of the laser output is important for gas detection applications. The simplest

style of diode laser often emits several nearby wavelengths simultaneously and is not suitable

for the proposed application. In the case of InGaAsP diode lasers, single mode (single

fi equency) operation can be obtained with distributed feedback or "DFE" lasers that include

a grating etched along the gain region. The laser output has a wavelength spread of only

0.003 cm"' compared with gas absorption linewidths of 0.1 cm"'; in contrast, laboratory

spectrometers such as FTIR devices typically show resolution of 1 cm"', or worse.

Extremely narrow laser linewidths allow selective probing of a single absorption line, even

in the presence of many other gases. This point is crucial to the success of the proposed fire

detection technique: wavelength selectivity increases the sensitivity for detecting each target

compound while also minimizing the risk of false alarnis.

CO LASER CURRENT

CONTROLLER

50 kHz

SINE WAVE

CO, LASER CURRENT

CONTROLLER

SWEEP

RAMP

FIBER OPTIC BEAM COMBINER

40 kHz

SINE WAVE

PHOTODIODE

HERRIOTT CELL

LOCK-IN LOCK-IN AMPLIFIER AMPLIFIER

4. Feasibility Demonstration

Figure 1 is a schematic diagram of the

apparatus first used to test

modulation frequency multiplexing

for simultaneous detection ofmultiple

gases. The feasibility study focuses

on simultaneous measurement of CO

and COt with a separate laser used for

each gas. The CO laser is modulated

at 50 kHz while the CO, laser is figure 1 - Schematic diagram of apparatus used

to test modulation frequency multiplexing for the

modulated at 40 kHz. Output from simultaneous detection of two gases.

the lasers is injected into a single

mode fiber using a 2 x 1 fiber optic beam combiner. The resulting light exits the fiber, is

collimated by a graded index (GRIN) lens and directed into the Herriott [20] cell containing

50 torr each of CO and CO.. After traversing 26 passes within the cell (13.9 m). the beam

exits and is focused onto an InGaAs photodiode. Output from the photodiode is applied to

362
a pair of lock-in amplifiers set to operate at 2/; one is referenced to the 50 kHz modulation

source, the other to the 40 kHz source.

Wavelength modulation spectra are acquired by sweeping both laser current supplies and

digitizing the output from the two lock-in amplifiers. The DC currents from each supply are

adjusted such that the peak due to CO appears at the left hand side of each scan with the CO

laser while the CO^ peak appears at the right hand side of each scan with the CO^ laser.

This approach^canning full wavelength spectra and offsetting the positions of the two

peaksis useful for determining signalto-noise ratios and for examining

potential cross-talk between the two

absorbance measurements.

Measurements show that the limiting

noise source is not due to the

multiplexing scheme but to unwanted

optical interference fringes which

change slightly in magnitude during

CO detection

CO, laser OFF

Wavelength

Figure 2 - Modulation frequency multiplexing

detection of carbon monoxide in a 50:50 mixture

the time required to collect all of CO and CO,.

pertinent data. Specifically, Fig. 2

compares CO spectra taken when only

the CO laser was on (lower trace) and

when both lasers were on (upper

trace). The WMS signal corresponds

to a peak absorbance of 2.7%

(measured in a separate experiment).

We define the noise as the standard

deviation of the right hand baseline

region. Spectra in Fig. 2 imply that the

minimum detectable absorbance

CO2 detection

CO laser OFF

Wavelength > Figure 3 - Modulation frequency multiplexing

detection of a carbon dioxide in a 50:50 mixture of

changes from 1.2 x 10 when only the qq ^ind CO,.

363
CO laser is used to 1.8 x 10"^ when both lasers are on. For these data, measurements are

extrapolated from the 20 Hz bandwidth used to a 1 Hz detection bandwidth. In contrast,

the data in Fig. 3. which shows CO, spectra, indicate the minimum detectable absorbance

actually improves from 1 .6 x

10"'' to 0.8 x 10'^ when the CO laser is activated.

Examination of the baseline regions in both sets of spectra. Figs. 2 and 3, shows that regular

interference fringes dominate random noise. This feature is seen most clearly in the lower

trace of Fig. 2. The presence of etalons is consistent with Southwest Sciences' prior

experience with nearly all diode laser applications and explains why we target a minimum

detectable absorbance of 1 x 1 i) ^

( 1 Hz) for field applications even though theoretical noise

limits are ~ 1 x 10 ** for typical laser powers and detector characteristics. More importantly,

these feasibility results imply that there is probably no performance penalty associated with

frequency modulation multiplexing. Additional shot noise associated with having more than

one laser impinging on the detector is exceeded by the etalons.

As the last test of frequency

modulation multiplexing. Fig. 4 CO2

modulation frequency multiplexing.

The spectra acquired to demonstrate

modulation frequency multiplexing {e.g.. Figs. 2-4) were not been scaled to the DC laser

power, Iq in Eq. (1). But, long term operation of commercial instruments would require

dividing the WMS signal by the laser power in order to get the correct absorbance.

Measurement of Iq is straightforward when only one laser is impinging on the detector; we

just measure the total photocurrent passing through the photodiode. For applications of

verifies no measurable cross-talk

between the two demodulations. The

CO and CO, spectra were acquired

simultaneously using the apparatus

depicted in Fig. 2. There is no excess

noise or residual signal due to the CO

peak in the CO, trace and vice versa. Wavelength —

Figure 4 - CO and CO, spectra acquired using

364
modulation frequency multiplexing, we anticipate using an indirect measurement of laser

power: modulation of the laser current introduces amplitude modulation as well as

wavelength modulation, and the magnitude of the amplitude modulation signal is an easily

calibrated measure of the laser power.

5. Construction and testing of a commercial prototype

The next step in demonstrating the effectiveness of the diode-laser-based technology for

early detection of fires is construction of a stand-alone prototype system for field testing.

That work, now in progress, includes simultaneous detection of carbon monoxide, carbon

dioxide, and hydrogen cyanide. Target detection limits for the three gases are 30 ppm for

CO and CO,, and 1 ppm for HCN. Key aspects of the prototype system include:

• Modularitv. Each laser system operates independently while reporting gas

concentrations and operating to a central computer using standard serial

communications protocols.

• Digital signal processing (DSP) technologv. A stand-alone, DSP single board

computer controls each laser system and provides numerical demodulation of the

detector signals as well as data reduction of the resulting wavelength modulation

spectra. The DSP hardware including four analog-to-digital converters (200 kHz,

16 bits), four digital-to-analog converters (also 200 kHz and 16 bits), and two

RS-232 serial ports provides a compact, low cost control and signal processing

platform.

• Line locking. Stable, long-term performance of each laser system is guaranteed, in

part, by using small optical cells filled with the target gases to provide a wavelength

standard for active wavelength control of each laser.

• 1 f power measurements. Accurate measurement of wavelength modulation spectra

requires separate measurement of the 2/ signal and "DC" power components ofeach

laser's contribution to the overall photodiode output. The present approach uses the

amplitude modulation that occurs synchronously with the laser's wavelength

modulation to provide a frequency-dependent signature for measuring separately

each laser's power reaching the photodiode.

365
1f modulation

waveform

pseudo-sine (2f)

pseudo-cosine (2f)

b

i a; c

d

1

; .'+1 ^ 0

-1

1 L2_ 0

-1

Each DSP system is programmed

to act as a digital lock-in amplifier.

The laser modulation waveform is

a modified square wave (the upper

trace in Fig. 5). In theory, the 2/

signal is {a + b) - (c + d) where the

time periods a, b, c, and d are

noted in Fig. 5. In practice, this

simple computation requires no

electronic phase delays between the Figure 5 - Modified square wave modulation

,

,

. - , , waveform (top) and the "in phase" (center) and "out

modulation waveform and the ^ , x , ,

,

• or phase (bottom) demodulation waveforms used for

photodector output. A more useful generating an all-digital wavelength modulation ... spectrum,

approach, and the one used in the

prototype instrument, requires digitizing the detector output at twice the frequency used for

the modulation waveform-indicated by the vertical digital lines-and obtaining the vector

dot products of the digitized signal with a pseudo-sine vector and with a pseudo-cosine

vector. The 2/ signal is the sum of the two dot products weighted by a demodulation phase.

This calculation is repeated at each wavelength step in order to generate a complete 2/

spectrum.

This demodulation approach is applied to both the signals from the reference cell and from

a multipass cell similar to the

Herriott cell used in the feasibility

study. The signal from the

reference cell is used for linelocking; a small offset is applied to

the laser current control in order to

keep the peak of the reference

spectrum at the center of each scan.

Figures 6 and 7 show the CO

reference cell (line-locking) signal

and the sample cell signal for 1000

CO reference spectrum

Wavelength »

Figure 6 - CO reference cell 2/ spectrum used for

line-locking.

366
ppm CO. respectively. The

spectrum in Fig. 7 shows the

effects of unwanted optical

interference fringes (etalons) which

distort the line shape as well as

introduce some high frequency

noise. These etalons reflect

mechanical problems in the

alignment of the fiber optic

collimating lens used to direct the

combined laser beams into the

1000 ppm CO

Wavelength »

Figure 7 - 2/ spectrum of 1000 ppm CO.

sample cell. We anticipate that a modified lens mount should improve alignment stability

and reduce the size of the etalons.

Gas concentration measurements are obtained from spectra such as Fig. 7 by representing

each raw 2/ spectrum as the sum of the "true" 2/ profile, a DC offset, a baseline slope, and

a baseline curvature term. This separation is shown pictorially in Fig. 8. The numerical

decomposition can be performed quickly using a fast multilinear regression cast as a simple

matrix equation:

raw = A ' X , (2)

where A is the "library" matrix with

columns comprising a representative

2/ spectrum, the DC baseline offset,

the baseline slope, and the baseline

curvature, respectively. Vector x

contains the coefficients identifying

the amounts of the four terms that

make up a given raw spectrum.

Solution of the matrix equation (2)

is straightforward.

2/ component

raw spectrum

baseline curvature

Figure 8 - Decomposition ofraw 2/ spectrum into

the target 2/ component plus a baseline slopes and

curvature terms. The baseline offset term is not

shown.

367
jc = (A'"A)~'A^Tflw , (3)

and the matrix (A^ A)"' A^ can be calculated beforehand. In practice, only the first element

of vector x is important - the magnitude of the 2/ component within the raw spectrum -

which is simply the dot product of the first row of (A^ A) ' and the vector raw. The first row of (A^ A) 'A' is stored on disk within the host computer and is downloaded to the

DSP board soon after the RS-232 link is established.

Long term detection limits are presently an order of magnitude worse than the target levels

due to problems in maintaining optical alignment in the sample Herriott cell. We are now

redesigning one of the key optical mounts with the expectation of significant improvement

in pointing stability and, concurrently, gas detection sensitivities.

6. Acknowledgment

This work supported by NIST under Phase 1 and 2 SBIR contract 50-DKNB-7-90149.

7. References

[1] Levin, B. C. and Gann, R. G., in Fires and Polymers, ACS Symposium Series 425,

Nelson, G. L., Ed., (American Chemical Society, Washington, 1990), p 2.

[2] Tsuchiya, Y., Proc. First International Symposium on Fire Safety Science, Grant, C.

E. and Pagni, P. J., Eds., (Hemisphere, 1986), p. 349.

[3] Lundgren, B., and Stridh, G. in Fires and Polymers, ACS Symposium Series 425,

Nelson, G. L., Ed., (American Chemical Society, Washington, 1990), p 35.

[4] Pitts, W. M., Twenty-Fourth Symposium (International) on Combustion, (Pittsburgh,

1992), p. 1737.

[5] Hartzell, G. E., Grand, A. F., and Switzer, W. G., in Fires and Polymers, ACS

Symposium Series 425, Nelson, G. L., Ed., (American Chemical Society, Washington,

1990), p 13.

[6] Silver, J. A., Appl. Opt. 1992; 31: 707-717.

17] Bomse, D. S., Stanton, A. C, and Silver, J. A., Appl. Opt. 1992; 31:718.

[8] Silver, J. A., and Stanton, A. C, Appl. Opt. 1988; 27: 4438.

[9] Silver, J. A., Bomse, D. S., and Stanton, A. C, Appl. Opt. 1991 ; 30: 1505.

368
[10] Bomse, D. S., Appl. Opt. 1991; 30: 2922.

[11] Silver, J. A., and Hovde, D. C, "Near-Infrared Diode Laser Airborne Hygrometer,"

Rev. Sci. Instrum. 1994; 65: 1691.

[12] Stanton, A. C, and Silver, J. A., Appl. Opt. 1988; 24: 5009.

[13] Stanton, A. C, and Hovde, D. C, Laser Focus World 1992; 28: 1 17.

[14] Silver, J. A., and Stanton, A. C, Appl. Opt. 1988; 27: 1914; U. S. Patent No.

4,934,816.

[15] Cooper, D. E., and Carlisle, C. B., Opt. Lett. 1988; 13: 719.

[16] Reid, J., Shewchun, J., Garside, B. K., and Ballik, E. A., Appl. Opt. 1978; 17: 300.

[17] Reid, J, Garside, B. K., Shewchun, J., El-Sherbiny, M., and Ballik, E. A., Appl. Opt.

1978; 17: 1806.

[18] Reid, J., El-Sherbiny, M., Garside, B. K., and Ballik, E. A., Appl. Opt. 1980; 19:

3349.

[19] Cassidy, D. T., Appl. Opt. 1988; 27: 610.

[20] Herriott, D. Kogelnick, R. H, and Kompfner, R., Appl. Opt. 1964; 3: 523.

369
A. Pfefferseder

BOSCH Telecom GmbH, Ottobrunn, Germany

Requirements to Gas Sensors in Fire Alarms for Residential Use

1. Introduction

There are fire alarms for two basically different areas of application: The fire alarms for

professional application and the fire alarms for residential use. The fire alarms for the

professional application are Lisually operated by a central unit, which analyses and

passes the alarms on. Fire alarms for residential use however are single devices for the

application in households, which are operated without central unit. Alerting is effected

by integrated acoustic and/or optical components.

Fire alarms, which are offered for the two areas of application specified above, differ

strongly in their technical level of development. Within the professional area, the

frequency of false alarms could be clearly reduced by the introduction of multiple

sensor alarm units e.g. the combination of light scattering and thermal sensors. The use

of gas sensors as an additional component in multiple sensor alarm units has been

discussed very intensively for several years. This invention will further reduce the rate

of false alarms. For this there are very promising beginnings, which are already

transferred partly into products which are ready for the market.

In contrast to this the technical development of fire alarms for residential use is far

back. The majority of the world-wide used alarm units are of the ionization type, a

smaller proportion are light scattering smoke detectors. The multiple sensor technology

is not represented in fire alarms for residential use so far.

There is a strong motivation for reducing the frequency of false alarms and thus for

using multiple sensor alarm units for residential use. It is well-known that unfounded

alarming can reduce the acceptance for fire alarms and so the owner often deactivates

the alarm unit. To that extent there are serious reasons to increase the reliability of fire

370
alarms for residential use by the application of the multiple sensor technology in

particular also by using gas sensors.

This article considers the requirements for gas sensors, which arise form their use in

private homes.

2. Environmental conditions

When talking about environmental conditions temperature and humidity have to be

mentioned first. In private homes 0°C to +50°C as well as 10% to 90% RH seem to be

sufficient. However if garages are added for example, this values have to be extended at

least to -25°C to +70°C. which is also typical for industrial applications, as well as 10%

to 95% RH.

Besides environmental conditions in operation, also terms of transport are to be

considered. The gas sensor may not be destroyed or damaged during the transport, e.g.

from the manufacturer to the dealer or from the dealer to the users home. Extreme

conditions may occur: The alarm unit can be situated in a car for several days. During a

day in stout sun 100°C can be achieved or -40°C m cold winter nights. These conditions

have to be taken into account, when defining packaging of the detector.

Since a fire alarm unit is a safety-relevant device, it has to get over all these scenarios

without damage or it has to announce its malfunction. The status signal of malfunction

however is only the second best solution.

3. Sensitivity, Selectivity, disturbance variables, deceptive alarms

For a gas sensor for this application it is important that it is sensitive to one or more

combustion gases. At the same time however it has to be insensitive on possible

disturbance \ ariables. The gases CO. CO2, NO/NO2 and H: seem to be suitable for fire

recognition. The following table 1 shows some concentrations of combustion gases

which were determined within fire tests according to EN54.

371
For the application in dwellings the test fires TFl to TF4 are relevant primarily. A pure

plastic fire (TF4) is rather improbable. Textiles (TF3) and wood (TFl/2) will always be

involved. As a result it can be counted on a significant high CO-concentration in fire

situation. CO2 and NO/NO2 occur in higher concentration at open liquid fires than at

cellulose fires.

CO [4] CO [3] CO [5] C02[5] C02[3] N02[3]

TFl 230 90 1800

TF2 50 80 100 880 350 0,15

TF3 300 100 90 1560 600 0,03

TF4 15 19 1500 0,75

TF5 15 27 2100 0,9

TF6 5 25 2000 1,7

Table 1: Measured values of gas concentrations for different ires in ppm [1]

Concerning the measured combustion gases, electrochemical cells, semiconductor gas

sensors, NDIR/DIR sensors, optodes and pellistors are suitable for fire detection.

Semiconductor gas sensors however have problems with reproducible measuring of

small concentrations of CO, as they occur at TF4, TF5 and TF6. Due to their poor

sensitivity pellistors are only suitable for certain fire scenarios with high CO production

(cellulose fires).

NDIR/DIR sensors provide the best selectivity due to the physical measurement

principle of infrared absorption. Optodes and electrochemical cells also provide a very

good selectivity on combustion gases. For electrochemical CO cells in particular the

highest cross-sensitivities are observed on NO/NO2 and H2. These however are also

combustion gases and thus they are no real disturbance variables.

Semiconductor gas sensors and pellistors show a very large sensitivity to all

inflammable gases and vapours. This can lead to substantial disturbance variables by

372
solvents, lacquer vapour, alcohol vapour, cleaning agents and so on and thus cause false

alarms.

At present piezo-electric gas sensors (quartz micro balance) are not suitable for fire

detection due to their sensitivity and spectrum of the measurable substances.

Capacitive and resistive gas sensors only got importance in measuring humidity. So far

suitable gas sensitive coatings for fire detection are missing, for both types (piezoelectric gas sensors and capacitive /resistive gas sensors).

When using CO concentration as an indication for a fire situation, all other sources of

CO potentially cause false alarms. The CO levels, which have to be detected for fire

alarm, are in many cases significantly lower than the Threshold Limit Value (TLV)

(table 1). Figure 1 shows the CO signal when ten persons smoking in a room of 197 m"\

The CO level is about the same as it can be expected at open liquid fires (TF5/6). Only

the combination with an optical smoke detector avoids a false alarm. Other possible

sources for CO are e.g. exhaust gases of vehicles and feature fireplaces.

373
100

a

O

o

time/s

Figure 1: Optical and CO signal when ten persons smoking in a room of 197 m''.

On the other hand the CO-Signal prevents false alarms from dust, steam, fog and other

optical disturbance variables. So an additional gas sensor and in particular a CO sensor

considerably improves false alarm behaviour of an optical smoke detector. But a CO

sensor on its own would be a poor fire detector.

4. Power consumption

A further important point is the power consumption of the gas sensor in operation.

Since fire alarms for residential use are battery powered usually, a very small power

consumption is admissible regarding an acceptable lifetime of the battery.

For this reason all gas sensors, which are operated at high temperatures (semiconductor

gas sensors, pellistors) and therefore use an integrated heating have to be regard

critically. Meanwhile there exist some approaches to reduce power consumption by

micromechanical structure and pulsed operation to make battery operation possible [8].

Particularly electrochemical cells and optodes with a power consumption of some /xW

are best suited for battery-operated devices.

374
5. Operational reliability, Maintenance

Since in the private area no regular maintenance schedule can be supposed, the lifetime

of the sensor must be at least as long as the battery life. This also implicates that fire

alarms for residential use are not periodically checked in a way that the sensor is

actually exposed to test gas. Here the operability of the alarm unit has to be guaranteed

with other measures. This can e.g. be ensured by a self test feature of the gas sensor.

Thus there are two types of self test e.g. for electrochemical cells: Measurement of cell

impedance represents a very good indication for the operability of the sensor. In gas

measuring equipment for industrial application this is usually regarded sufficient. More

sophisticated cells provide the capability to produce hydrogen by electrolysis. This

hydrogen causes a real gas signal in the cell. Thus it is guaranteed that the cell actually

reacts to gas. This self test can be executed regularly and automatically (e.g. once a

day). However it is not suitable for calibrating the cell.

Effects of poisoning by substances from the environment (e.g. silicone) are well-known

for semiconductor gas sensors and pellistors. These may cause insensitivity of the

sensor. The only chance, to recognise such a poisoning is to test the sensor with real

gas. However such a test does not seem to be practicable for fire alarms in private

homes.

6. Conclusion

From technical view only an optical smoke detector in combination with an

electrochemical cell, with a NDIR/DIR-sensor or with optodes seem to be suited for fire

detectors for residential use. The advantage of the NDIR/DIR sensors concerning their

high selectivity must be paid by an accordingly high expenditure for the structure,

which will not be interspersable however for fire alarms in private homes.

Electrochemical cells are high sophisticated and reliable. Optodes on the other hand

offer the advantage that several gases (e.g. three) can be measured with one component,

which is also clearly smaller than usual electrochemical cells.

375
7. References

[1] Oppelt, Ulrich: Auswerteverfahren fiir die Multisensorik fur eine sicherere

Meldungsentscheidung bei Brandereignissen, AUBE 1999

[2] Hippauf, Barbara: Intemer Untersuchungsbericht Institut fiir MeB- und

Automatisierungstechnik, Universitat der Bundeswehr, Neubiberg, 5/95

[3] Linden, 0.:Messungen im VdS Brandraum. BUGH Wuppertal, 10/98

[4] Fischerauer G., Mauder A., Miiller R.: Acoustic Wave Devices

(SAW and BAW) in Sensors Vol. 8, Wiley-VCH 1995.

[5] Grosshandler, William L.: A Review of Measurements and Candidate

Signatures for Early Fire Detection

NISTIR 5555 Building and Fire Research Laboratory, 1995

[6] Hensel, A.: Further development of optode membranes for the selective

detection of combustion gases. VdS-Conference: Gas Sensors for Fire Detection,

Cologne 2000.

[7] Pichlmaier J.: Kalibrierung von Gassenoren in befeuchteter Atmosphare und

Modellierung des Feuchteeinflusses auf kapazitve S02-Sensoren, VDI-Verlag,

Dusseldorf 1994.

[8] Katsuyuki Tanaka, Shin-ichi Matsumoto, Munehiro Ito, Kazuo Onaga:

Development of Semiconductor CO Sensor for Battery Operation, 8'*^

International Fair and Congress for Sensors, Transducers & Systems, Niimberg

1997

376
Currently

used

in

security

systems

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377
Jeffrey S. Goldmeer

Southwest Sciences, Inc., Santa Fe, NM, USA

A Rugeed LED-Based Sensor for Fire Detection

1. Introduction

An ideal instrument for early detection of aircraft cargo compartment fires would

combine high sensitivity, rapid response time, automatic self-testing, and would have a

zero false alarm rate. In addition, such systems could provide continuous data to the

pilots, giving them the ability to monitor the growth and possible extinction of a fire

after activation of the on board suppression system. Current aircraft cargo compartment

fire detection systems, which are primarily smoke detectors, do not meet this ideal.

These detectors have false alarm rates, defined as the percentage of alarms with no

verified smoke in the cargo compartment, as high as 99%, resulting in over 150

unscheduled landings in the last four years [1]. Current gas detection systems also fall

short of the ideal; tests of commercially available CO detectors showed various failures

that included false alarms at low CO levels, and worse, no alarms at dangerous CO

levels [2].

Southwest Sciences is developing a novel optical absorption spectroscopy technique for

fire detection. Key gases produced by incipient fires are detected by optical absorbance

measurements using inexpensive, rugged mid-infrared Light Emitting Diodes (LEDs).

Fully engineered sensors will achieve rapid response with low false alann rates, will be

small lightweight, fully automatic, self-checking, and use little power. The reduction

in false alarms is accomplished by measuring the concentrations of multiple gases in

order to detect incipient combustion. This multi-parameter detection approach has been

examined by NIST [3].

Target gases for the Southwest Sciences' instrument include carbon monoxide (CO),

carbon dioxide (CO2), hydrogen cyanide (HCN) and acetylene (C2H2). Carbon dioxide

is an important gas to monitor because its non-zero ambient concentration allows the

detection system to automatically confirm correct performance. In addition, in flaming

378
combustion carbon dioxide concentrations are well in excess of ambient levels (350

ppm) and can reach concentrations in excess of 1500 ppm [4]. In contrast smoldering

fires tend to produce large amounts of carbon monoxide [5, 6]. Monitoring both CO

and CO2 reduces false alarm rates because the ratio of these gases due to combustion is known [7] and should be significantly different from the ratio due to emissions from

biological cargo. Acetylene has a zero ambient concentration and plays a critical role in

the development of soot [8. 9]. Hydrogen cyanide, which also has a zero ambient

concentration, is produced during the thermal degradation of nitrile containing plastics

and foams [10. 11. 12]. Thus, the presence of either hydrogen cyanide or acetylene

could indicate the occurrence of a fire.

This paper reports on absorbance measurements of CO and CO2 using this innovative

technique and examines the feasibility of detecting hydrogen cyanide and acetylene.

2. Experimental Hardware

2.1 Mid-IR LEDs & Detectors

A benchtop LED-based absorption spectroscopy gas detection system {Patent Pending)

was built with off-the-shelf

components. Compact mid-infrared -—^^^^^K

LEDs that operate at a center

wavelength of 4.6 \im were used as the

light sources for this sensor (Fig. 1). They are inexpensive, rugged, and

compact (~ 5 mm dia.). The LEDs

have a spectral bandwidth of 0.85 \im

(FWHM) and an output power of 1

1

LiW. Each LED package includes a 1 cm diameter parabolic reflector used to

direct the output. Because the

emission bandwidth is much greater Figure 1 - Mid-IR LEDs

379
than the target absorption bands (~ 0.2 - 0.3 microns), bandpass fihers were used to

achieve selective detection of the target species. Three such fihers were used in the

tests reported in this paper: 3.91 - 4.09 |am (reference), 4.16 - 4.45 \xm (CO2). and 4.53 - 4.77 ^im (CO).

Mid-infrared LED output was measured with an infrared detector. Initially a

thennoelectrically cooled mercury cadmium telluride (HgCdTe) infrared detector,

which is well suited for continuous monitoring applications, was used. After a failure

with this detector, a liquid nitrogen cooled indium antimonide detector was used. Both

detectors have good spectral response to 5|im and a 1 .0 mm^ active area.

2.2 Prototype Detection System

A schematic diagram of the gas detection system (Patent Pending) is shown in Fig. 2.

This system uses two LEDs that are each pulsed at lOkHz with a 50% duty cycle. Light

from the LEDs is bandpass filtered and combined using a cadmium telluride (CdTe)

flat, and then focused onto the detector using two plano-convex, calcium fluoride

CdTe Beam Combiner CaF, Lens

LED #2

& Filter Absorption Cell i

LED#1

& Filter \

1

t

Pre-Amplifier

Detector

CaF, Lens

Controller #1

,

Controller #2

Oscilloscope

Generator

Pulse m Lock-In Amplifier

Computer

Patent Pending

Figure 2 - Schematic of the prototype detection system

380
lenses. The light passes through an absorption cell that has a path length of 35.5 cm

before reaching the detector. The pre-ampHfied signal from the detector is sent to a

lock-in amplifier that provides phase-sensitive detection at the LED modulation

frequency. The output from the lock-in can be viewed directly, or captured using an

analog-to-digital data acquisition board in a personal computer.

3. Spectroscopic properties

Spectroscopic detection of CO and CO2 is possible using commercially available LEDs

having a 4.6 fim center wavelength. The V3 band of CO2, centered around 4.27 |iim,

and the CO fundamental vibrational band, centered at 4.67 )im, are both strong

absorption features, and are sufficiently well separated to allow selective detection of

the two gases using bandpass filters in combination with the LEDs, Fig. 3.

2.0E-17 -|

1.8E-17 - 3 1.6E-17 -

0)0 1.4E-17 - E

fM 1.2E-17 - E

0^ 1.0E-17 - c

0 8.0E-18 - 'S0

6.0E-18 - (0

(A 4.0E-18 - (A0ki 2.0E-18 - 0

O.OE+00 -

3.8

CO, filter LED Emission Profile

4.2 4.4 4.6 4.8 Wavelength (microns)

5.2

-- 0.6

C

0-8 -i

(AE

LUQ

LU

"O

0.4 «

N

TOE

- 0.2

5.4

Figure 3 - LED emission profile, CO and CO2 filter bandwidths, and

absorption bands of CO and CO2.

This technique utilizes a broad emission (from the LEDs), covering a large portion of an

absorption band, instead of a single line feature that would be used for a high-resolution

tuned diode-laser. Effective cross-sections can be calculated by integrating molecular

381
line strength data (from the HITRAN database [13]) across a bandpass filter

transmission window:

CTeff = 2 S(T) / Avfilter (1)

Although individual molecular line strengths, S(T), are functions of temperature, the

resulting effective cross-section (cteff) is invariant to temperature over the expected

range (300-lOOOK). The effective cross-section is related to an effective absorbance

(oceff) through the Beer-Lambert Law:

OtEFF = cteff N L (2)

in which (L) is defined as the optical path length, and (N) is the number density of the

absorbing gas. Measured cross-section and absorption values are compared against

experimentally derived data in the next section.

4. Experimental Results

Two techniques were used to measure the concentrations of CO and CO2: direct

absorption measurements using a single LED and differential absorption measurements

using two LEDs.

4.1 Direct Absorption Measurements

In these experiments, measurements were performed using a single LED modulated at

10 kHz, and with a lock-in amplifier set to a one second time constant. For carbon

dioxide, data was obtained with the test cell evacuated (0 ppm CO2) to provide a

baseline (lo), and then filled with air (~ 350 ppm CO2 at 600 Torr). (The ambient

pressure at Southwest Sciences, located in Santa Fe at 7000 feet above sea level, is 600

Torr.) The experimentally determined absorbance (0.17) was within 10% of the

effective absorbance (0.16) computed using Eq. (1) and (2). The experimental

absorption cross-section was 7.1 xlO"'^ cm^/molecule in comparison to the computed

effective cross-section of 6.4 x 10' cm /molecule.

For carbon monoxide, measurements were made with the cell filled with 1 00% nitrogen

(0 ppm CO) to provide a baseline (lo). Calibration data were obtained with varying

concentrations of CO that ranged from 20 to 10,000 ppm. All measurements were made

382
at 296 K and at 600 Torr. Figure 4 shows the measured absorbance data plotted against

CO concentration. A hnear best fit of these data is then used to compute an

experimental cross-section (7.4 x 10""" cm7molecule), which is within 1.3% of the

20 2 numerically predicted cross-section (7.5 x 10" cm /molecule). The excellent

agreement between the experimental and theoretical values validates the numerically

computed effective cross-section and absorbance. Figure 4 also shows the FAA's CO

minimum performance standard, 200 ± 50 ppm [1,14,15].

1.E-01 T

1.E-04 -I

'

^—.

. .

'—.

.

.

I ^ ' . .—.—. . .I 10 ICQ 1000

CO Concentration (ppm)

Figure 4 - Direct absorption measurements and the FAA's CO minimum performance

standard for CO [1,14,15]

4.2 Differential Absorption Measurements

One approach to reducing the fire detector false alarm rates is to increase the detector

sensitivity, making it possible to differentiate between ambient levels and fire generated

concentrations of the key gases. Standard absorption spectroscopy techniques, such as

direct absorption, measure small changes on a large baseline signal making detection of

trace levels more difficult. Our approach uses a novel (Patent Pending) modulation

method that improves detection sensitivity and helps to distinguish among the target

gases and to reject signals due to background gases such as water vapor. This is to be

accomplished by measuring small changes on a zero baseline. Specifically, the goal of

383
this technique is to discern small concentration changes, potentially as small as the limit

set by the intrinsic detector noise.

This technique uses two LEDs (as shown in Fig. 2) pulsed 180 degrees out of phase,

and uses the difference between the two LED intensities at the detector to measure

absorbance by a gas. The LED emissions are filtered to provide a reference signal and

an absorption signal. Initially, the LED input currents are adjusted such that the

detection signal level (lo) is the same for each LED/filter combination with no

absorbing gas present in the cell, as shown in Fig. 5. When the lock-in amplifier

processes these signals, the resuhing output is zero because the signals cancel each

other out. When there is an absorbing gas, such as CO or CO2 present in the cell, the

sample beam intensity decreases while the reference intensity is unchanged and the

lock-in amplifier displays a non-zero output. The technique is effective because it

allows measurements of small absorbances against zero baseline and minimizes the

dynamic range requirements of the lock-in amplifier.

No Absorbing Gas in Cell

Intensity -reference beam

f

Intensity - sample beam

Net signal

time

lo

Absorbing Gas in Cell

Intensity -reference beam

f

Intensity - sample beam

A Net signal

—I 1—1

1—1 r

time

Figure 5 - Differential absorption measurement technique

384
Calibration measurements were made with concentrations of CO that ranged from 1 0 to

10,000 ppm. The differential technique was able to measure a CO concentration of 1

0

ppm, which is lower than the direct absorption measurements. Thus, the differential

absorption technique has a higher sensitivity than the direct absorption technique! All

measurements were made at 296K and at 600 Torr. Figure 6 shows the measured

change in intensity plotted against CO concentration and the FAA's CO minimum

performance standard, 200 ± 50 ppm [1,14,15]. The detection limit for the differential

absorption technique is below the FAA's CO alarm level [14,15] and below the 8 hour

weighted average exposure (40 ppm) established by National Institute for Occupational

Safety and Health [16].

o

1

'coc

G)

-*—> _C

_c

0

D)C

CD O

100

10

>

_J

E

<

<

<

LL ...

tr

1 1 1 1 1 1 10 100

CO Concentration (ppm)

1000

Figure 6 - Differential absorption measurements and the FAA's minimum

performance standard for CO [1,14,15]

4.3 Cross Talk

Successful implementation of Southwest Sciences' fire detection method requires

sensitive and selective detection of the target gases. Selectivity refers to measurement

of one gas in the presence of the others. Our results show trivially small cross talk

between CO and CO? . Filling the sample cell with 0.8 atm of CO2 shows a false

385
positive response equivalent to 50 ppm CO; that means selectivity for CO in the

presence of CO2 is 16,000:1. We expect no detectable false positive CO signals for all

realistic CO2 concentrations. In the other direction, filling the cell with 10,000 ppm

CO gives a false CO2 signal of 77 ppm. This corresponds to a selectivity of 1 30: 1 . The

small false positive CO2 signals are unimportant relative to the large CO2 background:

430 ppm of CO - a substantial CO concentration that will certainly exceed alarm

threshold - would introduce a false CO2 signal equal to only 1% of the 350 ppm CO2

background.

5. Spectroscopic Modeling

5.1 Feasibility of measuring HCN and C2H2

These gases have absorbance bands in the mid-IR that can be reached with

commercially available LEDs and filters. There is strong overlap between HCN and

C2H2 absorbances, which may prevent this technique from selectively measuring these

gases. However, even if these gases cannot be detected separately, monitoring for HCN

and C2H2 is useful because trace amounts of either gas, which have a zero ambient

concentration, would clearly indicate the presence of combustion. Using the validated

numerical simulation, the effective cross-sections for both gases are nearly identical to

each other (6.1 x 10'^^ cmVmolecule and 8.1 x 10"^° cm^/molecule for C2H2 and HCN,

20 2 respectively) and to CO (7.3 x 10" cm /molecule). This translates into a minimum

detectable concentration of similar to that of CO - 10 ppm.

5.2 Interference by Water Vapor

Cross-section calculations, Eq. (1), also allow us to predict potential interference by

background gases. Water vapor, because it is ubiquitous in air and has numerous

infrared absorption bands is the most important background gas. Spectroscopic

modeling computations show that there is no significant interference bv water vapor for

detection of carbon monoxide, carbon dioxide, hydrogen cyanide, or acetylene. These

computations use the spectral line parameters in the HITRAN database [13], which are

386
known to be accurate for these low molecular weight gases (including water vapor) at

temperatures near ambient. Under worst case conditions - 100% humidity at 310 K

which might occur in an open cargo bay on the ground during the summer at a coastal

location - the optical absorbance due to water vapor contributes a false positive signal

of 15 ppm CO. This concentration is below the OSHA maximum workday exposure

[16] and is well below FAA CO minimum performance levels (200 ± 50 ppm) [14,15];

moisture levels during flight will be at least a factor of three lower.

6. Conclusions

A new, innovative technique (Patent Pending) has been utilized for measuring trace gas

concentration. The mid-IR LEDs used in this system are ideally suited for continuous

monitoring in situations that require a small, low-cost alternative to conventional

systems, such as in aircraft cargo compartments. Using multiple LEDs, the system has

the ability to separately measure the instantaneous concentration of multiple gases,

greatly reducing the likelihood of false alarms, and increasing the likelihood for

detecting incipient fires. In addition, continuous monitoring of carbon dioxide, which

has a non-zero ambient concentration, can be used for real-time testing of instrument

performance.

This instrument could be used for early fire detection in aircraft cargo compartments

and other inaccessible locations within an airframe. This type of instrument could be

integrated into a sophisticated aircraft control system, such as the Aircraft Command in Emergency Situations system prototyped by Boeing [17]. The commercial applications

of the proposed instrument extend to fire safety in any location requiring fast and

accurate detection of a fire, including, but not limited to: storage warehouses, industrial

settings, large office buildings, shopping malls, etc. This technology could also be

applied to spacecraft fire safety, providing fire detection for long-duration manned

space missions. Additionally, the same technology used for fire detection can be used

for air quality monitoring, for a wide range of gases in almost any setting.

387
7. Acknowledgements

Dr. Goldmeer would like to acknowledge financial support provided by the NASA

Glenn Research Center's Small Business Innovation Research (SBIR) Program (NAS3-

00058), as well as technical assistance from Drs. David Bomse, Daniel Oh, Jeffrey

Pilgrim, Joel Silver, and Alan Stanton at Southwest Sciences.

8. References

1. Blake, D. Aircraft cargo Compartment Smoke Detector Alarm Incidents on U.S.

Registered Aircraft, 1974-1999. DOT/FAA/AR-TNOO/29, 2000.

2. Hedrick, R. L., and Billick, Irwin. Chamber tests of Residential CO Alarms. Gas

Research Institute 1997.

3. Grosshandler, W. A Review of Measurements and Candidate Signatures for Early

Fire Detection. NISTIR 5555, January 1995.

4. Milke J., and McAvoy T. Analysis of Fire and Non-fire Signatures for

Discriminating Fire Detection, Fire Safety Science - Proceedings. Fifth

International Symposium 1997; 819-828.

5. Pfister, G., Fire Safety Journal, 1983; 6: 165-174.

6. Jackson. M. and Robins, I. Gas Sensing for Fire Detection: Measurements of CO,

C02, H2, 02, and Smoke Density in European Standard Fire Tests. Fire Safety

Journal 1994; 22: 181-205.

7. Milke, J. Using Multiple Sensors for Discriminating Fire Detection. Proceedings -

Fire Suppression and Detection Research Applications 1999; 150-164.

8. Glassman, I. Soot Fonnation in Combustion Processes. Proceeding of the

Combustion Institute 1988; 22: 295-310.

9. Kennedy, I.M. Models of Soot Formation and Oxidation. Progress in Combustion

Science 1997; 23: 95-132.

10. Levin, B., and Gann, R. Fires and Polymers, ACS Symposium Series 425,

American Chemical Society 1990.

11. Lundgren, B., and Stridh, G. Fire and Polymers, ACS Symposium Series 425,

American Chemical Society 1 990.

388
12. Hathaway, G., et al. Proctoer and Hughes' Chemical Hazards of the Workplace, Van Nostrand Reinhold, NY, 3"^ ed. 1991.

13. L. S. Rothman, et al. Journal of Quant. Spectrosc. Radiat. Transfer, 1992: 48.

14. Cargo Compartment Fire Detection Instruments. Federal Aviation Administration

TSOClc, 1987.

15. Cargo Compartment Fire Detection Instruments. Society of Automotive Engineers,

Inc., (SAE) Aerospace Standard (AS) Document No. AS 8036, 1985.

16. Recommendations for Occupational Safety and Health. Department of Health and

Human Services (NIOSH) Publication No. 92-100, 1992.

17. Reynolds, T.L. Improvements in Aircraft passenger Safety: High Costs,

Diminishing Returns. Interior Safety of Passenger Transport Seminar, Institution of

Mechanical Engineers, London, England, November 1992.

389
U. Oppelt

BOSCH Telecom GmbH, Ottobrunn, Germany

Measuring results of a combined optical, thermal and CO detector

in real sites and classifying the signals

1 Introduction

One of the major problems of fire detectors is the number of deceptive

alarms. Though the detectors themselves are not unreliable, they are

sensitive to other aerosols and their large number in the field can lead to a

significant amount of false actions of the fire brigades. It is fact, that

deceptive signals, mainly caused by aerosols or dust, without any danger of

fire, happen much more often than real fire situations. Various statistics

prove, that fire brigade actions preponderate for other reasons than fire.

The number of deceptive alarms depends on the application and the

installation site, but there is also a human factor in not properly recognizing

critical installation areas. The aim is, to find solutions for a better enhance

disturbance behavior of today's detectors. For special applications like fire

detection in airplanes or in coil processing plants, there are already

sophisticated special solutions. But these solutions cannot be transferred

easily to general purpose applications.

The range of applications for fire detectors can be broadened by using

additional information from the signals provided by the fire accident. This

additional information can be obtained from time analysis of the smoke

density signal, from information on the installation site or from additional

signals of other sensors. This paper reports on measurements with a new

fire detector that comprises a light scattering detector, a temperature sensor

and a CO-gas sensor.

390
2 Influence of variations of light scattering on the signals from a

light scattering detector

Figure 1 shows the electrical signal of a light scattering detector over a time

course of 9 hours in a normal environment without any signal processing or

filtering.

intensitN/

3 i 6 Z ID Fig. 1: Light scattering signal in normal environment

An analysis of the signal with a -test shows, that a Gaussian distribution

can be assumed for this sample. With an typical signal to noise ratio S/N=20

and sample rate of one second the threshold will be exceeded once in 2 x

10"^^*^ years. This dramatically changes with signals sampled from a

susceptible environment. Fig. 2 shows for the signal taken in a smokers

room as an example.

amplitude/digits

13 0

12 0

110

10 0

S 0

8 0

7 0

li I \

[ I 1 I I I I L {t /h]

1 0 1 s 2 0 2 S

391
Fig. 2: Raw signal from a smokers room

Distinct signals peaks are recognizable in the distribution density diagram.

The values around zero have been suppressed, to highlight the distribution

of the disturbance signal.

frequency

10

ampl/

digits

10 30 50

Fig. 3: Distribution density from signals of a smokers room

To estimate the probability of a deception alarm, the distribution density

was approximated with a logarithmic normal distribution density function.

density

• h

—

\

4

: : : r—

1

ampl./

digits

>

1 20 30 40 50

Fig. 4: Approximation of the measured distribution density with a

logarithmic normal distribution density function

392
With this model one can estimate for a given alarm threshold, when a false

alarm will occur.

12r

time to deception alarm

10-

threshold

120 140 160 18 0 20 0

Fig. 5: Schematic rate of deception alarms depending of the alarm threshold

It can be seen, that the deception alarm rate decreases more than

proportional. Doubling the threshold lowers the false alarm rate by a factor

of 10. This consideration was done with raw signals, still without any

aspects of signal analysis, and shows, what improvement can be expected

using additional information to raise the threshold for the optical alarm

signal. So the use of more than a single sensor is an attractive possibility for

obtaining additional information. To get an impression about the usability

of combined sensors in fire detection, a new detector comprising a light

scattering chamber, a temperature sensor and a CO gas sensor was

investigated with test fires of EN54 and some additional defined fires and

artificial produced disturbances.

The CO sensor was an electrochemical cell. The properties of

electrochemical cells have been significantly improved over the last few

years. The life time of today's cells is specified with more than 5 years and

the costs lie in a range now, making their use in fire detectors attractive.

The selectivity is sufficient and the power consumption is low in contrast to

393
metal oxid sensors, which need power to heat them. To analyze the new fire

detector further, it has been analyzed under different environmental

conditions for a longer time beside the pure fire tests.

3 Fire tests

The new detector was tested in a fire room with standard test fires. Table 1

shows the CO sensor values at the end of the fires defined by EN54. In case

of a fire situation a significant concentration of CO is expected, at

smoldering fires (TF 3) it is particularly high (Fig 6).

intensity

H! smoke

gas

r

500 600 700 800 900 t/S

Fig. 6: CO and smoke signal of smoldering cotton fire (EN54)

One has to be aware that CO can also be measured at other occasions than a

fire, e.g. in environments with cars with operating engine.

394
Test fire according EN54 CO-concentration at the end of fire in

ppm

TFl open wood fire 20 ... 35

TF2 smoldering wood fire 10 ... 47

TF3 glowing smoldering cotton fire 130

TF4 flaming plastics (polyurethane) 25

TF5 flaming liquid, n-heptan 14 ... 33

TF6 Aethanol 1,5... 10

TF7 Decalin 16 ... 27

cable fire 3 ... 6

paper bin fire 35

Table 1: CO-concentration at standard fires

Installations at real sites should reveal, therefore, which signals are to be

expected, and whether the combination of the values of the scattering,

temperature and CO concentration signals can give information about the

origin of the sensor output.

4 Measuring arrangement

To analyze different applications for the new fire detector, it was exposed to

various influences. In some situations the optical detector may react

insensible, while the other sensors will be very reactive and vice versa. The

list below shows some critical factors, to which a fire detector has to adapt

in normal environments.

- cigarette smoke

- draught in entrance areas

- heater fans in industrial halls

- high humidity, dew

- fog

- all kind of dust (color, particle size)

395
- various climates and temperature cycling (operating < 0°C)

- fibers

- insects

- damp, smear films, solvents

- gasoline gases

- emanation in plastic manufacturing

- direct sunlight

For the selection of test sites as much different surroundings as possible

were chosen. The results presented here include a smokers room, a welder

room, a hall with open sides to the free air, a repair garage for cars and a

normal industrial environment. Fig 7 shows the data acquisition system.

The fire detectors were installed together with a customary fire panel. They

sent all the actual sensor values on demand. The data were collected about

every 5 minutes, depending from the number of detectors in the loop, and

are temporarily stored in the fire panel. In case of an alarm or a pre alarm

(75% threshold) the measurement ensemble was immediately transmitted,

in order not to loose the values of the alarm situation. Furthermore the

values of this special detector

analogue

values

combined

fire

detector

PC

control with

panel ISDN

interfacs

ISDN

sen/er

Fig. 7: recording of measuring values

in the loop were sampled with a higher sampling rate. Every hour, the

stored values were transmitted to a personal computer, where the data were

packed and saved in a sequential file on a mass storage. Once a day, these

data were transmitted via an ISDN data link to a server at the development

396
location for further data analysis. For a detailed evaluation some data

collection was also done in real time.

5 measuring values

The following figures show typical curves from the critical application areas

with disturbance signals. A closer view at the data of the welding cabin

shows, that the optical and CO signals are mainly short peaks. This could

not only be seen in amplitude analysis but also in a time analysis. Specific

experiments in the fire room show, that the emission of CO is less with

electric welding than with autogenious welding.

intensity, temerature/°C, CO/ppm

pollution

temperature

Thu 2 Fri 3 Sat 4 Sun s Mon

Fig 8: Measurement curve in a smokers room for 4 days

397
intensity, temperature/°C, CO/ppm

120

100

80

60

40

20

pollutio n

tempe "ature

1 4

J- IU- 1 V r-\^

t/dayj

1.5 3.5 4.S Fig. 9: measurements in a welding cabin

Contrary to welding, the cars driving in and out of a repair garage gave a

significant CO signal. The values are higher than the values at the majority

of the test fires. It is interesting, that the light scattering signal does not

show significant amplitude values.

intensity. ternperature/°C. CO/ppm

2 3 4 Fig. 10: signal curve in a garage

t/days

398
All shown disturbance values were especially chosen, because aerosol as

well as CO occurs simultaneously even though in different amplitudes.

However many disturbance effect only the optical part of the fire detector.

For this belongs beside the general creeping dust accumulation, making the

detector more unstable, all kind of dust and foggy substances corrupt the

intensity. temperatur/^C. CO/ppm

pollution

1 If!

J CO

\

\

V Mb- V

\ te npenature

. \ 500 1000 1200 1400 1600

optical signals. An extreme example is an experiment with disco fog (Fig.

11). Similar to dust, there is no CO signal as well as there is no increase in

temperature.

Fig. 11: experimental measuring with disco fog

6. Classification of the signals

The single signals of the detector have been divided into rough classes. The

classes were named as "nothing", "some", "more", "much", "very much".

"Nothing" means, there is no signal which can be assigned to the event,

"some" means a signal in accordance to the event, but it is to small to make

a decision on this signal without a priori knowledge. "More" means a value

near but not reaching the alarm threshold. "Much" stands for alarm and

"very much" is a value far beyond the alarm. The presence of disturbance

signals increase the risk of false alarms dependent on the probability of

399
their occurrence. In table 2 it is seen, that at all test fires except the alcohol

fire CO is generated in a measurable amount. Therefore it seems, that CO

could be used for the validating a fire situation, but contrary it cannot be

concluded that it is possible the CO signal alone for suppressing disturbance

signals. With disturbance signals like cigarette smoke and welding CO

concentrations are measured similar to some test fires. The combination of

the light scattering signal and the CO signal is also not clear enough to

make a distinction between fire and deception. The temperature and the

optical signal cannot distinguish clearly between smoldering fires and

disturbance signals caused by dust and aerosols. It is the nature of the

deception signals caused by combustion processes have a similar behavior

as caused by injuring fires.

400
event scatter signal temperature CO signal remarks

TFl some more more

TF2 very much nothing much

TF3 very much nothing very much

TF4 much some more

TF5 much more more

TF6 nothing very much nothing

TF7 very much some more

dico fog very much nothing nothing

cigarette some nothing more CO cone,

similar to

TFl

welding

autogenious

some nothing/some more CO cone, to

TFl

welding

electro

much nothing some

car garage nothing nothing very much high covalues

diesel

aggregate

some nothing very much

Table 2: classifying of signals

Fig. 12 shows the recording of value triples. The large points show the

values of the test fires, the smaller points represent the values of

disturbance signals. In this three dimensional graph the disturbance signals

could be separated from the fire signals, there is no overlap. Disturbance

signals with a high amplitude in only one category could be suppressed with

clever positioning a threshold area. The obtainable gain can be estimated

from fig. 5 and is expected to be in one order of magnitude.

401
scattering-value s

" 0 4

Fig. 12: Classifying of disturbance and fire signals (small points:

disturbance signal, large points: signal from fire)

7 Conclusion

Measurement triple of new fire detector combining optical, temperature and

CO sensors were be presented. Using all three signals in environments with

disturbance signals caused by dust and aerosols a significant improvement

of disturbance rejection can be expected. Deception signals caused by

combustion processes resemble real fires with a high grade. Improved

rejection of deception signals could be reached by selecting a threshold area

between the different triple values of fire and non fire signals. Not

mentioned in this report was the potential to validate signals by a time

domain analysis. With the a priori knowledge about the application field,

the detector combining optical, temperature and CO sensors promised a

significant improvement in reliability in many disturbance areas.

402
GroBer. J. : Kubon, C. ; Linden, O. ; Holemann, H.

Bergische Universitat - GH Wuppertal

Department of Safety Engineering (Fachbereich Sicherheitstechnik); Section for Fire

and Explosion Protection (Fachgebiet Brand- und Explosionsschutz)

Measurements for fire detection by mean of gas sensors in an insulation material

factory

Abstract

The characteristics of fire detection systems is improved by a proper application of gas

sensors. Particularly in sun^oundmgs, where dust or smoke produced by industrial

processes cause false alarms in conventional smoke detectors, gas sensors show

advantages.

Analysis of the background level in comparison to the firesignals is necessary in many

fields of apphcation. As an example tests carried out in a plant producing pulp-based

insulation material are described. As a result statements on the suitability of gas sensors

for fire detection are maked. Investigations on long therm stability started.

1 Einleitung

Gassensoren haben sich bereits in vielen verschiedenen Anwendungsgebieten etabliert,

so z.B. in der Klimatechnik, zur Messung explosionsfahiger Atmospharen oder zur

schnellen Gasdetektion. In der Brandmeldetechnik konnte sich diese Technologic bisher

nicht durchsetzen, obwohl es bereits 1983 Ansatze fur den Einsatz von

Halbleitergassensoren in der Brandmeldetechnik gab [1]. Dies liegt zum einen am

relativ hohen Preis der Sensorelemente und zum anderen an der geringen

Langzeitstabilitat, dem problematischen Ansprechverhalten sowie dem hohen

Stromverbrauch der meisten Sensoren.

Durch die konsequente Weiterentwicklung der Sensortechnik in den letzten Jahren ist es

jedoch moglich geworden, preiswerte und zuverlassige Gassensoren mit immer

geringerem Energieverbrauch in groBen Stuckzahlen anzubieten. Hieraus eroffnet sich

403
die Moglichkeit, freigesetzte Gase zur Detektion von Branden zu nutzen. Versuche im

Brandraum des VdS Schadenverhiitung, Koln haben gezeigt, daB Gassensoren als

Detektorelemente bei sich langsam entwickelnden Schwel- und Glimmbranden

wesentlich schneller und signifikanter ansprechen, als herkommliche Brandmelder auf

Basis einer Detektion von Flammen, Rauch oder Warme [2]. Die Freisetzung von

Gasen bei verschiedenartigen Branden, wie z.B. Zigarettenglimmbrand und offenen

Benzinbrand, ist ebenso untersciiiedlich wie charakteristisch. Durch die simultane

Erfassung und Auswertung verschiedener Brandgase kann eine Tauschung der

Brandmelder durch brandahnliche Vorgange, wie z.B. SchweiBen oder Rauchen,

verhindert werden. Erfolgreich eingesetzt werden solche mit H2-, CO- und NOxsensitiven Sensoren bestuckten Melder bereits in Bekohlungsanlagen von

Braunkohlenkraftwerken.

Der Einsatz von Gassensoren in der Brandmeldetechnik bringt jedoch auch Probleme

mit sich. Die Langzeitstabilitat vieler Sensoren ist noch relativ unbekannt. Der EinfluB

von aggressiven Dampfen und abgelagerten Stauben auf den Sensoren ist ebenfalls

wenig untersucht. Fur eine sichere Branddetektion ist weiterhin der Abstand zwischen

den betriebsbedingten Hintergrundsignalen und den vom Brand verursachten

Signalpegeln entscheidend. Untersuchungen zu diesem „Hintergrundrauschen" von

Gassensoren in den verschiedensten moglichen Einsatzgebieten sind bisher nicht

bekannt. Diese Probleme sollen im Rahmen des Forschungsprojektes "Untersuchungen

iiber den Einsatz von Gassensoren in der Brandmeldetechnik" an der Bergischen

Universitat - GH Wuppertal bearbeitet werden [3].

2 Ziele des Projekts

Um die Vor- und Nachteile der verschiedenen Sensorprinzipien herauszufinden, wird

durch Langzeitversuche mit Gassensoren und herkommlichen Detektoren das

„Hintergrundrauschen" in verschiedenen potentiellen Anwendungsbereichen erfaBt.

Hierbei sollen zum einen die Signale von brandgasspezifischen Sensoren (CO, CO2, H2,

NOx, CH4) in der Umgebungsluft erfaBt werden. Zum anderen werden Stor- und

TauschungsgroBen, sowie Sensorgifte in den Umgebungen ermittelt, der

404
Wartungsaufwand untersucht und das Verhalten der Sensoren bei unterschiedlichen

klimatischen Bedingungen (Temperatur und Luftfeuchtigkeit) beurteilt. Stor- und

TauschungsgroBen konnen beispielsweise auch Abgase von Kraftfahrzeugen oder

Gabelstaplem sein.

Gleichzeitig zu den Messungen des „Hintergrundrauschens" wird eine

Literatun^echerche zu den Hintergrundkonzentrationen in der allgemeinen

Umgebungsluft sowie in verschiedenen Anwendungsbereichen durchgefuhrt. Hierbei

werden vor allem Messungen des Landesumweltamtes und des

berufsgenossenschaftlichen Instituts fur Arbeitsschutz (BIA) zu Hilfe genommen.

Da an den MeBstandorten neben dem Hintergrundrauschen im allgem. keine Brande

aufgezeichnet werden konnen, werden mit der MeBapparatur typische Brande sowie

genormte Testfeuer in Brandraumen aufgezeichnet und mit dem „Hintergrundrauschen"

an den MeBstandorten verglichen.

Um die bei der Interpretation der MeBergebnisse vermuteten Querempfindlichkeiten

und TauschungsgroBen naher zu untersuchen, werden die Sensoren in einer

Priifkammer gezielt mit Tauschungssubstanzen beaufschlagt. Dabei handelt es sich um

Substanzen, die zum Teil schon bekannt sind, zum Teil aber auch aus den

Langzeitversuchen heraus vermutet werden.

3 Aufbau der MeBapparatur

Fur die Durchfiihrung dieser Langzeitversuche werden die verschiedenen

Sensorprinzipien in unterschiedHchen baulichen Ausfiihrungen eingesetzt. Im einzelnen

kommen folgende Sensoren zum Einsatz:

Sensor Zielgas QuerempfindlichkeitenMeBbereich Hersteller

Besonderheiten

Halbleitersensor mit

Zinndioxidsubstrat

(l.SnOz)

NOx;

O3

Alkohole;

HC

ppm Herst: UST [4]

Interdi gi tal strukturen

der Substratkontakte

Halbleitersensor mit

Zinndioxidsubstrat

(2. Sn02)

HC

NO2

NH3 ppb - ppm Herst: UST [4]

405
Halbleitersensor mit

Galiumoxidsubstral

(Ga203)

H: CH4;

Losimgsmittel

ppm Herst: SIEMENS [51

Hochtemperatursensor

fiir Oberwachung von

Gasturbinen

elektrochemische

Zelle (EC-CO)

CO Ethanol;

H2

0-500 ppm Herst: Sensoric [6]

elektrochemische

Zelle (EC-NO)

NO keine

Angabe

0-100 ppm Herst: Sensoric [6]

elektrochemische

Zelle (EC-NH3)

NH3 keine

Angabe

O-lOO ppm Herst: Sensoric [6

Infrarot-MeBzelle mit

Referenzdetektor

(IR-C02)

CO2 0-5000 ppm Herst: Sensor Devices

[7]; Verwendung zLir MAK-Uberwachiing

Infrarot-MeBzelle mit

Referenzdetektor

(IR-HC)

HC 0-4 % Herst: Sensor Devices

[7]; Verwndung zur UEG-Uberwachung

Zusatzlich wird auf jedem Sensoran^ay ein Rauchmelder nach dem Streulichtprinzip mit

separatem Ausgang der MeBgroBen genutzt. Weiterhin gemessen werden Temperatur

und relative Luftfeuchte.

Alle Sensoren sind in einem

Sensorarray gemaB Abb. 1

zusammengefaBt. Die einzelnen

Sensoren des Arrays werden an

einen MeBkoffer angeschlossen,

der bis zu 2,5m abgesetzt vom

Sensorarray angeordenet werden

kann und die komplette Sensorelektronik enthalt. Die MeBapparatur wird iiber einen Lowcost

Industriebus (M-Bus) an einen

Personalcomputer angeschlossen.

Der M-Bus ist eine protokollunabhangige Zweidraht- Schnittstelle mit integrierter Femspeisung

fur den AnschluB mehrerer mikroprozessor-basierter Endgerate.

Abb. I - Sensorrray und MeBkoffer

406
4 Anwendungsbereiche

Die Umgebungen, die mit dem MeBsystem untersucht werden, umfassen sowohl

allgemeine Anwendungsbereiche wie Biiros, Tagungsraume und Heimbereiche als auch

spezielle Anwendungsbereiche. In den allgemeinen Anwendungsbereichen wird

besonderes Augenmerk auf Raucher- und Nichtraucherbereiche und die Einflusse von

Kopierem, Drucker und Reinigungsmitteln gerichtet. Als spezielle Anwendungsbereiche werden Bereiche der Lagerwirtschaft, offentliche Gebiiude mit hohen

Menschenansammlungen und industrielle Fertigungseinrichtungen mit rauhen

Umgebungsbedingungen mit uberduchschnittlicher Staub- und Aerosolbelastung

untersucht. Es handelt sich hierbei um Bereiche, in denen herkommliche Brandmelder

groBe Probleme bereiten.

Zur Zeit sind bzw. waren MeBapparaturen in einem Motorenpriifstand, in BUro-, Laborund Heimbereichen, in der Automobilindustrie (Harteanlagen. Erodieranlagen,

Emulsionsaufbereitung, Garagen), iiber verschiedenartigen SchweiBarbeitsplatzen und

in einem Dammstoffwerk installiert. In dem folgenden Abschnitt werden exemplarisch

die Ergebnisse der Messungen in einem Dammstoffwerk vorgestellt.

5 Untersuchungen in einem Dammstoffwerk

5.1 Dammstoffe

Die Dammplatten bestehen aus Altpapierflocken, die mit Jutefasem verstarkt werden.

Zudem werden diverse Zusatzstoffe in geringen Mengen beigegeben: Ligninsulfonat

wird als Bindemittel eingesetzt, Tallharz und Aluminiumsulfat wirken

wasserabweisend, ein Borsaure/Boraxpuffer dient einerseits als Brand- und

Glimminhibitoren, andererseits auch als Pilzschutzmittel (Fungizid).

Herstellung:

Altpapier wird zerkleinert und im Wirbelstrom mit Borax und Borsaure vermischt. Die

Jutesacke werden zu Fasem zerrissen, danach werden Aluminiumsulfat, Tallharz und

Ligninsulfat beigegeben. Die Komponenten werden vermischt und in einer

407
VerfahrensstraBe zu Flatten gepreBt, mit heiBem Wasserdampf getrocknet und

formaliert.

Beim Zerkleinem des Altpapiers und beim Zerkleinem der Dammstoffplatten sowie im

gesamten Verfahrensablauf entsteht viel Staub. Im Laufe einer Woche bildet sich

schnell eine Staubschicht von 3 bis 5cm. Vor allem auf den Stahltragem lagert sich der

fertigungstechnisch bedingte Staub ab. Am Wochenende kommt es zeitweise durch

Vogelflug zu Staubaufwirbelungen, durch die ein Fehialarm der lonisationsmelder

verursacht wird. Die Nutzung von Gassensoren als zusatzHches

Branderkennungskriterium erscheint daher als sinnvoll.

Die Aniagen starten bei normaler Produktionslage gegen 8.00 Uhr morgens. Zur

Mittagszeit werden die Aniagen voriibergehend heruntergefahren und laufen nach

Beendigung der Mittagspause bis ca. 18.00 Uhr.

5.2 Branderkennung

Die Branderkennung erfolgt zur Zeit iiber lonisationsrauchmelder, die an eine

Brandmeldeanlage angeschlossen sind. Ein eventueller Alarm lauft tagsuber (wahrend

der Betriebszeiten) bei dem Pfortner und nachts direkt bei der Feuerwehr auf.

5.3 MeBorte und Installation

Im Dammstoffwerk wurden zwei MeBapparaturen installiert. Ein Sensorarray wurde

iiber einer Miihle zum Recycling von Altpapier aufgehangt. Die Papiermiihle stellt den

kritischsten Ort in diesem Dammstoffwerk dar. Bei diesem Verfahrensschritt kommt es zum groBten Staubanfall innerhalb des Produktionsprozesses verbunden mit starken

Vibrationen. Das zweite MeBsystem wurde in dem sog. Technikum aufgebaut, wodurch

Vergleiche zwischen den Hintergrundsignalen zweier ahnlicher Aniagen und den

MeBwerten von Testbranden gezogen werden konnen. Das Technikum stellt die

Versuchsabteilung dar, in der die gesamte Produktionsanlage verkleinert noch einmal

aufgebaut ist (Abb. 2), um Untersuchungen zur Verbesserung der Dammstoffe und der

Produktionsprozesse durchfuhren zu konnen. Dort werden insbesondere auch

408
verschiedene Stoffe zum Brand- und Glimmschiit/ getestet. woz.u Briindc in dt-n

Dammstoffen ausselost werden.

Abb. 2 - MeBapparalLir im Technikiim

5.4 MeBergebnisse

Die Abbildungcn 3 und 4 zeigen eincn i\pischen Wochen\eiiauf ausgewiiMier Sensorsignalc im Technikum init cinem Tcsibrand am Montag urn 13.00 Uhr. Hieraus werden

die Signalabstilnde sowie die Klassifikation der Sensoren abgeleitet. Auf der hnken

Ordinate ist leweils das Sensorsignal Libei" der Zeit aufgelragen. Die rechte Ordinate gibt

die aquivalenten Konzenirationen flir die ermittelten Sensorsignaie bezogen auf das

Zielgas dcs jeweiligen Sensors an, denen der Signalpegel bei Beaufschlagung mit dem

reinen Gas entsprechen w Lirde.

Das oberste Diagramin \on Abb. 3 zeigl den Wochenverlauf des Ga:0-,-Sensors. Da

Wasserstoff em t\pisches Brandprodukt isl. sollte dies auch im Projektverlauf niiher

untersuchl werden. Es ist jedoeh in diesem Anwendungsfall ein unerwaiteter Absinken

des Leitwertes zu beobachlen. das sieh nur geringfugig von den Hintergrundsignalen

unlerscheidet. Es kann davon ausgegangen werden, dafi wahrseheinlieh NO\ deleklieil

409
wurde. Der ansonsten relativ starke Hintergrundpegel ist duch das Cracken von organ.

Hilfsstoffen (Losungsmitteln) an der heiBen Sensoroberflache zu erklaren.

Der erste Sn02-Sensor besitzt eine hohe Sensitivitat fUr das reduzierende Gas NOx, was

sich in der kurzzeitigen Leitwertabnahme zeigt. Der auf das Brandereignis folgende

Starke Leitwertanstieg ist auf das Freiwerden von Kohlenwasserstoffen durch den

Glimmbrand zuruckzufuhren. Kohlenwasserstoffe sind typische Brandprodukte bei

Schwelbranden und eignen sich

1 ,0

0,8

0,6

0,4

0,2

0,0

— Ga203 sensor:

Conductivity [mS]

equiv. H2 concentration (ppmj

60

40

20

— 2. Sn02 sensor:

Conductivity [mS]

equiv. NH3 concentration [ppm]

equiv NOx concentration [ppb]

50

40

on

1 u

c

0 o

1600 (0c

1200 0

O

800 o

o

w

400 CO

0 To

>

320

O"

0) 240

160

80

0

80

60

40

E

20

0

Sat. Sun. Mon. Tue. Wed. Thu. Fri. Sat. Sun.

Abb 3 - Signalverlauf der Halbleitersensoren

deshalb sehr gut zur Detektion in

rauher Atmosphare.

Ammoniak bzw. Amine sind

ebenfalls Gase, die durch ihr

oxidierendes Verhalten auf der

Sensoroberflache sehr gut zur

Detektion von Branden genutzt

werden konnen. Einen

entsprechenden zur Ammoniakdetektion optimierten Sensor

stellt der zweite Sn02-Sensor

dar. Auch hier hebt sich das

Signal des Brandes deutlich vom

Hintergrundsignal ab. Die

zyklischen Schwankungen der

Signale der Halbleitersensoren

sind auf Anderungen der

absoluten Luftfeuchte zuriickzufuhren. Diese Beeinflussung

wurde bereits mehrfach nachgewiesen. Die Temperaturschwankungen konnen als

Ursache ausgeschaltet werden,

da die Sensoren temperaturgeregelt sind.

410
CD 0 CO 3000

2500

2000

1500

1000

500

0

c

10

Ui 8

O

o 6

0

0 4

a

CD 2

o

0

E

CO

Current of electrochemical cell [nA]

"

•equiv. NHS concentration [ppm]

A i.il..n

m.

- Light obscuration module [%/m]

/Vlarm- threshold

[M Ml

Sat. Sun. Mon. Tue. Wed. Thu. Fri. Sat. Sun.

0

CC 18 >

15 cr

CD 12

9

6

3

0

c

10 D)

CO 8

o

6 o

CD 4 CD T3

2 CD

0

O

E

CO

Abb 4 - Signalverlauf der elektrochemischen

Zellen und des Rauchmelders

In Abbildung 4 sind die Signale

der elektrochemischen Sensoren

als zweite Gruppe der

Gassensoren dargestellt. Als

erstes Diagramm ist der

Signalverlauf einer NOempfindlichen Zelle dargestellt.

Zwischen dem Brandereignis

und den sonstigen Hintergrundsignalen ist ein sehr hoher

Abstand zu sehen. Das langsame

Abklingen des Brandsignals laBt

vermuten, daB nach dem Brand

nicht alle Schwelriickstande

beseitigt wurden, so daB es in

der Folgezeit zu weiteren Ausgasungen kam.

Die elektrochemischen Sensoren

fiir CO und NH3 besitzen die

groBten Signalabstande bei

diesem Testfeuer.

Um die gewonnenen Daten mit

dem Verhalten der bisher

ublichen Brandmeldetechnik

vergleichen zu konnen, wurden

die MeBwerte des OT-Melders

ebenfalls aufgezeichnet. Der

Temperaturverlauf ist im untersten Diagramm von Abbildung 3 dargestellt und zeigt

wahrend des Brandes keinen signifikanten Anstieg. Die Rauchdichte dieses Melders 1st dem untersten Diagramm von Abbildung 4 zu entnehmen. Darin ist der ubliche

Alarmbereich dieser Melder eingetragen. Die Alarmschwellen liegen herstellerabhangig

411
zwischen 3 bis 6 %/m. Deutlich zu erkennen ist eine Alarmauslosung wahrend des

Brandereignisses. In der dargestellten Woche hatte dieser Melder jedoch zusatzlich 14

weitere Alarme ausgelost. Zu diesem Zeitpunkt war dieser Melder 3'/2 Monate

installiert.

Die Signale der InfrarotmeBzellen soUen hier nicht weiter dargestellt werden, da sie

keinerlei charakteristisches Verhalten auf das Brandereignis zeigten. Dies liegt beim

C02-Sensor an den stark schwankenden Signalpegeln wahrend des Betriebes. Die CO2-

Konzentration ist abhangig von Luftung und der Anzahl der Personen, die sich im

Raum aufhalten. Der HC-Sensor besitzt eine zu geringe Auflosung, urn

Konzentrationen der einzelnen Kohlenwasserstoffe im ppm-Bereich nachweisen zu

konnen. Andere Ereignisse, wie Freisetzung von Losungsmitteln, treten viel starker in

Erscheinung als das Brandereignis.

6 Ergebnisdiskussion

Die bisher vorgestellten Ergebnisse zeigen die zeitlichen Verlaufe der Sensorsignale.

Urn eine endgultige Aussage liber die Eignung der verschiedenen Sensoren treffen zu

konnen, miissen die Signalabstande der einzelnen Sensortypen sowie deren sensorische

und technischen Eigenschaften naher betrachtet werden. Eine grafische Ubersicht der

Brandsignalpegel der einzelnen Sensortypen im Vergleich zum maximalen

Hintergrundsignal (Basis = 1) ist Abbildung 5 zu entnehmen, wobei sich deutlich die

prinzipielle Eignung beider Sensorprinzipien zeigt.

Bei Halbleitersensoren ist vor allem der erste Typ zu bevorzugen. Er zeigt einen sehr

starken Leitwertanstieg auf Schwelbrande. Auch sein Ansprechverhalten auf NO laBt

eine Vermutung nahelegen, daB mit ihm auch offene Flammenbrande detektiert werden

konnen. Sein vom Hersteller spezifiziertes Verhalten auf Ozon laBt jedoch auch

Probleme an warmen Sommertagen erwarten, so daB Systeme mit nur einem

Halbleitersensor schwierig beherrschbar sein werden. Das Verhalten der

unterschiedlichsten Halbleitersensoren wird im weiteren Verlauf des oben genannten

412
Projektes noch naher untersucht, wenn eine groBere Anzahl verschiedener Sensoren den

genormten und neu konzipierten Testfeuem ausgesetzt werden.

20

15

45

10

5

0

Ga203 1.Sn02 2. Sn02 EC-NO EC-CO EC-NH3 IR-HC OT

Abb. 5 - Brandsignal bezogen auf den maximalen Hintergrundpegel

Bei den elektrochemischen Zellen erweist sich eine CO-empfmdliche EC-Zelle als am

besten geeignet. Diese Aussage sollte jedoch nicht verallgemeinert werden, da CO ein

Gas ist, das regular bei vielen Produktionsprozessen frei wird. So zeigte sich bei einer

Versuchsanordnung, die in einer Harterei installiert war, daB ein Hintergrundpegel von knapp unterhalb des MAK-Wertes pemnanet vorherrscht. Beim automatischen

Beschicken der Ofen kann dieser Wert kurzzeitig Uberschritten werden, was dem

Alarmpegel des hier durchgefuhrten Versuchsbrandes entspricht. Desweiteren sind ECSensoren fiir dauerhaften Einsatz in Atmosphare, die mit dem Zielgas belastet ist, eher

ungeeignet. Dort wird die prinzipbedingt begrenzte Lebensdauer solcher Zellen

erheblich verkiirzt.

Die klimatischen Einflusse spielen im hier beschriebenen Anwendungsfall nur erne

untergeordnete Rolle, da die Luftfeuchte nur geringe Anderungen aufweist und mit

einem Mittelwert von ca. 50% rel. Feuchte bzw. 15g/m^ abs. Feuchte konstante

Betriebsbedingungen fur die Sensoren darstellt. Auch die Temperatur sollte keinen

EinfluB auf das Arbeitsverhalten der Sensoren besitzen. Der Betrieb m einer

geschlossenen Werkhalle laBt selbst im Winter keine fiir elektrochemische Sensoren

kritischen Temperaturen von unter -10°C erwarten.

413
Auch im Sommer besitzt die Temperatur nur untergeordnete Bedeutung. Bei

Temperaturen von bis zu 45'^C muB dies nur bei der Wahl der Installationsorte

entsprechender Brandmeldetechnik Beachtung finden, da Warmepolster eine frlihe

Detektion verhindem.

Der eingesetzte optische Rauchmelder bestatigt die Erfahrungen des Betreibers mit der

konventionellen Brandmeldetechnik. Fehlalarme durch Staubaufwirbelungen sind keine

Seltenheit und Schwelbrande wurden bisher nur unzureichend detektiert bzw. erst nach

Ubergang zu einem offenen Brand erkannt.

7 Fazit

Als Ergebnis der Versuchsreihe wird eine Erganzung der bestehenden Brandmeldeanlage durch gassensorische Branddetektoren als sinnvoll erachtet.

Eine Kombination von konventionellen Rauchmeldem mit Gassensoren eliminiert deren

Staubempfindlichkeit. Hierbei bieten sich insbesondere elektrochemische Sensoren flir CO und NH3 an. Fiir diesen Sensortyp spricht weiterhin die geringe Stromaufnahme,

was eine Integration in bestehende Brandmeldesysteme vereinfacht. Nachteile dieser

Kombination sind der relativ hohe Preis der Sensoren sowie die sehr begrenzte Lebensbzw. Einsatzdauer. Weiterhin wird das Detektionsverhalten gegenuber Schwelbranden

im Vergleich zu einem reinen Rauchmelder nicht beschleunigt.

Eine weitere Moglichkeit besteht im Einsatz sog. Schwelbrandmelder auf Basis

mehrerer Halbleitergassensoren. Deren Sensorelemente sind relativ kostengiinstig und

besitzen eine hohe Lebensdauer. Nachteihg wirkt sich der hohe Energieverbrauch sowie

die komplexe Auswertung der Sensorsignale aus. Zur Zeit wird ein erster kommerziell erhaltlicher Schwelbrandmelder mit

Halbleitergassensoren [8] im Bereich der Papiermiihle getestet und seine Signale im

MeBsystem aufgezeichnet.

414
Literaturnachweis

[1] Pfister, G.: Detection of smoke gases by solid state sensors - A focus on research

activities; Fire Safety Journal; 6 (1983); Seite 165 - 174

[2] Linden, O.; Holemann, H.; Research on the Suitability of Gas Sensors for fire

Detections; EUSAS-Workshop No. 10, The Detection of Combustible Gases in

Connectionwith Fire Detection; Rauischolzhausen; 1998

[3] Kubon, Ch.; Holemann, H.; Linden, O.; Research on the suitably of gas sensor based fire detectors for various applications; VdS-Fachtagung, Gassensorik in der

Brandmeldetechnik; Koln; 2000

[4] N.N.: Gassensoren GGS, Produktinformation und Applikationshinweise; UST

Umweltsensortechnik GmbH, Geschwenda; 1998

[5] Fleischer, M.; Seth, M.; Kohl K. D.; Meixner H.; A study of surface modification

at semiconducting Ga203 thin film sensors for enhancement of sensitivity and

selectivity; Sensors and Actuators B - chemical; 35-36 (1996); Seite 290 ... 296

[6] N.N.: Technical Data CO 3E 300, NO 3E 300, NH3 3E 200; SENSORIC

Gesellschaft fur angewandte Elektrochemie mbH; Bonn; 1998

[7] N.N.: Datasheet - IR-Gassensor GS30; for CO2 and HC; Sensor Devices GmbH,

Dortmund; http://www.home.t-online.de/home/sensor-devices; 1998

[8] Petig, H.; Kelleter, J.; Schmitt, D.; Gas Sensor Fire Detector Prove Effective in

Coaling Plants; Global Risk Report; Allianz Versicherungs AG; 4 (1999); Seite

19 ...22

415
M. Harms, J. Goschnick

Institute for Instrumental Analysis, Karlsruhe Research Center GmbH,

Hermann-von-Helmholtz-Platz 1, 76344 Eggenstein-Leopoldshafen,

Germany, Phone +49-7247-822580, Fax +49-7247-824606,

Email: michael.harms@ifla.fzk.de

R. C. Young

National Aeronautics and Space Administration, Kennedy Space Center,

Florida 32899, USA

Early Detection and Distinction of Fire Gases with a Gas Sensor

Microarray

1. Introduction

The necessity of gas-anal3^ical instruments for fire detection has been discussed in the past and is widely accepted at present. Conventional fire detectors usually work on a poor information basis, incapable of identifying

the nature of the fire. Therefore, false alarms are often set off as gas concentrations, smoke densities or a high air temperature are mal-interpreted.

This leads to the fact that many people do not consider fire alarm systems

as reliable. Moreover, false alarms cause high costs due to the security

measures triggered. For these reasons much faster and more reliable detectors are needed that are based on an additional discrimination power able to

distinguish between the different occasions of gas release, but on the other

hand incur no more costs than a conventional instrument.

The measurement of the electrical conductivity of semi-conducting metal

oxides represents one way of realizing cheap and sensitive detection of gaseous components. Provided an array of metal oxide gas sensors is used, a

high discrimination power in gas analysis can be realized. Using this principle, a novel t3^e of gas sensor microarray based on the segmentation of a

monolithic metal oxide layer by a set of parallel electrodes, has been devel416
oped at the FORSCHUNGSZENTRUM Karlsruhe, that allows a sensitive detection and discrimination of gases at a very low cost.

2. Detection Principle

The principle of conductivity measurement at n-semi-conducting metal oxide fields for detection of gaseous atmospheric components has been wellknown for about 40 years [1]. If a metal oxide surface is held at a temperature of some hundred degrees Celsius, adsorption and catalytic reactions of

nearly all types of gases take place at the surface (see Fig. 1). The release of

electrons by catalytic reactions (e.g. the oxidation of adsorbed hydrocarbons)

or immobilization of conduction band electrons by adsorptive species, cause

a gas specific change of the electrical conductivity of the metal oxide surface

if the ambient gas composition is changed. If oxidizable gases such as methane are adsorbed on the surface, catalytic oxidation takes place, with electrons released to the conductivity band. As a result of adsorption and dissociation of gaseous oxygen molecules, negative oxygen ions are consumed

during oxidation. Therefore, their level is kept low in the presence of air

containing hydrocarbons or other oxidizable gases. However, if the latter

disappear from the ambient atmosphere of the model oxide surface, the level

of adsorbed oxygen ions rises to saturation, causing the conductivity to drop

accordingly. By different mechanisms, not only oxidizable gases can be detected, but reducible ones as well - such as NOg and Og - or less active gases

- as CO2 [2]. Nearly all gases are detectable except for rare gases and other

extremely passive gases, such as nitrogen.

417
CH

H,0 H2O

selector

membrane

transducing layer

measurement of

electrical conductivity

Voltage Current

Example: Detection of Methane

CH, + 40 4 e- + CO, + 2 H,0

Fig. 1: Gas detection principle of metal oxide conductivity sensors. The detection of methane is shown as an example.

3. Gas Sensor Microarray

The great diversity of detectable gases can turn out to be a disadvantage for

a single sensor if high selectivity for a particular atmospheric component is

required. A sensor system with the ability to differentiate between gases

can be set up if a multitude of different sensors is combined. Provided the

sensitivity spectrum of each sensor, the so-called selectivity, is different

from the other sensors of the array, exposures of the array to gases or gas

ensembles result in a conductivity pattern, characteristic of the type and

quantity of the gases contained in the gas mixture. The technological novelty of the microarray invented at the FORSCHUNGSZENTRUM K/\RLSRUHE.

however, is the arrangement and differentiation between the gas sensors. Contrary to conventional macroarrays and other gas sensor microsystems, a

single monolithic metal oxide film alone forms the basis of the whole array.

This film is separated into 38 sensor segments by parallel electrode strips to

measure the electrical conductivity of the individual segments |3|. The necessary operation temperature (usually between 200°C and 40()°('t is provided by four meandering heating elements, placed at the reverse side of the

chip (see Fig. 2). The heating power is controlled by two platinum thet inoresistors, placed on the upper side of the chip. The whole array is coated with

a permeable SiO, layer of variable thickness across the 38 sensor segments.

418
Fig. 2: Gas sensor microarray mounted in its

housing. The front side

consistis of the metal

oxide detector field,

separated into 38 sensor

elements by 39 electrode

strips. The reverse side

carries four separate

heating elements (on the

upper right side).

4. Gradient Technique

The gradient technique serves to differentiate gas detection selectivity via

the 38 individual sensor segments. The thickness of the ultra-thin gaspermeable SiO.; membrane layer deposited on top of the metal oxide film

varies across the array (s. P'ig. 3). Additionally, a controlled temperature

gradient, e.g. of 50 K, is maintained across the array. Depending on the nature of the gases, due to diffusion through the membrane and the warmth

caused by gas reactions at the metal oxide interface, gas detection selectivity is gradually modified from sensor segment to sensor segment. Therefore,

the exposure to single gases or gas ensembles (like odors) cause characteristic conductivity patterns at this gradient microarray. The dependence of the

conductivity pattern on the type and quantity of ambient gases allows gas

discrimination and quantification.

Hence, this gradient microarray can be applied to realize an electronic nose

system at a low cost: the Karlsruhe Micro Nose (KAMINA). Microfabrication is uncomplicated and thus inexpensive, especially thanks to the

simple but high level integration of the sensor elements into the gradient

419
array structure. Further functional advantages are reliability, stability and

sensitivity of the gas-analytical performance.

membrane 2 - 30 nm

strukture of electrodes

MOS gas detector

i substrate

d

4 heating elements

longitudinal section

Fig. 3: Longitudinal section of the microarray. A SiOg-membrane of a few

nanometer. Thickness covers the metal oxide layer including the electrode

structure. The thickness of the membrane varies across the sensor array

in order to differentiate between the selectivity of the individual sensor

segments.

5. Experimental

In the first stage of the fire detection study, two different kinds of microarrays were tested using defined concentrations of fire relevant gases. Representing typical components of fire gases, benzene, formaldehyde, tetrafluoro

methane, hydrogen cyanide, and carbon monoxide were chosen as test

gases. Two types of microarray chips were used in order to determine the

most appropriate one for this kind of application: one was coated with tin

dioxide doped by 1 % platinum (SP chip) and the other with tungsten trioxide (WO chip). Furthermore, the best operation temperature for the microarray was determined. In this stage, gas concentrations in the range of 1 - 100

ppm were used. The concentrations were set by a computer-controlled mixing system, producing defined pulses of test gases alternating with clean air

at a r. h. of 60 %. The signal response to the test exposures was used to determine the analytical performance of the microarray chips including sensitivity, detection limits, and response times. The gas concentrations were

gas exposure

Pt

420
checked by applying conventional analytical methods (e.g. PID, FTIR). The

experiments were repeated four weeks later using the selected chip with its

optimal operation temperature in order to determine long time stability.

Gas concentrations in the range of 0.5 - 250 ppm were then used. Additionally, the gas discrimination power was tested using the standard pattern

recognition algorithms Principal Component Analysis (PCA) and Linear

Discrimination Analysis (LDA). Signal reproducibility was examined in an

experiment exposing the microarray to five gas pulses of 10 ppm. Furthermore, the influence of changing humidity on the microarray was tested.

The studies were completed by practical tests using the KAMINA. Several

materials were burnt in a furnace and the gases formed were examined. In

addition to response behavior, possibilities were investigated of distinguishing between different burning materials. In another experiment, the

ability was tested to differentiate between solvents and the gases of over- heated wire insulation. Furthermore, the system was trained to distinguish

between fire gases and diesel engine exhaust.

6. Results and Discussion

6.1 Microarray test with defined single gas exposures

Measurements of the microarray's response to single model gas exposures

showed that the SP chip was the most appropriate microarray for fire gas

analysis. The optimal temperature span for chip operation turned out to be

200 - 250 °C. The following results have been achieved using SP chips.

Fig. 4 shows the resistances of all 38 elements versus the elapsed time or

the median in a typical pulse exposure experiment. In this example, the gas

sensor microarray was tested with pulse exposures of CO concentrations in

the range of 0.5 to 250 ppm. During the time in between the testing gas

pulses, the microarray was exposed to clean humid air of 60 % r. h., with the

sensor response immediately following the rising CO concentration. The tg^

response times were usually below 1 min. Using the KAMINA, response

421
times of a few seconds are possible. As contrary to conventional laboratory

electronics, the KAMINA has an output rate of 1 signal per second. A sensor

signal for 0.5 ppm CO was produced.

The signal S is defined as the relative conductivity change with respect to

the reference gas (clean humid air) S - [Ro / R] - 1 with Ro = reference resistance. Fig. 5 shows the median signal of all sensor elements. It can

clearly be seen that the median signals of all gases (formaldehyde, benzene,

tetrafluoro methane, hydrogen cyanide, carbon monoxide) are above the significance level, which is defined as triple noise level. Moreover, the comparison of the signal from the first test to that of a repeated test (one months

later) only shows a slight decrease of the sensor response. This means sensor stability is reasonably good. Moreover, separate long term investigations

with alcohol exposures have shown that this kind of sensitivity loss only occurs during the first 100 days after fabrication, while later sensitivity nearly

remains constant (it was tested up to 400 days).

422
100

100 200 300

Time [min]

400 500

100 -

O

1. 10

0)o

c

(0

+-» (AW

0)c

0>

0.1 0

-- 250

300

<no

200 w

3

Q.

(A 150 «

o

(0 100 «

"(5 >

-- 50 0

100 200 300

Time [min]

400 500

Fig. 4: Measurement of CO pulse exposures (0.5 - 250 ppm); upper plot: the

resistances of all 38 sensors versus time; lower plot: the median signal and

the set values of the CO concentration (broken line)

423
H2C0 C6H6 CF4 HCN CO

Fig. 5: Median signal of the SP chip (T = 200 - 250 "C, gas concentration 10

ppm); comparison of the 1'' to a repeated test after 1 month

E

01st test

Repeat test

m 1

. . . . .

-

\

m

H2C0 C6H6 CF4 HCN CO

Fig. 6: Detection Hmits of SP chip (T = 200 - 250 °C); comparison of the 1''

to the repeated test after 1 month

424
Furthermore, reproducibility was examined measuring sequences of five 10

ppm pulses for every gas. The mean deviation of the sensor signal was

3.6 %, at a maximum of 6 %. No cross sensitivity to humidity could be detected in the range of 40 -90 % r. h. Only a statistical scatter of the median

signal with a standard deviation of 8 % was measured in the examined humidity range.

Fig. 6 shows the detection limits of the test gases. The detection limit of CF^

was 1 ppm as CF, is less active and only the adsorption of the molecule established a change of the metal oxide conductivity. All other detection limits

were far below 1 ppm. Especially hydrogen cyanide could be detected very

easily. Repeated tests showed that the detection limits mostly remained

constant: the detection limits of benzene and formaldehyde were a little

higher than before, whereas the detection limits of CF^, HCN, and CO were

even slightly lower. These results again stand for good long time stability.

6.2 Distinction between different burning materials

The most important feature of an intelligent electronic nose system, as the

gradient microarray, is the ability to distinguish between various kinds of

gases. Therefore any difference in the composition of the gas ensemble released by different kinds of burning materials should cause the signal pattern of the microarray to respond in a characteristic way according to the

burning material. Even the heat-up phase before burning should give rise to

a characteristic signal pattern according to the specificity of the gas. However, the spreading of the fire changes the composition and the temperature

of the burning materials which in return results in a continuous variation of

the fire gases. Nevertheless, the signal inventory of the microarray obtained

from real fire gases was examined in terms of its significance of discrimination of burning materials.

The signal patterns of the microarray consisting of resistances normalized

"to the signal median" were examined by an LDA model. In Fig. 7 the resulting LDA diagram clearly depicts separate fields of clean air, fire gases

425
from wool and PVC. Fire gases from wood, PE, PS and PU form a further

field, which is separate from the other ones. Thus, first of all the occurrence

of fire gases can be clearly distinguished from clean air and it does seem

possible to make predictions about the tyipes of burning materials with the

help of the microarray. The spread of the signals within their cluster is due

to the pattern variation in the course of a burning process. In all fire ex- periments the microarray responded to the gases within a few seconds only.

o

S

oO

-+J

03

\ PVC

Wood/PE/PS/PtF

10 Pattern Component 1

Fig. 7: LDA of the signal inventory obtained while the microarray was ex- posed to real fire gas. The resistances were normalized to the median signal.

Discrimination between fire gases of different burning materials and clean

air is attain to high extent (PE: polyethylene, PS: polyst3Tene, PU: poljoirethane, PVC: pol3rvinyl chloride). A gradient microarray with 38 sensor segments based on Pt-doped Sn02 was used for the measurement. The surface

temperature of the array chip was hold at 250 - 300 °C. The area limits describe a confidence range of 95 %.

426
6.3 Distinction between solvents and overheated wire insulation

An important feature of a fire detector is its selectivity in terms of recognition of fire events, thus preventing false alarms caused by other sources that

emit solvent vapors, such as cleaning processes. Fig. 8 shows the result of a

test carried out with different kinds of overheated wire insulation as they

could occur in pre-fire situations and a selection of solvents. The microarray

very quickly responded to every single sample. By using the signal pattern

evaluation technique of an LDA, not only differentiating between the classes

fire gases and solvents but also distinguishing between individual solvents

and insulation materials became possible. Hence, a reliable detection of

overheated wire insulation is feasible. However, more data has to be collected from pre-fire situations and situations in which there is no danger of

fire, but similar gas components are in the air. Although the gradient microarray hardware already provides a sufficient gas discrimination power, a broad data base is necessary to design appropriate recognition of signal patterns, ensuring a reliable detection of pre-fire situations.

427
-25 I > 1 1 ' 1 1 1

-30 -20 -10 ^ 0 10 20 30 40 Pattern Component 1

Fig. 8: Distinction between clean air, solvents and overheated wire insulation in an LDA diagram; solvents (isopropanole, ethanole, xylene, toluene,

acetone, WD40) are clearly depicted separately from pre-fire gases of hot

wires mantled with kaptone, fluorine-containing and unknown materials. A

gradient microarray based on Pt-doped SnO.^ was used for measurement. Its

temperature was kept at 250 - 300 "C. The area limits describe a confidence

range of 95 %.

6.4 Distinction between fire gases and diesel exhaust

A deficiency of conventional fire detectors is false alarm set off by particle

contaminated exhaust gases of engines, e.g. diesel engines. This problem

was dealt with in another test. The following signal patterns (s. Fig. 9) were

produced during measurements of the exhaust of a diesel engine and of

gases from paper and polyethylene fire.

428
Fig. 9: Radar plots

of fire gases and

diesel exhaust signal patterns, nor- malized by the median signal.

The lower plot

shows the signal

patterns resulting

from measurements of different operation

modes of the diesel

engine.

Thickness gradient 37 ^ ^

36 ,

thin

3 Temperature gradient

5

Fire Gases " „/ /

23 / 22 Reference air ^ 'burning paper melting PE film — burning PE

Thickness gradient

thick

Reference Air qp. mode 2

- - starting Motor — op. mode 3

op. mode 1

From these simple radar plots the difference between the patterns can be

told. Accordingly, a corresponding LDA (s. Fig. 10). shows clear separate

fields representing clean air and the two gas ensembles caused by burning

materials and the output of a diesel engine.

429
Fig. 10: Result

of LDA to distinguish between

fire gases and

diesel exhaust

based on the

data of fig. 9.

10

8

o

Eo n

n3 Q.

-15

• Clean Air D Fire Gases

A Diesel Exhaust

-10 -5 0 Pattern Component 1

10

7. Summary and Conclusions

Typical fire gases were sensitively detected in laboratory tests on defined

conditions with a gas sensor microarray equipped with 38 sensor segments

based on platinum-doped tin dioxide. The determined detection limits were

1 ppm for CF4 and far below 1 ppm for CO, hydrogen cyanide, benzene and

formaldehyde. All these model gases could be clearly distinguished according to their signal patterns. Furthermore, the microarray showed very short

response times within the range of a few seconds only. In several practical

tests with fire gases or precursor gases of fires, the gas discrimination power

of the microarray was tested, namely its signal pattern results caused by

the gradient differentiation of the array's sensor segments were checked.

Possible interfering gases for the detection of pre-fire and fire gases, such as

solvents or diesel exhaust, could reliably be distinguished from other true

fire gases. Furthermore gas discrimination of the gradient microarray has

proved to be successful for discrimination between different burning materials. Hence, the gas sensor microarray with its unique sensor element gradient differentiation offers a promising basis for development of a gas analytical fire detector.

430
8. References

[1] Gopel, W. and Schierbaum, K.-D., Chemical and Biochemical Sensors, in:

Gopel, W., Jones, T. A., Kleitz, M., Lundstrom, J. and Seiyama, T., Sensors, Vol. 2, VCH, Weinheim, New York, 1991

[2] Madou, M. J. and Morrison, S. R., Chemical sensing with solid state

devices. Academic Press, Inc. (London) Ltd.. 1989

[3] Althainz. P., Goschnick, J.. Ehrmann, S. and Ache, H. J., Multisensor

microsystem for contaminants in air. Sensors and Actuators B 1996; 33:

72-76

431
Martin Berentsen, Thomas Kaiser

Fachbereich 9, Fachgebiet Nachrichtentechnik

Gerhard-Mercator-Universitat, Germany

Bismarckstrasse 81, 47048 Duisburg

e-mail: berentsen@sent5.uni-duisburg.de, th.kaiser@uni-duisburg.de

FIRE LOCATION ESTIMATION USING TEMPERATURE

SENSOR ARRAYS

1. ABSTRACT

This paper reconsiders how to estimate the location of a fire in a closed room with an

array of temperature sensors. The very important question is: "Where is a fire located

?". The answer is of interest for research on fire detection and for a deeper understanding

of the case of fire for prevention and extinguishing. The answer to this question can be

given with two small arrays of temperature sensors. Here, "small" means in the same

dimensions like a standard fire detector. The data signal processing for the fire location

estimation can be done by a digital signal processor (DSP). It seems to be possible to give

a rough fire location estimation with such an array in the same time which is required by

a standard fire detector to give a fire alarm.

2. INTRODUCTION

One of the main concerns in fire research is to detect a fire in a short time with a low false

alarm rate. It is also of interest where a fire is located. For example two scenarios will

be given. First an automatic fire extinguishing in a sensible area like a ware house or a

computer room. The damage in such an area can be minimized with knowledge of the

fire location and a more exact automatic extinguishing can be done. Hence, the cost can

be reduced. The second scenario is a fire extinguishing by a fireman in a smoky room. Knowledge of the fire location before entering the room minimizes the extinguishing time.

It can also minimize the danger for his life while entering a room with such deadly smoke.

432
Now we take a look at a fire in an early stage. The hot gases are rising up from the fire

place near the floor to the ceiling shown in figure 1 a). Under the ceiling they propagate

in a circular shapes as shown in figure 1 b).

ceiling

floor

Figure 1: a) fire in a closed room (sideview) b) circular wave shapes (topview)

Due to more or less strong turbulences and interferences caused by the rooms walls the

shapes will not be perfectly circular. If we take now an average in time we expect an

almost circular shape. This observation is fundamental for the following idea. However,

these circular behavior seems to be time limited. When a fire grows up, the hot gases

become more turbulent, and the propagation becomes more and more non-circular shapes.

Here, our basic approach. For the following considerations some boundary conditions

must be fulfilled:

• The ceiling should be flat with a low heat conductivity.

• The flow current velocity v of the hot gases should be nearly constant under the

ceiling in the early state of fire.

• The fire is near the floor level

• The walls are of the same temperature.

• The fire is not located directly at a wall.

• Other air currents,e.g. caused by a heating system, should be neglected.
These assumptions are needed to validate the waveform as of almost circular shape. With

these assumption the hot gases grow up vertically from the fire and reach the ceiling with

the shortest distance. From this point the circular shapes starts under a nearly flat ceiling.

In this case the estimation can be reduced from a three dimensional problem into a two

dimensional problem.

In the following always an array of four sensors is used. The sensors are arranged in a

quadratic way and located at the points {x„,y,i), n — 1(1)4 with a distance d — 5cm. The

active part of the sensor (diameter 0.13mm) is mounted 8mm under the ceiling. These

parameters differs from earlier publications on this topic [3], [4], [7]. Modem ceramical

NTC resistors are used in a temperature range from 0° C up to more then 150° C. Thus,

a high dynamic of these sensors is required. The used sensors have a low response time

around 0.11s in air [8]. Also a low tolerance is required, which is guaranteed here by

careful selection.

3. THEORETICAL FUNDAMENTALS

The propagation of the hot gases under the ceiling can be seen as a temperature wavefront

T{x,y^t) with (jtQi.Vo) the location of the fire. With a flat ceiling a radius r can be

defined as

Since the wavefront T{x,y,t) is assumed to be of a circular shape it is only a function

of the radius and time. Hence, we use the notation T{r,t) instead of T{x,y,t) in the

following. For a sensor on a fixed point (x,;,y„) the temperature function T{t)\r„ is only

a function of time. Sampling is required for the data processing. The frequencies in the

case of fire are limited up to ~ lOHz [2], so that we assumed to use a sample frequency

of = 20Hz . So the temperature samples from b sensors at the location [x,j,y„) can be

seen as T,{k)^S„{k)+N,{k), n^\{\)b

where Sn{k) is interpreted as a deterministic signal caused by the fire and N,i{k) as noise

caused by the unavoidable turbulences. If the range r is large between the fire place projection under the ceiling (vq, Vq) and the location of the sensor array (x„, >',;), the temperature wave under the ceiling can be seen as a quasi planar wavefront from the perspective

of the array with the dimensions d xd and d <^r.

434
Figure 2: geometrical arrangement of the sensor array

In figure 2 the sensor array with sensors Sn,n — 1(1)4 is shown in the distance r from the

fire place projection under the ceiling. The wavefront is shown as quasi planar with the

velocity v.

Figure 2 also shows, that with knowledge of the geometric order of the sensors in the

array and the assumption of a quasi planar wavefront with the velocity v first reaches the

sensor SI, then S3, then S2 and last S4. With this model a time delay can be defined

between SI and S2 as Dj-, and also D^-, as delay between SI and S3.

With knowledge of this coherence and our signal model the problem of estimation the

direction can be reduced using the geometry of the sensor array to a time delay estimation

problem

T^{k)^S{k)+N,{k)

T„{k) = a„S{k-k^^,) + N„{k), n+\

with the delays k^^^. The deterministic signal S{k) — 5, [k) as a part of [k) is interpreted

as a time delayed signal S{k - k^^^ also in 7^, (A), /z / 1.

We have investigated several signal processing algorithms known on time delay estimation, e.g. the PATH-algorithm, the SCC-algorithm, the SCOT-algorithm, the WiENER

Processor, the Roth Processor, the ML-algorithm and the Adaptive Time Delay

Estimation method. For details see [9]. In the following only the SCC-algorithm (Simple

435
Cross Correlation) is explained to understand the principles of time delay estimation. The

cross correlation between the first and the n sensor output is

R,,iK)^E{T,{k)Uk + K)}, ,2 = 2(1)4.

By the assumption of uncorrected noise N„{k) in the signal model, it can be written as

RJk) = E{S{k)S{k - + K)}, n = 2(1)4.

The maximum of Ri„{k) occurs for k — k^^^, since the argument takes for arbitrary s{k)

a positive value. For estimation of k^^^ now only the cross correlation has to be estimated

and its maximum has to be found. Normally the estimation R^^^{K,k) is given by averaging

the temperature sample vectors from T", [k] and T„{k).

1 ^+"'"" m

^uM^^) =

l S T,{I)Tn{l + K), « = 2(1)4, k = -,

1=111 m = 0{M)K-L, K^-K,„a.x ( 1 ) WvIn the following the hat indicates an estimation. The in-stationarity of the signals is taken

into account by the time dependence k— ~ of R^^j{K,k). The measured signals are composed to non overlapping blocks with the length L. The estimation is only calculated each

Mth time. Ki„ax should be not too large to minimize required calculation power.

After knowing the estimated time delays, we just need the relations between these delays

and the parameters r, a and v. We have assumed that the fire is far away from the array,

so that r ^ d and we can see them as quasi planar wavefronts. So the velocity vector can be seen as

|v| = dcosa

Ja

'

' fa

So the parameters a and v can be written as

a — arctan(^)

dcosa

Actually the fourth sensor S4 is unused, so it can be used to estimate another pair of delay

times. These delay times can be also used to calculate an estimated angle and velocity

436
to verify and also to improve the first calculation. For the location estimation a second

estimated angle information is required. In our case we place another angle estimation

unit in the same observation room. With these two estimated angles «[ and 02 a location

of the fire can be calculated.

4. FIRE EXPERIMENTS

All fire experiments were carried out in the fire detection laboratory of the GerhardMERCATOR-Uni\ersity Duisburg. The fire room of the laborator}' has a ground size of

10.5m X 9m and the variable ceiling (it can be varied from 2.87m up to 6.57m) was fixed

at 3.40m. Some first experiments [3]. [4] with spirit fire were carried out to test out the

generic w ork of the location estimation algorithm. After an optimizing process for some

boundary conditions on the algorithm parameters seven kinds of test fire were done. The

test fires are listed in table 1 by name, number and burning material.

TFl Open wood fire (beechwood)

TF2 Smoldering (pyrolysis) wood fire (beechwood)

TF3 Glowing smoldering fire (cotton)

TF4 Flaming plastic fire (polyurethane)

TF5 Flaming liquid fire (n-Heptane)

TF6 flaming liquid fire (metylated spirit)

TF7 Flaming liquid fire (dekalene)

Table 1: Test fires by name and material

To verify the different algorithms with test fires a fixed angle between the sensor arrays and the fire location was used. For the fire experiments two sensor arrays are mounted

under the ceiling. The physical parameters are — 45°, c^, = 45°. r, = 3w and — 3m,

the fire was always located at (-Vo,>'o) — (0.0).

437
Figure 3: Sensor array, signal processor and PC

The measurement system, shown in Hgure 3. uses a disc v\ ilh a diameter of 23cm. In

its center the temperature array containing four sensors is placed. The figure also shows

the used Digital Signal Processor (DSP) board, liquid crystal display and data acquisition

computer. For a detector solution with DSP we minimized the required calculation pov\er

and used memory. The sample rate was shifted down to / = 20 Hz. The observation w in- dow length L was minimized to 40 samples. All shown angle estimations are calculated

with the .S'CC-algorithm. The reason for this selection was the first portation of this algorithm into a DSP for an automatic angle estimator. In |9| is shown that the error between

the 5CC-algorithm and other, more calculation intensive algorithms is small enough for a

Hrst solution.

To verify the results from the DSP solution an additional computer-based simulation with

the algorithm under test has been done. The estimation algorithms has been tested v\ ith

all of the listed kinds of test hres. But exemplified by three of these tires should be shown

that the estimation works. Only the first 80 seconds of a test fire are shown in the follow -

ing figures, because the goal of the estimation was to give a first fire location estimation

at the time of fire alarm. For example the detection time for a TF6 and an European standard temperature detectors of class Al is around 60 seconds. The detector must give an

alarm after the room temperature increases by 29" Celsius. The alarm here is given by a

temperature of 50° Celsius depending on a room temperature of 21^ Celsius before the

fire starts.

438
time/s

Figure 4: TF2 temperature data

Figure 4 shows the temperature curve from a sensor which is typical for smoldering fire

type TF2. This signal and the following signals shown in figure 6 and figure 8 showed

no temperature dc offset, so they can be interpreted as shown after a high pass filtering.

These signals includes the lightning of the fire in their first ten seconds.

100

a

0 20 40 60 80

time/s

Figure 5: Estimated angle a for a TF2

The figures 5, 7 and 9, shows the estimated angle value a for the first 80 seconds of a fire.

There is an angle a value for every two seconds depending on a 40 sample correlation

window length L and a =20 Hz sample rate. The estimated angle a, shown in figure 5

varies Aa = — 12... + 8 degrees around true real value after 60 seconds.

Figure 6 shows the temperature curve from a sensor which is typically for a flaming fire

439
bdemp

1

0

-1

"0 20 40 60 80

time/s

Figure 6: TF4 temperature data

type TF4. The typical increasing temperature values over the time indicate that the time

is limited for estimating the fire place. This is caused by increasing the turbulences of the

hot gases under the ceiling from the increasing fire.

100

90

a 80

70

60

50

40

30

^°0 20 40 60 80

time/s

Figure 7: Estimated angle a for a TF4

The estimated angle a, shown in figure 7 varies Aa = — 10... + 3 degrees around the real

value after 40 seconds.

440
8

6

2

0

-2

-4

time/s

Figure 8: TF6 temperature data

Figure 8 shows the temperature curve from a sensor which is typically for a flaming liquid

fire type TF6.

80

60

50

40

"^°0 20 40 60 80

time/s

Figure 9: Estimated angle a for a TF6

The estimated angle «, shown in figure 9 varies Aa — —8... +4 degrees around the real

value after 20 seconds and Aa = —5... + 3 degrees after 40 seconds.

0

441
Figure 10: The location estimated error area

Figure 10 shows the error area of the location estimation in the fire laboratory under our

test conditions. For example the TF6 is a flaming liquid fire in a quadratic basin with

43.5cm X 43.5cm and the error area from the estimation is in the same dimensions.

5. CONCLUSION

A method to locate a fire using two temperature arrays was proposed. A first DSP solution was shown and it's results were verifyed by fire experiments. It was shown that it is

possible to give a first fire location estimation in the same time as needed for detecting

the fire with a class Al detector. Unknown is the time for an useful location estimation

of smoldering fires of type TF2 and TF3. For the future it is planed to port some other

promising time delay estimation algorithms to the DSP solution for a higher accuracy. It

is also planned to test some other sensor types e.g. pyroelectric sensors for their useful

work in such a detector type.

References

[1] G. C. Carter, "Coherence and Time delay Estimation", Proceedings of the IEEE, vol

75, no.2, pp.236-255, February 1987

[2] G.Cox. R. Chitty, "Some Stochastic Properties of fire Plumes", Fire and Materials,

vol. 6. no. 3+4, 1982

442
[3] L. Eikermann, "Ortsbestimmung von Branden unter der Verwendung eines Temperatursensorfeldes", Studienarbeit,Gerhard-Mercator-Universitat Duisburg, 1997

[4] L. Eikermann, "Weitere Untersuchungen zur Ortsbestimmung von Branden", Diplomarbeit, Geriiard-Mercator-Universitat Duisburg, 1997

[5] D. Wang, "Untersuchungen moglicher Fehlerquellen bei der Ortsbestimmung von

Branden", Studienarbeit, Gerhard-Mercator-Universitat GH Duisburg, 1997

[6] H.Luck: "Theoretische Grundlagen der Nachrichtentechnik 3,4", Vorlesung an der

Gerhard-Mercator-Universitat Duisburg.

[7] T. Kaiser, "Ortsbestimmung von Branden mit Temperatursensorgruppen", AUBE99

proceedings, pp.52-66, 1999

[8] A. Telljohann "Vorarbeiten zum Aufbau eines Prototypen zur Ortsbestimmung von

Branden", Studienarbeit, Gerhard-Mercator-Universitat Duisburg, 1999

[9] R. Sprenger "Ein erster Ansatz zur signalprozessorbasierten Brandortbestimmung",

Diplomarbeit, Gerhard-Mercator-Universitat Duisburg, 1999

[10] T. Kaiser "Fire Detection with Temperature Sensor arrays", IEEE Camahan Conference proceedings, 2000

443
N. Miyamoto, T. Someya, and T. Omori

Energy & Environmental Technology Laboratory, Tokyo Gas Co., Ltd., Minato-ku,

Tokyo, Japan

FIELD MODELING OF AN INITIAL STAGE OF FIRE IN A COMPARTMENT

-Comparison with a Fire Experiment Measured in an Enclosure

1. Introduction

Wider installation of the fire detector is effective in preventing disastrous fire. Given a

broad variety of fire, effects of the fire and situation of a fire-containing room on fire

detection should be well understood, thereby making more effective implementation of

fire detectors possible.

The present study focuses on an early stage fire, during which an initiated fire in the

compartment develops to trigger a fire detector installed, to elucidate fire phenomena

and predict accurately heat transfer and diffusion of combustion-generated intermittent

chemical species. To this end, simulation of compartment fire is performed using the

field model approach. Existing work of field model fire simulation has largely

simplified complex fire phenomena involving turbulent buoyant flow, unsteadiness,

chemical reaction of combustion, thermal radiation between the high temperature flame

and solid walls, among others.

It has been recognized, however, that in the initial stage of fire, combustion, fluid flow,

unsteady heat conduction of building walls, radiative heat transfer from the fire source

to the combustion gas as well as walls, and convective heat transfer all play major roles.

Hence, putting early stage-fire simulation into practice need to integrate these important

factors without oversimplification and by balancing with constraints from computational

resources and allowable time frames.

444
r

2. Brief Description of Fire Experiment

According to the European standard of fire alarm test method [1], response time of the

ceiling-mounted fire sensor provided is measured when a prescribed amount of

combustible is ignited in an enclosure for fire experiment (Fig. 1). Six different types of

fuel, designated TFl for wood through TF6 for ethanol, are prepared to represent fire

ranging from flaming fire to smoldering. Here, experimental data for TF6 collected in a

thermal detection test [2] are employed for validation of numerical simulation results.

4rri

• Thermocouple O Anemometer

3m

Im

0.5m

I Jl. 4m

10m

Fig. 1 Schematic diagram of Test Fire Cell

Table 1 Properties of walls, ceiling and floor

Surfoce M oteriol

T he rm ol conductivity

T tiickness Density

W/mK m kg/m' W oils C one re te (L igh t) 0.523 0.1 1 ,350

C eilinq Flexible board 0.61 6 0.1 723

F 1 oor C oncrele 1.53 0.1 2,250

Table 2 Experiment condition

Comortment size 1 0 mu mij '4 mfH

)

Fuel Ethanol 2500cc

' Pan size 0.435 m u -0.435 m

Heot release rate 100 kW

T hermocouDle dIoc ement (K tvoe)

Ceiling temperature 5cm under ceilina (horizontal array) 1m horizontal intervals from fire

Verticol temperature 4m awav horizontally from fire (vertical array) 0.5m verticol intervals from ceilina A nemometer ploc ement (uitrosomc anemometer)

V elocity nearby ceiling 5cm under ceiling 3m awav horizontallv from fire

Velocity nearby woll 30cm under ceiling 5cm from wall

For representative flame combustion, 2500 cc of TF6 ethanol fuel fills a square pan

having side length of 43.5 cm placed on a scale that is centrally located on the floor. The

thermal properties of the wall material used in the test compartment are summarized in

f Table 1. When the fuel is spark ignited, ethanol temperature rises by receiving heat from

1 the flame by convection and radiation as well as conduction through the pan walls. After

' the liquid temperature reaches the boiling temperature of ethanol (351.7 K), all the heat

input is consumed by evaporation and both the temperature and the evaporation rate stay

constant thereafter. The measured combustion rate using the scale is converted to the

' heat generation rate and is presented in Fig. 2.

I

445
" 140

0 50 100150200250300350400

Time [s]

Fig. 2 Heat release rate history

3. Numerical Simulation

3-1. Combustion model

The heat generation rate increases with time in the initial stage of fire studied here and,

therefore, its effects should be properly accounted for when one wishes to gain accurate

prediction of temperature rise inside the room. To this end, the eddy break up model of

Magnussen for combustion [3] is employed. The computations are made under the

condition that vaporized ethanol with minimum heat generation of 26.8 MJ/kg, latent

heat of evaporation of 0.85 MJ/kg, and specific heat of liquid of 2.42 kJ/kg-K, flows

uniformly from the fuel pan into the room. The incoming velocity is set equal to the

experimental data.

3-2. Radiative Heat Transfer

Ethanol undergoing liquid surface combustion generates glowing flame with a measured

absorption coefficient of approximately 0.4 to 0.5 m"' [4]. The present investigation

adapts 0.5 m"' for the absorption coefficient that is specified in a rectangular

parallelpiped region above the fuel pan.

At 7 minutes after ignition, the concentration of carbon dioxide (CO2) below the ceiling

is measured to be about 1.1 % in standard wet gas condition, while that of water vapor

(H2O) is 1.6 %. Using the measured gas temperature of 90 °C and by assuming 2.7 m as

the thickness of gas layer, the mean light path is estimated to be 2.8 m. The absorption

coefficient calculated from the emissivity of gas using the Hottel chart is 0.08 m '.

446
As to the absorption coefficient underneath the ceihng, two values - 0.1 m ' (Case 1)

accounting for a small amount of soot, and 0.2 m ' (Case 2) - are specified to assess

effects of soot radiation.

3-3. Incoming Turbulent Energy of the Fuel

Application of the standard ^-e model to an intensely buoyant thermal plume flow that

may arise in fire situations has been found to give overprediction in the axial

temperature of plume. This has been attributed to the suppressed momentum and heat

diffusion near the fire source [5]. Yoshie et al. [6] obtained closer agreement with the

experimental correlation of Yokoi [7] when larger incoming kinetic energy of

turbulence k was specified at the height seven times the radius of the fire source, which

leads to enhanced diffusion. Effects of this practice are examined in the present study by

varying the incoming value of k for the following three cases: 0.0001 (Case 3, baseline

case), 0.02 (Case 4), and 0.05 m^/s^ (Case 5).

Table 3 simulation cases

Case K_pr. k_inlet

1 0.1 0.0001

2 0.2

3 0.1 0.02

4 0.05

3-4. Numerical Method

Simulations are conducted using the CFD package CFX 4.2 [8] with the standard k-z

model of turbulence and under the weakly compressible assumption. Radiative heat

transfer is analyzed using CalcRad [9]. Convective heat transfer to the wall is calculated

by the generalized logarithmic law-based wall function. The convection terms are

discretized using the hybrid finite-difference method and the pressure-velocity

correction follows the SIMPLEC algorithm. The time step At is gradually increased as

explained in Table 4. The initial increment At is set small so that relatively fast changes

in the room temperature distribution may be sufficiently resolved. Due to the

geometrical symmetry having the seat of fire centrally located in the room, the

computation is carried out for a quarter domain only. The incoming flow boundary

condition is set at the fuel pan and the drain is treated as a pressure boundary. The

447
simulation model is depicted in Fig. 3. The mesh systems displayed in Fig. 4 have

4ni

3nt

Fuel pan

Symmetry

plane _ Outlet

5m

Table 4 Simulation condition

1 ime [sj Ou 1 u 3 0 10 G 20 G 40 G 420

Q b\s] 0.1 0.2 0.5 1.0 2.0 5.0 Heot T ransfer

to Walls

Wall Function(Log law)

A bsorption

coefficient [m"'l

Flome 0.5 Gas iaver 0.1

Fig. 3 Simulation model

36,176 grid points consisting of 38 (x) x2S (y)x 34 (z) divisions for the CFD mesh and

38 X 28 X 34 nodes for radiation calculations. The first CFD grid near the ceiling is

located 0.5 cm below the wall. All the computations are run using a SGI Origin 2000

with a single CPU, which consumes about 25 hours per case.

4. Results and Discussion

4-1. Comparison of the Simulated Cases

Figure 5 shows the time history of combustion gas temperature at the point 5 cm

underneath the ceiling and 4 m horizontally separated from the fire source. It is seen that

the result of Case 1 almost overpredicts the experimental data. On the other hand, in

Case 2 for which the absorption coefficient is 0.2 m"', the computed temperatures

448
increasingly underestimate the measured values with time, indicating the excessively

large absorption coefficient. The predicted result of Case 3 with the larger turbulence

energy is overall in good agreement with the experiment, except for the temperature

overshoot up to 10 °C during t = 100 and 300 seconds. Further increasing turbulence

energy (Case 4) leads to much higher temperature with degraded convergence properties

stemming from somewhat unstable computation. Based on these observations, the

computational results of Case 3 that achieve the best agreement with the measured data

are considered for further discussion.

4

3 . 5

3 ^2.5

2

-H

a: 1.5

1

0 . 5

0

' ' ' '

.

—

' ^/ —

// ?— /

J'

[min i Exp. Calc.

: •

3

6 -- -

7 .... \ .... \ ... . .... .... i .... 1 .... 1 .... 20 30 40 50 60 70 80 90 100

Temp [ ] Fig. 6 Transition of vertical temperature

distribution (4m from fire)

5 4 3 2 1 0 Distance from fire [m]

Fig. 7 Transition of horizontal temperature

distnbution near ceiline

0 50 100 150 200 250 300 350 400 Time [ s

]

Fig. 5 Comparison of Simulation cases (ceiling. 4m from fire)

449
4-2. Variation of Vertical Distribution of Air Temperature with Time

The vertical temperature distributions of air at 1,3, 6, and 7 minutes after ignition at the

horizontal location 4 m away from the fire source are represented in Fig. 6. The

numerical predictions are seen to be globally in good agreement, although the

temperature below the ceiling at 3 minutes is overestimated by roughly 7 °C and the

mid-height temperature undershoots by almost 8 °C at 7 minutes.

4-3. Time Variation of the Near-Ceiling Temperature Distribution of Combustion Gas

In Fig. 7, the horizontal gas temperature distribution at the monitoring point 5 cm below

the ceiling from the fire source toward the wall is shown. Except for the computed

higher temperature seen right above the fire, the numerical prediction agrees well with

the measured results beyond the location 1 m away from the fire.

The European Standard EN54 [1] imposes that the fire detector be positioned on the

ceiling and 3 m horizontally away from the location of test fire. The computed

temperature will trigger properly a detector with a given temperature sensitivity.

4-4. Flow Velocity

Table 5 provides the measured and computed velocities at 7 minutes after ignition at the

location below the ceiling and 3 m separated horizontally from the fire source, and the

near wall (distance 5 cm apart) location 30 cm away from the ceiling. The simulation is

seen to slightly attain the higher flow speed in both positions. However, in the actual

experimental situations in which there are instrumentation and other protrusions on the

ceiling area, the flow speed may have been reduced compared to the unobstructed

ceiling condition and, in turn, the two sets of data should come closer to be in good

agreement.

450
4-5. Time Elapse to Fire Detector Activation Temperature

The fire detector is designed to activate when the combustion gas temperature at its

mounted location reaches 65 °C. Figure 8 depicts the time elapsed from ignition for

triggering the alarm. Note the enlarged scale of temperature centered on 65 °C. The

activation time in the experiment is roughly 170 seconds, while in the simulation it is

about 40 seconds shorter to have 130 seconds. The difference may stem from the

manner the combustion rate is approximated: in the simulation it varies linearly with

time, as shown in Fig. 2. More realistic fuel consumption pattern should be utilized for

achieving better agreement.

Table 5 Velocity comparison at time=390 s

Location Exp. Cole.

Ceiling 0.59 0.&

'

WaU 0.28 0.37

451
4-6. Variation of Indoor Air Temperature

Changes in the air temperature in the symmetry plane (corresponding to the central

vertical plane of the room) together with a plane 5 cm below the ceiling are displayed in

Fig. 9. The temperature range exceeding 100 °C is colored in red. After the thermal

plume rising from the fire source impinges on the ceiling, it spreads over the ceiling

wall in a concentric fashion. Upon hitting the vertical walls, the flow turns downward

until it loses momentum and reverses its direction upward due to the buoyancy force.

The nearer the ceiling jet is from the fire source, the larger the momentum contained

therein. This manifests into the descending combustion gas that penetrates farther down

near the fire source (toward the right of the figure). This demonstrates the course of

developing thermal stratification properly captured in the present simulation results.

5. Conclusion

The early stage-fire experimental situation using ethanol fuel (TF6) that conforms to the

Time[s]=10 Time[s]=180

Fig. 9 Transient temperature distributions

452
EN 54 semi-enclosure is simulated numerically by way of the field model approach. The

following conclusion is drawn from the results obtained:

1. In the stagnation region where impingement of the thermal plume on the ceiling takes

place, the predicted temperatures are significantly higher compared with the

measurement data. However, excellent agreement is achieved in the ceiling jet region

that is located about 1 m away from the stagnation point.

2. For the numerical simulation of the TF6 fire experiment, the absorptivity of

combustion gas layer for thermal radiation is roughly 0.1 m"' that is given by the

Hottel chart.

3. With the eddy break up model used for modeling combustion, higher turbulence

energy should be assigned as the boundary condition for the incoming fuel. For TF6,

good agreement with the experimental result in the ceiling jet region is found by

setting k = 0.02 m^/s^.

4. The present numerical simulation captures successfully a series of phenomena

characteristic of the early-stage compartment fire: When the ascending thermal plume

from the fire source impinges on the ceiling, it transforms to the ceiling jet that

spreads concentrically. The ceiling jet, after it shortly heads downward upon hitting

the vertical walls and then loses momentum, rises due to the buoyancy effects. These

lead to the well-developed thermal stratification as time elapses.

References

[1] British Standard Institution, Components of automatic fire detection systems: Part

9. methods of test of sensitivity to fire. EN 54 1984.

[2] Doi, M., Manmoto, A. and Yamauchi, Y. Prediction of response time of the

thermal sensor using the RTI-C model. Proc. Fire Society of Japan 1999; 50-53 (in

Japanese).

[3] Magnussen, B. F. and Hjertager, B. H. On mathematical modeling of turbulent

combustion with special emphasis on soot formation and combustion, 16'*^ Symp. (Int.)

on Combust. 1976; 719-729.

[4] Tanaka, T. Introduction to Building Fire Safety Engineering 1993; 138-141 (in

Japanese).

453
[5] Nam, S. and Bill, R. G. Jr. Numerical simulation of thermal plumes. Fire Safety

Journal 1993; 21: 231-2.56.

[6] Yoshie, R. CFD Analysis of Fire Plumes (2nd Report). Proc. Arch. Inst. Japan

1998; 147-148 (in Japanese).

[7] Yokoi, S. Study on the prevention of fire-spread caused by hot upward current.

Report of the Building Research Institute 1960; No. 34.

[8] CFX Release 4.2 User Guide. 1994; AEA Hyprotech K.K.

[9] Omori, T., Yamaguchi, S. and Taniguchi, H. Accurate Monte Carlo simulation of

radiative heat transfer with unstructured grid systems, ll"^ Int. Symp. on Transport

Phenomena 1998; 567-573.

454
Thomas Cleary, Michelle Donnelly, George Mulholland, and Bakhtier Farouk*

Building and Fire Research Laboratory, Natl Inst of Stds and Tech Gaithersburg, MD

20899, USA

Mechanical Engineering and Mechanics Department, Drexel University

Philadelphia, PA 19 104, USA

Fire Detector Performance Predictions in a Simulated Multi-room Configuration

1. Introduction

Modeling fire detector performance requires detailed information on the environment

surrounding the detector, the species transport (heat, particulate smoke, and gases) from

the surrounding to the sensing surface or volume, and the sensor response. The details

of the environment surrounding a detector can be gathered from full-scale fire

experiments, however, that approach affords very little flexibility. One may be able to

find information gathered from standard fire sensitivity tests or other single-room fire

tests, but not for complex configurations. In a performance-based approach, an ideal

situation is one where modeling replaces full-scale experiments wherever possible.

Luck and Sievert [1] refer to the environment surrounding the detector as the "outer

world" , where all aspects important for fire detection must be modeled including both

fire and non-fire conditions. Once the detector environment is known, the species

transport to a detector's sensing surface or volume and its response can be modeled if

sufficient detailed information on a particular detector exists. An alternative approach is

to perform detector exposure experiments in the fire emulator/detector evaluator to

ascertain sensor responses for modeled, realistic fire scenarios.

2. Modeling Detector Fire Environments

Modeling of detector environments has evolved from the ceiling jet correlations applied

to (thermal) detector activation, zone modeling, to more detailed computational fluid

dynamics models. Davis [2] has developed a zone fire model "Jet" which has a ceiling

jet correlation embedded in the computational algorithm to facilitate better temperature,

smoke and species concentrations, and flow conditions at detector locations. The model

455
formulation has been used in a sensor driven fire model that utilizes thermal and/or

smoke sensor outputs to predict fire conditions [3]. Andersson and Holmstedt [4]

performed a computational fluid dynamics (CFD) study to predict temperature and

smoke light extinction at a detector location in a simulated EN 54 part 9 fire sensitivity

test. Davis et al. [5] used CFD computations to study complex ceiling geometry effects

on detector activation. Cleary et al. [6] used the Fire Dynamics Simulator, (FDS; a

computational fluid dynamics fire model based on large eddy simulation technique,

developed by NIST [7]) to predict the smoke, thermal and flow environment at a

detector located in a simulated EN 54 part 9 test room subjected to test fire 4.

Here, the fire model FDS was used to predict the fire environment at multiple detector

locations in a three room suite. Specifically, FDS was used to compute velocity,

temperature, smoke and CO gas concentrations at detector locations in each room of a

simulated fire located in one of the three rooms. A diagram of the room layout, fire and

detector locations is shown is Figure 1 . Room 1 is the fire room, where the fire source is

located at the floor in the center of the room. The ceiling height is 2.90 m and the door

openings are 0.91 m wide by 2.44 m high. Supply and return HVAC vents (0.3m wide

by 0.9 m high) are located in the walls at a height 0.3 m from the ceiling, and have fixed

flows between 0.04 m^/s and 0.2 veils. The return in room 2 acts as an open vent. The

ambient temperature was 20 °C and the surfaces were adiabatic (i.e., no heat loss to wall

or ceiling). The computational grid spacing was x = 150, y = 75, and z = 27, for a total

of 303,750 cells and a physical grid spacing of 17.2 cm, 20.8 cm, and 10.7 cm for x, y, and z directions respectively.

The simulated fire consisted of a flaming fire that starts out with a heat release rate

similar to the EN54 TF4 flaming polyurethane foam mat fire. It transitions to a "medium ^ fire" after the mat fire reaches its peak output at 200 s (Figure 2). The radiative

fraction was set at 0.35 with a heat of combustion fixed at 16 kJ/kg. The smoke and CO

yields of 0.03 g smoke/g burned and 0.0 Ig CO/g burned are in the range of what would

be expected from a flaming plastics fire. The detector locations represent two separate

detector spacings of 9.1 m (30 ft.) for locations 1, 3, 8, and 9, and 6.4 m (21 ft.) for

locations 1, 2, 4, 5, 6, 7, 10, 11, and 12. The environment was simulated for 500 s with

456
door

open

supply

m ©

Room 2

17.4 m

®

© 0

6.4 m

©

open

door

open

return

Room 3

25.9 m

door

closed

Figure 1 . Three room suite layout; circled numbers are detector locations.

1000 100

200 300

Time (s)

Figure 2. Heat release rate curve used in FDS computation.

the temperature, smoke and CO concentrations, and the x and y flow velocity vectors

recorded at each detector location (i.e., the computational grid at the ceiling which

encompasses a typical detector's vertical position). The x and y velocity vectors were

used to compute the scalar horizontal flow speed as a function of time at each detector

457
location. The smoke concentration was converted into an extinction coefficient by

multiplying the smoke concentration in grams per m^ by a specific extinction coefficient

for soot of8.7m^/g[8].

Figures 3 and 4 show the temperature, flow speed, CO volume fraction, and smoke

extinction coefficient computed at detector location 1 (LI) in the fire room. In Figure 3,

the temperature rise curve has the same shape as the heat release rate curve. The plume

centerline temperature at the ceiling was computed from Heskestad's strong plume

correlation at ambient background temperature of 20 °C [9]; AT = 25 Qc^'^ z

'^'^

, where

AT is the excess temperature (°C), Qc is the convective heat release rate, and z is the

height of ceiling from the fuel source. The fact that the correlation predicts higher

ceiling temperatures early may be due to the course grid size used in the FDS calculation

and the fact that the plume correlation was developed from steady-state fires. The

under-prediction later in the computation is due to the fact that the correlation is valid

for unconfined plumes, and the effects of entrainment of hot layer gases is not accounted

for. The horizontal flow speed is somewhat vague since the plume velocity is turning

600

500

o

400

oki -2 300

oI

200

o

100

0

Figure 3. Temperature and flow speed at detector location 1 in the fire room.

from mostly vertical to horizontal directly above the plume. In Figure 4, the CO volume

fraction gradually rises (noticeably starting at 60 s) to a peak volume fraction greater

than 500 X 10"^ at the end of the simulation time. The extinction coefficient started

100 200 300 400 500

Time (s)

458
500

c 400

o

cv ^300

u. O

oE

X

3 >-200

O

>

O 100

o

-^CO.LI 0 /

r Smoke, LI

-

p

a'

—m 1 I JKiea^^X—1 1 1 1 1—

4 E

3 -

.2

2 5

c

o

1 1

X

UJ 100 200 300 400 500

Time (s)

Figure 4. CO volume fraction and smoke extinction coefficient at detector location 1,

to rise at 60 s and continued to rise above 5 m' by the end of the simulation. For

comparison, an extinction coefficient of 0.13 m-1 is equivalent to a smoke obscuration

of4%per0.3m(l ft).

Figures 5 and 6 show the temperature, flow speed, CO volume fraction and extinction

coefficient values computed at detector locations 5, 8, and 10. These locations were

grouped together due to their proximity to one another and to the door opening from

Figure 5. Temperature and flow speed at detector locations 5, 8, and 10 in room 3.

459
Time (s)

Figure 6. CO volume fraction and smoke extinction coefficient at locations 5, 8, and 10.

room 1. In Figure 5, note that the flow speeds reach levels of over 0.8 m/s before

temperatures rise by 25 °C, and peak between 1.9 m/s, 2.5 m/s, and 3.3 m/s for locations

5, 8, and 10 respectively. These flow speeds are created from the jet issuing from the

doorway. In Figure 6, the CO volume fraction curves look similar to the CO volume

fraction in room 1 except that the initial rise started at 120 s and the peak volume

fractions were lower than at location 1. Smoke extinction followed a similar path

compared to location 1 , however, initial rise was delayed by 40 s to 80 s.

Figures 7 and 8 show the computed values at detector locations 6, 7, 9, 11, and 12.

Again, these were grouped due to their proximity. As expected, all computed values

begin to rise later than at locations closer to the fire. Except for location 1 1 after 300 s,

the temperatures, flow speeds, CO volume fraction and extinction values are quite

similar, however, shifted in time by 20 s to 60 s. The flow speed at location 11

continued to rise after 300 s, achieving a speed of 1 .5 m/s at the end of the simulation,

which suggests it was seeing the effects of the doorway jet more directly than the

locations 6, 7, 9, and 12.

Figures 9 and 10 show the computed values at detector locations 2, 3, and 4, all located

in room 2, and the furthest from the fire source. All computed values began to rise

460
0 100 200 300 400 500

Time (s)

Figure 9. Temperature and flow speed at locations 2,3, and 4 in room 2.

461
260 s and 320 s. The maximum speed at location 4 was nearly twice as large as the

maximum at location 3, owing again to its relative position to the doorway opening and

the jet issuing from room 3.

3. Fire Emulator/Detector Evaluator Tests

The fire emulator/detector evaluator (FE/DE) was used to reproduce the computed flow

speed, temperature rise, CO and smoke concentrations at select detector locations. The

FE/DE is a single-pass wind tunnel where room air is drawn into the opening, and

exhausted to a hood at the end of the duct. It was designed specifically to reproduce the

environment surrounding a detector during fire or nuisance events [10,11]. In the

FE/DE, air velocity at the test section can be controlled over a range of flows between

0.02 m/s to over 2 m/s by means of a computer-controlled axial blower. The flow is

conditioned before it reaches the test section by passing it through a 10 cm long

aluminum honeycomb with 5 mm rectangular openings. The goal was to provide a

nominally flat flow profile indicative of what would be expected by a detector in a

ceiling jet flow. The flow was monitored at the test section by a bi-directional probe

located at the duct centerline. For fixed fan speeds, the flow profile is nearly top-hat

with a velocity that fluctuates indicating turbulent flow. Thermal energy is added to the

flow by forcing it through a series of 9 annular finned heating elements. Each element is

rated at 5 kW for a total maximum heat input of 45 kW. Power td the heating elements

is controlled by a feedback controller that receives set-point values automatically from a

462
computer file and compares them to the air temperature exiting the heaters. An air

temperature difference between the heater exit and test section locations is due to heat

losses to the duct section between those two points. A rate of rise in air temperature of

0.5 °C/s is achievable at the test section, up to maximum temperature of about 80 °C.

Air temperature at the test section was recorded with type-K thermocouples.

CO, CO2, or other gas blends may be metered into the flow via electronic mass flow

controllers. CO, CO2, H2O, and hydrocarbon gas concentrations are monitored by nondispersive infrared analyzers. The standard uncertainty in the CO volume fraction

measurement is stated as 2.5 x 10"^. The ability to control gas concentrations

independently benefits both fire and nuisance alarm scenario emulation. For example,

both CO and CO2 may be normally present in ever-changing concentrations in a building

due to the external environmental sources such as attached parking garages, or internal

sources such as the diurnal CO2 variation due to occupancy and ventilation levels. The

FE/DE can be programmed to reproduce such conditions as part of an evaluation of a

fire detector that includes gas detection.

Various smoke and non-combustion aerosols may be introduced into the flow. Here, a

propene smoke generator, which provides black soot typical of flaming hydrocarbon or

plastics fire smoke, was used. The concentration of smoke in the flow is varied by

changing the fiiel flow of the burner and opening or closing dampers allowing more or

less flow fi-om the burner to enter the duct. Laser light transmission measurements

across the duct at the test section were used to calculate the extinction coefficient of the

propene soot. A He-Ne laser at 632.8 nm wavelength is the light source, and a stabilizer

utilizing a liquid crystal polarizer maintains a nearly constant laser intensity. The beam

is split and introduced at two heights: the center of the duct, and 5 cm below the ceiling

(here, the extinction measurement fi'om the beam 5 cm below the ceiling was used).

Each light beam is reflected off two mirrors inside the duct and directed at a

photodetectors placed on the opposite side of where the beam enters the duct. The total

path length inside the duct is 1.5 m. The photodetector output voltage is linear with

respect to the transmitted light intensity. The standard relative uncertainty due to random fluctuations in output is 0.06% of the measured light transmittance.

463
A multi-sensor, analog output fire detector was used to record continuous photoelectric

and thermal sensor signals during the emulated conditions. These outputs are actually 8

bit numbers from an analog to digital converter. Here, offsets were subtracted so the

outputs are zero to start. A CO sensor removed from a residential CO detector was

placed in the FE/DE test section during the tests and the voltage drop that developed

across a resistor placed between the two sensor electrodes was recorded.

The simulated detector environments that were emulated in the FE/DE and reported here

are detector location 2 in room 2, and detector location 1 1 in room 3. The computed

values at these locations represent the range of flow speeds and temperature rise

achievable in the FE/DE. The length of simulation time emulated depends on the flow

and temperature rise; the complete 500 s simulation at location 2 was emulated, while

only the first 330 s of the simulation at location 1 1 was emulated. Figure 1 1 shows the

duct velocity and fan settings for two repeats of a test that was designed to emulate

conditions developed at location 2. The fan setting was controlled such that the duct

velocity matched the simulated flow speed as indicated. Figure 12 shows the duct

velocity and fan setting for a test designed to emulate conditions developed at location

11. Again, good agreement between the simulated flow speed and the duct velocity was

achieved.

Time (s)

Figure 1 1 . Duct velocity and fan setting for emulated conditions at location 2.

464
Figure 13 shows the duct air temperature at the test section and the thermal sensor output

for repeated tests of the location 2 emulation. The computed temperature and the

emulated temperature compare favorably until about 450 s when the emulated

temperature starts to deviate from the computed temperature which continued to climb.

The thermal sensor lagged the thermocouple temperature due to its response

characteristics. Figure 14 shows the duct air temperature and thermal sensor output for

the location 1 1 emulation. Good agreement between the computed temperature and the

emulated temperature was maintained until about 330 s when the simulated temperature

rose above the operational range of 80 °C of the FE/DE. Note that the lag between the

200 250 300 350 400 450 500

Time (s)

Figure 13. Duct temperature and thermal sensor output for conditions at location 2.

465
0 50 100 150 200 250 300 350

Time (s)

Figure 14. Duct temperature and thermal sensor output for conditions at location 1 1.

thermal sensor and the thermocouple is less pronounced. This is most-likely due to enhanced convective heat transfer to the thermal sensor in this emulation due to the

higher duct flow velocities.

Figures 15 and 16 show the duct CO volume fraction for location 2 and location 11

emulations respectively. The agreement between the duct CO volume fraction and the

computed value is not very good. This is due in part to the fact that the lower limit of

the selected mass flow controller used to introduce the CO gas into the duct was too high

200 250 300 350 400 450 500

Time (s)

Figure 15. Duct CO volume fraction and CO cell volts for emulated conditions at

location 2.

466
o

II <aO

E X

3 w

O

>

O

o

60

50

40

30

20

10

0

I ' I

I I

I

I I I I

I

I I I . I ~*~Duct

Simulated

"6>" CO Cell

n'f^ratfr'. I .... I .... i

0.1 >

0.05 £

o

w

o

1

O

ow«<O

o

tu

-0.05

-0.1

-0.15 8

0 50 100 150 200 250 300 350

Time (s)

Figure 16. Duct CO volume fraction and CO cell volts for emulated conditions at

location 1 1.

to produce smooth continuous flows of CO needed to achieve the target concentrations.

For comparison, the electrochemical cell output is shown, and it compares favorably

with the duct CO concentration, with only a short lag time between the curves.

Figures 17 and 18 show the duct extinction coefficient and the computed extinction

coefficient for emulation tests of location 2 and 1 1 respectively. The smoke produced

by the propene burner is sufficient to emulate the computed extinction coefficients at

200 250 300 350 400 450 500

Time (s)

Figure 1 7. Duct smoke extinction and photoelectric sensor output for emulated

conditions at location 2.

467
0.3

I 0.2

£

©0.15

o

o

o

.EO.05

«*X

Ul 0

0 50 100 150 200 250 300

Time (s)

Figure 18. Duct smoke extinction and photoelectric sensor output for emulated

conditions at location 1 1

.

each location up to about 0.15 m-1. Owing to the ever increasing flow velocity, the

smoke production cannot keep up with the simulated smoke build-up. However, a high

smoke concentration that produces a large extinction coefficient is not necessarily

relevant to detector performance. The photoelectric sensor output tracked the duct

extinction coefficient in each emulation. Here, the duct flows are sufficiently high to

reduce the smoke entry lag to negligible times.

The particular simulated fire scenario chosen pushed the operation of the FE/DE to its

limits. Refinement of the FE/DE for these emulated conditions is required to overcome

the temperature, smoke production, and CO flow limitations.

4. Conclusions

From this work, it is concluded that multi-room fire simulation with the FDS software

can yield environmental conditions a detector or sensor may experience during an actual

fire. The FDS code computes the smoke, heat, and gaseous species transport needed to

predict detector performance. The specific fire scenario simulated here was chosen to

produce rapidly changing smoke and gas concentrations, heat, and flow velocities at

detector locations. Such a fire test would be quite expensive to perform a single time

since the fire room approaches flashover conditions during the computational time.

Once a detector environment is specified, the FE/DE can reproduce the important

-^Duct

Simulated

-€> - Photoelectric

* lO 1 1

1 1 ll

O

(0c

o

o

o

oo

468
variables of the environment in a repeatable fashion, and actual detectors or sensors can

be exposed to the defined environment for performance evaluation.

5. References

[I] Luck, H., and Sievert, U., "Does an Overall Modelling Make any Sense in

Automatic Fire Detection?", Proceeding of the 11th International Conference on

Automatic Fire Detection "AUBE '99", March 16-18, 1999, Gerhard Mercator

University, Duisburg, Germany, Luck, H., Ed., pp 1-9, 1999.

[2] Davis, W., "The Zone Fire Model Jet: A Model for the Prediction of Detector

Activation and Gas Temperatures in the Presence of a Smoke Layer", NISTIR 6324,

National Institute of Standards and Technology, Gaithersburg, MD, May, 1999.

[3] Davis, W., and Forney, G., "A Sensor-Driven Fire Model, Version 1.1", NISTIR in

preparation, National Institute of Standards and Technology, Gaithersburg, MD,

January, 200 1

.

[4] Andersson, p., and Holmstedt, G., "CFD-Modelling Applied to Fire Detection - Validation Studies and Influence of Background Heating," Proceeding of the 10th

International Conference on Automatic Fire Detection "AUBE '95, April 4-6, 1995,

Gerhard Mercator University, Duisburg, Germany, Luck, H., Ed., pp 429-438, 1995.

[5] Davis, W., Forney, G., and Bukowski, R., "Developing Detector Siting Rules from

Computational Experiments in Spaces with Complex Geometries," Fire Safety Journal,

Vol. 29, pp 129-139, 1997.

[6] Cleary, T., Anderson, M., Averill, J., and Grosshandler, W., "Evaluating Multi- sensor Fire Detectors in the Fire Emulator/Detector Evaluator," Proceedings of the 8th

Inter. Conf. on Fire Science and Eng., Interflam '99, Edinburg, Scotland, June 1999.

[7] McGrattan, K., Baum, H., Rehm, R., Hamins, A., and Forney, G., "Fire Dynamics

Simulator - Technical Reference Manual," NISTIR 6467, National Institute of Standards

and Technology, Gaithersburg, Maryland, January, 2000.

[8] Mulholland, G., and Croarkin, C, "Specific Extinction Coefficient of Flame

Generated Smoke," Fire and Materials, Vol. 24, pp 227-230, 2000.

[9] Heskestad, G., "Engineering Relations for Fire Plumes," Fire Safety Journal, Vol. 7,

pp. 25-32, 1984.

[10] Cleary, T., Grosshandler, W., and Chemovsky, A., "Smoke Detector Response to Nuisance Aerosols," Proceeding of the 1 1th International Conference on Automatic Fire

Detection "AUBE '99", March 16-18, 1999, Gerhard Mercator University, Duisburg,

Germany, Luck, H., Ed., pp 32-41, 1999.

[II] Cleary, T., "Performance Characterization of Multi-sensor, Multi-criteria Fire

Detectors in the Fire Emulator/Detector Evaluator: Particulate, Thermal, and Gas

Sensing Combinations," Proceedings of VdS Conf on Gas Sensors for Fire Detection,

November 15-16, Cologne, Germany, 2000.

469
Frank Gockel

Gerhard-Mercator-Universitat Duisburg, Germany

Fire Sensor Modelling and Simulation

1. Introduction

In automatic fire detection it is desirable to know exactly how fire sensors incl.

their housing work.

A fire sensor in its housing is the link between physical quantities in the sensor

housing environment and the usually electrical signals generated by the sensor. The physical quantities (fig. 1 ) in the room to be watched are converted into elecalarm

decision

fire non-fire

signal processing

fire detection algorithms

physical

quantities

signals

Figure 1 : Automatic Fire Detection

trical signals by the sensor element itself. The physical quantities must penetrate

through the housing, which surrounds the sensor element in order to protect it

against touch, damage, dirt, etc. and, in case of optical sensors, against disturbing external light.

The actual measure.'nent of the physical quantities as well as the process of enter470
ing the housing have some influence on the measuring result. Exact knowledge

how these mechanisms work is required in order to correctly describe fire sensors and interprete their signals in a correct way.

The computing t/n/f (fig. 1 ) represents the "intelligence" of a fire detection system.

It processes the sensor signals, it combines those of a group of sensors if necessary, watches for sudden changes or crossed thresholds etc. in order to form an

alarm decision.

The sensor in its housing together with the computing unit represent the fire detector.

When it becomes possible to model and simulate the fire sensor in its housing

then complete fire detectors can be simulated in computer program on the base

of physical quantities which have been measured and recorded during test fires. This is even possible for a new fire detector while it is under development if the

basic model is correct.

The sensor element is the device that actually measures physical quantities and

converts them into an electrical signal.

2.1. lonistaion chamber type smoke sensor

The ionisation chamber can be described according to Hosemann [12]

2. The sensor element

y (1)

d=0

y = smoke density

rj = chamber constant [12]

d = particle diameter

471
N^'^^d) = particle size distribution

Though there are more accurate theories available Hosemann's formula [12] is used for simplicity. The chamber current ij., which depends on the smoke density

y according to

y = --- (2)

/q = chamber current without smoke

is also somewhat influenced by the velocity of the air through the chamber. This

effect is not contained in Hosemann's formula. It can be described:

hi-) /,(v = 0)/,(— ) (3) -Er kx X < { 2 (4)

X > 1 constant

2.2. Optical smoke sensors

Optical smoke sensors i.e. the scattered light sensor and the light extinction

sensor, can be described using the MiE theory [16, 13]

/ = /o/^(/,(f^,m,e) + /2(^,m,0))dyv(^)(rf) (5)

d=0

^ext =

j C,AdW\d)

I = intensity of scattered light = intensity of incoming light

d = particle diameter = scattering volume

A = wavelength of incoming light

n = 3.1415926...

r = distance between observer and scattering volume

^1,2 = M IE scattering functions [13]

472
III — romnlpx rpfrr^ptivp indpy

fc) = scattGring angiG

^ext = Gxtinction coGfficiGnt

c = Gxtinction cross section [13] = particlG size distribution

2.3. Elektrostatic sensor

ElGCtrostatic SGnsor can bG dGscribGd according to [9]

oo = ^ /

PoC-^M'-^J^^^'^Hd) (7)

d=0

P,ia) = min{^;l} (8)

/ = SGnsor curront

K — SGnsor constant

a = particlG radius

d = particlG diamGtGr

qj.{a) = GlGctrlcai chargG on a particlG

N^^\d) — particlG sizG distribution

b[a) = GiGCtrical mobility of a particlG

U = voltagG of SGnsor

V = vGlocity of air through thG SGnsor

h.d^ — gGomGtrical sizGS

2.4. Semiconductor gas sensors

SGmiconductor gas SGnsors usually prGSGnt an GiGCtrical output quantity according to:

g = c^K^+c^K^ + ... + c,K„ + Cj{T-Tq) (9)

473
g = sensor output signal

r- = sensitivity constants

Kj = volume concentration of gases

T = temperature

Tq = reference temperature, usually room temperature

3. Particle filtering

The sensor housing can be interpreted as particle filter. It is a particle-size distinctive filter according to the five principles described by Ogawa [20, 21]:

• Inertia Deposition: decreases number of large particles

• Interception: decreases number of large particles

• Diffusion: decreases number of small particles

• Electrostatic attraction: decreases number of small particles

• Gravity settling: decreases number of large particles

Particle deposition also occurs in the inside of the sensor housing. Altogether,

these effects result in a kind of particle size bandpass so that the particle size

distribution in the inside of the housing differs from the particle size distribution

outside:

Nj^^cl) = N^^\d)f,p{d) (10)

d = particle diameter

Nj^^d) = particle size distribution, inside

/v(^^(c?) = particle size distribution, outside

fgp{d) = bandpass function

Another way to achi-eve this is the following: In an abtract point of view, if the

particle size distribution can be described by a set of parameters /?,,. ..,;?„ the

474
particle bandpass transforms these parameters into another set For

example, we can assume a logarithmic normal distribution with the parameters

(volumetric number concentration), /i (geometric mean particle diameter), and cr (geometric standard deviation) which turn into n^^, ia-, and cr,, respectively.

4. Entry lag

Entry lag was first decribed by Heskestad [11] and has been extended by

Cleary e.a. [5]. It is modelled as an air velocity dependent system with the

impulse response

h{t) = AE{t-TQ)ce-'^'-'o^ (11)

c = (12)

t, = k/' • (13)

A = constant, area under the function

[ 0, t<0

e = Euler number

k^.,d^-,ki,d, = parameters

V = velocity of air through the sensor housing

5. Combining things into a general model

For a complete spot-type fire sensor the following general model based on the

model proposed by Fissan and Helsper [8] is proposed:

The model (fig. 2) consists of the four parts A, B, C, D while B is distributed

Into the two parts B1 and B2. The vektor x, representing the model input, is a

combination of all physical quantities important for fire detection. Fig. 3 shows an

example.

475
c D

input filter filter sensor output filter

Figure 2: fire sensor in its housing

/ comment

1 volumetric particle number concentration

2 geometric mean particle diameter

3 G geometric standard deviation

4 m complex refractive index

5 V velocity of air

6 T temperature

Figure 3: Example of physical quantities in vector Jc

Part A is a memoryless system. Its output vector is a vectorial function of x

i.e. each component of the output vector may depend on each component of the

input vector. The idea is that part A represents the particle filter.

Part B is a system, with memory modelling the entry lag. It is based on the theory

of linear and time-invariant systems though the system is not necessarily linear or

time-invariant. First, part B1 constructs a function h-{T) for each component of the

input vector. Then, this function is used as an impulse response in part 82. So the

output vector Xg consists of components which are the results of the convolution

of the respective component of with the associated impulse resonse. Note that

the impulse response may be time dependent because it is formed by part B1

476
which gets time dependent input signals.

Part C describes the sensor element itself. This means that it combines the components of its input vector jcg into a single-component output signal .v^ according

to the physical laws describing the sensor (Mie theory, Hosemann theory etc).

Part D is an output filter representing for example sensor signal amplifier characteristics (lowpass, limiter). It is optional.

6. Simulation results

Simulation results presented reflect the state of the study from July 2000 and are

preliminary. The study is continued. Simulations are based on particle measurements by Tamm, Mirme, e.a. [23, 17] which result in a particle size distribution

in the form of N^^\d,t) during the test fires. All tests have been made in the fire

detection laboratory at the Gerhard-Mercator-Universitat Duisburg. Also, the air

velocity has been recorded.

In the following figures 4 to 6, the measured output signals of industry-standard

fire sensors are compared to the simulated signals according to the model proposed above. As an example, test fire TF1 according to EN54/9 [7] is shown. The

results for other fires are similar.

7. Conclusion

A new, complete, and highly modular model for fire sensors including the housing

has been introduced. The model is based on the work from Heskestad,Fissan

and Helsper, Cleary a.o. and has been designed to be suitable for computer

simulation. Simulation results have been presented and compared to signals

measured with industry-standard fire detectors.

477
current in pA

12000

0

simulation - measurement

measurement -

100 200 300 400 500 600 700 800 900 1000

time in seconds

Figure 4: ionisation cinamber response during TF1

References

[1] N.G. Bernigau, H. Luck: The Principle of ttie Ionization Chamber in Aerosol Measurement Techniques - A Review. Journal of Aerosol Science

Vol 17 No 3, 1986, pp. 511-515

[2] J. Bjorkman, D. Baroudi, M. Kokkala, R. Latva, M. Tuomissaari: Determination of Characteristic Parameters of Point-type Smoke Detectors, in:

H. Luck (Herausgeber): Tagungsband der 11. Internationalen Konferenz

uber automatische Brandentdeckung AUBE'99, Duisburg, Marz 1999, Agst

Verlag Moers 1999, S. 22-31

[3] CP. Bohren, D.R. Huffman: Light Scattering and Absorption by Small Particles. Wiley, New York 1 983

[4] Th. Cleary, W. Grosshandler, a. Ghernovski: Smoke Detector Response to Nuisance Aerosols. In: H. Luck (Herausgeber): Tagungsband der 1 1 . Internationalen Konferenz uber automatische Brandentdeckung AUBE'99, Duisburg, Marz 1999, Agst Verlag Moers 1999, S. 32-41

478
rel. intensity

7000

6000

5000

4000

3000

2000

1000

0

-1000

1 1 1 1

1 1 I.J.I

Simulation

measurement _ J

III! 1 1 1 i 1 0 100 200 300 400 500 600 700 800 900 1000

time in seconds

Figure 5: scattered ligint sensor response during TF1

[5] Th. Cleary, a. Chernovski, W. Grosshandler. M. Anderson: Particulate Entry Lag in Spot-type Smoke Detectors. In: Proceedings of the Sixth

International Symposium on Fire Safety Science, Poitiers, 1999

[6] I. COLBECK, B. Atkinson, Y. Johar: The Morphology and Optical Properties of Soot produced by different fuels. Jo urnal of Aerosol Science Vol 28

No 5 July 1997. pp. 715-723

[7] CEN EUROPAISCHES KOMMITTEE FUR NORMUNG: EN 54 Tell 9, Bestandtelle automatlscher Brandmeldeanlagen, Erprobungstest. 1 984

[8] H. FissAN, C. Helsper: Zur Reaktlon von Rauchdetektoren auf BrandkenngroBen. In: H. Luck (Herausgeber): Tagungsbericht des 8. internationalen Vortragsseminars uber Probleme der automatischen Brandentdeckung AUBE'82, Duisburg, Oktober 1982, S. 20-37

[9] F. Gockel: Messung der elektrlschen Ladung bel Testbranden. In: H. Luck

(Herausgeber): Tagungsband der 11. Internationalen Konferenz uber au- tomatische Brandentdeckung AUBE'99, Duisburg, Marz 1999, Agst Verlag

Moers 1999, S. 160-169

479
Figure 6: light extinction sensor response during TF1

[10] C. Helsper, H. Fissan, J. Muggli, A. Scheidweiler: Verification of Ionization Chamber Ttieory, Fire Technology 19, 1 , 1983, pp. 14-21

[11] G. Heskestad: Generalized Characterization of Smoke Entry and Response for Products-of-Combustion Detectors. In: V. Aschoff, H. Luck

(Herausgeber): Tagungsbericht zunn 7. internationalen Vortragsseminar

uber Probleme der automatischen Brandentdeckung AUBE'75, Aachen,

1975, S. 267-310

[12] J. P. HosEMANN, H. GiLSON: MeBkammer nach dem Kleinionenanlagerungsprinzip zum quantitativen Nachweis von Aerosolpartikeln.

Forschungsberichte des Landes Nordrhein-Westfalen Nr. 2336, Westdeutscher Verlag Opiaden, 1973

[13] M. Kerker: The Scattering of Light. Academic Press, New York 1969

[14] CD. Litton: A Mathematical Model for Ionization Type Smoke Detectors

and the Reduced Source Approximation. Fire Technology 13, 4, 1977, pp.

266-281

[15] G. Meyer: Ein mathematisches Modell fur lonisationsrauchmelden

In: H. Luck (Herausgeber): Tagungsbericht des 8. Internationalen

480
Vortragsseminars uber Probleme der automatischen Brandentdeckung

AUBE'82, Duisburg, Oktober 1982, S. 38-62

[16] G. Mie: Beitrage zur Optik truber Medien, speziell kolloidaler Metalldsungen.

Annalen der Physik, Band 25, Folge 4, Nr. 3, 1908, S. 377-445

[17] A. MiRME, E. Tamm, U. Sievert: Performance of an Optical and an lonisation Smoke Detector Compared to a Wide Range Aerosol Spectrometer In:

H. Luck (Herausgeber): Tagungsband der 11. Internationalen Konferenz

uber automatische Brandentdeckung AUBE'99, Duisburg, Marz 1999, Agst

Verlag Moers 1999, S. 380-391

[18] G.W. Mulholland: Smoke Production and Properties, in: Ph. J. DiNenno

(Editor): The SFPE Handbook of Fire Protection Engineering, National

Fire Protection Association, Quincy 1995, pp. 2/217-227

[19] J.S. Newman: Prediction of Fire Detector Response. Fire Safety Journal 12,

1987, pp. 205-211

[20] A. Ogawa: Separation of Particles From Air and Gases. Vol. 1, CRC Press,

Boca Raton, Florida 1984

[21] A. Ogawa: Separation of Particles From Air and Gases. Vol. 2, CRC Press,

Boca Raton, Florida 1984

[22] F.N. Simon, G.D. Rork: Ionization Type Smoke Detectors. Rev. Scient.

Instrum. 48, 2, 1976

[23] E. Tamm, A. Mirme, U. Sievert, D. Franken: Aerosol Particle Concentration and Size Distribution Measurements of Test-Fires as Background

for Fire Detector Modelling. In: H. Luck (Herausgeber): Tagungsband der 11. Internationalen Konferenz uber automatische Brandentdeckung AUBE'99, Duisburg, Marz 1999, Agst Verlag Moers 1999, S. 150-159

481
B. Farouk, G. W. Mulholland*, and K. B. McGrattan*

Department of Mechanical Engineering and Mechanics, Drexel University

Philadelphia, PA

*Building and Fire Research Laboratory, National Institute of Standards and

Technology, Gaithersburg, MD

Simulation of Smoke Transport and Coagulation for a Standard Test Fire

Abstract

Large eddy simulations of a standard test fire (EN 54-9, TF4) were carried out. The

development of the large-scale air movements and temperature fields generated by the

enclosure fires are calculated. In addition, the smoke transport and time evolution of the

size distribution of smoke aerosol due to coagulation are also predicted. The mass and

number densities of smoke particles are computed at a detector location, as specified in

the standard test fire procedure. Recent measurements of the number and mass densities

of smoke using electrical aerosol spectrometry compared favorably with the model

predictions.

Introduction

Smoke detector response is sensitive to both the concentration of smoke ingested and

the size distribution of the aerosol in a fire scenario. The local smoke characteristics

actually sensed by the detector must be related to the physical and geometrical

properties of the fire and the enclosure, so that the response of a smoke detector can be

understood.

Generally the ionization detectors are found to be more sensitive to the number density

of the smoke. As a class, smoke detectors using the ionization principle provide

somewhat faster response to high energy (open flaming) fires, since these fires produce

larger numbers of the smaller smoke particles. On the other hand, photoelectric smoke

482
detectors respond to the volume (mass) density of the smoke particles rather than the

number density.

Prediction of smoke transport and coagulation has been attempted in the past [1,2]

where the evolution of size distribution of smoke aerosol under the influence of

coagulation as well as the large scale fluid motion and temperature fields were studied.

Specifically, Lagrangian particles or 'thermal elements' were used to model the burning

of fuel in the fire plume. Each thermal element (blob) represents a given mass of smoke

(containing many smoke particles), which is proportional to the instantaneous heat

release rate. The transport of the thermal elements is also used to model smoke

movement (without smoke coagulation) m a Lagrangian sense. The evolution of the size

distribution in space was calculated deterministically from the solution to the

Smoluchowski equation.

A promising methodology for the prediction of large-scale gas movement, temperature

field and smoke movement m fire plumes and enclosure fires has been recently

introduced [3]. The model and computational methodology have reproduced mean

temperature and buoyant velocity correlations for large fire plumes [4]. In this paper,

we present numerical results for the velocity and temperature fields induced by a

standard test fire (EN 54 part 9) [5] by applying the above model, which incorporates

large eddy simulation techniques. The simulations reported here are for the test fire TF4

(open plastic fire) [5]. The smoke particles are again represented by a large number of

the thermal elements - continuously introduced at the burning surface, while the fuel is

being consumed. Based on prescribed rates of smoke yield (Y kg/kg of fiiel consumed),

the smoke transport within the enclosure is reported. While the ionization detectors are

found to be more sensitive to the local number density of the smoke, the photoelectric

detectors are more sensitive to the mass density. Hence it is necessary to determine both

the mass and the number densities of the aerosol reaching a detector. A smoke

coagulation sub-model based on the Smoluchowski equation [6] is incorporated to track

the mean number density of smoke particles in each blob with time. The mean size of

the smoke particles m each thermal element increases with time due to coagulation. The

smoke mass and number- densities at a specified detector location are computed directly

483
from the number of thennal elements and the corresponding number densities of smoke

particles in each themial element (blob), present at the detector location. The time

evolution of the mass and number densities of smoke at the detector location are compared with the recent measurements reported by Mirme et al. [7]. The model

predictions for smoke coagulation compare favorably with the reported measurements.

Problem description

The test fire TF4 in EN 54-9 [4] is an open plastic fire, which is allowed to bum freely

with no restriction of air supply in an enclosure. The overall enclosure dimensions and

the detector location are specified in the test procedure. For the present simulations, a

9.5 m X 6.3 m X 4.0 enclosure was considered (see Figure 1 below). A small vent is

considered at a bottom comer of the enclosure to allow for constant pressure condition

during the combustion process. The fire source is located at the center of the enclosure

(0.5 m X 0.5 m) on a 0.25 m high pedestal. The walls, floor and ceiling are considered to

be thermally insulated. The rate of heat release qit) for the TF4 fire is estimated from

the measurements reported by Ahonen and Sysio [8].

Model description

A fire plume is a three-dimensional transient buoyant flow that can be modeled by the

motion of a thermally expandable ideal gas [8]. The Navier-Stokes equations are solved

for such a fluid driven by a prescribed heat source Following Rehm and Baum [9] the

pressure is decomposed into three components, a background (average) pressure, a

hydrostatic contribution, and a perturbation to the hydrostatic pressure. High-frequency

acoustic oscillations are eliminated while large temperature and density variations

typically found in fires are retained. The resulting equations are thus referred to as

weakly compressible and are valid for low Mach number flows. Constant pressure

specific heat of the gas is considered in the formulation. An elliptic partial differential

equation for pressure perturbation is formulated by taking the divergence of the

momentum equation. Further details of the mathematical formulation can be obtained in

[3].

484
4.0 m

Po ly u reth ane Foam M als

6.3 m

Figure 1 . Schematic of the test fire laboratory

Large eddy simulation technique

The present application of the large eddy simulation (LES) techniques to fire is aimed at

extracting greater temporal and spatial fidelity' from simulations of fire performed on

the more finely meshed grids allowed by modem fast computers. The small-scale eddy

motion is modeled via a sub-grid description. One such representation is the

Smagorinsky model. There have been numerous refinements of the original

Smagorinsky model but it is difficult to assess the improvements offered by the newer

schemes. In this study, we have used the Smagorinsky model, which produces

satisfactory results for most large-scale applications where boundary layers are not

important [3].

Combustion model

A sub-grid thermal element model (TEM) is formulated to represent the fire. A large

number of Lagrangian elements (blobs) are introduced into the plume, releasing heat as

they are convected by the thermally induced motion [3]. The overall heat release rate

q (t) from the fire is discretized as thermal elements that represent pyrolized ftiel. At a

specified surface, such as the fuel bed, thermal elements are ejected at a rate of h" blobs

per unit time per unit area. The heat release rate of a single thermal element 'j' is given

where q" (to) is the instantaneous heat release rate per unit area of the fuel bed and tb is

the burnout time (t - to < tb) of the thermal element and to is the time the element is

by

q"(to) 1 (1)

485
ejected from the burning surface. The burnout time is obtained from the plume

correlations of Baum and McCaffrey [4].

Smoke transport model

A specified percentage of the fuel consumed (smoke yield, Y kg/kg of fuel) is assumed

to be converted to smoke particulate. The smoke transport is simulated by tracking the

motion of the thermal elements. The smoke mass in any thermal element (blob)

introduced at time to is given by

= (2)

' AH^ n"

where q" (to) is the instantaneous heat release rate, Y is the smoke yield and AHc is the

heat of combustion of the fliel. Again, li" is the number of thermal elements injected

per unit time per unit area of the burning surface. In the present model, the total smoke

mass in each blob remains constant as it is convected by the buoyant plume. At any

instant, the smoke mass density at the detector location can be determined if the total

number of smoke blobs in the detector volume is known. The smoke mass density is the

summation of smoke masses of the individual thermal elements in a grid cell of volume

5x5y5z

.

C (t) = ^

(3)

oxoyoz

Smoke coagulation model

The most important physical mechanism acting to change the smoke aerosol sized

distribution once it leaves the flame zone of a fire is coagulation. The particles present

at high concentration levels collide as a result of Brownian motion and stick together.

The time evolution of coagulating particles for a uniform system is described by the

Smoluchowski equation

^H^^^ - v' ,v')n(v -v' ,t)n{v\t)dv' - 2n{v,t){r{v,v')n(v' ,t)dv' (4)

dt

where n{v,t)dv is the number concentration in the particle volume size range v to v +

dv and r(v, v ') is the coagulation frequency. In this analysis, we assume that Ffv, v') is a

constant. The total number concentration, N(t) and the volume concentration V(t) are

obtained as the first two moments of the size distribution. Integrating equation (3) with

respect to volume leads to the following differential equation:

486
Smaller particles collide and stick decreasing the number concentration, but the volume

concentration is unchanged. For the present calculations, the time evolution of the

number density of smoke particles ns (t) in a given thermal element is modeled as [5]

n°

;7jt) = ^ (6)

(l + TAA'V)

where is initial number of smoke particles in a thennal element at the time of its

injection, F is the coagulation frequency and N° is the initial number concentration of

smoke particles (number of particles/volume) in the flame zone. For the present

calculations, the value of G (G= FN" ) is estimated from measurements reported in

literature. The initial number of smoke particles «° in a thermal element (injected at

time to ) is calculated from

AH, .."(j^JTD; ft)

where Dp is the initial smoke particle diameter and Ps is the soot density. The smoke

number density is the summation of smoke masses of the individual thermal elements

in a grid cell of volume §x5y5z

:

C.v(t) = (8)

oxoyoz

where ns,j is defined in equation (12) above for the j-th particle.

Results and discussion

We present the results for a hypothetical fire with a 'fixed heat release rate'

first. The computational domain is a parallelepiped region with a rectangular base (9.5

m X 6.3 m) and a height of 4.0 m. The origin of the Cartesian coordinate system is at

the right bottom comer. A uniform mesh (64 x 32 x 32) was used for the simulations.

This gives a cell size with dimensions 0.15 m x 0.20 m x 0.12 m. All simulations were

carried out for a period of 300 s. For all calculations presented, only 65% of the energy

from combustion is considered to be the convective heat release. The rest of the energy,

converted to radiation, did not play any role in the simulations as none of the surfaces

487
were specified in the model as thermally active. The heat of combustion of the fuel

(AHc) was taken as 20,000 kJ/kg. . A smoke yield value of Y = 0.05 kg/kg of fuel

consumed) was used for the present simulations. Figure 2 shows the time-dependent

convective heat release rate for the simulation with 'a fixed heat release rate'.

According to EN54 - 9 specifications, the detectors and measuring instalments are to be

located along a 3 m radius from the center of the fire source. A detector location was

chosen near the ceiling (z = 3.9 m) at x 2.25 m, y = 3.15 m (see Figure 1). For the

present mesh, one computational cell was found to be large enough to represent a

detector. The temperature rise at the detector location is shown in Figure 3 as a function

of time. The temperature at the detector location continues to increase as the heat

release rate is held fixed in the simulations. A large number of thermal elements were

introduced into the flow field to properly characterize the smoke transport and

coagulation. Five thermal elements were introduced per each cell covering the burning

area per time step (AT = 0.05 s). All thermal elements introduced were tracked for the

entire period of the calculations (200 s) unless they leave the enclosure by the small

vent (see Figure 1).

3C0

Figure 2. Convective heat release rate for

the 'fixed heat release rate' case.

Figure 3. Predicted temperature profile

at the detector location

488
The predicted smoke number density variation (averaging time = 3 s) at the detector

location for the 'fixed heat release rate' case is shown in Figure 5. Coagulation effects

G = 0.1 Fixed heat release rate Averaging time . 3 s

50 tai 1513 Time is'i

Figure 5. Predicted smoke number

density Cm(t)at the detector location for

the 'fixed heat release rate' case

HIT'

a

lie' £

7TD-"

Q = 0.1 FiKed heat release rate Averaging time : 3 s

St> ICC ISC- Tim 9(2) Figure 6. Ratio of smoke mass/number

densities Cm(t)/CN(t)at the detector

location for the 'fixed heat release' case

were considered by setting G = 0.1 s. The time-averaged evolution of the ratio of the

smoke mass/number densities at the detector location is shown in Figure 6. The ratio

increases initially as the buoyant plume reaching the detector develops. The effect of

aging on the smoke particles is evident after about 150 s when the ratio of mass/number

densities starts decreasing.

The simulations of the test fire (TF4) are presented next. The EN54 -9 specifications

suggest a fuel source of soft polyurethane foam mats (0.5 m x 0.5 m). The heat release

rate for the fuel source is obtained from the measurements of Ahonen and Sysio [7].

The heat of combustion of the fuel (AHc) was taken as 20,000 kJ/kg and the smoke

yield value was Y = 0.05 kg/kg. Figure 7 shows the time-dependent convective heat

release rate for the simulations of test fire TF4.

IDS JEC Tlm9(£)

Figure 7. Convective heat release rate for the TF4 fire

2 4D

Time (s)

Figure 8. Predicted temperature profile

at the detector location for the TF4 fire

489
The TF4 fire simulations were carried out for a period of 300 s. The large eddy

simulations capture the evolution of the characteristic structure of the fire-plume and the

associated buoyant flow field. The temperature rise at the detector location is shown in

Figure 8 as a function of time. The temperature (non-averaged) at the detector location

remains unchanged for the first 20 s, then increases rapidly. After the fuel source is

fially consumed, the temperature is found to decrease gradually.

The predicted time variation of the smoke mass density (averaging time: 3 s) at the

detector location is shown in Figure 9 for the TF4 fire. The smoke mass density

increases continuously for the about 1 20 s (see Figure 7 for the corresponding rise in

heat release rate) then levels off as the strength of the fire decreases. The recirculating

flow generated in the test section maintains the level of smoke mass density at the

detector location. When the calculations are carried out with no coagulation model (G =

0 s), the predicted evolution of smoke mass and number densities are similar and the

ratio of mass/number densities at the detector location is invariant.

: Averaging time: 3 s

8

o

gll?

^11? r

_i 1 I 1 I I L

IDD 3D0

"nmi9(s)

Figure 9. Time-averaged (3 s intervals) prediction of the smoke density Cm(t)

at the detector location for the TF4 fire

Recent measurements of the TF4 fire smoke characteristics are reported in Mirme et al.

[7]. An electrical aerosol spectrometer (EAS) was used to measure the smoke aerosol

characteristics including number and mass densities. The EAS is able to measure the

extremely fast changing aerosol in a broad particle size range. The measured time

variation of the smoke number density (top curve) and the smoke mass density (bottom

cur\''e) are shown in Figure 10.

490
Mirme el al. f 1999)

Time {i-i

II?

2

1 1?"

Figure 10. Measured time variation of

the fire sensor readings for the

plastic fire (TF4), Mirme et al.

M\m% 9t al. (1999)

IDD ICC

"nfi'i«i,s;i

Figure 1 1 . Measured time variation of

the ratio Cm(t) /CN(t) for the plastic

fire (TF4), Mirme et al.

The ratio of the measured smoke/mass number densities is shown in Figure 11. Both the mass and number densities detected tend to increase as the fire grows with the growth

rate of mass density higher than the growth rate of the number density.. While the

growth rate of the mass density levels off, the number density keeps decreasing as the

aerosol ages.

The model predictions for smoke coagulation for the TF4 fire (for different values of

the coagulation frequency, G ~s are now presented and qualitative comparisons with the

measurements (shown in Figures 10 and 11) are made. Figure 12 below shows the

predicted time-averaged (averaging time: 3 s) smoke number density for the TF4 fire at

the detector location with G = 0.1 ^s. As the fire gains strength, the number density

predicted increases, then starts decreasing as the coagulation effects become apparent.

The trend observed in the measurements (Figure 10) is similar to that shown in the

predictions in Figure 12. However, differences exist quantitatively between the

measurements and the predictions. While the peak value of the measured smoke number

density is about lO''' Ini , the predicted peak value of the smoke number density is about

10"* W. Also the coagulation rate predicted with G = 0.1 -s appears to be somewhat

faster than the rate observed in the measurements (Figure 10, top curve).

491
go"

|ii>'' C

o

leftG = 0.1 Averaging time: 3 s

IDD ICC

Figure 12. Time-averaged (3 s intervals)

predictions of the smoke number density

CN(t) at the detector location (G =0.1 s)

for the TF4 fire

cx2

8

o

G = 0.1 Averaging time: 3 s

_j I I 1 1 I ' 7lm9(s)

Figure 13. Time-averaged (3 s intervals)

prediction of the ratio Cm(t) /CN(t) at the detector location for the TF4 fire

The predicted evolution of the ratio of mass/density number densities is shown in

Figure 13 with G = 0.1 "s. The measured ratio (as shown in Figure 1 1) increases as the

fire develops which signifies onset of coagulation effects. In the predictions, the Cm(t)

/CN(t) ratio remains somewhat invariant for the first 100 s and then starts increasing.

This is consistent with the number density variation shown in Figure 1 1 . Predictions

were also obtained with a slower coagulation frequency, G = 0.01 s. The predicted

time variation of the ratio Cm(t) /CN(t) is shown in Figure 14. As expected the ratio

-.10'

Gn = 0.0

1

Averaging time: 3 s

IDC 2CC

Figure 14. Time-averaged (3 s intervals) prediction of the ratio Cn,(t) /CwCt) at the

detector location (G = FN" = 0.01)

shows a much gradual rise with time now, indicating that the model simulates the

prescribed physics realistically. Better qualitative and quantitative agreement with the

492
measurements can be obtained by systematically varying the adjustable parameters in

the model, namely, the coagulation frequency, G and the smoke yield Y.

Conclusions

Numerical simulations of smoke transport and smoke coagulation in the fire TF4 (EN54 part 9) were conducted. The simulations were based on the heat release

rate as prescribed in the test fire. The present results provide interesting insights to the

smoke movements as seen by a detector placed near the ceiling of an enclosure.

Acknowledgements

One of the authors (BF) gratefully acknowledges support from the Building and Fire Research

Laboratory at NIST, Gaithersburg, MD where he served as a Guest Researcher during the academic year

1999-00

References

[1] Baum, HR, Rehm RG and Mulholland GW, 'Computations of Fire Induced Flow and Smoke

Coagulation' 19''^ Symposium (International) on Combustion, The Combustion Institute, 1982, p. 921-931

[2] Baum HR, Rehm RG and Mulholland GW, 'Prediction of Heat and Smoke Movement in Enclosure

Fires', Fire Safety Journal, 1983, 6, p. 193-201

[3] McGrattan KB., Baum HR. and Rehm RG., 'Large Eddy Simulation of Smoke Movement', Fire

Safety Journal, 1998,30(2), p. 161-178

[4] Baum HR., and McCaffrey BJ., 'Fire Induced Flow Field - Theory and Experiment' Fire Safety

Science - Proceedings of the Second International Symposium, 1989, p. 129-148

[5] Jackson MA, and Robins 1, 'Gas Sensing for Fire Detection: Measurements of CO, C02, H2, 02,

and Smoke Density in European Standard Fire Tests', 1994, Fire Safety Journal, 22, p. 181-205

[6] Mulholland GW, 'Smoke Production and Properties', NFPA Fire Protection

Handbook, Sec. 2, Ch. 15, 1993

[7] Mirme A., Tamm, E. and Siever, U, 'Performance of an Optical and an Ionization Smoke Detector Compared to a Wide Range Aerosol Spectrometer', Internationale Konfemz uber Automatische

Brandentdeckung, AUBE 1999, Proceedings, p. 380-391

[8] Ahonen, AI, and Sysio PA, 'A Run-in Test Series of a Smoke Test Room - Tests according to the

proposal prEN 54-9', Research Report 139, Technical Research Center of Finland, 1983

[9] Rehm RG. and Baum HR., 'The Equations of Motion of Thermally Driven Buoyant Flows'. Journal

of Research, National Bureau of Standards, 1978, 83(3), p. 297-308

493
William D. Davis & Glenn P. Forney

National Institute of Standards and Technology, Gaithersburg, MD. U.S.A.

A Sensor-Driven Fire Model

1. Introduction

Modern building fire sensors are capable of supplying substantially more information

to the fire service than just the simple detection of a possible fire. Nelson, in 1984,

recognized the importance of tying all the building sensors to a smart fire panel [1].

In this paper, a sensor-driven fire model is described that is designed to achieve a

smart fire panel configuration such as envisioned by Nelson. A sensor-driven fire

model makes use of signals from a variety of detectors such as smoke, heat, gas, etc.

to detect, verify and predict the evolution of a fire in a building. In order to

accomplish this task, the fire model must be able to discriminate between fire and

non-fire conditions, must be able to recognize detector failure for both fire and nonfire scenarios, and must be able to determine the size, location, and potential hazards

associated with a growing fire.

The fire model must be flexible by having the capability to handle fire scenarios in

rooms where there may be anywhere from a suite of detectors to no detectors. In the

latter case, detectors in adjacent rooms would provide the necessary sensor input to

the model. For the suite of detectors, the model must be able to take advantage of the

increased amount of information in order to provide earlier and more reliable

detection. The model must also be able to accommodate detector failures due to a

growing fire and still continue to provide estimations of fire growth and location.

Finally, the model must be able to handle a large number of rooms and must complete

its calculation cycle in a time interval that is shorter than real time.

494
2. Sensor-Driven Fire Model

X'ersion 1.1 of the sensor-driven fire model, SDFM, is designed to predict the heat

release rate (HRR) of a tire based on signals from either smoke or heat detectors

positioned below the ceiling that sample the ceiling jet produced by the fire. The

estimated HRR is then used by a variant of the zone model CFAST to predict layer

temperature and heights in the fire room and in the adjacent rooms in the building.

Based on the predicted layer temperature and height, room conditions such as limited

\ isibility and flashover potential can be deduced. In non-fire situations, the SDFM is

designed to look for sensor failure, to discriminate between nuisance signals and fire

induced signals, and to monitor the condition of detectors that degrade over time.

The model will spend virtually all its time monitoring the building detectors in the no

fire mode. In this mode, the signal received from each detector will be compared

with the historic detector record to identify any deviation from normal operation.

Detector failure modes will be checked and sensor signals that fall into these modes

will resuh in a trouble (sensor failure) signal being sent to the appropriate monitoring

location. Version 1.1 of the sensor-driven fire model has only a simple checking

algorithm available that detects sensor failure based on either no signal or a saturated

signal from the detector.

When the model receives a detector signal that indicates a HRR increasing with time

and has reached a target threshold, the model will try to verify that it is a true fire by

assessing the signals received by other available detectors in the area. Such detectors

might include CO or COj detectors as well as heat or smoke detectors. If other

detectors do not support the fire signal, a trouble signal will be issued and the

program will revert to its normal detector polling. If no other detectors are available

in the room or if other sensors also support the presence of a fire, a fire alarm will be

issued.

The target threshold for the model to start checking for a fire is based on two

alternative methods of .defining a fire signal. The first method used by the model is to

495
compare the sensor signal with a user prescribed signal. This signal would be one

that has been developed by observing the response of the detector to small test fires.

The second method would be based on looking at the time history of the detector

signal once an estimated HRR based on the detector signal has been reached. If the

detector signal indicates a time growing hazard that has reached a particular HRR, a

fire alarm will be issued. This second method may allow for earlier detection of fires

as well as fewer false alarms since it depends on a time history as well as a signal

magnitude.

The determination of a HRR from a detector signal requires knowledge about the

characteristics of the detector and its position with respect to the fire. Detector

characteristics include the calibration curve for the analog/digital signal generated by

the detector as a function of temperature or smoke/gas concentration and the delay

time introduced by thermal lag or flow conditions into the sensing element. Once the

detector characteristics have been defined, the HRR may be estimated using modeling

correlations coupled with a zone fire model. In the following discussion, it will be

assumed that only one detector is present in each room. The detector will be located

close to the ceiling where it can be considered in the ceiling jet. Presently, version

1.1 of SDFM contains algorithms to estimate HRR from either the excess temperature

or the smoke concentration in the ceiling jet [2 3 4].

3. Estimating the extent of fire hazards

Once a HRR has been obtained for one or more of the identified fire sources, this

information will be passed to a version of CFAST in order to calculate layer height,

temperatures and smoke concentrations in each room of the structure. From this

information, hazards such as limited sight, high temperatures, toxic gases and

potential for flashover may be identified on a room by room basis for the current fire

conditions.

The layer temperatures and smoke concentrations calculated using CFAST are also

used to estimate fire spread from the room of origin to adjacent rooms. The signals

496
from sensors in these adjacent rooms are compared with calculated signals based on

the estimated layer temperature and smoke concentration predicted by CFAST. If the

ceiling jet temperature as estimated from sensor signals is 30% higher than the upper

layer temperamre predicted by CFAST, it is assumed that a fire has broken out in the

adjacent room. If the predicted upper layer temperature exceeds the flashover

temperature, 500 °C, it is assumed that a fire has started in that room. In addition,

with a known HRR history, projections can be made using CFAST to estimate tire

growth and spread. The present version of SDFM does not have this capability.

4. Model Verification

Since the SDFM is designed to operate in a space with a large number of rooms,

verification of the algorithms becomes a major problem. One method of verification

will be to use the results of multiple room fire experiments and to test the predictions

of the SDFM against these experiments. The number of these fire experiments is

quite limited, so an additional method of verification is being used. The Virtual

Cybernetic Building Testbed at NIST is a computer platform where the building

ventilation, heating and cooling, and sensor activities in a multiple room building can

be simulated.

HSM

G2 —

Room 3 y—

/

Room 3 Construction Mate rials

Room 2 7

Room 2

Constructloii Materials Room 1

j Gad burner Room 1 ionstrucUon Matsrials

03 Thsrrnccouole "ae

Gi CO, COe

32 02 @ Static prsssura

HS<.( Smoke Obscuration, Horliontal VSM Smoke Obsc^ralun. Vertical

Fill. t>t[.-k ^

Figure 1 Three-room test plan view.

497
The present structure modeled in the test-bed contains three rooms and will soon be

increased to nine rooms. Using CFAST [5] or FDS [6], a fire scenario can be

generated for the testbed room geometries and the resulting sensor signals used as the

model input for the SDFM. In this way, the SDFM can be tested using a virtual fire

source and receive signals which are representative of what may happen in an actual

fire scenario in the virtual building.

Two fire experiments were selected to provide initial verification of the SDFM. The

first is a three room, single level experiment where the fire source was a methane

burner [7]. The second experiment is a seven room, two story full scale fire test

(Sharon 2) where the major fuel source was wood pallets and tlashover was achieved

in the burn room [8] . Thermocouple data was available for both experiments and

350

100 200 300

Time s

400 500

Figure 2 Calculated (sdfm) vs measured (exp) upper layer

temperature for each room - 1, 2, 3.

single thermocouples near the ceiling were used for the SDFM inputs to mimic the

response of ceiling mounted heat detectors

498
A plan view of the Three-room test is shown in figure 1 . The test was modeled as a

three-room structure with two short corridors connecting the rooms being modeled as

doors. While multiple thermocouple trees and sensors were available in each room,

only one thermocouple per room was used to provide a sparse data set for comparison

with room conditions. A sparse data set does not provide sufficient information to

locate the fire and therefore provides a test of the default positioning algorithm used in

the SDFM. The experiment consisted of a methane burner operating at 2.8 kW for

the first 330 s of the test. The 2.8 kW fire was increased to 103 kW at 340 s into the

lest. The SDFM detected a fire in the burn room at 350 s into the test. The initial

fire of 2.8 kW was below the threshold setting for fire detection in the SDFM.

Figure 2 provides a comparison of the predicted upper layer temperatures of the

2.5

.Sf 1 5

* sdfm

ia exp

0.5

0 I 1 0 100 200 300 400 500

Time s

Figure 3 Upper layer height comparison for room 2

SDFM with the calculated upper layer temperatures of the experiment. The plots start

from time 0 that is the time that the SDFM detected a fire (350 s into the experiment).

The SDFM predictions are higher than the calculated upper layer temperatures of the

499
experiment for the second room but are in good agreement for rooms one and three.

The average 95 % confidence interval for this data based on five identical experiments

is ± 18 °C, ± 6°C, and ± 4 °C for rooms 1, 2, and 3 respectively. The layer height

comparisons for room 2 are given in figure 3. The experimentally measured layer

heights are based on observations of the height of the smoke layer in the experiment

and are not inferred from temperature measurements. Agreement between the

observed layer heights and calculated layer heights are quite good. The average 95%

confidence interval for this measurement is ± 0.2 m.

The SDFM is designed to provide information concerning the fire threat that fire

fighters might encounter in a building. The fire threats presently in the model include: a smoke layer less than 2 m above the floor (limited visibility), a smoke layer above a

temperature of 50 °C and layer height below 1.5 m (toxic gas/themial hazard), and a smoke layer at a temperature higher than 500 °C (flashover). For the three-room

experiment, the upper layer temperature did not reach flashover and no flashover

warnings were issued. A comparison of the SDFM predictions with the experimental

measurements is given in the table below. The SDFM was run with a reporting interval

of 20 s.

Room 1 EX 1 SD 2 EX 2 SD 3 EX 3 SD

Visibility Limited na 20 s 20 s 40 s na 60s

Toxic Gas - Themial

Hazard; Layer

na 100s 190 s 100 s na nr

Toxic Gas - Thermal

Hazard; Temperature

0 s 20 s 50 s 100 s 460 s 80 s

Table 1 Comparison of time of occurrence for three hazard predictions. The symbol

"na" stands for not available and "nr" is not reached. 1 EX is the first room

experimental results while 1 SD is the first room model prediction.

Experimental layer heights were given for only room 2 in the experiment. Hence, the

toxic gas/themial hazard warning has been separated into two parts, one the smoke

layer temperature in excess of 50 °C and the other for a layer height less than 1.5 m.

500
In room 1, the SDFM provided a toxic gas/thermal liazard warning as quickly as it

could (based on temperature) and is in good agreement with the experimental

measurement.

In room 2, the limited visibility warning was in good agreement with the experimental

measurements. The toxic gas/thermal hazard warning was issued roughly 90 s ahead

of the criteria being met experimentally. This difference is due to the experimental

layer height staying just above the 1.5 m layer height criterion while the calculation

predicted a layer height that is just below the 1.5 m criterion (see figure 3).

In room 3, the layer temperature in the experiment remains just below the 50 °C

criterion while the calculated value is just above the 50 °C criterion (see figure 2) for

an extended time period. The small differences in the experimental and calculated

temperatures account for the large difference in the time to issue the warning. Based

on the results for rooms 2 and 3, the warning levels used to issue hazard warnings

should be set at a lower value than the hazard level so that small differences in the

calculations will not delay potential hazard warnings.

n» 1

Ro» 1 C0.C02

- -Uii]. CO. CO: . (grrca Or

Upstairs

Figure 4 Sharon 2 Townhouse Plan View
To simulate the Sharon 2 test, the seven-room townhouse was divided into eight

spaces and thermocouple data was used to provide ceiling jet temperatures in six of

1200

1000

800

Uo2 600

a; 400

200

• brm

B brm c

fi (J n li I

X (J n li I c

50 100 150

Time s

200 250

Figure 5 Upper layer temperature comparison for the burn room (brn)

and the lower level hallway (dn hi). The "c" indicates the calculation.

the eight spaces. A plan view of the townhouse, showing locations of the

instrumentation, is shown in figure 4. In the simulation, room 2 was partitioned into a

hallway and a room with the thermocouple tree, TC6, providing the temperatures for the

hallway. Thermocouple trees TCI, TC2, and TC3 were used to provide data for the

upstairs spaces while TC8 and TC4 were used for the other downstairs spaces. The

partitioned room 2 on the first floor and the stairway were modeled spaces with no

thermocouple measurements. Only the themiocouple near the ceiling was used for the

input data from each tree.

The first 231 seconds of the fire was modeled for the Sharon 2 fire since, in the

experiment the wood pallets in the fire room began to fall off the load cell after this

time. Figures 5 and 6 provide a comparison of a representative layer temperature as

502
400

• up li I

B u p h I c

i s br

X s br c

X n br

• n br c

250

Figure 6 Upper layer temperature comparisons for the upper liallway (up ill), soutli bedroom (s br), and nortli bedroom (n br). Tlie ""c" indicates tlie calculation.

measured by the thermocouple trees with the layer temperature calculated by the

model for each room. Agreement between the calculations and the measurements is

quite good for all rooms although the model tends to overpredict the temperature.

The experimental layer height was detennined by estimating the location of the

midpoint of the temperature transition between the lower layer temperature and

theupper layer temperature. The location of the midpoint above the floor was taken as

the upper layer height. The calculated layer heights agree well with the estimated

layer heights for most of the comparison interval. Only at the last time intervals for

the second floor bedrooms do the calculated upper layer heights drop significantly

below the estimated layer heights.

The predicted fire threats correlated well with the estimated occurrence of these

threats for the lower level rooms using the data shown in table 2 below. The layer

503
heights were predicted to be lower for the upper level rooms than measured which

accounts for most of the differences shown in the table for these rooms. For the

bedrooms, the smoke in the room may become well mixed with a two-layer structure

beginning to disappear. The cycle time for the calculations and measurements was 12

s, meaning those two or three calculations or measurement times produced several of

the time differences.

Room Burn Burn LL LL UL UL N N S S

EX SD EX SD EX SD EX SD EX SD

Visibility 24 12 49 12 88 24 78 49 80 36

Limited

Toxic Gas - 24 12 49 24 99 36 103 61 94 49

Thermal

Hazard

Flashover 48 24 97 97 nr nr nr nr nr nr

Table 2 The table presents a comparison of the times in seconds between the

experiment (EX) and the SDFM (SD) for the hazard conditions in the burn room

(Burn), the lower level (LL) hall, the upper level (UL) hall and the two upper level

bedrooms labeled N (north) and S (south). The symbol "nr" indicates that this

condition was not reached.

5. Summary

The goal for the SDFM is to provide adequate warning of fire threats within a

structure using the building sensors as detectors. For comparisons with two fire

experiments, fire warnings were given that were in reasonable agreement with

measurements. This agreement was obtained using data from only one sensor per

room. Additional sensors in each room would permit the fire source to be more

accurately located and as a result better predictive capabilities would be expected.

The present results, using only single detectors in each room, provide information that

would be of value to fire fighters.

504
The smoke detector algorithms have not yet been tested experimentally and the model

needs to be tested in buildings that contain real detectors and HVAC systems. There

are additional algorithms that need to be added or expanded in the model. These

algorithms include but are not limited to improving the multiple detector algorithm for

a single room, adding lag time algorithms for detectors, expanding the false alarm

algorithm in the model, and adding a wall heating algorithm.

Version 1.1 of the SDFM has demonstrated that this type of fire model can give useful

results in both simple fire conditions and in fire conditions where flashover occurs.

[1] Nelson, H. E. Funcfional Programming/Research Planning for High Technology

Federal Office buildings. National Institute of Standards and Technology 1984;

NBSIR 84-2828: 28.

[2] Davis, W. D. The Zone Fire Model Jet: A Model for the Prediction of Detector

Activation and Gas Temperature in the Presence of a Smoke Layer. National

Institute of Standards and Technology 1999; NISTIR 6324: 1-51.

[3] Evans, D. D. Calculating Sprinkler Actuation Time in Compartments. Fire Safety

Journal 1985; 9: 147-155.

[4] Davis, W., D. and Reneke, P. Predicting Smoke Concentration in the Ceiling Jet.

National Institute of Standards and Technology 2000; NISTIR 6480:1-12.

[5] Peacock, R. D., Forney, G. P., Reneke, P., Portier, R., and Jones, W. W. CFAST, the

Consolidated Model of Fire Growth and Smoke Transport. National Institute of

Standards and Technology 1993; NISTTN 1299: 1-235.

[6] McGrattan, K. B. and Forney, G. P. Fire Dynamics Simulator - User's Manual. National

Institute of Standards and Technology 2000; NISTIR 6469: 1-49.

[7] Peacock, R. D., Davis, S., and Lee, B. T. An Experimental Data Set for the

Accuracy Assessment of Room Fire Models. National Institute of Standards and

Technology, 1988; NBSIR 88-3752: 1-11 1.

[8] Levine, R. S. and Nelson, H. E. Full Scale Simulation of a Fatal Fire and

Comparison of Results with Two Multiroom Models Volume II - DATA. National

Institute ofStandards and Technology 1990; NISTIR 90-4268: 1-125.

505
O. Keski-Rahkonen

VTT Building and Transport, P.O. Box 1803, FIN-02044 VTT, Finland

01avi.Keski-Rahkonen@vtt.fi

Revisiting modelling of fluid penetration into smoke detectors for

low speed ceiling jets

1. Introduction

A smoke detector is in principle a container, which has partially permeable

walls separating the gas volume in the detector from the volume around it. Walls delay fire detection as compared to a fully open detector. Heskestad

[1] drafted a theory using dimensional analysis arguments for the time lag

Ar of a products-of-combustion fire detector

At = yl/v (1)

where v is the mean convective flow velocity around the point detector, I

the characteristic length scale, and y a non-dimensional coefficient

characteristic for the geometry. According to [1] Eq. (1) is valid presuming

Viscosity effects are not considered important'.

Brozovsky [21 observed the simple relationship of Eq. (1) did not hold for low

ceiling jet velocities. In Fig. 1 his observations (dots) are plotted as a

function of velocity v. Thin solid lines represent exponential fits by

Brozovsky [2] on his limited set of data. Further data manipulation showed,

an equation of the form

At = rl/(V-vJ (!')

is also a plausible fit on the data. The numerical values of the parameters

were: yl = 0.8 m and = 0.075 m/s, which Brozovsky called a critical

velocity. Fitting Heskestad's correlations on these data according to Eq. (1)

506
would yield curves similar to # 1 and # 2 in Fig. 1. These predictions are in

contrast with experiments. No value of yl would fit experimental data.

1

-

I V ''

'

' ' I

' '

' i 0 0.1 0.2 0.3 0.4 0.5

Velocity (m/s)

Fig. 1. Entry lag time dependence on flow velocity past a detector. Dots

(experimental data). (1) and (2): The delay time according to Eq. (1) as

explained in text. (3): Fit according to Eq. (V), = 0.075 mis). (4) and (5)

Calculated models explained in text.

2. Flow and pressure fields around solid bodies

2.1 Principles of calculation

There is a wealth of literature on flow around solid objects in a flow field

using different techniques at different level of approximation [6 - 10]. No

general analytical solutions are available for all flow velocities.

Furthermore, in the whole body of this literature only some references were

given on works, where the body is partially transparent 17 - 8]. For these

reasons here general^ techniques of flow modelling are attempted in the

regions, where analytical solutions are available. Flow through the detector

507
is treated as perturbation. To grasp the salient physics of the flow we first

calculate the pressure field around a totally solid body for a constant

velocity, and use that information to calculate flow through the mesh in and

out of the fire detector. The validity of perturbation approach is not granted.

Failure of it demonstrated by related drag problem. Despite that, anal3^ical

approaches are so simple, it was decided to try them first. For calculating

the flow around the detector Reynolds numbers are typically 100 ... 1000.

For flow through the mesh Reynolds numbers are in the range 0.1 ... 10.

Therefore, the calculation can be made in two stages, and requires different

approximations in both ranges.

A smoke detector can be modelled as a solid object in a flow field, which is

partially permeable. For modelling we set the center of the detector in

origin, and arrange a flow along the negative x-axis with a velocity far

from the origin towards positive x. The point on the detector, where

negative x-axis penetrates the outer surface is a stagnation point. On the

opposite lee side, there is a point, where the dynamic pressure is the lowest.

For the smoke flow through the detector the insect mesh provides the

biggest flow resistance. Without quantitative calculations, only through

visual inspection of some detector constructions all other channels of the

flow have much bigger cross sections, and relative to the mesh penetration

pressure drop can be considered only as small.

Therefore we concentrate here only on the ultimatelly simplified

geometrical model of the detector: (i) a cylindrical or hemispherical outer

mesh, (ii) small channels through the mesh into (iii) the interior of the

detector of total volume V". Due to additional light scattering barriers and

flow channeling guides inside the detector the volume V is considered a

well stirred reactor, which communicates with the space outside the insect

mesh through the holes on it. The other alternative in the inner space is to

use laminar plug flow, and calculate time of smoke front moving from the

508
mesh the center point. For a smoke detector two geometrical, highly

idealized models are used: (a) a two dimensional, infinitelly long circular

cylinder neglecting the presense of the ceiling, and (b) a three dimensional

hemisphere setting the frictionless ceiling in the equatorial plane. First,

potential flow is applied to both problems to account for the disturbance the

detector causes on the free flow field.

For calculation we set the coordinate system as shown in Fig. 2. The origin

O is set to the center of the detector in the plane of the ceiling.

Perpendicular to the plane of the ceiling and along the center axis of the

detector points the z-axis downwards. The detector screen is cylindrical or

spherical showing as circle in the x-y-plane of Fig. 2. The screen is partially

permeable for the flow, and idealized to a surface of zero thickness. The

ceiling jet is assumed to be a free constant velocity flow approaching the

detector parallel to the x-axis far left. The detector screen divides the space

into two regions: (1) inside, and (2) outside. For modelling flow equations

are written for both regions, and boundary conditions along the surface

presenting the screen bound the regions together.

509
1 ^—

y —7>\

1

(2)

Figure 2. Coordinate system used for detector modelling.

2.2 Inviscid potential flow

Experimenting with different combinations of boundary conditions between

the inside and outside regions showed, that no extra effects were obtainable.

Therefore, potential flow a free stream of velocity is superposed with a

two (cylinder) or three (sphere) dimensional doublet [8]. For solid bodies the

radial velocity component disappears on the surface. For a permeable

body the first order perturbation theory is attempted, which would as

mismatch yield to radial velocity distribution

= £ cos (p (2)

where the numerical value of s is determined using experimental pressure

distribution around the body slightly simplified [7]. For laminar flow from

510
the stagnation point to the separation angle the (p^ the velocity obeys Eq.

(2), after which it has a constant value reached at separation point. The

experimental pressure amplitude for laminar flow is

Ps -Pk^ ^f^l ' (3)

where q = 1 for a cylinder and 0.75 for a sphere. For turbulent flow the

pattern is similar, but numerical values of q differ slightly. The flow was

not yet turbulent around the detector, and thus shis combination is not

discussed further.

2.3 Laminar viscous flow

A creeping laminar flow past a cylinder results in a logarithmic singularity

(Stokes paradox) but past a sphere (Stokes's first problem) the pressure

distribution is

symmetric, given on the surface by [10]

p-p^-^-^coscp (4)

2a

By coupling to that field an additional radial flow velocity component into

the spere in the form of Eq. (2), it can be shown to be of the higher order in

perturbation expansion than the second therm in the pressure distribution

of Eq. (4). Therefore, two-stage calculation is plausible. The flow inside the

sphere satisfying Navier-Stokes equations to the same degree of

approximation as for the outer flow takes place at constant velocity parallel

to the flow far from the detector. Oseen has extended the calculation of

Stokes as a power series of Reynolds number [11]. The second term of

511
pressure averages to zero over the sphere. Therefore, it is not taken here as

a separate case.

3 Flow through small openings

Calculating the smoke density inside the detector for a flow, where particles

do not separate from the fluid, the time constant z„ of well stirred volume V

is given asymptotically by

T^=V/V (5)

where V volume flow in the detector. For a plug flow a similar time

constant is obtained by dividing the radius of the detector by the average

velocity inside the detector. Both consepts are used here.

When the flow is laminar inviscid through the mesh volume flow is

proportional to the square root of pressure difference [7].

For viscid creeping fluid the flow through the holes takes place in a layer

attached onto the surface of the mesh. For an order of magnitude estimate

we look fully developed viscous flow, which can be solved accuratelly in a

closed form for rectangular channels (Poiseulle flow) [7] or for circular tubes

(Hagen-Poiseulle flow) [6-7]. Taking the circular variation volume flow V

through a hole of radius a is given by

y^_^r^±

(6)

8 jj ds

where r] is the dynamic viscosity of air, dp I ds the pressure drop along the

channel, and the radius of the circular hole . In Table 1 calculation forms

are given for the lag time 7,,, (s) for different reasonable combinations of

ambient and grid flow conditions. The formulas are preceeded by numerical

factors C(. Radius of the detector is a, ratio of combined hole areas to total

grid surface area k, and effective length of pressure drop through mesh S. In

512
Fig. 1 these time lags are plotted as a function of velocity v„ for all

calculated cases using rough estimations of the numerical coefficients c^ in

those cases they were not direct results from the derivation of the formulas.

This is plausible for an order of magntude calculations.

Curve # 2 is viscid-viscid combination, and curve # 1 plug flow model for the

same case. Curve # 5 is laminar-laminar combination with a very big

difference between theory and observations. Curve # 3 presents laminar

ambient causing viscid grid flow, which is still rather far from measured

data.

Table 1. Lag time z„ (s) calculation formulas.

Ambient laminar Ambient creeping viscid

inviscid

Grid laminar inviscid a NA

Grid creeping viscid va5 a'5

2 2 2 0 00

4. Conclusions

Order of magnitude calculation formulas were derived for the lag time of

fire detectors using two different scales of flow: (i) ceiling jet flow around the

fire detector, and (ii) flow through the mesh. At different jet velocities

different flow approximations apply. The experimental data do not support

any of the proposed theoretical models since both the magnitudes of

calculated lag times differ considerably from the observations, and

513
asymptotically the theoretical curves do not have a pole at a finite velocity.

As for the flow through the mesh, viscosity effects cannot be neclected.

The reason for the negative result is not known for sure. Some guesses could

be made to guide further research. Perturbation approach may not be valid,

because the disturbation might be finite even for a very small flow through

the detector. There is an analogy with the drag problem of a sphere [10].

The flow through the grid is never fully developed. This was not taken into

account, because of difficulty of modelling.

No apparent experimental errors were found in [21. Therefore, the

behaviour described by Eq. (1') seems to indicate the flow might have a 'nonnewtonian' character plugging the flow at velocities lower than Vg

.

Qualitativelly, such a flow occurs, if the fluid, which here is an aerosol,

behaves collectivelly like a non-newtonian continuum fluid of Bingham

plastic [3]. The analytical theories of such fluids would in principle allow

calculation of the flow in and out of the detector [4, 5]. However, there

seemed not be available experimental data of rheological properties of

smoke. The tacit assumption has always been, smoke behaves like air, an

almost perfect newtonian fluid. 'Non-newtonian' behaviour might also be

caused by electric rather than fluid phenomena. Most of the smoke particles

are charged. They might stick on the grid at low velocities. However,

without further detailed experimental observations, if not yet readily

available, these questions cannot be settled.

Acknowledgements

This study has been partially financed by the Finnish Centre for Radiation

and Nuclear Safety, the Ministry of Trade and Industry, Fortum

Engineering Ltd, Teollisuuden Voima Oy, and the Finnish Fire Research

514
Board. I am indebted to E.L. Brozovsky for sending me the original

experimental data.

References

[ 1 1 Heskestad. G. Generalized Characterization of Smoke Entry and Responce for

Products-of-Combustion Detectors, Fire Detection of Life Safety, Proceedings of a Symposium, March 31 and April 1, 1975, National Academy of Sciences, Washington ,

D.C., (1977), p. 93 - 127.

[2] Brozovsky, E.L. A Preliminary Approach to Siting Smoke Detectors Based on

Design Fire Size and Detector Aerosol Entry Lag Time, Master's Thesis, Worcester

Polytechnic Institute, Worcester, MA, 1991, 96 p.

[3] Irvine, T.F., Jr., & Capobianchi, M., 1998. Non-Newtonian Flows, in R.W. Johnson

(Ed.) The Handbook of Fluid Dynamics, CRC Press, Boca Raton, Chapter 22.

[4] Kawase, Y. & Moo-Young, M., 1992. Flow and Heat Transfer in Turbulent

Slurries, International Communications in Heat and Mass Transfer 19, 485 - 498.

Bukowski, R.W. & Mulholland, G.W. 1978. Smoke Detector Design and Smoke

Properties, NBS Technical Note 973, National Burea of Standards, Washington, D.C.,

51 p.

[5] Patel, N. & Ingham, D.B., 1994. Flow and Heat Transfer in Turbulent Slurries,

International Communications in Heat and Mass Transfer 21, 75 - 84.

[6] Frank, P. & von Mises, R. 1943. Die Differential- und Integralgleichungen der

Mechanik und Physik, Mary S. Rosenberg, New York, 2. Auglage, 1 106 p.

[7] Truckenbrodt, E., 1980. Fluidmechanik, Springer, Berlin, Band 1, 371 p., Band 2,

426 D.

[8] Katz, J. & Potkin, A. 1991. Low Speed Aerodynamics, McGraw-Hill, New York,

632 p.

[9] Kirchhoff, R. H. 1998. Inviscid Incompressible Flow - Potential Flow, in R.W.

Johnson (Ed.) The Handbook of Fluid Dynamics, CRC Press, Boca Raton, Chapter 7.

[10] Gursul, I. 1998. Incompressible Laminar Viscous Flows, in R.W. Johnson (Ed.)

The Handbook of Fluid Dynamics, CRC Press, Boca Raton, Chapter 9.

515
[11] Clift, R., Grace, J.R. & Weber, M.E. 1978. Bubbles, Drops, and Particles,

Academic, New York, 380 p.

516
Claudia Rexfort and Thomas Kaiser

Gerhard-Mercator-Universitat Duisburg, Germany

e-mail: rexfort@sent5.uni-duisburg.de, th.kaiser@uni-duisburg.de

A General Approach for Simulating Signals

of Scattering Light Detectors

1 Introduction

The simulation of fire signals is an important tool in automatic fire detection. It is useful

for developing new detection algorithms or for testing existent algorithms. Simulations

make it possible to develop algorithms for fire detection without expensive measurements

in fire detection laboratories. This article introduces a first approach of a general method

of simulation. The basis of such a simulation is a statistical model of the measured signal

which describes the fire. In this article the point of view lies on the signal processing

and decision making part of the automatic fire detection [1], where we concentrate on

scattering light detectors. Those signals (figure 1) often include impulsive parts, which

have been seen as noise in earlier approaches [2. 3]. There, the impulsive noise has been

12000

Y{k)

1 1000

10000

9000 -

8000 -

7000

6000 -

5000

O 200 400 600 800 1000 1200

k

Figure 1: Signal of scattering light detector (sampling rate Ihz)

517
suppressed in a first step of data processing and has been added in a further step as additve

noise signal. Here, in our general approach the impulses are seen as a part of the signal and

will be simultaneously considered by simulating the signal to circumvent the inefficient

and maybe unprecise two-step approach.

2 Motivation

One advantage of simulating fire signals is the possibility of testing detection algorithms

with a few number of measured fire signals. Fire signals which were measured by testfires

can be used to simulate new fire signals. So after changing the parameters of an algorithm

or developing a new algorithm, these simulated signals can be used to test the algorithm.

This test can be done on a PC without implementing the algorithm on any hardware. To

achieve comparable testresults a large number of tests with different data are necessary.

With simulating fire signals it is possible to generate a large number of data without measuring a lot of test fires. This means saving time and costs. Difficulties with simulating

the signals of a scattering light detector are described in the next section.

3 Model of the Signal

The basis of the simulation is an additive model of the signal, that can be described as

follows:

Y{k)=X,{k)+X{k)

with

K (A:): measured signal

X^j{k): deterministic part of the fire signal

X{k): statistic part of the fire signal including noise

518
The figures 2 and 3 show the deterministic and the statistical part of the signal shown

in figure 1. The deterministic part is supressed in the first step of processing the signal

simply by highpassfiltering, only the statistical part will be simulated. The figure 3

1000 1200

Figure 2: deterministic part of the fire signal

500

X{k)

-500

-1000 -

-1500

-2000

-2500

200 400 600 800 1000 1200

Figure 3: statistical part of the fire signal

shows that the fire signal is not stationary, the statistic properties change with time. But

the signal can be separated into stationary segments of different length. This is done after

filtering the deterministic part of the signal. Therefore the signal power of the signal is

evaluated. It is obvious that the signal energy of the signal in figure 3 for 0 < /: < 700 is

519
less than the energy for k > 700. he stationary segments of the fire signal are described

by an autoregressive AR) process X{k) of the order p, which looks as follows

X{k) = J^a^X{k-i)+N{k),

where X{k — i) are former samples of the signal X{k) and N{k) is a so-called stable distributed random process. So X{k) at the discret time ^ is the sum of former values of the

signal X{k — i) which are weighted with the coeffients and the noise signal N{k) at the

same point of time. For simulating the signal the coefficients cij have to be estimated by

the given measured signal X{k). Figure 4 shows such an AR model, where is a time

Nikl

a1

<3| —t »

«2 »

m

delay of one sample.

Figure 4: autoregressive (AR) model

4 Symetric a-Stable (SaS Noise)

In this general approach symetric a-stable (SaS) noise with zero mean is used as input

signal for the AR model to simulate the fire signal. A ra ndom variable is called a-stable,

520
if its characteristic function (the Fourier transformation of the probabilty density function)

can be described as follows [61

(p{z) = e^'^'"

with

0 < a <2: characteristic exponent

y > 0: dispersion

Figure 5 shows an example for a SaS stable random signal N^^^{k) with a = 1.5. A

special case of the symetric a-stable distribution is the gaussian distribution which is

described by a = 2. Figure 6 shows the probability density function p^^ (n) of SaS

k

Figure 5: a-stable distribution with a = 1.5

signals with different characteristic exponents a. The diagram shows that with decreasing

a the tails of the function becomes heavier. For a = 2, the gaussian case, the tails are

the smallest. Because of the heavy tails there is one problem with processing signals with

a-stable distribution. The moments with a order of a or higher do not exist except for the

special case of the gaussian distribution. For this reason estimation procedures which use second or higher order moments can not be used in the case of a-stable distribution with

a < 2 as the popular least squares (LS) method for example. In this case other methods

have to be used. In the literature there are different solutions for this problem [6. 4|. Some

of these solutions are modified LS methods or use lower order {p < 2) moments.

521
k

Figure 6: a-stable distribution with a = 1.5

5 Simulation with gaussian Noise

The object of this approach [2] is the estimation of the parameters of the AR process

which describes the signal. As described before the signal X{k) has to be separated in

stationary segments. The segmentation is done with the Maximum-Likelihood method

where the energie of the prediction errors of different signal segments are compared. The

prediction error is calculated in the following way

n{k) = j;^a,r{k-i)+x{k),

where ;7(/:) and dj are estimated values. The AR parameters d^ are estimated by minimizing this prediction error. During this segmentation algorithm the impulsive part of the

signal is supressed. If the energy of the prediction error exceed a threshold the signal is

extrapolated at this point of time. The extrapolated value of the signal is calculated by an AR process using former values of the signal as input in the following way

x(^) = Xfl,(/:)x(/:-0,

where the AR parameter change with every step of iteration. After finding the steady

segments their AR parameters are estimated by using the least squares methods for each

522
of the segments. With this AR parameters and the Hmits of the segments the stationary

segments of the tire signal can be simulated. After the simulation a random signal is

generated which contains the impulses. This signal is added to the simulated signal.

Figure 7 shows a schematic diagram for this classical approach.

X{k) segmentation and

impulse supression

all samples

processed

X{n) impulse

addition

.A_R-model

- standard deviation

- segment limits

- AR parameters

Figure 7: Structure of classical simulation approach

6 Simulation with a -stable Noise

In our approach of simulating a fire signal, the impulses are not interpreted as noise but

as a part of the signal. Contrary to the algorithm described in the in section before the

impulses are not supressed but considered at the estimation of the AR parameter. There

are different methods descnbed in the literature to estimate AR parameter for models with

impulsive noise as input signal [6. 4]. In this w ork a method is used w hich minimzes the

p-th order moment. The minimizing problem of this /;;-norm looks as follow s:

K A argmm^ X ~ S anx{k-n)\P

A-l «=1

w here

d — a^.a-. .flv^"^- estimated AR coeffients

x{k) = 0 for < 0

\ < p <a

K: single -length of a-(A:)

A^: order of AR model

523
This Ip norm is a generalization of the least squares deviation where p is equal to 2. In

this work the iteratively reweighted least squares (IRLS) algorithm is used to solve the

minimization problem [4, 8]. The algorithm starts with the LS solution and in every

step it solves a weighted LS solution whereby the weights are the iteratively calculated

residuals of the signal. The algorithm looks as follows [4, 5J

1- a(0) = (x^x)"'z''s

2- r^(/) = [s-xa(0]^

3. W,,{i)=p\r,{i)\P-^

4. a(i + l) = (z^W(/);t)-';t^W(Os

5. IF ^IfL^-^ <. then stop

else go to step 2)

where

a(/'): the parameters of the AR model at the i-th iteration

s: one value shifted version of the data vector jc = [x{k — \),x{k — 2), . . .]

r: residual vector

W: diagonal matrix of the residuals (weight matrix) ^ = 0\ f i j

ll(^) the p-th norm \r{i)\^p^ = ^/STMOI^

X

'. matrix of the data

(

-^(1) 0

x{2) x{l)

X —

x{P) Jc(P-l)

^x{L) x{L-\)

P: order of the AR model

L: length of the signal

The structure of the algorithm is shown in fig

0 \

0

x{\)

. x{L-P+l)

J

8. This algorithm has been implemented

524
X{n)

initialisation

AR parameter

R > threshold

weight matrix AR parameter testvalue R

AR parameter

Figure 8: Structur of the IRLS algorithm

to estimate the AR parameter for simulating the signal of a scattering light detector. The

structure of simulating a signal with the IRLS algorithm is similar to the one shown in

figure 7 except that the impulse supression is not necessary and the input of the AR filter

is SaS noise.

6.1 Simulation of Fire Signals

After estimating the AR parameter the stationary segments of the measured fire signal can

be simulated by using an AR model with the estimated parameters. This leads to another

signal which exhibits the same statistical properties as the measured signal. Of course it

does not look the same. The input of this filter is «-stable noise with the same standard

deviation as the fire signal, a can be estimated from the signal. There are different

approaches for the estimation of a. In this work the }og\SaS\ [6. 7| estimator is used.

With the log\SaS\ estimator a new random variable Y is evaluated where Y{k) is

Y{k)^log\X{k)l

X{k) is a SaS random variable. For the estimation of a exists the following equation:

v..(rW) = f(ij + i)

which only depends on a. The variance of Y{k) exists and can be estimated from the

samples of the known signal Y'{k) and the mean value. This estimation gives very good

525
results for a large number of samples ( e.g. 10000 samples) for signals with few samples

( e.g. 100 samples) the simulated value of a deviates from the real value. Figure 9

1500

1000

500

-500

-1000 800 1000

Figure 9: Measured and highpass-filtered fire signal part

shows the high frequence part of the signal of a testfire measured with a scattering light

detector, figure 10 shows a simulation of this fire signal with stable noise with the characteristic exponent a = 1.8. It can clearly be seen that both signal have he same statistical

properties.

7 First Conclusion

Both the model using gaussian noise as well as the one which uses general stable noise

have advantages and disadvantages. In the first algorithm not all the impulses of the signal

are suppressed and after the simulation not all the found impulses are added to the signal

again. The correlation of the impulses gets lost by fitting the impulses into the simulated

signal in a brutal manner. One benefit of the estimation of AR model parameters by

using least squares is that it is a well known procedure and the algorithm converges fast. By using the IRLS algorithm the supression of the impulses is not necessary. On the

other hand using an AR model with impulsive noise to simulate the fire signal exceeds

the following problem. The number and the amplitude of the impulses depends on the

value a. If a decreases the number of impulses and the amplitude increases. It is not

526
X{k)

1500

1000

500

0 -500

-looor

-1500

-2000

-2500

-3000

-3500

100 200 300 400 500 600 700 800

Figure 10: Simulated fire signal part

possible to generate noise with less impulses but with a high amplitude by using a-stable

noise. In the signals of scattering light detectors there are few impulses with a high

amplitude compared to the signal. So it may be necessary to choose another input signal

then a-stable noise.The figures 9 and 10 show an example where the simulated signal

corresponds very good to the measured signal, but this must not be. One advantage of the

algorithm using stable noise is that the coiTelation of the impulses is still contained in the

simulated signal.

Actually a combination of both described algorithms is already realised. The segmentation is done with the algorithms using the LS method, then the AR parameters for the

model are estimated with the IRLS algorithm for every found segment. In the literature

exist fast evaluation echniques for the IRLS algorithm which are based on FFT [9], so

that the implemented IRLS algorithm become faster.

References

[1] Luck, H.O. Does an Over-All Modelling Make Sense in Automatic Fire Detecfion.

Proceedings AUBE'99, Duisburg, Germany, 1999

[2] Klose J. Ermitdung der Eigenschaften von Signaldetektoren durch Analyse und Sim527
ulation von Zufallsprozessen mit einer Anwendung in der automatischen Brandentdeckung. PhD Thesis, Universitat Duisburg, Germany, 1992

[3] Fischer A. Ein nichtlineares Signalmodell fiir Zufallsprozesse auf der Basis der Multilayer Perzeptrons. PhD Thesis, Universitat Duisburg, Germany, 1995

[4] Kuruoglu E.E. Signal processing in a-stable noise environments: a least /^-nonn

approach. PhD Thesis, University of Cambridge, 1998

[5] Kuruoglu E.E., Ray P.J.W., Fitzgerald W.J. Least Lp-norm estimation of autoregessive

model coeffients of symmetric a-stable process. IEEE signal Processing Letters, July

1997, Vol.4 7:210-203

[6] Nikias C.L., Shao M. Signal processing with alpha-stable distributions and applications. John Wiley & Sons, 1995

[7] Ma X., Nikias, C.L. Parameter estimation and blind channel identification in impulsive signal enviroments. IEEE Trans, on Signal Processing, Dec 1995, Vol.43,

12:2884-2897

[8] Burrus C.S., Barreto J. A., Selesnik I.W. Iterative reweighted least-squares design of

FIR filters. IEEE Trans, on Signal Processing, Nov 1995, Vol.42, 11:2926-2936

[9] Yarlagadda M.D., Bednar J.B., Watt T.L. Fast algorithm for Ip deconvolution. IEEE

Trans, on Acoustics, Speech, Signal Processing, Feb. 1995, Vol.ASSP-33 1:174-182

528
Robert P. Schifiliti, P.E.

Fire Detection Modeling - The Research-Application Gap

1. ABSTRACT

Fire detection is a rapidly evolving discipline. New sensor technologies and

sampling algorithms are being developed to monitor traditional parameters

of smoke and heat as well as new parameters involving gas and radiation.

However, the engineering application of fire detection has lagged behind

and has remained essentially unchanged for the past 20 years. In fact,

there are very few engineering tools for fire detection applications. Also, the

few tools that do exist do not have a known degree of accuracy. This paper

uses smoke detection as a catalyst to discuss the widening gap between the

fire researcher, the sensor designer, and the application engineer.

2. INTRODUCTION

In order to model smoke detection, the application engineers need a fire

model or fire data and a detector I sensor model. The relationships between

these models are shown in Figure 1. The fire model or fire data must be in a

form suitable for use by the sensor model.

Figure 1 Smoke Detection Model

Smoke

Detection Model

Smoke

Detector Model

Smoke

Entry Model

Smoke

Response

Model

Smoke

Production Model

Smoke

Dynamics

Model

Smoke

Transport Model

529
For example, fire researchers most often measure and report data on heat

release rate, temperature and velocity of fire gases, and the optical density

or obscuration per unit distance of the smoke at various locations. Of these,

only optical density or obscuration relates directly to smoke. Although

called obscuration, it is more accurately called attenuation since the light

beam may be absorbed, reflected or refracted by the smoke.

Optical density and obscuration are useful data for evaluating visibility.

However, the only commercially available smoke detector that operates by

sensing the attenuation of a light beam is the projected beam type smoke

detector. Further, these measurements are sensitive to the wavelength of

light used. Thus, to be valuable for estimating the response of a projected

beam smoke detector, the data must be measured and reported using the

same wavelength as the light source used by the detector. Alternatively, the

error introduced by modeling or using data generated at a different

wavelength needs to be incorporated into the results. The two most

common types of smoke detectors are ionization type and photoelectric type.

Neither type operates using light attenuation. Without a correlation

between the optical density data and the response characteristics of a

particular detector, accurate modeling is not possible.

Examining Figure 1 it seen that when data is presented for smoke

measurements at a detector location, it is only valid for use in modeling

situations where the Production, Dynamics and Transport are substantially

the same as in the test that generated the data.

In addition, detectors often use complex response algorithms rather than

simple threshold or rate-of-change response levels. These algorithms vary

from detector to detector and are generally not published by the

530
manufacturers. Thus, even if correlations between optical density and the

response of scattering and ionization type smoke detectors were available,

the signal-sampling algorithm affects the actual response of each model.

In order to determine whether or not a smoke detector will respond at a

given time after ignition or at some threshold heat release rate, a large

number of factors must be evaluated. These include: smoke aerosol

production and characteristics, aerosol transport, including dynamic

changes during transport, detector aerodynamics, and sensor response.

Smoke aerosol characteristics at the point of production or generation are a

function of the fuel composition, the combustion state (smoldering or

flaming), and the degree of vitiation of the combustion air. The

characteristics considered include particle size and distribution, particle

number or concentration at various sizes, composition, color, and refractive

index. Given the dynamic nature of fire growth, fire spread and fuels

involved, ventilation conditions will change over time, thus affecting the

characteristics of the smoke produced.

Smoke dynamics during transport changes to the aerosol characteristics

that occur with time and distance from the source. For instance, do

particles agglomerate or coagulate? How does the size and number

concentration vary? How do the optical properties change?

Smoke transport considerations include transport time and velocity as well

as soot deposition causing changes in airborne concentrations. Transport

time is a function of the characteristics of the travel path from the source to

the detector, and includes ceiling height and configuration (sloped, beamed.

531
etc.), intervening barriers such as doors, and buoyancy effects such as

layering and thermal inversions.

Once smoke reaches the detector, other factors become important, namely

the aerodynamic characteristics of the detector and the type of sensor. The

aerodynamics of the detector relate to the ease with which smoke can pass

through the detector housing and enter the sensor. In addition, the location

of the entry portion of the housing relative to the velocity profile of the

detector normal to the plane of the ceiling is also a factor. Finally, different

sensing modes (e.g., ionization or photoelectric) will respond differently,

depending on the characteristics of the transported aerosol. Within the

family of photoelectric devices, there will be variations depending upon the

wavelengths of light and the scattering angles employed. Also, algorithms

used to sample and weight the sensor's response are introduced by the

manufacturer and affect the detector's response.

Standard practice for the design of smoke detection systems is much the

same as that for heat detection systems. Recommended spacing criteria are

established based on detector response to a specific parameter, such as the

optical density within an enclosure. A variety of smoke tests are used to

verify that the detector responds between defined upper and lower

activation thresholds and within required response times to a range of

different types of smoke. This information translates into recommended

spacing criteria that are intended to ensure that the detector responds

within defined parameters. In some cases, the recommended spacing can be

increased, or must be decreased, depending on factors such as compartment

configuration and airflow velocity.^

532
In applications where estimating the response of a detector is not critical,

the recommended spacing criteria provides sufficient information for design

of a basic smoke detection system. If the design requires detector response

within a certain time frame, optical density, specified heat release rate, or temperature rise, additional analysis may be required. In this case,

information concerning the expected fuel, fire growth, sensor, and

compartment characteristics is required.

3. MODELING SMOKE DETECTOR RESPONSE - GENERAL

The response of smoke detectors to fire conditions is not easily modeled.

The response characteristics of smoke detectors vary widely compared with

thermal detectors. In addition, less is known about the production and

transport of smoke in the early stages of a fire. Natural and forced air

currents have a larger effect on the movement of smoke at the time of

interest (very early in the fire) than they do on the stronger thermal

currents required to alarm heat detectors.

A comparison of how smoke detectors operate with the smoke measurement

methods most often employed and reported by researchers shows that

smoke measurements do not generally include the factors that we need to

model smoke detector response^ Thus, there is a gap between the data

generated by fire researchers and the data needed to model smoke detector

response.

Researchers most often use optical density or obscuration as a measurement

of smoke. These are calculated as follows:

533
Percent obscuration, %0b.:

%0b. = 100

Percent obscuration per unit distance,

%0.. =100 I

Optical density, D:

D = Iog ^^'^

>io = -logio

Optical density per unit distance, (m '):

O 1,

D„ = — = -log,„

1, -ylog,„ m

Where is the initial intensity of a light beam reaching a photocell and I is

the intensity of the light beam in the presence of smoke.

Optical density and obscuration are useful data for evaluating visibility.

However, the only commercially available smoke detector that operates by

sensing the attenuation of a light beam is the projected beam type smoke

detector. Further, these measurements are sensitive to the wavelength of

light used. Thus, to be valuable for estimating the response of a projected

beam smoke detector, the data must be measured and reported using the

same wavelength as the light source used by the detector.

534
The two most common types of smoke detectors are ionization type and

photoelectric type. Neither type operates using hght attenuation. Without

a correlation between the optical density data and the response

characteristics of a particular detector, accurate modeling is not possible.

In addition, detectors often use complex response algorithms rather than

simple threshold or rate-of-change response levels. The algorithms are used

to reduce false and nuisance alarms and to enhance fire signature matching.

These algorithms vary from detector to detector and are generally not

published by the manufacturers. Thus, even if correlations between optical

density and the response of scattering and ionization type smoke detectors

were available, the signal-sampling algorithm affects the actual response of

each model.

Nevertheless, there are methods that can be used to grossly estimate smoke

detector response. These estimation methods may not provide accurate

prediction of time to detector response because the potential errors in the

estimation methods are not generally known and because the response

algorithms for a particular detector are not known. Without knowledge of

the accuracy of the models and the potential errors, these estimation

methods should not be used to compare detector response to other model

calculations such as egress time calculations or time to untenability.

Estimation methods are best used to compare changes in the response of a

particular detector as a result of changes in spacing or location, while

holding all other variables constant.

In addition to these estimation methods, actual fire tests with detectors

present may provide information to compare smoke detector response to

other factors such as egress time, structural response, heat release rate, etc.

535
Product performance tests may be sources of data. Although, the actual

response may not be reported in manufacturer's literature, the minimum

and maximum permissible performance imposed by the test standard

provides ranges of possible response.

4. MODELING SMOKE DETECTOR RESPONSE - LIGHT

OBSCURATION SMOKE DETECTORS

For projected beam type detectors, fire or smoke models that calculate the

optical density per unit length, D^, in a space or the total optical density in

the path of the detector, D, may be used to determine when the detector

would respond. Manufacturer specifications will typically indicate at what

levels of total obscuration or total optical density the detectors respond.

Projected beam smoke detectors generally have adjustable response

thresholds.

Many fire models estimate the unit optical density, D^,, in a uniform upper

layer or volume. This is referred to as zone modeling. The optical density

over the entire length of the beam is then determined by multiplying by

the path length, 1. The path length is the distance between the source and

receiver or the projected beam smoke detector. This method assumes

homogenous distribution of smoke throughout the path, an assumption that

may not be valid.

Another method to model the response of projected beam obscuration type

detectors is to calculate the unit optical density, D^, at several discrete

points or in several discrete segments between the source and the receiver

of the projected beam smoke detector. This is a form of field modeling. The

536
optical density per unit length is then multiplied by the length of that

particular segment. The total optical density of the path is then the sum of

all of the densities for the individual segments.

5. MODELING SMOKE DETECTOR RESPONSE - LIGHT

SCATTERING (PHOTOELECTRIC) SMOKE DETECTORS

The amount of light scattered by smoke is very complex and is related to

factors such as the particle number density and size distribution, refractive

index, the wavelength of the light source, and the angle between the source

and the receiver. The manufacturer for a particular detector can describe

some of these variables. Some require information about the smoke

produced by the fuel and its transport to the detector location.

Information about smoke properties related to light scattering is presently

limited to a few types of fuels and is not readily available to practicing fire

protection engineers. In addition, the data may not be in a useable format.

For instance, the data must match the wavelength of the light source used

in the detector being modeled. Scattering data at other wavelengths

introduces errors and uncertainties.

Meacham has shown that it is possible to model the response of light

scattering detectors using information about smoke properties obtained by

small scale testing of various fuels. ^

^ However, the recommended test

methods have not been further developed, tested and incorporated into fire

test programs.

At the present time, there are no practical methods available to directly

model the response of light scattering type detectors. However, obscuration

537
or optical density modeling, as was discussed above for obscuration type

detectors, can be used in a limited way to estimate scattering type smoke

detector response.

A scattering type detector will respond at different optical densities for

different tj^Des of smoke. For example, a scattering type smoke detector

that responds at an optical density of .029 m ' (2.0%/ft obscuration) to smoke

produced by a smoldering gray cotton lamp wick may not respond until an

optical density of 0.15 m^ (10%/ft) is reached for smoke from a kerosene fire.

At the response threshold, both t3^pes of smoke are scattering the same

amount of light to the receiver of the scattering photoelectric smoke

detector. There are many factors involved in this effect. One is that the

darker smoke from the kerosene fire does not reflect as much light as the

lighter colored smoke from the lamp wick.

Another way to understand the differing response of a scattering type

detector to two types of smoke is to consider the amount of light being

scattered when both smoke samples have the same optical density. Both

samples of smoke equally block our vision of the light reflected by an object.

One type of smoke may be composed of large, highly reflective smoke

particles that cause the incident light to scatter in many directions. Thus, it

reduces the amount of light in the forward direction. The other type of

smoke may consist of a smaller number of larger particles that absorb light

more readily than they reflect it. Though they have equal optical densities,

one is more likely to scatter light and set off a scattering type detector.

In order to model the response of a scattering type detector using

obscuration or optical density, it is necessary to know the optical density

required for a particular type of smoke to alarm a particular model detector.

538
For example, many manufacturers label their smoke detectors with a unit

optical density, D^, or unit obscuration, %0„ based on a calibration test that

is part of UL standard number 268.' That number indicates the unit optical

density required for that detector to respond to smoke having very specific

characteristics. The optical density required to alarm a particular detector

as quoted by the manufacturer is just one value for a given particle size

distribution, concentration, color, etc. used in the laboratory calibration test

of that model detector. If the smoke and conditions are similar to that used

in the test of the detector, the specified alarm threshold can be used in

calculations.

It is not sufficient to have data for a particular fuel and detector

combination. It is known that smoke changes as it moves away from a fire.^

There may be changes in the number of particles, their size, shape and

velocity. The optical density at response to any smoke signature other than

the laboratory calibration test will be different and will vary with different

fuels and burning modes.

Threshold response data to various fuels for a particular detector are not

readily available. Some manufacturers may provide data if available and

when requested. Product performance and safety tests as well as fire tests

with detectors present are useful sources of limiting performance data.

Product standards t\'pically test detectors in rooms with specified fuels and

smoke build-up rates and velocities. The detectors must respond at certain

levels or within certain time limits. While the exact performance data may

not be made available, the test limits are useful for estimating the range of

possible detector response.

539
6. MODELING SMOKE DETECTOR RESPONSE - IONIZATION

SMOKE DETECTORS

The signal produced by the chamber of an ionization detector has been

shown to be proportional to the product of the number of particles and their

diameter.^' ^'°' The exact signal produced by an ionization smoke detector is

given by a more complex equation in the literature and requires an

additional number called the chamber constant. The chamber constant

varies with each different model of detector.

Given the quantity and size distribution of smoke particles and the chamber

constant (from the manufacturer), it is possible to model the ionization

smoke detector. Unfortunately, there are no fire models that provide the

required detector model input. In addition, manufacturer specifications do

not presently include chamber constants.

Newman modified the chamber theory to account for ionization detector

sensitivity to the small electrical charge carried by some fire aerosols."

Newman also developed a method to model ionization smoke detector

sensitivity as a function of the soot yielded by a particular fuel. Using his

method, the change in a detector's signal, AI, can be related to the optical

density of smoke measured at a particular wavelength, D^^-

To use the method proposed by Newman it is necessary to know what

change in detector chamber signal, AI, will cause a detector or system to

alarm. Although manufacturers do not presently provide this data they

may be persuaded to do so in the future.

* 1

540
Newman's work was done using a small-scale apparatus and three

ionization smoke detectors. A wider range of tests, including some full scale

testing is needed to verify this method.

Presently, the only way to model ionization detector response is to use the

optical density estimations as discussed for obscuration type detectors.

7. MODELING SMOKE DETECTOR RESPONSE - ENTRY

RESISTANCE

In addition to smoke characteristics and the detector's operating

mechanism, the ability to get the smoke into the chamber effects the

response of the unit. For spot type photoelectric and ionization t5rpe smoke

detectors, bug screens, chamber design and the detector's aerodynamic

characteristics cause entry resistance.

In a scenario where the optical density at the detector location is increasing

with time, the optical density inside the detector chamber will always be

less than that outside the detector chamber. Similarly, if a detector is

placed in a smoke stream having a constant optical density, there will be a

time delay before the optical density inside the chamber approaches that

outside the detector. As with heat transfer to heat detectors, smoke entry

resistance can be characterized by a detector time constant, i:

dD.ui -{D.-D,„) s T

-1 -1 m

dt

541
Where D^. (m *) is the optical density per unit length inside the detector

chamber, (m is the optical density per unit length outside the detector

and T (s) is the detector time constant.

If the time constant and the rate of change of optical density outside the

detector are constant, then this equation can be solved. Further,

substituting D^^ for the optical density outside the detector at response and

for the optical density required inside the detector to produce response

yields the following

D =D +T

dt

1-exp

T

( dD.

dt

J-i m

Heskestad proposed that the time constant could be represented by the

following:

T = — seconds

u

Where L is the detector's characteristic length and u is the velocity of the

ceiling jet flowing past the detector.

The characteristic length is thought to be a property of the detector that is

independent of the smoke and ceiling jet properties. It is interpreted as the

distance the smoke would travel at the velocity u before the optical density

inside the detector reaches the value outside of the detector. Combining

the equations:

542
I"

The exponential term is small compared to the rest of the equation, allowing

the equation to be simplified.'^ Simplification of the equation is not

necessary when calculations are made using a computer. However, the

simplified form clearly shows the effect of entry resistance.

This form of the entry resistance equation clearly shows that when the

optical density outside a detector is increasing with time, the optical density

inside the detector will lag behind if there is any entry resistance.

Heskestad and later Bjorkman et al. have plotted test data to determine the

L number for a variety of smoke detectors. Additional work has been done

by Marrion and by Oldweiler to study the effects of detector position and gas

velocity on the L number."

Bjorkman et al., Marrion and Oldweiler all observed variations in L that

may be attributed to a dependence on velocity. Marrion and Oldweiler's

data also imply that there may also be a dependence on the characteristics

of the smoke. Table 1 below summarizes the results from the works cited

above.

or D =D + — III 11(1 m

543
Table 1- Range of Characteristic Length (L) Numbers

Researcher Ionization Detector L (m) Scattering Detector L (m)

Heskestad 1.8 15 (a)

Bjorkman et

al.

3.2 +/- 0.2 (b) 5.3 +/- 2.7 (c)

Marrion Not tested 7.2 (d), 11.0- 13.0(e), 18.4(f)

Oldweiler 4.0 - 9.5 (g), 4.3 - 14.2 (h) Not tested

Notes:

a) Older style detector with more elaborate lab3rrinth.

b) L determined by best fit for three test velocities.

c) L based on a single test velocity & a limited number of tests

(complete equation used).

d) Low L number at low test velocity.

e) Range of L for several fuels and detector positions.

f) L increased by adding "fence" to further restrict smoke entry.

g) Range of L for a variety of velocities using simplified equation

for entry resistance

h) Range of L for a variety of velocities using complete equation

for entry resistance

Examination of the data and analysis work cited above shows that more

work needs to be done to study the effects of low velocities and the effiects of

smoke characteristics on detector entry characteristics. The sharp increase

in L at lower velocities appears to indicate that entry resistance may be

related to smoke particle size. It is also possible that L is a function of the

smoke momentum at low velocities. Thus, the time lag would be inversely

proportional to the velocity squared.

544
Engineers can use L as a measure of entry resistance and the resulting time

lag. However, in scenarios where the ceiling jet velocity is low, there will be

greater uncertainty in the results.

Without validation of L as a measure of lag time, manufacturers and test

laboratories are not measuring or reporting L in their literature.

Nevertheless, the range of L numbers reported in Table 1 can be used to

estimate possible errors in detector response time.

8. MODELING SMOKE DETECTOR RESPONSE - SMOKE

DILUTION AND THE EFFECTS OF FORCED AIRFLOW

Smoke dilution refers to a reduction in the quantity of solid and liquid

aerosols (smoke) available for detection. Dilution can occur through natural

convection (entrainment in the plume or the ceiling jet) or through the

effects of by forced ventilation systems.

Air flow effects become less as fire size increases. Conversely, when

detection is desired at smaller fire sizes, air flow effects may become a

dominant factor in the response of the smoke detector. Velocity profiles of

the air movement within a room or smoke tests under various conditions

may assist in determining optimum smoke detector placement.

Computational fluid dynamics (CFD) modeling has been used to show the

effects of ducted air supply and air return on smoke spread."^ That work

used three dimensional color graphics plots to show areas of non-activation

for given scenarios and assumptions. That research effort verified a long

standing rule that smoke detectors should not be located within

545
approximately one meter of conventional air supply difFusers and air return

grills due to the high level of dilution. However, because air dilution and

smoke movement in a compartment is such a complex problem with a large

number of variables, the research program was not able to create simplified

engineering relations or tools for use in design of smoke detection systems.

CFD modeling can be a useful tool for complex scenarios involving airflow.

However, potential sources of error exist. For example, CFD models

generally do not model fires. Instead, they model the transport of the mass

and energy "injected" or "produced" at the fire location. Thus, many of the

same discussions above concerning mass optical density, soot deposition,

smoke particle characteristics, optical density vs. temperature correlations

are also applicable to this type of modeling.

9. TEMPERATURE APPROXIMATION METHOD FOR MODELING

SMOKE DETECTION

The temperature approximation theory is a method used to estimate the

optical density produced by flaming fires. The theory h5^othesizes that the

mass concentration of smoke particles at a point is proportional to the

change in temperature due to the fire (at that point). The following

assumptions are necessary:

• Particle size distribution is constant in space and time

• Mass generation rate is proportional to mass burning rate

• There is no heat transfer between particles or between the particles

and the confining surfaces

• The smoke does not continue to react as it travels

546
Heskestad then hypothesized that the ratio of optical density to

temperature rise would be a constant for a particular fuel and burning mode

(flaming, smoldering, vertical combustion, horizontal combustion, etc.).

There are actually three parts to this hypothesis.

The first is that each fuel and burning mode results in a unique optical

density required to alarm a particular model and type of detector. This was

discussed previously regarding photoelectric, ionization and projected beam

smoke detectors. This phenomenon is regularly observed, explained by

theory, and accepted by the scientific and engineering community.

The second part of the hypothesis is that for each fuel and burning mode the

optical density at a point is proportional to the mass concentration of

particles.

The final part of the hypothesis is that for each fuel and burning mode the

mass concentration of particles is proportional to the change in temperature

at a point.

Combining these proportionalities, optical density is proportional to the

change in gas temperature for a given fuel and combustion mode:

D ocC (10)

(11)

D ocAT (12)

547
Therefore, the ratio of optical density to temperature rise is constant for a

given fuel.

= CONSTANT

This hypothesis assumes that the only way to move the smoke particles

from the source to the detector at the ceiling is by buoyant forces.

Heskestad and Delichatsios examined experimental data for obscuration

and temperature rise at various locations on a ceiling for different fuels.

They concluded that while the data showed some variation in time at

different radial positions relative to the fire source, the ratio could be

approximated as a constant. Table 2 lists the ratios recommended by

Heskestad and Delichatsios for various fuels.

Table 2

Material 10' D,, f 1

"l M ft^'F

Range of Values

Wood 0.02 0.015 - 0.055

Cotton 0.01/0.02 0.005 - 0.03

Paper 0.03 Data not

available

Polyurethane 0.4 0.2-0.55

Polyester 0.3 Data not

available

PVC 0.5/1.0 0.1 - 1.0

548
Foam rubber PU 1.3 Data not

available

Average 0.4 0.005 - 1.3

Examining the original data, the last column has been added to show the

range of values for each fuel. Averages have also been calculated and listed

in the last row of the table for reference.

Others experiments have resulted in data that differ from that of Heskestad

and Delichatsios. Bjorkman et.al. reported values for polyurethane that are

approximately one half that reported by Heskestad and Delichatsios.^°

The data produced by Heskestad and Delichatsios show the ratio of optical

density to temperature rise was not constant. The authors concluded that

the variation was the result of slowly changing characteristics of the smoke

particles as they left the flaming source and traveled in the plume and

ceiling jet. Nevertheless, they concluded that a constant value could be used

as a rough approximation to allow engineers to model optical density

produced by a fire. Although it has not yet been done, it is possible to

examine their original data and place error bars on the values recommended

in the above table. With today's availability of desktop computers to

perform modeling calculations, it may be possible to develop and use a

functional relationship for the ratio of optical density to temperature rise in

lieu of a constant.

A fire model can be used to calculate the temperature rise at a smoke

detector location or in a layer. Then, using the ratios reported by

549
researchers, the optical density at that location as a function of time can be

approximated.

10. MASS OPTICAL DENSITY METHOD FOR MODELING SMOKE

DETECTION

The fuel characteristics of primary concern for smoke detection are: (1)

material and (2) mode of combustion. These two parameters are important

for determining pertinent features of expected products of combustion, such

as particle size, distribution, concentration, and refractive index. Assuming

a well-mixed smoke-filled volume, data on smoke characteristics for given

fuels can provide an estimation of detector response. Some fire models

calculate the optical density of smoke in a space using the following

relationship for the optical density per unit length, D^:

D ^ Am , K

Du= — = Dn, (m )

1 is the path length (m)

is the mass optical density (mVg)

Am is the mass of the fuel burned (g)

is the volume that the smoke is dissipated in (m^)

This method will be referred to as the mass optical density method.

varies depending on the fuel, and is determined experimentally. Tables of

can be found in the SFPE Handbook of Fire Protection Engineering.^'

The mass optical density method for estimating the optical density of smoke

produced by a fire requires that the variable be selected for the

particular fuel and burning mode. The most complete set of data available

550
are in the SFPE Handbook of Fire Protection Engineering. However, not all

fuels and burning modes have been tested and reported. Also, more

complex fuel packages, such as upholstered chairs, require that be

chosen for one fuel even though more than one may be burning at the same

time. When a model using this method is employed, users should check to

see if the model selects an appropriate value of for the fuel being studied.

Some models may use a single value of D,,, regardless of the fuel. In that

case, answers can be modified by dividing the calculated D^, by the used

in the program, then multipljdng by the appropriate value of selected

from the available data.

D_r» ^mUu-u) -1 /-I p-\

Mass optical density can be derived by burning a sample in a closed

chamber and measuring the optical density in the chamber. The following

equation is used to calculate D^:

A.,=^

^

(16)

Where

D is the optical density measured in the test

1 is the path length over which D was measured (m)

is the volume of the test chamber (m^)

Am is the mass of the fuel sample consumed in the test (g)

A different equation is used for open test arrangements that involve a flow

of air and combustion products through a test chamber.

data are often measured in small scale tests due to the need for accurate

measurements of mass loss and optical density. The use of from small

551
scale tests to calculate the resulting in a large scale scenario introduces

error. Some comparisons show qualitative correlation. However, it has

been reported that the correlation breaks down with complex fires.

The value of optical density, D, measured in the test depends on the

wavelength of the light used. For a given set of test conditions, if the

wavelength of the measuring light beam is reduced, the measured optical

density will increase. This occurs because particles must have a diameter

on the same order of magnitude or larger than the light wavelength in order

to obscure the light. Shorter wavelength lights will be obscured by the

smaller particles not seen by the longer wavelength light. Also, most light

sources produce a range of different wavelengths, usually having some

specific distribution and some nominal peak. The exception is when a

precise laser light source is used. Finally, the receiver that senses the light

and produces a corresponding output also has a response distribution curve

for various wavelengths.

Examination of test data reported by Tewarson show that found using a

nominal wavelength of 1.06 (microns) might be as much as 5 times less

than D^^ found with a 0.458 [i light source. Unfortunately, the tables of

data for various fuels in the SFPE Handbook do not all list the wavelength

used to determine D^. If the wavelength (and distribution) of the light used

to determine is the same as the wavelength of light used to test a smoke

detector and report it's sensitivity, then no error is introduced. For these

data to be useful in modeling smoke detector response, they should be

determined using a light source and receiver having characteristics similar

to most commercially available smoke detectors. Commercially available

detectors use light sources having peak wavelengths in the infrared band.

552
Meacham has reported that two manufacturers use LED sources having

peak wavelengths on the order of 880 to 950 nm (0.880 to 0.950 ^m).'

Another method for determining involves a calculation using the yield of

smoke for a particular fuel. Tewarson examined data from several sources

and arrived at the following equations^^:

In these equations is the 5deld of smoke in grams per gram of fuel

consumed. These equations provide an additional method for determining

when test reports include Y,. However, the determination of Y, may also

introduce errors in the calculation of Dm and hence Du. The nature and

extent of these possible errors is beyond the scope of this paper.

Models that calculate the optical density in a space using assume that

the smoke produced is distributed evenly throughout a specific volume.

This is called a zone model. In short, one zone is the hot upper layer which

has a uniform temperature and smoke optical density throughout. The

second zone is the cooler, near ambient lower layer. Some models treat the

lower layer as being at the initial, ambient conditions, while others consider

the amount of smoke and heat added to the layer. Since real fires tend to

produce varying optical density throughout the space, the uniform zone

assumption introduces error in any detector response modeling. Larger

for flaming fires (17)

D,„ =0.17 In F +0.65 — for smoldering fires

I S J

(18)

553
volumes and larger horizontal distances from the fire plume increase the

errors caused by the use of simplified zone modeling.

When the mass optical density method is used, all smoke produced by the

fire is assumed to contribute to the optical density of the smoke in the

assumed volume. However, the actual mass loss and smoke production

occurs over time. Depending on the actual conditions in the space and the

nature of the fuel, some amount of the smoke will have been deposited on

the walls, ceilings, furnishings and other surfaces as soot. In addition, the

potential for transport out of the space exists whenever vents are present.

Most computer fire models do not contain a routine to account for soot

deposition when calculating the resulting optical density.^

Most fire models determine the volume of the upper smoke layer by

calculating the distance below the ceiling at which the temperature drops

off significantly. In an actual fire, the demarcation between the two layers

is not necessarily distinct, as some amount of the smoke cools and diffuses

into the lower layer.

It has also been shown that smoke ages, changing its characteristics with

time. Smoke aging effects include agglomeration of small particles into

larger particles, possible continued oxidation of unburned fuel mass in the

smoke layer and other chemical and physical changes that affect the optical

density of the smoke. The effects of smoke deposition and smoke aging on

smoke detector response modeling may be negligible in the early stages of a

fire and when a detector is close to and in the same room as the fire. In

cases where the detector is far from the fire, in another room, or where the

smoke must travel an indirect path to the detector, results will be less

554
accurate. These phenomenons have been investigated by Yamauchi'^^ but

have not yet been incorporated into available detector models.

11. DISCUSSION RELATED TO THE USE OF FIRE MODELS FOR

HEAT AND SMOKE DETECTOR MODELING

Some computer fire models or sets of computational tools include routines

for calculating heat or smoke detector response. It is important for users to

understand the underlying detector models being used so that limitations

and potential errors can be understood. For heat detection, most use a

lumped mass model. However, for smoke detection some use a temperature

rise model and some use a mass optical density or specific extinction area

model. The specific extinction area, is similar to the mass optical density

except that it is based on calculations using the natural log, e, rather than

logjo- Most do not include entry resistance modeling. Some permit the use

of fuel specific parameters for smoke yield and mass optical density. Others

use preset values.

12. CONCLUSIONS

It is not possible to accurately compare smoke detector response to other fire

related models such as egress time or structural response to heat. If smoke

detection is to be a part of tomorrow's performance based solutions fire

models and detector models must evolve and work together. The evolution

of the fire and detector models must include a feedback loop to ensure that

they can be integrated to form a detection model.

555
Additional research is needed to test and verify certain aspects of detector

models. Most important is additional work on the entry resistance model

and the scattering detector model. Additional work should also be done to

further test and refine Newman's model for ionization detector response.

Detector manufacturers, product certification laboratories and application

engineers need to agree on a method for cataloging specific detector

response algorithms. This needs to be done in a way that protects

manufacturer's patents and confidentiality rights while still providing

useful engineering information. Further, it must be done in a way that is

easy to measure and verify.

Fire researchers need to examine detector models and develop

instrumentation and data gathering methods that match the detector

models. Raw data should be made available for input to detector models.

Where possible, functional models produced from data should be developed

in a format suitable for incorporation into detector models.

Detector and fire models, whether modular or integrated to form a detection

model, need to better report and present limitations and potential errors.

Finally, application engineers must be more conscious of the limits on a

model's accuracy and precision and must provide useful feedback to

researchers involved in product development and fire research.

13. REFERENCES

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*

556
R.P. Schifiliti and W.E. Pucci, Fire Detection Modeling: State of the

Art, the Fire Detection Institute, Bloomfield, CT, 1996.

Brian J. Meacham, "Characterization of Smoke from Burning

Materials for the Evaluation of Light Scattering-Type Smoke Detector

Response," MS Thesis, WPI Center for Firesafety Studies, Worcester,

MA (1991).

B.J. Meacham and V. Motevalli, "Characterization of Smoke from

Smoldering Combustion for the Evaluation of Light Scattering-T3^e

Smoke Detector Response," J. of Fire Protection Engineering, SFPE,

Vol. 4, No. 1 (1992).

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Signaling Systems, Underwriters Laboratories, Inc., Northbrook, IL

(1989).

G. Mulholland, "Smoke Production and Properties", SFPE Handbook

of Fire Protection Engineering, Chapter 2-15, 2"' ed. NFPA 1995.

J. Hoseman, "Uber Verfahren zur Bestimmung der

Korngrossenverteilung hokkonzentrierter Polydispersionen von

MiePartikeln,", Ph.D. Thesis, Aachen, 1970.

CD. Litton, "A Mathematical Model for Ionization Type Smoke

Detectors and the Reduced Source Approximation," Fire Technology,

Vol. 13, No. 4, NFPA, November 1974.

R.W. Bukowski and G.W. Mulholland, "Smoke Detector Design and

Smoke Properties", US Dept. of Commerce, National Bureau of

Standards, November 1978.

557
[M C. Helsper, H. Fissan, J. Muggli and A. Scheidweiler, "Verification of

Ionization Chamber Theory," Fire Technology, Vol. 19, No. 1, 1983

1 J. Newman, "Modified Theory for the Characterization of Ionization

Smoke Detectors," Fire Safety Science-Proceedings of the Fourth

International Symposium, 1994.

[^^

] G. Heskestad, "Generalized Characteristics of Smoke Entry and

Response for Products-of-Combustion Detectors," Proceedings, 7th

International Conference on Problems of Automatic Fire Detection,

Rheinish-Westfalischen Technischen Hochschule Aachen, March 1975.

[^^

] M. Kokkala et al, Measurements of the Characteristic Lengths of

Smoke Detectors," Fire Technology, Vol. 28, No. 2, National Fire

Protection Association, Quincy, MA (1992).

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on the Placement of Optical Smoke Detectors," MSThesis, WPI Center

for Firesafety Studies, Worcester, MA (1989).

['^

] A. Oldweiler, "Investigation of the Smoke Detector L Number in the

UL Smoke Box," MS Master's Thesis, Worcester Polytechnic Institute,

Worcester, MA, 1995

] Klote, J. H.; Forney, G. P.; Davis, W. D.; Bukowski, R. W. Simulating

the Effects of HVAC Induced Air Flow From Slot Diffusers on Detector

Response. National Institute of Standards and Technology,

Gaithersburg, MD National Fire Protection Assoc., Quincy, MA

NISTIR 5908; 79 p. December 1996.

Klote, J. H.; Forney, G. P.; Davis, W. D.; Bukowski, R. W. Field

Modeling: Simulating the Effects of HVAC Induced Air Flow From

558
Various Diffusers and Returns on Detector Response. International

Fire Detection Research Project. Technical Report. Year 3. National

Institute of Standards and Technology, Gaithersburg, MD National

Fire Protection Research Foundation, Quincy, MA Technical Report;

Year 3; 79 p. April 1996.

Klote, J. H.; Davis, W. D.; Forney, G. P.; Bukowski, R. W. Field

Modeling: Simulating the Effects of HVAC Induced Air Flow From

Various Diffusers and Returns on Detector Response. International

Fire Detection Research Project. Year 4 Report/Data. National

Institute of Standards and Technology, Gaithersburg, MD National

Fire Protection Research Foundation, Quincy, MA Year 4 Report/Data;

1998.

['^

] Heskestad, G. and Delichatsios, M.A., "Environments of Fire Detectors

- Phase I: Effect of Fire Size, Ceiling Height and Material," Volume I

-

"Measurements" (NBS-GCR-77-86), May 1077, Volume II - "Analysis"

(NBS-GCR-77-95), June 1977, National Technical Information Service

(NTIS), Springfield, VA 221

r° 1 J. Bjorkman, O. Huttunen, and M. Kokkala, "Paloilmaisimien

toimintaa kuvaavat laskentamallit (Calculation models for fire

detector response)," Technical Research Center of Finland, Research

Notes 1036, 1989.

P 1 A. Tewarson, "Generation of Heat and Chemical Compounds in Fires",

Chapter 1-13, in Philip J. DiNenno Ed., SFPE Handbook of Fire

Protection Engineering, National Fire Protection Association, Quincy,

MA, 1988.

559
Y. Yamauchi, "Prediction of Response Time of Smoke Detectors

Enclosure Fires, US Dept. of Commerce, National Bureau

Standards, January 1988.

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Z. Liu, J. Makar and A. K. Kim

Institute for Research in Construction

National Research Council of Canada, Ottawa, Canada, Email: zhigang.liu@nrc.ca

Development of Fire Detection Systems in the Intelligent Building

Abstract

Fire detection and its corresponding safety systems are crucial parts of an intelligent

building. This paper reviews the current state of development of fire detection and alarm

systems in the intelligent building. New technologies and concepts developed in

intelligent buildings, such as advanced multi-function sensors, computer vision systems

and wireless sensors, real-time control via the Internet, and integrated building service

systems, have also been reviewed and discussed. These new technologies and concepts

will improve the capability of fire detection systems to discriminate between fire and nonfire threats and will increase the time available for property and life protection. However,

much effort is still needed to remove barriers to the further development of these new

technologies.

Introduction

An intelligent building can be defined as one that combines the best available concepts,

designs, materials, systems and technologies to provide a responsive, effective and

supportive intelligent environment for achieving the occupants' objectives over the full

life-span of the building [1-6]. Compared with traditional buildings, intelligent buildings

should be able to reduce energy consumption, reduce maintenance and service operation

costs, provide improved security services, improve ease of layout planning and replanning, and increase the satisfaction of building occupants [4-7]. Other benefits should

include its adaptability to changing uses and technology and its environmental

performance in providing safer, healthier and more comfortable working conditions.

Intelligent building proponents also believe that these buildings will improve worker

productivity through improved work environments. Over the last two decades, the

561
intelligent building concept has become an important consideration in the planning of

many new or upgraded office buildings [3-6]. It has also been further developed to

embrace other types of living and working environments such as homes, factories and

education facilities.

Fire detection and the corresponding safety systems are crucial parts of an intelligent

building. Billions of dollars are spent annually to install and maintain fire detection

systems in buildings to assure safety from unwanted fires [8]. Intelligent systems

developed in the intelligent building offer opportunities to meet this task more

effectively, efficiently and economically. New sensors will produce earlier and more

reliable fire detection. Wireless systems will eliminate the need for cabling and offer

opportunities for fire fighters to work out fire fighting strategies before arrival at the fire

scene. Integrated building systems hold the potential for reducing false alarms, speeding

building evacuation and assisting in fire fighting. These changes will create new ways to

provide fire safety and new markets for fire detection, alarm and fighting systems [9].

As these technologies mature, changes to building practices may also result.

This paper reviews the current state of the art for fire detection and alarm systems in

intelligent buildings. It identifies new technologies and concepts developed for

intelligent buildings that could be used to improve the capability of fire safety systems.

The potential effects of integrated building service systems and barriers to the

development of fire detection and alarm systems in intelligent buildings are discussed.

The paper concludes by examining how these new systems may be combined to provide

the next generation of intelligent fire safety systems.

Emerging Sensor Technologies

New sensor technologies will be key components in the next generation of intelligent

buildings. Current intelligent buildings often have embedded processors and dedicated

information networks. The new generation is expected to add the capability to learn

about the building's circumstances and its occupants' needs and change the behaviour of

its control systems accordingly [10]. The employment of a large number of sensors

562
within the building will allow it to operate in a responsive manner, rather than using preprogrammed control models as are employed in the first two generations of intelligent

building. The information provided by sensors includes changes in both internal and

external environments of a building, such as smoke, temperature and humidity, air

quality, air movement, and the number of building occupants as well as a host of other

properties. The system will use sensors to identify how a particular person tends to react

to particular circumstances and to learn different behaviours for different people.

The number of sensors required to obtain this type of functionality is quite high,

especially since one of the major goals of intelligent buildings is to allow individualized

control of an environment. This need will increase the cost of intelligent buildings and

make it difficult to manage the resulting large amount of data. Development of costeffective sensors has consequently been identified as a key need for intelligent buildings

[11]. Fortunately, many of the properties that need to be monitored can be used for

multiple purposes. Security systems that can track the entry and exit of occupants from

an office building can also be used to ensure complete evacuation of a building during a

fire or even, in more advanced forms, determine where occupants may be trapped and

unable to escape. Similarly, parameters such as temperature and air movement are as

relevant to fire detection as the maintenance of the indoor working environment. Dual

use sensors and sensor systems that are flexible enough to interpret data from different

events will be key to making cost efficient intelligent buildings.

Efforts are being made to develop multi -function sensors for simultaneously detecting

fire and monitoring indoor air quality (lAQ). Multi-function sensors that combine inputs

from several different chemicals or physical processes would be expected to reduce the

rate of false alarms and increase the speed of detection of real problems. They should

therefore enhance fire safety while at the same time lowering total system costs. The

chemical gas sensor has potential for this type of application. Chemical sensor

techniques are now available for measuring almost any stable gaseous species emitted

from materials and prior to or during combustion [12]. Chemical species can be sensed

through a multitude of interactions, including catalytic, electrochemical, mechanicchemical, and optical processes. In one square inch, several hundred individual sensors

563
can be placed in an array. By coating each sensor with a different semi-conducting

material, several hundred different readings for gas signatures can be made by an expert

system [13]. Recently, one olfactory sensor array system has been developed for

environmental monitoring and for fire and smoke detection [14]. Such a system consists

of an array of broadly-selective chemical sensors coupled to microprocessor-based

pattern-recognition algorithms so that the changes in environmental conditions, such as CO, CO2 and smoke, can be detected.

A major issue in any sensor system is differentiating between different causes of the

event being detected. Higher than expected levels of CO2, for example, may be the signs

of a fire, but may also be a sign of poor air circulation within a room. When separate

sensors installed in the building for fire safety, thermal comfort control and

environmental monitoring can be integrated, sensitivity to fires and false alarm immunity

can be significantly enhanced [15]. These sensors are located in different positions in

the building. Once a fire occurs, the system can take multiple fire signatures and the

spatial relationship and status of adjacent detectors into account in making decisions.

Separate fire sensitivity information produced by these sensors would be transmitted to a

control panel where fire signal processing and alarm and fault determinations are made.

The use of a powerful central processing unit (CPU) in the control panel would also

allow the system to use complex algorithms and advanced signal processing for fire

signature identification.

The role of the control panel in improving fire detection capability has already been

recognized, with a system using control panels for decision making being one of two

main versions of intelligent fire detection systems [16]. Modem control panels are much

more powerful and flexible because of the widespread use of integrated circuits and

digital components that allow functions to be fully computer controlled. These control

panels have powerful signal processing capability and use artificial intelligent techniques,

to improve fire detection system reliability, response times to incipient fires, false alarm

rates and maintenance requirements. The Building and Fire Research Laboratory at NIST

has recently initiated a project to further develop advanced fire detection and alarm panels

[8]. This project aims to use information provided by sensors and advanced models of

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fire growth and smoke spread in buildings to discriminate between fire and non-fire

threats, identify the exact location of a fire in a building, and provide continuous

estimates on the short and long term behavior of fire growth and smoke spread in the

building. Such fire information will allow building operators and fire fighters to make a more accurate and responsive evaluation of any fire -related incident in the building, to

control fires and supervise the evacuation from the building.

Computer vision systems can also be used as a type of multi-function sensor. Computer

vision applications have included building security, improving response rate and energy

saving for HVAC systems by identifying occupant numbers and their locations [17],

monitoring electrical power switchboards and control panels [18] and lighting level

sensing and control [2]. Computer vision also has strong advantages for use in sensing

and monitoring a fire. Cameras and corresponding facilities required in the computer

vision system are already standard features of many buildings for other applications.

Additional fire detection capability can therefore be added with minimal cost through

changes in software and correlating results between the computer vision system and other

sensors. One such application is the machine vision fire detection system (MVFDS),

which uses a combination of video cameras, computers, and artificial intelligence

techniques [19-22]. It processes multiple spectral images in real time to reliably detect a

small fire at large distances in a very short time. It can also identify the location of a fire,

track its growth and monitor fire suppression. For some applications, the MVFDS is

further combined with radiation sensors (UV and IR) to enhance its detection capabilities

or a CCD camera to automatically evaluate the scene through identification of bright

regions associated with the fire radiation and increase system reliability [21, 22]. The

development of this computer vision system is still ongoing and is viewed as being

restricted due to the need for expensive and sophisticated software and hardware

components.

Wireless sensors are another important emerging technology for intelligent buildings.

Wireless fire detectors are already available in the market. An alarm signal is transmitted

to the control panel by radio, infra red transmission, ultra sonic and microwaves when

smoke or rapid temperature changes are detected. Their significance comes not from

565
their ability to measure new parameters, but because they do not require a hardwired

connection to the data acquisition system that will record their readings. This capability

not only allows wireless sensors to be located anywhere inside a room, but also means

that they can be installed in the exterior envelope or other locations that would be too

expensive or physically impossible to monitor in any other way [2]. Wireless technology

may also be a necessity for retrofitting intelligent building technology in older buildings,

where the difficulty and cost of installation is a significant barrier. In many cases

installing intelligent building systems in older buildings requires major renovations. It

can rarely, if ever, be done without damage to existing walls, floors and ceilings. It is

likely that wireless networks will need to be developed to retrofit older buildings.

Without such techniques, these older buildings will gradually become uncompetitive with

new construction, reducing the value of the existing built environment.

In large buildings, wireless sensors communicate with other building systems through

wireless networks in the building. Intra-office data networks based on 10 GHz wireless

networks are already becoming widely available [23]. Wireless networks are expected to

become the dominant media for low to medium bit rates for many intelligent building

network applications. However, significant further development will be necessary for

them to reach their full potential, and to overcome attenuation problems, such as

absorption by office partitioning and reflection from wall, windows and other surfaces.

Other major problems include the need to significantly lower the cost of wireless sensors,

and the requirement for the development of suitable power supply systems that will allow

the long-term operation of these sensors.

Development of Remote Monitoring and Control Techniques

There is increasing interest in remotely monitoring building service systems. Intelligent

remote monitoring can significantly increase efficiency and reduce costs for building

management operations. They may be especially important for small facilities where

skilled technical supervision would otherwise be too expensive to consider. These

systems could let a single person supervise a number of buildings.

566
Most commercial monitoring systems use a modem and remote dial-up to access the

building's operating system. Alarm messages from the building systems can also be

directly sent to the equipment's manufacturer without intervention from the building's

operator. More recently, studies have been carried out using the Internet for real-time

control of a building automation system [24, 25]. Compared to "voice/touch-tone"

interface, the Internet is able to provide more information (text, images and sound clips).

Researchers at the University of Essex in the United Kingdom are developing an

embedded-internet within a building that will allow building users or manufacturers to

directly communicate with the building service systems [24]. The City University of

Hong Kong has carried out an initial research project to use the Internet for real-time

control of building automation systems [25]. Their studies have shown that the Internet

has the potential to extend the monitoring and control of a typical building automation

system out of the building so that users can gain access to it at anytime and from

anywhere. Their work also shows that one central 24-hour management office is able to manage a real estate portfolio with hundreds of buildings.

Remote monitoring and control also has the potential to improve fire safety. It is

estimated that 67 percent of all fires occur outside of office hours [26]. Remote

monitoring of fire detection and alarm systems can reduce response time and improve

response effectiveness by providing adequate fire information to the building supervisor,

activating fire suppression systems and immediately summoning the appropriate fire

brigade.

Some current advanced fire control panels have already incorporated a modem for remote

access control. With the development of real-time control via the Internet, fire detection

systems will perform automatic fault detection and diagnosis with early warning of sensor

contamination before the overall integrity of the system is affected. Human intervention

at the first sign of a warning should permit more efficient discrimination between fire and

non-fire threats. When a fire occurs, detailed and adequate local fire information could be

directly sent to the appropriate fire department. Firefighters could also access information

from the Internet to identify the locations of potentially hazardous materials or occupants

who will need special assistance to leave the fire location. Fully integrated remote access

567
systems will allow planning for fighting fires to take place enroute to the fire, rather than

at the building's fire panel. Remote access systems should therefore provide valuable

additional time for property and life protection.

However, real-time control via the Internet, is still in its infancy [25]. Development of

the advanced, Internet based remote access fire protection systems described above has

not yet begun. In addition, significant issues, such as real-time control of security and

safety, still need to be considered. Internet access to fire safety systems also creates its own unique fire safety issues concerning computer and network security. The full

implementation of Internet based monitoring systems will require strong assurances of

data integrity and resistance to computer hacking. Without these protections, fire

fighters may receive false information about the existence, location or size of fires.

Integrated Building Service Systems

Today's fire detection and alarm systems have been partially integrated with other

building systems. Once a fire occurs in a building, fire detection and alarm systems in

some buildings activate various fire safety systems, such as smoke control, and various

pressurization and smoke exhaust system. They also activate elevator recall, the door

release system, flashing exit signs and fire suppression systems [27]. Currently,

however, the level of integration of all the disparate building systems is still limited.

Even though building service systems that have similar functionality, such as fire safety

systems and security systems, or HVAC systems and lighting systems, have been

integrated together [5, 6], there is a limited level of information-sharing among the

systems. Systems on the same cabling backbone are all provided by the same

manufacturer. Various building service systems involving HVAC, lighting, fire safety

and security monitoring in the building are not integrated together on the basis of a common communication protocol. This is mainly due to fragmentation of the building

and communication industries, a reluctance to change established practices as well as the

lack of standardized, broadly-based communication protocols that allow different types

of building service systems to communicate with each other. Many tenants and

developers also prefer to have a lesser degree of systems' integration due to fears of

568
excessive complexity, potential total system failure and possible slowdown of the central

control [28, 29].

Various methods and concepts have been developed to enhance integration of building

systems and to increase reliability of the integrated systems [5, 6, 30]. Efforts are also

being made to develop communication protocols that enable different manufacturers to

"interoperate" together and allow the building systems to communicate with each other

over a network [8]. These protocols include BACnet, LonWorks, CAN, NEST, EHSA

and CAB [10]. They prescribe a detailed set of rules and procedures that govern all

aspects of communicating information from one cooperating machine to another.

BACnet prefers a hierarchical model in which the whole system is divided into a number

of subsystems, each with a separate central processing unit [31]. The coordination of the

subsystems is achieved by hardwired interconnection or software interconnection. This

method simplifies installation and maintenance and the damage caused by the failure of

the CPU to EMCS and the fire safety systems is only limited to the local level, instead of

to the whole integrated system. BACnet is most suitable to the traditional processing and

communications models used by current HVAC hardware. However, BACnet does not

support dynamically structured networks, nor does it emphasize distributed processing.

Efforts are being made to expand BACnet beyond the HVAC realm. The first

commercial BACnet fire system products will be introduced within the next two years,

and new features are also being added to the protocol that will enhance the use of BACnet

in life-safety systems [8].

Other communication protocols, such as LonWorks, on the other hand, prefer

"networking integration" in which there is no central processing unit, just intelligent field

cabinets. Each intelligent field cabinet is a "node" on the network and has equal status to

the other nodes. Each cabinet controls local or zoned all energy management functions,

all fire alarm functions and smoke control functions. It does not depend on any remote

central processing unit or another intelligent field cabinet. The microprocessors in the

field cabinet can support advanced diagnostics and manage all the local building

functions. The nodes in the network can communicate with each other and can be

approached and managed through a central station or by a personal computer. This type

569
of network further simplifies installation and maintenance, and increases the reliability of

the system. Once a fire damage or a fault occurs, only the immediate area is affected, and

the fire command station or any other man/machine interface location could maintain

communication with any other field cabinet on the network loop by transmission of data

in two directions. Response of this type of network to a fire threat is very fast, because

there is no need for a CPU to scan and process whole building systems. The intelligent

field cabinet recognizes the fire alarm within its own area and acts upon that event within

the cabinet.

Conclusion

New intelligent building technologies have strong potential to improve fire safety. Multifunction sensors (i.e., chemical gas sensors, integrated sensor systems and computer

vision systems) and wireless sensors will not only reduce expenditure on sensors, but also

reduce false alarms, speed response times and reduce fire-related losses. Real-time

control via the Internet will extend the monitoring and control of building service systems

and fire safety systems out of the building, which will increase the efficiency and reduce

costs for building management operations, more efficiently discriminate between fire and

non-fire threats, and increase the time available for property and life protection. The

integration of fire detection and alarm systems with other building systems should also

increase fire safety in the building.

However, the application of intelligent building technology may also create completely

new risks. Sensor technologies will need to be robust enough to prevent false alarms,

accurately discriminate between fire and non-fire threats, and ensure that vital

information such as the location of occupants is not lost due to data overload during a fire.

Internet based monitoring and control of building service systems will need to be

completely secure to prevent false fire infoiTnation being provided to building owners and

fire brigades. Integrated building systems will need to be designed not only to give fire

safety priority over other building activities but also that fire emergencies do not crash the

building service system. A close examination of the concept of system integration will

need to be conducted as intelligent building systems become rnore prevalent in order to

570
determine whether a full integrated building system has sufficient redundancy to provide

adequate fire safely.

In addition to the need for further research in developing new fire safety systems and

ensuring that intelligent building systems do not hinder fire safety, additional work is

needed to overcome the problems that are common to all parts of the intelligent building

industry. Fragmentation of the building and communication industries, a reluctance to

change established practices, the complexity of intelligent building systems, and the lack

of universal communication standards have all slowed intelligent building progress.

Much effort is needed to remove these barriers.

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Security and Fire Detection System," Fire Technology, Vol. 30, No. 3, 1994.

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25. So, A. T. P., Chan W. L. and Tse W. L., "Building Automation on the Information

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573
Walter W. Jones and Richard W. Bukowski

Building and Fire Research Laboratory

National Institute of Standards and Technology, Gaithersburg, MD 20899

Using High Reliability Detection for Fire Service Response in

Buildings

1 Introduction

Reliable fire detection is an essential aspect of fire protection in all

constructed facilities, first for the safe evacuation of occupants and second

as a means to initiate manual suppression to for control and extinguishment

of unwanted fires.

Firefighting in buildings is complicated by lack of information about the

environment inside the building. Even residential buildings (one and two

family dwellings) are equipped with detection and alarm devices, that

provide early warning for occupant evacuation. As technology for device

interconnection, such as embodied in the the IEEE 802.11b standard,

becomes more wide spread, the capability for communication even within

residences increases and reporting such signals over a residential network

will provide increased reliability.

Most commercial and industrial buildings have fire detection systems that

supply limited information from detectors in the building to fire alarm

panels, generally located in a designated area of the of the building. The

information available today, and likely to be available in the future in new

buildings with advanced sensors, can be used to improve the fire service

effectiveness and improve safety of the firefighting effort.

574
In order to enhance the safety and effectiveness of fire fighting operations in

buildings containing modem fire alarm systems, we need to improve the

t3rpe of information that is made available to the fire service, the means by

which it is presented, the channels through which it is distributed. More

timely information on the state of the fire and the building environment will

lead to better tactical decisions by the fire service.

The range of uses for modern transducers covers building management and

indoor air quality, as well as 1" responders. While building management

information display will be available on high resolution monitors, 1^'

responders need a much wider range of devices from laptops for vehicles use

to handheld devices such as "pagers." The delivery of information must scale

across this wide range of input and output devices, obviously with the detail

available on small footprint displays being much less than on the high

resolution devices.

The National Fire Alarm Code (NFPA 72-1996) requires that,

"The primary purpose of fire alarm system annunciation is to enable

responding personnel to identify the location of a fire quickly and

accurately and to indicate the status of emergency equipment or fire

safety functions that might affect the safety of occupants in a fire

situation.

In light of this requirement it is surprising that many fire departments

report they seldom use the features provided by alarm panels. The root

cause appears to stem from inconsistent interfaces, displays and controls.

This paper describes an improvement in the type of information that is

made available from buildings, describes a means to achieve that end and

proposes an interface protocol that meets the diverse range of the needs of

the fire service.

575
2 Background

As transducers become more commonplace in the built environment, it is

desirable to utilize this information in a more complete way to assure safety.

There are two facets to doing this, incorporating our knowledge of fires and

other extreme events into the measuring and reporting capability, and

insuring that all systems are functioning the way in which they were

intended. The former is commonly referred to as smart sensing, while the

latter deals with fault detection and redundancy. Combining the two is an

information delivery infrastructure. These are the prime components of a

system which will allow reliable real-time prediction of the environment in a

building.

To accomplish this objective, it is important to have access to information

about the building and its environment. The shortcoming in understanding

what the information implies is transcended by providing sufficient

computing and memory capacity to allow reasonable algorithms a chance to

work in real time.

Taken together, we are trying to understand what transducers actually tell

us about the environment in a building. In order to predict the

environment, we must first understand the meaning of the data that is

delivered. Then we can use the information in a system which is sufficiently

faster than real time that the predicted information is useful. What is

needed is an understanding of the measure that the sensor itself takes and

effect that the surrounding environment has on the data. Essentially this

means understanding entry characteristics of the sensing element, and the

response of the transducers themselves, such as the thermal lag of

576
thermocouples, accumulation of dust on optics, and similar instrument

functions.

Given the sensor information, the second part of the problem is being able to

modify an environmental model "on the fly" to change the parameters being

used as the initial conditions. CFAST has been able to do this since its

inception (using the restart function) but the process 1) assumes a well

defined consistent state, and 2) is not fast enough for this application. We

have developed a method to start (in the real sense of ah initio) the model

with (almost) arbitrary values. The results do not (and should not ) track

exactly, but asymptotically converge as they should.

The third is information delivery. The majority of the effort is in developing

consistent controls and icons which convey the critical information and

allow meaningful response. It is generally accepted that suitable graphics

convey a great deal more information than simple text messages'.

This information should be available whenever and where ever it is needed,

and to whomever will benefit from the knowledge. The stakeholders range

from the building owner, to the maintenance service contractor for the fire

systems, to the firemen responding to emergencies. The means by which it

is provided should provide for multiple transmission media, from low band

wireless to broadband wired lines. In the former, alarm prioritizing must

occur and in the latter, video can be provided. The amount of information

delivered must be coromensurate with the delivery capability.

3 Fire Service Needs

The immediate focus of this project is information delivery for those who

respond to emergencies. The fundamental questions that must be asked are

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1) what information is needed, 2) when is the information needed, and 3)

how can it best be presented to be most useful?

The first two are closely linked. Though the fire service information needs

differ with time, most relate to the most effective allocation of resources. There are three distinct operation times, dispatch, arrival and deplo)rment

and incident management.

Initially, the most important item is to provide some metric for the

likelihood that the alarm is genuine - particularly when it derives from a

single device. Perhaps a three level metric of low, moderate and high

confidence would be enough. The basis for assessing confidence is currently

unclear but may involve heuristic algorithms based on sensors keeping

history data and reacting to excursions from that history. There is

significant concern among the fire service over liability for damage they

cause by forced entry when an incident turns out to be false. They would

also like information they could use to decide what resources are required.

For small fires growing slowly a single unit may be enough. For a fast

growing major incident, additional units dispatched early can be of great

help in minimizing losses and assuring firefighter safety.

At arrival, the most important information is (1) the location and size of the

fire within the building, (2) the location of occupants, (3) how to get to the

fire, (4) a safe location to stage, location of standpipes, and other points of

interest (hazardous materials, locked areas), and (5) how fast is the fire

growing. In addition, there are specific bits of information that are needed

to make good choices about resource deplo5Tiient, including temperature,

carbon dioxide and monoxide concentrations, and whether conditions are

conducive to full room involvement. The initial decisions about tactics and

578
resource deployment for search and rescue, ventilation and suppression can

have a significant impact on the effectiveness of the attack.

Finally, during the incident, information on (1) location and rate of spread of

smoke/gas and of fire, (2) measures of operational effectiveness and safety of

crews, and (3) potential benefits or dangers of ventilation.

These ideas extend to the less extreme environment found during normal

conditions. Improved information gathering and processing could provide

building owners and managers with more cost effective ways to maintain

conditions which are acceptable for the occupants. The primary difference is

the range of sensor input, and their concomitant calibration.

4 What needs to be done

In order to extract information from both current and the next generation of

transducers, it is crucial to calibrate these sensor(s). There are two regimes

of sensing: low level which is appropriate to ignition and early fire growth,

and high level which occurs during the later phases of a fire, perhaps

extending to full room involvement and complete (visual) obscuration.

NIST has developed the fire emulator and detector evaluation (FE/DE)" test

chamber to calibrate sensors at extremely low signal levels. The FE/DE has

been designed to evaluate fire detection technologies such as new sensors,

multi-element detectors, and detectors that employ complex algorithms.

The FE/DE is useful for calibrating transducers at low levels. These are

important in early detection. However, in order to provide information and

tactical aids during an entire incident, the calibration of the sensors to more

extreme conditions must be accomplished.

579
There is a cost/benefit tradeoff in asking for such extensions. For example,

with current sensors, there is an hmit to how well they can be expected to

perform without hardening. While use of current technology for hardened

sensors is more expensive, as the new technologies come online, this cost

disadvantage will disappear. And as the expectation for reliability grows,

there will be a greater demand for such transducers.

5 Why we need to do it

From residential housing to complex office buildings, active technology is

plajdng a greater and greater role in assuring the well being of their

occupants. In the residential end, refrigerators which "know" about their

contents as well as maintenance needs are the bases for using technology to

improve the living conditions of the occupants. In the complex office

building, eliminating "sick building" syndrome is a desirable end. All of

these advances are fueled by monitoring and sensing of the environment

and providing this information at the appropriate place in a timely manner.

In the specific case of firefighting, the availability of tactically significant

information across a wide range of media facilitates the delivery of this

information to the hands where it can be used to best advantage. For

example, information on the current location and intensity of the fire

delivered wireless to pagers or PDAs could prove lifesaving to truck

companies doing ventilation on a roof, or search and rescue teams already

inside a building. Detailed information on the fire monitored at dispatch

could indicate the need for special units or additional resources before it

becomes critical to the "on scene" commanders. Critical information could

even be shared with Fire Wardens in high rise buildings undergoing partial

phased evacuation.

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6 How it can be done

The best scheme is to examine the sensor input from a building in the

normal (operating) state and look for deviations from this baseline. An

anomaly at this level should trigger a closer examination. The closer

examination would be to initiate a model of the environment using data

from the transducers. In algorithmic terms, one would need an initial guess,

followed by a prediction, followed by a comparison of the ongoing

measurements with the output from the model.

Since we are interested in the full range of environmental conditions,

sensing from very low levels is needed to extreme conditions is needed.

While current sensor implementation, hardness and calibration, is suitable

for the low level signals, decision aids for sustained fire fighting will require

a much broader range of detection and robustness.

Generally, it is not feasible to run a predictive model continuously. Indeed,

most of the time such computing power could better be used for other

purposes. The requirement is to filter signals including a means to detect

any deviations. This would provide a starting point for a prediction of the

environment. Even at this point, long term predictions are not useful.

However, the reliability of confirming trouble or alarm signals through

modeling is of great use. Even if a building cannot be specified completely, a

calibration of nominal and expected conditions can be done as an empirical

technique, much as a ventilation system is balance prior to occupancy. Such

calibration and spot checking of sensor systems can be done relatively

cheaply in terms of computer resources.

581
A typical building with a modem alarm system and environmental

monitoring will have in excess of 10 000 transducers. This argues for using

relatively simplistic filtering techniques to extract significant deviation, and

utilizing the readings fi*om a dozen nearby sensors as initial conditions for a

predictive model. We have demonstrated that such filtering can be done on

a real-time basis with current microprocessor technology\ A decision to

model the environment can be a relatively frequent occurrence, perhaps one

per second per zone in an occupied building. An example of appropriate

filtering would be the exceeding a nuisance alarm threshold for smoke

detectors. At this point in time, an quick estimate of the heat release rate or carbon monoxide buildup could be extracted as an initial fire signature and

posed as the initial conditions for estimating the time to a notable event.

The level of detail available is closely related to the resolution of the display

devices. This also affects the possible interaction. At the "high" end of

technology, one would expect high resolution screens. Pointing devices such

as touch screens or trackballs would complement this technology. At the

laptop ("in truck") level, the amount of information which can be conveyed

becomes constrained. The need to accommodate a wide range of lighting

conditions renders fine detail found in graphic display unsuitable. Similarly,

the freedom to point is constrained by vibration, distractions and possibly

inclement weather which necessitates gloves. Further reductions in

information availability and interactivity occur at the personal level,

exemplified by beepers and PDAs.

In all cases, touch screens or similar "point and click" devices are available;

toggles and similar switches are not scalable. Scaling both paradigms

argues for layers of information. At the highest level of resolution, one can

show building graphics and video signals to confirm alarms. Pointing at

582
rooms would bring up additional information such as text showing

temperature, geometry and other suitable data. At the laptop level, the

channel bandwidth will nominally be lower and video confirmation is

probably not feasible. At the level of a PDA, only a single text line will be

available. The information on alarm size and location can be conveyed with

such constraints, but details of the number of devices in alarm is beyond its

capability. And interactivity with a stylus and total display size l"x3" lends

itself only to acknowledgment and requests for status information.

The browser paradigm, shown in figure (1), is an example of an

implementation. It would include the basic panel information as elements.

We would propose three layers, corresponding to three resolutions of

devices: layer 1 for palm pilot, beeper, cell phones; layer 2 for basic panels

and fire service interaction; and layer 3 for building management, dispatch

and similar protected displays. In the example, layer 1 is represented by the

"Elevation" information, layer 2 by the 'Tire Service Controls," and layer 3

by the building schematic, 'Tlan view, 3rd Floor."

7 Display requirements

In order to achieve the goal of "information anywhere, anytime," it is

important that display of building conditions be possible across a wide range

of technologies, and through a wide range of conduits.

At the "high" end, for example in a building management or security center,

one would expect the luxury of high resolution displays with detailed

drawings and schematics of building components. In these cases, fragility of

the hardware, and ambient lighting conditions can be controlled. However,

as one takes even this same display capability to remote locations,

prioritizing of signals is necessary.

583
A similar push comes from lower resolution and ruggedized requirements,

such as would be imposed on information centers which might be carried in

command vehicles or even on-scene. In these cases, ruggedness must be

considered, as must the lack of control of ambient lighting.

In the extreme case of portable systems, i.e., Palm Pilots, beepers and handheld browsers, it will only be possible to convey a very limited amount of

information. Not only must the most important information be display first,

there will be only limited capability of interaction so in general the

information shown must be relevant to immediate needs, i.e.,

announcement, location and so on.

One might envision four levels of display to be in 1) a building management

office, 2) a command vehicle, 3) a small building annuciator panel, and 4) a

personal information manager. In each case, it is crucial to provide

information about the location and size of the fire . As the display capability

improves, additional layers of information can be accommodated. In the

realm of simple annunciator panels, the capability to access other systems,

such as the status of the elevators, would be possible. At the highest end,

devices such as CCTV would be available.

One of the basic concepts deals with the actual display of information.

Graphical displays are recognized as being a compact and efficient way to

transfer information from electronic signals to humans. It would seem to be

natural to use a set of icons to represent the information that can be

delivered which will be generally meaningful. Although we are not

intending for this to be "intuitive," the closer the symbols are to commonly

accepted notions of signals that are of interest the more reliable the

information transfer to the user.

584
An initial set of icons was developed for this purpose from icons used for

similar purposes in Japan and from standard symbols for engineering

drawings from NFPA170. An example of a set of icons is shown in figure (2).

The set and style chosen were dictated by scalability and the desire to allow

information to be displayed in monochrome.

8 Modeling the building in a hostile environment

Over the past decade we have developed a model of fire growth and smoke

spread (CFAST^) which has seen a wide variety of uses. This model is used

by specifying the geometry of the building, characteristics of the fire and the

venting available for combustion. It is based on solving a set of equations

that predict state variables (pressure, temperature and so on) based on the

enthalpy and mass flux over small increments of time. These equations are

derived from the conservation equations for energy mass, and momentum,

and the ideal gas law. The perspective has been understanding the

environment for a specified building. In this context the overall computation

time is paramount.

If we can presume sufficient information to make a prediction with

sufficiently small error bounds, an example of an approach that might be

taken would be the following: use transducer data to start a simulation of a

building; predict the environment for the next 10 seconds (30 seconds, ...); gather the actual conditions for this period of time, then compare the

curves. If these curves are close and the imputed heat release rate is

indicative of a fire, then an alarm is sounded. There are several other

possibilities. One is that the prediction and measurements do not agree.

This would indicate that some assumption in the building model is

incorrect, or that a transducer is giving an incorrect reading. Another is that

585
the cause of the discrepancy is from some cause other than a fire. Either

scenario would trigger an alarm. Another is, of course, that prediction and

measurement are in agreement and no untoward event is happening. The

latter is, hopefully, the case the majority of the time. An implied acceptance

criterion is that there be no false positives (false alarms) or false negatives

(missed fires). Actually, any extreme event is a candidate for an alarm, and

some thought will need to be given to the various conditions that warrant

intervention.

In order to implement such a paradigm, there are three areas in which we

need to make improvements: a real-time environmental response model of

fire growth and smoke transport, we must be able to make a very quick

assessment of how good a comparison there is between a prediction such a

model makes, and the actual data which are subsequently measured and we

need a way to interpret sensor signals to know what the environment being

detected is. There are several component to such an endeavor. The natural

evolution, at least for a first try, is to improve upon our current framework

of models, verification and sensor modeling.

We started with the framework provided by CFAST and have modified it to

read a sensor suite as might be delivered from an alarm panel, and compare

this curve with an actual data set. Figure (3) shows an example of

modifying a pair of time-velocity curves to bring them into the 'T3est"

agreement, which would then provide the basis for a restart at the "correct"

time. This is only a first step, and provides a match based only on making

the curves agree in shape and minimum difference between the curves. In

this example, the fire in the prediction was started too soon, that is the fire

was thought to have started earlier than the measurement data indicates.

The procedure was done for a single sensor, using 120 data points in time.

The time for this computation was 0.8 seconds. In order to make this

586
practical, we need to be able to apply the technique to -5000 transducers

using -20 data points (in time), and the total computation time must be

under one second. So the matching algorithm must be improved and the

time to do the comparison must be reduced.

A related issue is the ability to say how close two curves are, that is whether

a value extrapolated from a model of the process agrees with the actual

progression of events. In the alarm industry, this is manifested in the

Underwriters' Laboratory Test Standards for smoke detector suitability, UL

268. The assumption is that if the time series sensed by smoke detectors

were found in habited compartments, this would indicate that there is a

high likelihood that dangerous conditions would exist, absent corrective

action. This is an implicit statement of reliability, indicating a high level of

confidence that this series of fires is indicative of conditions which will

become extreme.

The paper by Forney" has examined the mathematical robustness of fire

models using the CFAST model as an example. While the ability to compare

a fire model with experimental data is the thesis of the paper, the issues are

the same for comparing real time measurements from multiple sensors, and

making an assessment of parameter extraction. Key to both sensitivity

analysis and fire model comparisons is the ability to quantify the difference

between two time series'^.

Functional analysis is a generalization of linear algebra, analysis, and

geometry. It is a field of study that arose around 1900 from the work of

Hilbert and others. Functional analysis is becoming of increasing

importance in a number of fields including theoretical physics, economics,

and engineering to answer questions on differential equations, numerical

methods, approximation theory, and applied mathematical techniques.

587
Functional analysis allows problems to be described in vector notation and

defines appropriate operations on these vectors to allow quantitative

analysis of the properties of the underlying physical system.

Figure (4) shows another simple example of fictitious experimental data

compared with three model predictions. Model 1 is simply the experimental

data multiplied by 0.9. Model 2 has the same peak value as model 1, but

with the peak shifted -25 s. Model 3 has the same peak as Model 1 and

Model 2, but with a 20 s plateau centered around the peak of the

experimental data. Weighted area comparisons would show that these three

models are essential identical. Clearly this comparison fails to capture the

differences. If such an algorithm were used, there would not be a high

degree of confidence that an alarm would be justified.

These examples are based on single point measurements and predictions.

The mathematics can be extended to multiple sensors, multiple

compartments and more than one sensible variable (temperature as well as

smoke, for example). Then the data fusion implied by the national fire alarm

code, chapter 2, section 3.4.5.1.1 would be rigorously defined. The

implication is that more detectors mean higher reliability of the ability to

detect and report fires. While the emphasis in existing codes and standards

are for single compartments, the ideas can be extended to different types of

transducers, placed in non-contiguous compartments and systems.

9 Emerging technologies

Current technology for sensors covers quite a wide range of measurement

capability. The commonly used measures are sensors for carbon monoxide

sensors, temperature (thermocouple) or thermistor, opacity (photo

588
detectors), smoke particle counters (ionization current), beam (laser), carbon

dioxide sensors, oxygen concentration, and water vapor (moisture).

These are the types of measurements needed to understand the

environment from normal operating conditions to the extreme environments

found in hazardous situations. Much of the future development of sensors

for additional gases will be in the arena called "electronic noses." These are

sensors which detect small amounts of polymeric substances. The particular

focus today is on detecting bombs and the vapors from organic materials

(foods), but the principle should apply to any odor or volatile hydrocarbon.

10 Recent Results

There are three separate thrusts: display of information, preprocessing to

provide more understanding of the sensors data, and tactical tools. For the

first, a proposal has been submitted to NFPA as a new appendix for the Fire

Alarm Code. This will incorporate the display paradigm discussed earlier.

We have demonstrated a prototype with new icon based display and

reported on an assessment of techniques and technologies to the

International Association of Fire Chiefs. The proposal is for the 2002

Edition of NFPA 72. To implement the second part, we have developed a consortium with NEMA and the major fire alarm companies to acquire insitu data from current buildings. The third will depend on the experience of

use of these systems in actual responses to emergencies.

11 Conclusion

We are using our knowledge and practical experience in developing

predictive models of fire growth and smoke transport to develop the

capability for making real time predictions in buildings using existing

589
transducers. There are three research threads involved: developing a

computer model which can make predictions in real time; understanding the

instrument function in order to use data from building transducers; and

finding a metric for the "goodness of fit" between two time varying curves. These avenues are being explored and there is progress in all three areas. This should allow for a prototype of such a tool in the near future. At

present we are pursuing these concepts using tools we have developed, but if

they are not suitable, or sufficiently robust, then we will develop ones that

are.

This will provide a higher level of information to the building industry for

monitoring the environment in buildings. The endpoint is to provide

appropriate information whenever and where-ever it is needed. This

includes early warning of system malfunctions, hazardous environments, insitu monitoring and prediction for building managers as well as real-time

assessment of fire fighter conditions.

The immediate application is to provide environment measurement and

prediction for "first responders," those who have to know very quickly where

a fire is and how large it has become and what is likely to occur in the near

future. A possible future development would be to use data reported from

PASS devices to indicate to scene commanders when a fire fighter is likely

to be in conditions in which it is not possible to operate safely, even with

appropriate gear.

590
12 References

[1] Shneideiman, B., Designing the User Interface, Strategies for Effective HumanComputer Interaction, Addison-Wesley Publishing Company, 1987.

[2] Cleary. T. G.; Grosshandler, W. L.; Chemovsky, A., Smoke Detector Response to

Nuisance

Alarms, International Conference on Automatic Fire Detection V9", 1 1th. Proceedings.

University of Duisburg.

[3] Using Sensor Data to Predict the Environment in a Building, WW. Jones, RD

Peacock, GP Forney and PA Reneke, FPRF Suppression and Detection Application

Symposium, Orlando (1998).

[4] A Technical Reference for CFAST: Engineering Tools for Estimating Fire Growth

and Smoke Transport, Walter W. Jones, Richard. D. Peacock, Glenn P. Forney, and

Paul A. Reneke, NIST Technical Note 1431 (2000).

[5] Forney, G. P., and Moss, W. F., " Analyzing and Exploiting Numerical

Characteristics of Zone Fire Models," Fire Science and Technology, 14, No. 1/2, 4960,

1994.

[6] Peacock, RD, Reneke, P A, Davis, W D, Jones, WW, Quantifying Fire Model

Evaluation Using Functional Analysis, Fire Safety Journal, 33, 167 (1999).

591
XiangyangLI Weihua LIU Aizhong WANG Jing SU

Gulf Security Tech. Co., Ltd., Qinhuangdao, Hebei Province, PRC

AN OPEN DISTRIBUTED FIRE DETECTION SYSTEM

Abstract: This paper introduces an open distributed fire detection system. This system

consists of three layers: base layer, middle layer and upper layer The base layer

connecting the detectors to control unit uses a special digitized bus. In order to have

reliable and effective data transport, the digital bus protocol should make requirements

to the physical layer, the link layer, the network layer and the transport layer. The

middle layer which links the control units to one another adopts LONWORKS as its

internal bus, implementing real distributed control between the control units. The upper

layer, providing the interfaces between the fire detection system and other control

systems, adopts standard network variables conformed to LONMARK protocol

completely. The characteristic of the open system makes the integration between the

fire alarm system and other control systems come true.

1 Epigraph

The devices of fire alarm system can be divided into three types according to

topological structure. The first one concerns the field devices including various fire

detectors, manual call points, special indicators and execution units of fire fighting

system. The second one is usually composed of the control units, which are connected

with the field devices, providing the interface of man-machine conversation for

monitoring and testing. The third one is the center of the whole system, monitoring the

alarms, sending off control signals, providing interfaces to security systems, building

automation systems and city fire alarm network systems.

With the developing of microelectronics and network communication technology, the

fire alarm system demands more adoption of these technologies.

592
Many new methods are adopted in fire detection in consequence of the progressing of

sensor technology. The fire detectors become more stable because of multi fire detection

technology. More information can be transmitted through the internal network with high

speed.

Because of the advanced network technology, the large-scale and super large-scale fire

alarm systems have been widely used. In order to insure the stability of the systems, the

control units must have the characteristic of independence in case of failure of the

internal network.

Now integration is a tendency of the control systems within buildings. So a fire alarm

system needs the open characteristic to fit the demand.

2 Introduction

The fire alarm system introduced by this paper is a kind of new and distributed

monitoring system developed by GST. The system is a typical one composed of three

layers. The first layer is the base layer using special digitized bus protocol and being

connected to the control unit and field devices. The network topological structure can be

ring type, 'T' type, or mixed type of both. The second layer is the control layer

conforming to LON bus protocol and being connected to various units of the system,

such as display unit, control unit, communication unit and printer unit etc. The third

layer is the interconnection layer satisfied by the requirement of LonTalk protocol,

accomplishing the integration with other control systems.

3 Network Formation

3.1 Special Digitized Bus

The special digitized bus uses the self-defining bus protocol to suit the data transporting

between control unit and field devices. In order to have reliable and effective data

transport, the digital bjjs protocol should make requirements to the physical layer, the

593
link layer, the network layer and the transport layer, which can be introduced

respectively as follows:

I. The physical layer: An ordinary twisted pair or an optical fiber link is used for the

electric connection. If a twisted pair is used, a direct connection is all right. If an

optical fiber is used, a fiber-optic transceiver is needed. The fiber-optic transceiver is

specially designed for data conversion purpose.

n. The link layer: The balance code is used with CRC calibration and sum calibration.

The signal bus supplies power to detectors when it transports signals. The

transmission rate is 16 kbps. Fig 3-1 shows the basic structure of the balance coding.

The coding is symmetrical and self-calibrating. It has no special requirement to the

wave-shaped edge and has the features of high resistance to disturbance, long

transporting time and high reliability.

T T T/2 T/2

1 1111 I—0]——

t

T is a one-digit cycle

H—^1 IHI

Frame head data+16 bit CRC Frame tail

Fig3-1 Coding structure of special digital bus

in. The network layer: An independent addressing is used and the node coding is not

restricted by the protocol.

IV. The transport layer: It provides acknowledged messages, and the calibration can be

CRC calibration or sum calibration according to the degree of data importance and

the spectrum of transport so that the communication is more reliable and effective.

594
The network between control units and detectors is a kind of master-slave, low speed,

ring-type network. It has a characteristic of collision detection. Using twisted pair to

transit signals and power, each control unit can be connected with 128 field devices.

The network uses a kind of special digitized protocol, which includes network

management, field device static signal transmission, and fire data pre-treatment.

Network management protocol regulates the manner of automatic distribution about

logical addresses of the field devices. Each field device has only one number. During

the period of network installation, the number is the only identifier of a detector by

means of which the system can do double addresses tests and the logical addresses redistribution.

Field devices static signal transmission protocol regulates the static signals exchange

mode between control units and detectors. The static signals include production date,

batch number, history and fire sensitivity etc.

Fire data pre-treatment protocol regulates the dynamic data exchange mode between

control units and detectors. The protocol of the data transmission mode is based on

interrupt, insuring that the signals can be transmitted rapidly and efficiently when

needed. The data can be fire alairn signals or field signals pretreated by detectors.

ASIC with the firmware of special digitized bus protocol will be used in field devices.

The matched circuits are also used in control unit. The data exchange is accomplished

by coordination of the firmware. The custom design insures the stability and speed of

the data transfer.

The protocol conforms to the requirement of the network in this layer and is also

applicable for various security systems.

595
3.2 Control Unit Management Network

Fire alarm systems are usually composed of several kinds of units, such as central

management unit, field devices control unit, repeater control unit, remote

communication control unit, and remote printer unit etc. Various units have separate

functions. More than one identical unit might be in the same fire alarm system. The

network interconnects the units to accomplish a complete task.

The internal network of the system is LonWorks, a kind of field bus network based on LonTalk protocol promoted by ECHELON USA in 1993. LonTalk protocol follows ISO

OSI reference model for network protocol and operates as national standard of

ANSI/EIA709.1. The protocol has been integrated in the Neuron Chip named Neuron

3120, 3150 Chip. The nodes using Neuron Chip for controlling can communicate with

the other nodes on the same network via the network variables. For the integrality of the

network protocol, the internal network communication is simplified.

The structure of the fire alarm system is very flexible. The topological structure of the

network can be changed according to the structure of protected area. The network

structure can be compacted mode, distributed mode, or mixed mode of both.

The control units in the system can be connected directly with twisted pair when the

compacted mode is adopted, and the transmission rate is 78kbps. Network

communication can be realized using various transceivers through various media, such

as twisted pair, power line, optical fiber, when the distributed mode is adopted. A router

is necessary in order to connect the compacted part with the distributed part when the

mixed mode is adopted.

596
Many management units might be in the same network when the distributed mode is

adopted. The functions of the management units can be set by software. Users can get

custom designs for their fire alarm systems.

Unit 1 Unit 2 Units

1

1

direct connection direct connection direct connection

Fig3-2(a) The second layer network topological structure-the compacted mode

Unit 1 Unit 2

] i Units

T/R I T/R

) - ( Unitn

T/R T/R

connection via multi media connection via multi media connection via multi media

Fig3-2(b) The second layer network topological structure-the distributed mode

597
598
3.3 Open Data Interface

The diversity and open characteristic of LonWorks makes this system be widely used in

the area of building automation and industry control.

The signals of the fire alarm system can be divided into private type and shared type.

The shared type signals are presented by network variables meeting LONMARK

standard. The standard network variables are approved and managed by LONMARK

organization specially. Because LONMARK standard is an open standard, the systems

approved by LONMARK organization can communicate with each other directly.

The interfaces have been designed for various media, such as twisted pair, optical fiber

to fulfill the needs of users. The fire alarm system can directly connect to the other

control systems meeting LONMARK standard via these interfaces.

4 Application Sample

We have designed an application system for Guangzhou subway using this fire alarm

system. The subway system has 20 stations, 2 transformer stations, 1 vehicle center, and

1 control center.

The network system consists of two LonWorks ring-type networks, one is a twisted pair

ring type network connected with the main control computer, standby control computer

and a fire alarm control unit. The other is a fiber-optic ring type network connected with

fire alarm control units of the stations. These two parts are connected with a router.

599
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The system is a distributed network system. The control unit of the station is a compacted mode fire alarm system. Each control unit is composed of a central

management unit, field devices control unit and a repeater control unit. The control

units are interconnected by the fiber-optic transceiver. Each node of the system can communicate with other nodes by peer to peer mode. The control center can get all the

information within the network and save it to the system database and sends commands

to the control units. The fire alarm control unit can work in network mode, performing

the orders of the control center, and it also can work independently when the network

failed. With the authorization the control units can also upgrade to a control center of

part of the network or the whole network when needed.

5 Review

This paper introduces a new kind of fire alarm control system. The flexibility of the

system makes it suitable for all kinds of fire alarm systems, especially the large

distributed fire alarm control systems.

6 References

[1] Yuhong YANG, "LON network control technology and application," XiAn

University of Electronics Technology press.

[2] Kuanming WU, "CAN bus theory and application system design," Beijing

University of aeronautics & astronautics press.

[3] Echelon Corporation, "Neuron Chip Data Book," Echelon Corporation press.

[4] LONMARK Interoperability Association, "LONMARK Layer 1-6 Interoperability

Guidelines," LONMARK Interoperability Association press.

[5] LONMARK Interoperability Association, "LONMARK Application Layer

Interoperability Guidelines," LONMARK Interoperability Association press.

601
CHEN Tao,WU Longbiao, FAN Weicheng, SONG Weiguo

(State Key Laboratory of Fire Science, University of Science and Technology of

China, Hefei 230026, P.R.China)

ANYLYSIS ON DATA STREAM MODEL OF NETWORK IMAGE FIRE

DETECTION SYSTEM

ABSTRACT

Along with the scale of IFD (Image Fire Detection) system continuously enlarging in

the field of fire detection, the capturing, transfer, and processing of a large quantity of

image information inevitably result in the longer response time for fire, which

contradicts the early alarm requirement of fire detection. In this paper, mathematics

model of data acquisition and data processing of IFD system is constructed with the

guidance of queuing theory. After the calculation and analysis of the result of

emulating program, appropriate scale of IFD system is determined under certain speed

of data processing and transferring.

Key words: Image Ore detection, Queuing theory, Model analysis, Emulator

INTRODUCTION

Typical development procedure of compartment fire characterized by the average gas

temperature can be described in four stages, growth period, flashover, fully developed

fire, decay period [1]. Flashoveris disastrous and has been an increasing problem

because of the use of new material in buildings [2]. So the alarm of fire must be sent

out in the early stage of fire, or in the early growth period. Grosshandler outlined

advances in sensor technology along with intelligence that could be implemented to

improve detection time while limiting the frequency of unnecessary alarms [3] so that

there is enough time for people to escape or for fire-extinguishing system to put out of

the fire.

602
\l;ikf use ol the temperature measurement methods based on primary colors [4] and

I he technology of mfrared imaging and digital image processing, the IFD system is a

vigorous developing intellectualized system that includes the technology of fire

filter

camera

other cameras

i J 1, X

video swith

electrocircuit

alarm

module

eralating

module

-i t communication

video capture card computer module

Figure I. Hardware of IFC system

detection, recognition, alarm and control. Figure I gives hardware used in IFD

svstem.

The infrared images from CCD are transfen-ed into the central processing unit (CPU)

through video cable and video card [6]. The CPU extracts the characteristic figure of

the early fire flame which is distinguished from the environment, and judges whether

the fire has taken place by computing the criterion of fire, then sends the command

codes through I/O card to control the alert function of IFD system. If the CPU is

assimilated to the brain of human, then CCD just acts as eyes.

In recent years, along with the fast development of Chinese economy, large buildings

with complicated circumstance such as large spaces appear continuously, bringing

forward, higher demand to the field of fire detection. Traditional fire detectors such as smoke detector, temperature detector can't be competent for the large and complicated

circumstance. For its high sensitivityl high reliability and high applicability, IFD

system is quickly accepted by society. Now IFD system becomes one of the

development trends of fire detection and control, and has broad application prospect..

However, modem buildmgs tend to larger-scale, which results in the increasing of fire

detection area. So the number of sub-area of fire protection becomes more and more. To meet with this requirement, the scale of IFD system must be enlarged through

increasing of CCD number. Consequently, in a certain interval, the number of infrared

images transferred to CPU for processing increases greatly. The speed of image

processing relies on frequency of CPU and time complexity of image processing

algorithm. Therefore, the increase of image number conduces to overtime of image

603
processing, which will affect the early alarm of fire detection, so as to limit the scale

of IFD system.. Given the speed of image capturing, data transferring and data

processing, the mathematics model is be constructed with the guidance of queuing

theory in this paper. Appropriate scale of IFD system is then gained through analyzing

of model.

BASIC QUEUING THEORY

2.1 Model and key points of queuing system [7]

Customer! Queuing

^ Arrivp QuClie Serve

Figure 2. Simple model of queuing system

Figure 2 gives the simple model of queuing system Key points of queuing system:

1. A Average arrival rate of customer (average arrival number per second);

The arrival interval between two contiguous customers /. is a random

variable, and the statistical mean of /, is called average arrival interval, expressed as

t = E(t. ) . The average arrival rate is given by

A = i 2D

2. ju Service rate of system, which is a parameter to express the number of

customer served by system every second. Define

1

t 3D

here i is called average service time. It is the statistical mean of service time per

custom, z. . And w. is the queuing time of every customer.

3. m Dlt is named number of window or server, showing the number of system

resource.

It is called single window queuing system when m = 1 , and called multi-window

queuing system when m>\

.

2.2 Service and queuing principle

1. Service principle:

604
The service principle is classified as FCFS (First Come, First Served), LCFS (Last

Come, First Served), SIRO (Stochastic Served) and PRI (Priority)

2. Queuing principle:

It is defined as waiting mode and cut-off mode. Waiting mode means arrival will

queue in turn until being served, while cut-off mode means arrival will be refused to

serve if necessary.

2.3 Primary parameter of queuing system

1. Queue length k

It points out the number of customers who are held up in system at a certain time,

which includes the one who is being served. The average queue length is given by

2. Queuing time w

It means the time length from arrival to beginning to be served, with average queuing

time marked as w

.

3. Service time z

It is the time while a customer is being served, and i is called average service time.

4. Standing time T

The whole interval, from the time when customer arrives to the time when he leaves

the system, is known as standing time. And it is given by r = w + t .

5. Little formula

For a queuing system with average arrival rate of customer A , the equation

k = ?i-T 4Dis

presented in the meaning of average, and it is called Little formula.

6. System efficiency

For multi-window queuing system with m windows, the acquisition probability is r/m

when r windows are engrossed. The system efficiency can be expressed as

rj = Eir/m) USD

7. Stability

The expression yo = /I / 6nis

entitled system strength. For waiting mode system, the system is stable when p<m

,

and it is unstable when p>m .

605
8. Sign for queuing system: A/B/m (N, n)

where A signifies arrival principle of customer, B signifies service principle, m is the

number of windows, indicates the number of potential customer (It can be omitted

when N and n means queue length of cut-off system (It indicates waiting mode

when n —> oo , and here n can be omitted).

MATHEMATICS MODEL

3.1 Two common characteristic distribution

1. Poisson distribution

Poisson distribution can describe the probability that there are k customers arriving in

t seconds, which is expressed as

p (/)-_= 1^^-'', k=0X2,-- (t>0) Din

k\

And this distribution must satisfy three characteristics mentioned as follows:

• Stationarity: The probability P^U) only depends on the time interval t,

while it is independent on start point of this time interval.

• No hereditary characteristic: The number of customers who appear in the

two mutually disjoint interval (///2) is mutually independent.

• Sparse characteristic: The maximum number of customer should be 1 in an

infinitesimal interval At.

2. Negative exponential distribution

When the arrival of customer satisfies Poisson distribution, it can be proved that the

arrival interval between two contiguous customers / submits negative exponential

distribution. The procedure is described as following.

Prove: The probability that there is no arrival (k=0) in time interval t can get from

equation (7)

The probability that there is arrival in t is given by

Pit) = l-P,(t) = l-e-''

* The density function probability of t is negative exponential distribution

dz

606
I Ik- s;miic rcsiill can be got from the analysis of service procedure.

3.2 Construction of mathematics model

In IFD system, the frame number of image that transfers from CCD to central

processing unit is affected by distance of data transfer, delay of video switchover and

other delay caused by hardware. So. the frame number of image isn't a simple direct

ratio of time. Instead, it has a certain characteristic distribution. On the other hand, the

time for image data processing varies along with the change of circumstance. For

example, the quantity of characteristic information drawn from image varies as the

background light intensity changes, and the area of the fire can't always be the same.

Thereby, the time for digital image processing based on certain time complexity of

algorithm is fluctuant. In another word, the frame number of image data which is

processed in a given interval submits a certain distribution character.

According to the queuing theory, the frames of image can be considered as customers

while the recourse such as video card and CPU can be regarded as window. Studying

the distribution characteristic of arrival frame number of image and that of processing

procedure in a given interval, it can be concluded that the two distributions are

provided with the three characteristics of Poisson distribution. Therefore, the negative

exponential distribution can be expressed as the model of arrival interval of image

frame and the processing interval.

3.2.1 Mathematics model about arrival of image frame

The time of fire growth period can defined by

1 "4V2Ml-fJ

where 6^ is a dimensionless characteristic of the radiation heat flux from the zone to

the upper fire bed, 6, is dimensionless temperature, and b is given by

b = [e,

It is easy to conclude that \s different as for different circumstance and fuel, which

result in different growth period of fire. The destination goal of fire detection and

In {\+b-e^- +^bd,)-

+ 27r-2tan

-Jlbd,

\-b-e:

607
alarm is to reduce loss as can as possible, then if fire occurs, enough time should be

provided for human to escape and for fire-extinguishing system to work. So fire

detection and alarm should give alarm as early as possible.

In IFD system, the image data acquisition is finished by circulation detection. To

satisfy the require of early fire detection, all CCD in the system should be circulation

detected in a relative short time. In the same time, a single CCD should capture

several continuous frames of image to implement the image comparison, which will

extract the dynamic characteristic to detect the fire.

Suppose that there are n CCD cameras for detecting in one system, every circulation

detection time is 1 minute, and every CCD camera will continuously shoot 6 frames

image, then, the average arrival interval between two contiguous frames image is

given by

~tA = 60 / 6n= 10 /n{s)

It can be concluded from formula (2) that the arrival interval between two contiguous

frames of image submits the negative exponential distribution

where = 1/?a = n/10 means average arrival rate of image.

3.2.2 Mathematics model about processing procedure

In my EFD system, the basic frequency of CPU is 300MHz, and high-velocity

colorized video card DH-VRT-CG200 is used. Based on PCI bus, video card can

control the speed of image data transferring without the help of CPU, and the highest

picking rate is 40MB/s. The image resolution ratio picked by video card is

640x480x24bit in NTSC standard[9], so the date size of one frame of image is = 0.88M5

.

The digital image processing time of CPU should consider the factors such as the

speed of data transferring of video card, CPU clock period and time complexity of

digital image processing algorithm. All the factors mentioned here will be regarded as

608
parts of service procedure, in which digital image processing algorithm manly

mcludes wnte-read process of memory, extracting the infrared image characteristic of

early fire. The extracting procedure are made up by threshold comparison, flame area

calculating and comparing and senes of fire pattern recognition such as development

of tlames' edge, orderliness of flames' twinkle, that of flames' layers and that of

flames' movement [5]. It is quite complex to determine the processing time for every

frame of image from the analysis of time complexity of algorithm. Therefore, in this

paper, it is provided b\ the mean of computer processing time from the results of

multiple tests, which is about 0.347s. Then the average service time of service model

is given by

rs = + 0.347 = —+ 0.347 - 0.3695

40 40

It can be concluded from formula (3) that the service interval time for one frame of

image submits the negative exponential distribution

f{T,) = A,e-'-^'^ =2.710^--''°'^ Dion

where = 1/ts = 2.710 means average arrival rate of image.

From the analysis described above, it can be concluded that the IFD system can be

simply modeled as ideal M/M/l queuing system with single queue, whose service

principle is FIFS.

609
STUDYING AND ANALYZING

4.1 Emulator program

In order to study the relation between the average queuing time for each image

and the scale of IFD system, emulator program for data stream of IFD system is

Initialize routine I

Construct dislrihulion/ , /

1 f Calculate arrival time /"u Queuing tune /u ; and Lea\e time I

u

Arrixal number .V, Lea\e number .Vv Hmulalor time

/ E\ent number AV Queue Length

Initial value of total queue length Kr=0

T=Tif[Nsl Customer leave number A'^t +

Arrival number A'^+h- I

K

Real time recording K N,.++ C alculate K,

Calculate w . and k =Kj/Nj:

I

(^^^^^^Data record ( / ^

. i ^ and I wj)^^

designed based on queuing theory. Figure 3 shows the program flow diagram.

Figure 3. Queuing model emulation floe process chart of IFD system

4.2 Analyzing of result

.\ccording to the queuing theory, the average queuing time \r can be theoretically

deduced.

p = ^J± = ^= Om^n (11)

A, t, 10/

n

- p ^ 0.0369/; ^j^^

l-p I - 0.0369/;

= 0.369 X . • (13)

l-p 1-0.0309/; 1-0.0369/;

610
Therefore w can

individual gained from

theoretical derivation or

emulator program. The

comparison of these two

results is showed by

figure 4. It can be

concluded that the two

comparison curves are

both indicate the relation

between w and /i, and the

different calculation

methods can get almost

the same tendency. So, the

result of emulator

program can be

considered right. That's to

say, emulator program can

be used to study the

problem of data stream of

IFD system.

Figure 5 clearly

shows the different tendency of w relative to variant n. If /z = 28 p = 1.259 > 1 is got

from equation (11). So the mathematics model is unstable, the curve /7 = 28 visibly

expresses the instability of the model. The curve n = 27 in the figure seems unstable

while it is theoretical stable for p= 0.997 <1 got from equation (11). And

u- = 122508(m5) can be calculated from equation (13). The reason is that the emulator

time is not long enough to reflect the stability in the end. It can be predicted that this

curve will be stable where w = 122508(m5-)

.

However, what should be paid more attention is that it is not inevitable to reach the

request if the model is stable. For example, the result of model analysis trends stable

when /?<28. But considering the early alarm require of fire detection, il is mu

611

\4<m

~ 1 2009 £- 100O0 -

V 8S0CI3-

U, 6000 < 4000

iOOO -

0 -

theoretical denvation

simulation relult

1—i——I—1—I'—I—1—1—I—I—1—I—1—1—I—1—1—

,

8 10 12 14 16 13 20 22 24 28 28

n (Number ol CCD)

Figure 4. Comparison of theoretical derivation and simulation result

120000 -

lODOOO

Wj 80000

3- 60000 - o

rsS 40000 - ><

20000 -

0 -

-I 1

1

1 1

1

•

1

1

1

1

1

1 1.

1

.

1 -500 0 500 1000 1500 2000 2500 3000 3500 4000 Frame number of arrival image

Figure 5. Average queuing time tendency against different /;
permitted if the queuing time is too long. Suppose that the IFD system does not

confirm to early alairn require for fire detection if the queuing time is more than 10

second. Then the suitable scale is n<22, for there are some frames of image whose

queuing times are more than 10 second if n>22.

DISCUSSION AND CONCLUSION

The data stream of IFD system is simulated using the emulator program based

queuing theory, which is more progressive than traditional analysis method in that

only the mean image processing time and that of image frame arrival interval. In

traditional opinion, the scale of the IFD system is appropriate as long as the mean

time of image processing is shorter than that of image frame arrival interval.

However, it is not always right from the opinion of queuing theory, which has been

analyzed clearly hereinbefore.

One second thoughts, it will be found that the simple theoretical analysis based on

queuing theory can get nothing but the average data. Obviously, this is still not

enough. However, the emulator program will solve this problem easily. The queuing

time of each frame of image can be obtained through the simulation process. More

dynamic curves could be achieved if necessary, which express queue length k, service

time 7 , standing time T and so on.

In a word, the analysis method introduced in this paper can exactly guide the scale of

IFD system.

EXPECTATION

From the analysis result described above, the conclusion is that the IFD system based

on a single CPU can not meet the requirement put forward by larger and larger

buildings.

The usage of distributed processing system (DPS) may be a feasible way to solve this

problem. That is to say, every sub-area of fire protection will be collocated a

612
distributed processor. So the digital image processing task could be decentralized into

each distributed processor. Every distributed processor will send information to

central processing unit after the digital image processing has finished. Of course, the

quantity of information here is much fewer than image data.

With the development of fire detection technology, micro-treatment technology, the

point intelligentized image detector based on digital image processing will come into

being, which is an integrated module of data acquisition components and distributed

processor components. This kind of detector can realize the communication with

central processing unit usage 2-bus mode. The advantage is that it thoroughly solves

the overtime problem that comes with the enlarging of IFD system. And based on the

existing integration technology [10], it can completely integrated with the traditional

detectors such as smoke detector, temperature detector that is widely used in fire

detection.

The signal of temperature, smoke and acoustic signal can be introduced into fire

image recognition technology. The compound detector will be formed in the future,

which holds the sensor of sight, feeling, taste, and hearing, just as human being. It

also holds "brain" (processor) for processing and recognizing. It can be predicted that

the new detection detector will greatly improve the accuracy and early-stage of fire

detection.

REFERENCES

[1] Drysdale, D. D., An Introduction to Fire Dynamics. Wiley, Chichester, UK, 1985

[2] Rasbash, D. J., Major Disasters Involving Flashover Fire Safety Journal,

17(1991)

[3] Grosshandler, W.L., A Review of Measurement and Candidate Signatures for

Early Fire Detection, NISTIR 5555, Gaithersburg, MD, National Institute of

Standard and Technology, 1995

[4] Cheng Xiaofang, Zhou Zhou Principle Study of Temperature Measurement Based

On Primary Colors, Science In China (Series E), October, 1997, Vol. 40 No. 5

[5] Song Weiguo, Wu Longbiao, Lu Jiecheng, Song Xiaowu, Study of the Image Fire

613
Detection Method. Proc 1998 ISSSP 1998. Sci Press.

[6] Song Weiguo, Wu Longbiao

[7] Wang Shuhe, Basic Mathematics Model, The University of Science and

Technology of China Press (in Chinese) May, 1996

[8] T. L. Graham, G M. Makhviladze, J. P. Robers, On the Theory of Flashover

Development, ¥'\rt Safety Journal 25(1995)

[9]

[10]Chen Tao, Wu Longbiao, Fan Weicheng, Integration of Image Fire Detection

System And Fire Auto-Alert System, Progerss in Safety Science And Technology,

Vol. II (700-704)

614
David Blake

Federal Aviation Administration Technical Center, Atlantic City, NJ, USA

Stefan Domino, Walt Gill, Louis Gritzo, Jill Williams

Sandia National Laboratories, Albuquerque, NM, USA

Initial Development of Improved Aircraft Cargo Compartment Fire

Detection Certification Criteria.

Abstract

Most of the cargo compartments on passenger carrying aircraft are required

to have fire detection systems that provide a visible indication to the flight

crew w^ithin one minute from the start of a fire. Flight tests are required to

demonstrate compliance with these regulations. The fire detectors in use

today are either photoelectric or ionization smoke detectors. While these

detectors are effective at detecting actual fires they are also prone to alarm

from airborne particles not associated with fires. The use of multiple sensors

and appropriate alarm algorithms have the potential to better discriminate

between actual fires and nuisance alarm sources. Certification guidelines

for using these types of fire detectors on aircraft do not currently exist.

Testing is being conducted to define the types of fires that should be

detected and the production of smoke, heat and gases from these fires. The

tests will be conducted in various sized cargo compartments to determine if

the threshold fire size for detection should vary with compartment size and

shape or if the fire size should remain constant and the time to detection

should be varied.

Concurrent with the initial fire testing, a transient computational fluid

dynamics simulation tool for the prediction of smoke transport in cargo

compartments is being developed. This simulation tool will couple heat,

mass, and momentum transfer in a body fitted coordinate system in order to

615
handle a variety of cargo bay shapes and sizes. Comparing the predicted

results with the results obtained from the full-scale fire tests will validate

the CFD model. Ideally, such a physics based CFD simulation tool can be

used during the certification process to identify worst case locations for

fires, optimum placement of detector sensors within the cargo compartment

and sensor alarm levels needed to achieve detection within the required

time.

Testing is being conducted at the FAA Technical Center. The code is being

developed by Sandia National Laboratories. Additional partners in the

project include the National Institute of Standards and Technology (NIST)

and the National Aeronautics and Space Administration (NASA) Glenn

Research Center.

1. Introduction

Incidents of aircraft cargo compartment inflight fires are very rare events

but as with all fires the consequences can be severe. Reliable detection and

effective suppression or fire containment within the cargo compartment is

perhaps more critical than in other occupied areas due to the inability to

quickly land the aircraft and perform a rapid evacuation. Aircraft operate

on some routes that could be in excess of three hours flight time from a

suitable airport. Even on domestic flights, 15 to 20 minutes is often needed

to descend from cruise altitude and land before an evacuation can be

initiated.

The number of incidents of false alarms from aircraft cargo compartment

detection system has been steadily increasing as the number of aircraft in

the US fleet increases [1]. In addition, the ratio of false alarms to the

detection of actual fires in cargo compartments is also increasing. For the

period 1995-1999, the ratio was approximately 200:1. Because the majority

of cargo compartments on passenger carrying aircraft are inaccessible

616
during flight, the required procedure in the event of a cargo fire alarm is to

discharge fire suppression agent if available and divert and land at the

nearest suitable airport. Not only are the direct costs of unnecessary

diversions due to false alarms significant but they also raise safety

concerns. Some of those concerns include passenger and crew injuries in the

event of an emergency evacuation and the possible increased risk of an

accident due to landing at unfamiliar airports, changes to air traffic

patterns, shorter runways, and inferior navigation aids.

2 Certification of Fire Detectors

The FAA, along with other regulatory agencies throughout the world,

require that aircraft cargo compartment fire detection systems provide a

visible indication to the flight crew within one minute after the start of a

fire [2]. A flight test is required to demonstrate compliance with the

regulation. The flight test is conducted during the certification process for a

new aircraft type or when there has been a significant change in the fire

detection system. The fire detectors that have been exclusively used in

aircraft cargo compartments have been photoelectric or ionization smoke

detectors. They are either spot detectors or in aspirated systems. A wide

variety of smoke sources have been used during the required flight tests to

demonstrate the functioning of the detection system. Some examples include

burning tobacco, rope and chemicals and a variety of theatrical smoke

generators. The methods for generating smoke and the quantities permitted

in difl'erent size cargo compartments have evolved within the different

certification offices over the years based on individual preferences. The FAA

has issued guidance material on smoke sources and a visual representation

of the appropriate quantity of smoke. However, the precise quantity of

smoke is still subjective and there is currently a lack of standardization

regarding exactly what the detection system is supposed to detect within

one minute. Multi sensor detectors have not previously been used in aircraft

cargo compartments but would seem to have the capability to reduce the

617
current rate of false alarms. However, there are infinite combinations of

threshold alarm levels, rate of rise values, and number of sensors that can

be used to trigger or suppress an alarm. There is currently very little

information available for the FAA to determine what are appropriate alarm

levels and algorithms for cargo fires. The environment inside a cargo

compartment is subjected to fairly rapid changes in temperature, pressure

and humidity as well as exposure to the exhaust from airplane and service

vehicle engines.

In addition to the need to better define what should be detected, a parallel

effort is underway to develop a transient computational fluid dynamics

simulation tool for predicting the transport of smoke, heat and gases within

a cargo compartment. Full-scale cargo fire tests will be used to validate the

model. Due to the high cost of flight testing, extensive ground tests are

typically conducted to define the best location for fire detectors, the worst

location for the fire source and the alarm levels necessary to achieve

detection in less than one minute. If enough confidence is developed in the

model it could replace much of the current testing.

Research has been conducted by NIST in support of this joint project. The

first phase of that effort has been a literature search to attempt to document

all of the fire detection technologies that currently exist and assess their

suitability for aircraft cargo compartments [3]. In addition, they have

conducted testing in a Fire Emulator/Detector Evaluator apparatus to

document the response of existing aircraft smoke detectors to three fire

sources and three nuisance alarm sources.

NASA Glen Research Center is also contributing to this project through

funding for the CFD model development and research into miniature gas

sensors that could be used in multi sensor fire detectors.

618
3. Fire Sources

The FAA Technical Center has undertaken an effort to standardize the fires

to be detected and develop data for selecting appropriate alarm levels and

algorithms. It is desirable for the standardized fires to be both repeatable

and realistic. Anything imaginable can be carried in an aircraft cargo

compartment and there is no typical cargo fire. The standardized fires

described in EN 54 and UL 217 were initially considered but it was felt that

those fires did not produce a realistic enough mix of the kinds of gases that

would reasonably be expected from a cargo fire involving typical luggage

material. Two new fire sources were developed and have undergone initial

testing with promising results. Both fire sources use a mix of six plastic

resins in pellet form that are heated and pressed into a 4" by 4" by 3/8"

molded resin block. A length of nichrome wire is embedded within the resin

block and is used as a heat source. A smoldering fire source can be produced

by energizing the nichrome wire alone. The same resin block is used to

produce a flaming fire source by pouring 2 ml of heptane onto the resin

block and simultaneously igniting the heptane and energizing the nichrome

wire. The resins used are: Nylon, Polyethylene, Poh-vinyl Chloride,

Polystyrene, Polybutylene Terephthalate, and Polyurethane. In the Cone

Calorimeter tests, it was shown that the burning behavior of both fire

sources and the production of combustion gases is very repeatable [4].

4. Test Results

The FAA Technical Center has conducted tests in a below floor cargo

compartment of a Boeing 707 test article. The compartment is instrumented

with smoke meters, thermocouples and gas analyzers. It also simulates

tj^ical ventilation flow in the form of leakage around the perimeter of the

cargo door. Figure 1 shows the 707 test article.

619
125"

Figure 1. 707 Forward Cargo Compartment

The initial testing attempted to quantify the smoke output from a

smoldering suitcase. Identical suitcases were purchased and filled with a

mix of cotton and synthetic rags. A coil of nichrome wire, wrapped around

several paper towels and connected to a 110 VAC supply was used as the

ignition source. Figure 2 shows the smoke levels measured by the mid

ceiling smoke meter during six smoldering suitcase tests. As expected, there

was considerable variation in the quantity of smoke produced despite a

relatively uniform fire load. Time zero on the chart is the time when smoke

was first observed. That time was fairly subjective because of the different

behavior from tests to test. An easily discernible smoke plume would start

for some tests while for others very light wisps of smoke could be seen

intermittently before a steady plume was observed. The rectangular outline

on the graph shows the desired target for the quantity of smoke produced.

The width of the box is the 60 second window in which detection is currently

required. The height of the box represents the range of alarm levels

required by Technical Standard Order (TSO) CIC which applies to aircraft

cargo compartment fire detectors. It would obviously not be practical to

expect a detector to alarm in the required time and at the required alarm

620
level if the quantity of smoke it was exposed to did not fall within that

range. The smoldering suitcase did not consistently produce the desired

smoke quantity.

72 -

68

Celling Mid 6

Celling Mid 7

Celling Mid 8

Celling Mid 9

Ceiling Mid 10

Celling Mid 1

1

60

' ill— = : 1 1 1 1 1 , 0 10 20 30 40 50 60 70 80 90 100 110 120 Time (seconds)

Figure 2. Smoke levels from a smoldering suitcase.

The results of initial testing using the molded resin block in a smoldering

state is shown in Figure 3. The results show better repeatability and the

smoke that is produced falls within the desired quantity and allotted time

for this particular volume cargo compartment. The flaming fire scenario also

produces the desired smoke quantity.

621
0 20 40 60 80 100 120 Time (seconds)

Figure 3. Smoke levels from smoldering resin block

The testing of both the smoldering and flaming resin blocks in the Cone

Calorimeter employed a FTIR mass spectrometer for gas analysis. The data

from those tests will be used to provide the source terms for heat release

rates and smoke and gas production in the CFD model. Additional testing is

also planned in the 707 cargo compartment as well as larger cargo

compartments to measure the concentrations of various gases. The sample

probes and the smoldering and flaming fire sources will be placed in various

locations. The data will be used to validate the CFD model and to provide

some guidelines for selecting appropriate alarm algorithms for multi sensor

fire detectors.

5. Computational Fluid Djmamics Model

A computational fluid dynamics simulator is currently being developed at

Sandia National Laboratories to predict the transport of smoke in cargo

compartment fires. The targeted platform is a midline personal computer,

622
i.e., 128 MEG of memory with a 750 MHz Intel® Pentium® III processor. The

simulation tool is to run quickly and efficiently at modest computational grid

sizes (10-30K) in an effort to provide a convenient platform on which to

identify worst case fire location scenarios for use during the certification

process. It is anticipated that the total "time to detection" simulation will be

on the order of one minute, whereas the total computational run time will be

on the order of hours.

5. 1 Turbulent Flow Simulation Background

Accurately modeling the complex physical phenomena associated with

heterogeneous combustion often requires physical models that couple

turbulent fluid flow, heat and mass transfer, radiant energy transfer, and

chemical reaction. The appropriate physical governing transport equations

are discretized and solved on a computational mesh. Unfortunately, the

computational expense of solving the turbulent reacting system directly for

all appropriate time and length scales frequently exceeds both the

computational resources of the user and the desired cost-to-accuracy ratio.

Therefore, models that are largely guided by reasonable engineering

assumptions have been developed to decrease the associated computational

expense in solving these t5npes of problems while attempting to preserve all

controlling physical phenomena.

The description of the conservation of mass and momentum for a continuum

fluid are described by the Navier-Stokes Equations [5]. These equations are

equally valid for turbulent flows since the molecular mean free path is much

smaller than the length scale associated with a tj^Dical eddy. Therefore,

solving the instantaneous Navier-Stokes equations in a turbulent system

would yield an instantaneous velocity field that, over time, would fluctuate

about some mean value. In most engineering numerical implementations of

turbulent flows, however, the instantaneous equations of motions are not

solved due to the excessive computer memory requirements associated with

623
resolving the small length and time scales. Rather, the time-averaged

equations are solved. This time-smoothing procedure is accomplished by

separation of each independent variable into a time-mean and fluctuating

part within the equations of motion and time averaging the result.

The technique of Reynolds averaging the equations of motion leads to cross terms known as Reynolds stresses [5]. These newly created cross fluctuation

terms are an artifact of the Reynolds averaging procedure and must be

adequately modeled. The proper modeling of these terms represents the

classic closure problem of turbulent fluid mechanics.

In variable density flows, the density must also be decomposed and its

inclusion within the time-smoothing technique augments the total number of

Reynolds stress terms by introducing cross terms involving a fluctuating

density component. In such variable density cases, it is convenient to utilize

the technique of Favre-averaging [6], which eliminates this complication, by

weighting the fluctuating quantities by the instantaneous density before the

time averaging step. Upon Favre averaging the variable density equations of

motion, triple correlation terms involving variable density terms are,

therefore, eliminated. Therefore, the Favre-averaged equations appear to be

exactly of the same form as the Reynolds averaged Navier-Stokes (RANS)

equations when density fluctuations are neglected.

Most engineering turbulence closure CFD codes employ a form of the

Boussinesq [5] hypothesis to model the Reynolds stresses that arise during

the time-smoothing procedure. In this formulation, the Reynolds stresses are

assumed to act analogously to molecular viscous stresses, i.e., in a gradienttype diffusion relationship. Therefore, the Re5molds stress terms are assumed

to be proportional to the mean velocity gradient multiplied by a

proportionality constant known as the turbulent eddy viscosity [7]. The

closure problem reduces to calculating an appropriate turbulent eddy

624
viscosity by the utilization of models such as the two-equation k-z model [7]

that relates the turbulent energy production and dissipation to the turbulent

eddy viscosity via the Prandtl-Kolmogorov relationship [71

.

5.2 CiiTTent CFD formulation

The unsteady, Favre-averaged, three-dimensional incompressible NavierStokes equations are solved using a finite-volume method whereby the bodyfitted partial differential equations are discretized. The use of a structured

body-fitted coordinate system within the CFD simulator is desired in order to

adequately represent the curvature of the cargo compartment walls while

maintaining a low amount of total required mesh points.

In addition to the time-mean equations describing the transport of

momentum, equations describing the turbulent time-mean transport of

germane species, e.g., CO, COg, soot, etc. can be computed and used for the

calculation of point wise mixture properties such as molecular weight and

heat capacity. A sensible enthalpy transport equation, including convection

heat loss to the cargo walls, is solved to determine the temperature field

using the mixture average heat capacity. Radiation effects within the code

are currently neglected.

The transient partial differential equation set, in strongly conserved form, is

solved for the primitive variables on a collocated grid. A fully implicit scheme,

which is first order in time, is used to solve the transient equation set. Face

values for the convective terms are determined by the hybrid scheme [8] that

results in second order spatial accuracy for Peclet numbers less than 2.0^ and

first order upwind differencing for Peclet numbers greater than 2.0. To

overcome the well-known pressure-velocity decoupling that can occur when

using a collocated grid, a convective flux interpolation method based on the

^ Second order spatial accuracy is formally true for only purely orthogonal

grids.

625
work of Parameswaran, et al [9] is used. A modified version of the SIMPLE

formulation [8] as described within Parameswaran et al. [9] is implemented.

In cases where pressurization can occur, the Extended SIMPLE algorithm is

used to include low speed compressibility effects [8]. The Boussinesq

hypothesis is assumed and the turbulent eddy viscosity is determined by the

standard k-^ equation [7].

6. Physical fire model formulation

Although it is possible to include physical models that adequately describe

the detailed chemical reactions germane to the fire process, such a simulation

would likely exceed both the targeted simulation run-time and platform

constraints. In fact, detailed multiple step kinetic devolatilization models for

the materials common in airplane cargo compartments are not available.

Therefore, the CFD simulator will not attempt to model the complex physical

process of species devolatilization, chemical dependent heat release, and the

chemical reaction interaction between high temperature free radicals.

Rather, the CFD simulator utilizes experimentally time-resolved species and

heat release data in lieu of simulating the complex physical phenomena

associated with physical objects burning.

The CFD simulator, therefore, numerically models the fire by the placement

of mass and heat source terms within the right hand side of the appropriate

transport equation. The overall volumetric mass source term appears on the

right hand side of the following equations: 1) continuity equation, 2) species

transport equation (multiplied by the appropriate mass fraction of that

particular species), and 3) the momentum equations in the form of a

momentum sink. The placement of volumetric heat releases on the

computational grid will model the buoyantly induced flow rather than the

associated heat release due to both homogeneous and heterogeneous chemical

reaction. Although the technique of prescribing source terms is certainly not

the preferred method for an entirely predictive CFD code, in this particular

626
application where source terms are available through a detailed timeresolved experiment, it is the preferred method.

7. Mathematical formulation

The partial differential equations describing momentum, species, turbulent

energy, turbulent dissipation, and sensible enthalpy transport are linearized

and discretized using the fmite volume method [81. The method of finite

volume discretization is a conseivative approach even at low discretization

resolution. The discrete continuity equation, which includes the appropriate

discrete volumetric mass source term, is used to form the pressure correction

equation [8|. The governing equations are solved iteratively using a

segregated approach. Updating the matrix coefficients through each sweep

captures the non-linearity inherent to the original PDE equation set. The

linear system of equations for the momentum field, species, turbulent

dissipation and production, and sensible enthalpy are solved using the

strongly implicit method of Stone [10] while the pressure correction equation

is solved via a preconditioned conjugate gradient method. A particular time

iteration is considered converged when the maximum residual of all

individual linear equations is below a user-defined value that corresponds to

the desired reduction in the normalized L2 norm.

8. Code Validation and Preliminary Simulation Results

A concerted effort between the FAA and Sandia National Laboratories is

underway to validate the CFD model by comparing the simulation results to

detailed experiments that measure species and smoke concentrations at

various locations within the cargo compartment. Figure 4 illustrates a preliminary simulated temperature profile ten seconds into a simulation.

This initial CFD simulation utilized a heat and mass release rate of 2.25 KJ

and 0.05 g/s. respectively. These boundary values correspond to a single

instance of time-resolved experimental data for a flaming resin from a FAA

627
cone calorimeter experiment. The total number of grid points represented in

this simulation is 10,000.

Figure 4. Simulated temperature profile, K, at 10 seconds.

Sandia is a multi-program laboratory operated by Sandia Corporation, a

Lockheed Martin Company, for the United States Department of Energy

under Contract DE-AC04-94AL85000.

References

[1] Blake, D. Aircraft Cargo Compartment Smoke Detector Alarm Incidents

on U.S.-Registered Aircraft, 1974-1999, DOT/FAA/AR-TNOO/29, June 2000.

[21 Code of Federal Regulations 14 CFR Part 25.858

[3] Cleary, T. and Grosshandler, W. Survey of Fire Detection Technologies

and System Evaluation/Certification Methodologies and Their Suitability

for Aircraft Cargo Compartments, NISTIR 6356, July, 1999.

628
[4] Filipczak, R., Blake, D., Speitel, L., Lyon, R., Williams, J. Development

and Characterization of a Smoldering Combustion Source. Fire and

Materials 2001 Conference, January 22-24, 2001

[5] Bird, R. B., Stewart, W. E. and Lightfoot, E. N. Transport Phenomena.

John Wiley and Sons, New York 1960.

[6] Favre, A. Problems of Hydrodynamics and Continuum Mechanics.

Society for Industrial and Applied Mathematics, Philadelphia 1969.

[7] Launder, B. E. and Spalding, D. B., The Numerical Computation of

Turbulent Flows. Computer Methods in Applies Mechanics and

Engineering. 1974; 3:269-289, 1974.

[8] Patankar, S. Numerical Heat Transfer and Fluid Flow. McGraw-Hill,

New York 1980.

[9] Parameswaran, S., Srinivasan, A. B., and Sun, R. L. Numerical

Aerodynamic Simulation of Steady and Transient Flows around TwoDimenstional Bluff Bodies Using the Non-Staggered Grid System.

Numerical Heat Transfer, Part A 1992; 21:443-461.

[10] Stone, H. Iterative Solution of Implicit Approximations of

Multidimensional Partial Differential Equations. SIAM Journal of

Numerical Analysis 1968; 5(3):530-557.

629
Klaus Schmoetzer

European Aeronautic Space and Defense Company EADS Airbus GmbH, Bremen,

Germany

Aircraft Fire Detection: Requirements, Qualification and Certification Aspects

1. Abstract

For aircraft applications other or additional qualification requirements need to be fulfilled

by the automatic fire detection instrument compared to those applicable to buildings. It is

important to distinguish between pressurized zones and non pressurized zones due to their

completely different environments (e.g. inside/outside temperature and pressure/altitude).

This paper will concentrate on fire detection aspects applicable to pressurized areas of

transport aircraft where passengers and cargo are located.

2. Introduction

For some aircraft compartments a fire/smoke detection system is required by the

regulations JAR[l].and/or FAA[2]. For example:

JAR/FAR 25.854 requires the installation of a smoke detector system or equivalent for

each lavatory

JAR/FAR 25.857 requires the installation of a separate approved smoke detector or fire

detector system for some cargo compartments.

JAR/FAR 25.858 defines details for cargo compt fire detection systems.

In addition, aircraft manufacturers install supplementary fire/smoke detection systems to

increase the level of safety. These systems must comply e.g. with subsequent regulations

JAR/FAR 25.1301 Function and Installation:

Each item of equipment must

(a) Be of a kind and design appropriate to its intended function

(d) Function properly if installed

Additional systems are installed in Airbus aircrafts to monitor areas which are not

permanently occupied or monitored by crew members or passengers like

630
• Main avionics compartment (computer room / electrical energy center)

• Customized electronics equipment bays (e.g. In - Flight Entertainment)

• Crew rest compartments / lower deck facilities

The urgency of the corrective action subsequent to a fire/smoke warning depends directly

on the risk and is reflected in the procedure to be applied by cockpit or cabin crew.

For example, a cargo compt smoke warning will be indicated to the flight deck crew as a

red warning, this means the crew has to perform the action immediately.

In this case the

• air ventilation system, if any, needs to be turned off and associated

compartment isolation valves have to be closed

• fire extinguishing system needs to be activated

• crew has to land the aircraft as soon as possible, etc As long as the crew is unable to differentiate between a true and a false warning, it has to

follow the certified procedure.

The impact of a false fire/smoke warning in non accessible compartments is extensive and

might include: flight diversion, declaration of emergency situation, eventually passenger

evacuation, compartment inspection, fire extinguisher replacement, customer/passenger

disappointment, loss of confidence in the warning system etc.

To minimize the risk, an early detection of an in-flight fire/smoke situation is mandatory

to initiate the corrective action at an appropriate time. On the other hand,

false/uncon firmed warnings could be critical as well.

With the 60 seconds detection time requirement as addressed in JAR/FAR 25.858 (a), the

system design is always a compromise between fast detection and warning signal

reliability.

This time was originally assigned to cargo compartment applications and has often been

required/applied by airworthiness authorities [3] and/or system designers to in flight

inaccessible compartments or remote located crew rest rooms

631
3. Fire Detection Requirement (JAR/FAR 25.858)

If certification with cargo compartment fire detection provisions is requested, the

following must be met for each cargo compartment with those provisions:

(a) The detection system must provide a visual indication to the flight crew within one

minute after the start of the fire.

(b) The system must be capable of detecting a fire of a temperature significantly below

that at which the structural integrity of the aeroplane is substantially decreased.

(c) There must be means to allow the crew to check, in flight, the functioning of each

fire detection circuit.

(d) The effectiveness of the detection system must be shown for all approved

configurations and conditions.

4. Experience

An analysis of false warning scenarios identified the following main problem areas:

• sensitivity to aircraft environmental condition changes (temperature, pressure /

altitude, humidity, power transients, electromagnetic interference, exhaust fumes

from ground loading equipment etc)

• insufficient knowledge of either sensor/detecor functions by aircraft manufacturers

or specific to type aircraft environmental conditions by fire/smoke detector

instrument manufacturers

• detector/sensor stability

• sensitivity to cargo/load transported in the compartment

• sensitivity to sprays used in lavatories

• insufficient detector cleaning (operator and/or interval)

• penetration of unexpected particles (contaminated/unfiltered air) into the

measurement measurement chamber(s)

• detectors evaluation (open-area /point type or flow-through /duct type)

• single detector alarm dependence

From the a.m. items we can identify the two main areas of possible improvements. The

fire/smoke detector itself, and the associated integration into the aircraft. The integration in

632
the aircraft specific environment and the need to customize specific to type solutions is a

real issue which makes it difficult to standardize detectors for the various aircrafts and

applications.

We, at Airbus Industries consider the minimum requirements for fire/smoke detection

instruments as defined in standard JTSO/TSO-Clc [4] as not sufficient as far as

fire/smoke detection performances and warning reliability is concerned.

In considerisation of this, EADS-Airbus uses aircraft specific integration guidelines and

purchases smoke detectors/systems according to own detailed technical specifications. In

addition new detection technologies are closely monitored by system design office [5]

The following graphic shows the occurrences of false smoke warnings in cargo

compartments of US-registered aircrafts within the last 25 years [6]. Of course air traffic

increased and the requirements to install smoke detection systems have been amended.

Therefore the general increase of false warnings per year could be explained.

False Cargo Smoke Alarms on Regional Aircraft vs. Transport

Aircraft (US-registered aircrafts)

160

COE

<

CDO

E

CD

CO LL

140 -I

120

100

80

60

40

20 -I

1

I Regional

Transport

i

n n jl n Ji n

2

<t CD CO O 1^ CO O) 05 05 0>

CM

00 CO

05

CD

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05

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CD CO

03

Source: David Blake, FAA Technical Center.Fire Safety Section
If we investigate in the a.m. graphic the share between regional aircrafts and transport

aircrafts, we can conclude that the efforts spent by transport aircraft manufactures in the

last years to reduce the rate of false warnings were efficient considering that the number of

installations increased whereas the occurrence of false cargo smoke warnings maintained

nearly the same level or decreased.

We are convinced that the main reasons for false smoke warnings are due to the

insufficient signal processing/confirmation applied in some types of smoke detectors

and/or the maximum allowed detection time of 60 seconds. If the maximum allowed

detection time could be changed to e.g. 120 seconds then a significant step in false smoke

alarm reduction could be made. [7]

5. Smoke Detector Requirements

The function of the equipment is to detect emerging smoke and thus to detect an arising

fire. Any compensation/filtering process shall not degrade the equipment's ability to detect

smoke.

5.1 Smoke Detection Performance

With reference to EN54-9 [8]"Methods of test of sensitivity to fire", the equipment shall

at least detect the test fires TF2 to TF5 within the range given in that document

Normal operating conditions

Temperature range -40 to 86 degree Celsius

Pressure range 1089 mbar abs to 57 1 .8 mbar abs

Depending on the sensor's measurement principle it may be necessary to justify the

ability of the equipment to detect smoke/fire in the a.m. temperature and pressure

ranges. For further environmental conditions refer to the chapter Qualification Test

The equipment shall be tested by application of an appropriate test stimulus, e. g. air

containing smoke having a light obscuration value of 3% per meter. For equipment in

634
which the sensitivity and/ or response time is affected by any factors which may be

varied from one installation to another tests shall be conducted with the least sensitive

and longest response time condition to be used.

Open-area/Point type smoke detector (e.g. cargo/lavatory): The equipment shall be

tested with an air sample as defined above which is introduced into the equipment under

standard atmospheric conditions. The equipment shall then actuate a smoke alarm

within a maximum time period of 30 seconds.

Flow-through/Duct type smoke detector (e.g. avionics): The equipment shall be

operated continuously by varying the pressure differential from 25% below to 25%

above the rated. This pressure variation shall have no influence on the smoke detection

performance.

Development smoke tests shall be carried out by the supplier to define a good compromise

between adjustment of the electrical alarm threshold and the housing layout to get an

acceptable response time (target within 20 seconds) if the equipment is exposed to a fire. The final sensitivity adjustment shall be recommended by the supplier and agreed with the

aircraft manufacturer.

The equipment shall not revert to „no smoke" signal condition following an alarm

indication when held immersed in smoke levels at least 25% greater than the „Smoke-"

response level for the equipment for a period of ten hours.

The equipment shall revert to a „no smoke" signal condition when the smoke level is

reduced to approximately 50% of the „Smoke-" response level.

The equipment shall include means to avoid/compensate effects caused by environmental

condition changes such as e.g. temperature, altitude, acceleration and possible

combinations of those parameters

The sensitivity drift due to temperature/pressure changes and component tolerances shall

635
not exceed +/- 15% of the corresponding thresholds. The measurement of the response

threshold value shall be carried out in a manner as described in EN54-7 annex B.

With reduced tolerances (see above) following EN54-7 tests are applicable:

Repeatability Refer to EN54-7 clause 6.

Directional dependence Refer to EN54-7 clause 7

Reproducibility Refer to EN54-7 clause 8.

Air movement Refer to EN54-7 clause 10

Ambient Light Refer to EN54-7 clause 12

Hermeticity of Flow-through/ Duct detector:

The leakage rate shall not produce a pressure variation inside the smoke detector more

than typically 5 mbar (max. 7 mbar) after 60 sec. With the equipment being exposed to an

internal pressure equal to the external pressure minus 20 mbar at the beginning of the test.

This test has to be applied on each unit.

Fire Resistance of open-area/ point type smoke detector

The equipment shall be able to withstand the effect of an open fire at 232,2 degrees C (450

degrees F) for 1 min. Smoke shall be indicated during test for at least 20 sec. Installation

in designated fire zones is not permitted.

5.2 Fatigue Test

Detector forced to smoke / standby conditions

The purpose of this test is to demonstrate that the item of equipment will retain its proper

characteristics when subjected to repeated tests. A minimum of 120000 cycles shall be performed. Each cycle shall last one minute and

consists of:

• 20 seconds alarm

• 40 seconds standby

The test shall be carried out under ambient temperature and pressure conditions. For the

purpose of test runtime reduction up to 10 smoke detectors may be interfaced.

636
Detector-power-up-test (Reason: System may not be energized if the aircraft is parked)

The purpose of this test is to demonstrate that the item of equipment will retain its proper

characteristic when subjected to power up.

Cycle type 1 Cycle type 2

Power-on time of 2 minutes or Power-on time of 2 minutes or more more

Power-off time of 2 to 3 seconds Power-off time of 5 minutes

1

• A minimum of 30000 cycles (type 1 and type 2) shall be performed.

• The number of each cycle applied shall be equal.

• Cycle type 2 sequence shall follow cycle type 1 sequence

It shall be verified during each test cycle that the smoke detector does neither emit a

detector "Fault", "Alarm", nor any other failure.

The test shall be carried out under ambient temperature and pressure conditions.

6. Environmental Conditions and Test Requirements Associated to

Qualiflcation

The environmental conditions applicable to airborne equipment are largely different

compared to industrial or maritime applications [9]. It is of great importance to qualify

the detection performances of a fire/smoke detection instrument in an approved

laboratory prior to it is release to qualification tests.

The Airborne smoke detectors to be used in Airbus shall comply with the environmental

conditions and test procedures as defined in document RTCA/DO-160 [10], and

ABDOIOO. [11], completed by the requirements contained in EN54-7 [12].

During the environmental tests, the tested smoke detector shall not:

• generate a smoke warning signal

• become inoperative

• generate a fault signal

637
The applicable tests and their category are defined in the technical specification to be

issued by the aircraft manufacturer for each equipment/system.

7. Equipment Specific Software Requirements

Software shall be produced in accordance with the definition contained in RTCA /DO178 [13]. The software level will be identified by the System Safety Process [14]. For

example Level B could be assigned to cargo compt application and Level C for lavatory

application. Software partitioning is required whenever different software levels are

used within an equipment..

8. Certification Aspects

The smoke detection equipment/systems will be certified together with the certification

of the aircraft following the aircraft type certification process or supplementary type

certification procedures when applied after type certification.

To achieve certification the suppliers have to provide the aircraft

manufacturer v^^ith all product relevant justifications to demonstrate

compliance with the applicable airworthiness regulations. The fire/smoke

detection performances of the fire/smoke detection instrument must be

proven by the supplier during equipment qualification phase. These

justifications will be added to those ones which have to be established by

the aircraft manufacturer.

The purpose of the smoke detection test in the aircraft is only to verify that smoke

generated at the most unfavourable place in a compartment will reach the approved

fire/smoke detection instrument and actuate an alarm within the required time frame.

Thus to demonstrate compliance with JAR/FAR 25.858 (a) for example in case of a

cargo compartment.

9. Summary

An early detection of an in-flight fire/smoke situation is mandatory to initiate the

corrective action at an appropriate time, but false warnings could be critical as well.

Investigations showed that the problem to safely detect a fire/smoke situation and to

638
distinguish this from all non-fire/smoke situations is not yet fully solved in the pressurized

areas of transport aircraft.

Further improvements are very limited as long as the 60 seconds detection time

requirement (ref. JAR/FAR 25.858) must be fulfilled to certify fire/smoke detection

systems.

10. References

[I] Joint Aviation Requirements, JAR-25, Large Aeroplanes, Change 14, 27May

1994, JAA, Hoofddorp, The Netherlands

[2] Federal Aviation Requirements, FAR part 25 incl. Amendments 1-98

[3] Federal Aviation Administration, Advisory Circular 25-9A, Smoke Detection,

Penetration and Evacuation Tests and related Flight Manual Emergency

Procedures, 1/6/94, Federal Aviation Administration, Washington DC, USA

[4] Joint/Technical Standard Order JTSO/TSO-Clc, Cargo Compartment Fire

Detection Instruments", lO.June 1991

[5] Freiling, A, New Approaches to Aircraft Fire Protection, see this book of

conference

[6] Blake, D. FAA Technical Centre, Fire Safety Section, Report No DOT/FAA/ARTN0029, June 2000

[7] Schmoetzer, K. "Improvement of Smoke Warning Reliability in Aircraft

Application", International Aircraft Systems Fire Protection Working Group

Meeting, May 2000, Atlantic Hotel, Bremen, Germany

[8] EN 54-9 Components of automatic fire detection systems

Part 9 Methods of test of sensitivity to fire",

[9] Alroe, H., "Environmental Test Requirements for Fire Alarm Equipment Used for

Marine Applications "AUBE '99", 11th, March 16-18, 1999, Gerhard Mercator

University, Duisburg, Germany, Luck, H., Editor, pp. 567-575, 1999.

[10] Radio Technical Commission for Aeronautics (RTCA) Washington D.C.,

RTCA/D0-I6OD Environmental Conditions and Test Procedures for Airborne

Equipment

[II] Airbus Directives (ABD) and Procedures, ABDOIOO-Equipment-Design, General

Requirements For Suppliers, Issue C, December 1998.

639
EN 54-7 Components of automatic fire detection systems

Part 7 Specification for point-type smoke detectors

Radio Technical Commission for Aeronautics (RTCA) Washington D.C.,

RTCA/DO-178B Software Considerisations in Airborne Systems and Equipment

Certification

Airbus Directives (ABD) and Procedures, ABD0200-Requirements and

GuideHnes for the System Designer, Issue C, December 1998.

640
A. FREILING

European Aeronautic Space and Defense Company EADS Airbus GmbH, Bremen,

Germany

New Approaches to Aircraft Fire Protection

1. Abstract

Currently, new fire detection technologies are under evaluation for aircraft application.

The goal is to reduce the false alarm rate drastically and to improve safety and reliability

figures. Gas sensor technologies, visualization devices and other multisensor/multicriteria

are under discussion. In this paper, an overview of currently fire protected areas in Airbus

aircraft is given. The potential to introduce specific fire protection by the means of new

technologies in dedicated aircraft areas is discussed. If new fire detection technologies are

used, there is the need to have modified integration tests. A comparison of a commonly

used aircraft integration test to a real fire scenario is given by the example of a gas sensor

based fire detector.

2. Introduction

A fire protection system in an aircraft includes passive and active fire protection means

[1]. Passive fire protection is realized by using fire proof or inflammable materials in all

areas of the aircraft including lining, cables, interior etc. In this paper, the active fire

protection system will be regarded which consists of scattering light smoke detectors

managed by a central control unit and a halon extmguishing system. Several aircraft areas

are equipped with fire detection instruments. These are the cargo compartments, the

electronic compartments and the lavatories. The most important and critical area is the

cargo compartment, which is inaccessible during flight.

For ground based applicafions, which includes building fire protection, new kinds of fire

detectors like multisensor/multicriteria- or gas sensor based fire detectors have currently

been developed or are under discussion [2, 3, 4, 5]. The main goal of using these kinds

of sensors is to reduce the false alarm rate. Also the aircraft fire false alarm rate and the

correlated consequences have to be reduced drastically [6, 7]. There are several

641
restrictions and additional requirements that come along with the airborne application

[8]. For new fire detection technology to be used in aircraft, there is the necessitiy to

revise the integration / validation test.

At EADS Airbus, new fire detection technologies are examined for aircraft application

to improve the alarm reliability and to provide additional means for monitoring fire or smoke in dedicated aircraft areas.

3. Fire protected aircraft areas - state-of-the-art

Lavatories

The fire protection of aircraft lavatories is realized by a scattering light smoke detector

near the air extraction and an automatic fire extinguisher in the receptacle. In case of a fire

alarm, the lavatory door can be opened and a crew member can extinguish the fire with a

handheld fire extinguisher. Figure 1 shows a drawing of a lavatory and installation points

of smoke detectors.

Fig. J: a) Sketch of an aircraft lavatory

b) Installation ofa lavatory smoke detector in Airbus

642
Avionics Compartment

In the avionics compartment, nearly all the electronics necessary to fly the aircraft is

located. Commonly, the compartment is positioned under the cockpit, in the front part of

the aircraft. In most aircraft, the avionics compartment is not accessible during flight. Only

in larger Airbus aircraft, there is a small access hatch. The compartment is ventilated, with

the extracted air passing through a common air extraction duct which is monitored for the

presence of smoke.

Fig. 2: a) Sketch of an aircraft avionics compartment

b) Installation ofa duct type smoke detector in Airbus

Cargo Compartments

More critical areas in the aircraft in which smoke detectors are installed, are the cargo

compartments. In transport aircraft, these compartments are normally located under the

actual passenger cabin, the forward (FWD) compartment in front and the aft compartment

behind the wing box. During flight, the cargo compartments are inaccessible. That means

that in case of a fire warning, the pilot has got no possibility to verify if it is a real or a

false alarm. The action the pilot has to take after a fire waming is to activate the

extinguishing system and to land as soon as possible, eventually on an unsuitable airport

[9].

A further reason for a high risk within the cargo compartment is that the freight cannot be

controlled by the aircraft manufacturer. Although there are restrictions on what is allowed

to be transported, there is still the possibility that dangerous ignition sources get into the

aircraft.

Concerning fire extinguishing, there fire extinguishing bottles installed in transport

aircraft. As extinguishing agent, halon is used. Although halon is generally banned by the

643
Montreal Protocol, there is a time limited exceptional regulation and it can still be used for

aircraft application. This regulation expires in 2003. Until then, alternatives have to be

found.

Fig. 3: Airbus Cargo Compartment Smoke Detector Positions and installation

Lower Deck Facilities

With the development and construction of larger aircraft, there comes the wish to use

additional space gained in the lower deck. In order to accommodate more passengers in

the main deck area, certain facilities will be located in the lower deck area of the aircraft.

These are galleys, toilets, crew rest areas (with beds for the passengers/crew to sleep), etc. Along with the installation of such facilities, there comes the necessity to install fire

detection.

4. State-of-the-art aircraft fire detection technology

The signal processing of the scattering light type smoke detectors currently applied in

the Airbus aircraft series uses specifically developed smoke discrimination algorithms.

Using specific light frequencies, modulations and correlation in the time domain with a

database allows to differentiate between typical smoke patterns.

The overall aircraft smoke detection system consists of the sriioke detectors at several

locations (see section 2) and the so-called Smoke Detection Control Unit (SDCU) which

644
controls and reads out the detectors. A block diagram of the system architecture is given

in Fig. 4. For redundancy reasons, the smoke detectors in the cargo compartment and in

the avionics compartment are installed in pairs. Each pair of detectors is supplied with

power by a dual redundant power supply (see Fig. 4). One detector in the pair is

mstalled on the Smoke Detection Control Unit (SDCU) loop A, the other on loop B.

The SDCU tests each loop to check whether it is functioning before it acts on a smoke

alarm from a single smoke detector.

When a smoke alarm is generated by the SDCU the ventilation and heating systems (if

installed) will be closed automatically.

Loop A

Battery

28VDC

Normal Bus

28VDC

Power

Channel 1

Power

Channel 2

Loop A

Loop B

SDCU

Smoke

Detection

Control

Unit

Loop B

Avionics —

n

Cargo

Lavatory

I Optional

Avionics

Cargo

I

Lavatory

I Optional

I

o

o

o

(0

o

o

0)

0)

o

Fig. 4. Smoke Detection Loop Schematic for A340

645
5. Approaches to new kinds of fire detection

Currently under investigation are advanced fire detection technologies with the aim to

identify the proper fire signatures (gas, smoke, heat etc.) as they may develop in a

crucial, inaccessible area of the aircraft and develop the algorithms which allow to link

these fire parameters to non-fire events that may be present in the aircraft. Technology

under consideration to reach adequate detection properties includes [10]:

Gas sensing with semiconducting metal oxide sensors in thick- or thin-film

technology or/and electrochemical cells

- Optical smoke sensing with light attenuation or back-scattering devices

- Near infra-red (NIR, wavelengths < 1.2/xm) and visible light sensing with CCD

(Charge Coupled Device) and/or CMOS (Complementary Metal Oxide

Semiconductor) technology

- Infra-red sensing with thermopiles (for wavelengths > 1.2^m)

It is possible to subdivide several aircraft areas to dedicated fire sectors with dedicated fire

protection systems. One example for that can be a special fire protection of avionics

compartments where the materials that can bum are relatively well defined. So, may be the

possibility to develop a system based on gas sensors that detects smouldering cable fires or

overheated equipment. A certain spatial resolution in fire detection would give the pilot a

decision means of what measures to take if an area of the electronics compartment

becomes overheated. If the heat source is an uncritical item, then this equipment can easily

be switched off.

In the Cargo compartment, where the kind of material that might bum is unpredictable, the

approach is different. Here, there is the necessity to know the non-fire case in order to

reduce false warnings. So far, it has never happened in Airbus aircraft that a fire was

undetected when a smoke detection system was installed. The problems are false alarms

caused by cargo. To improve the false alarm rate, knowledge about environmental

conditions in false alarm cases is necessary. Therefore, database studies have been

conducted in order to get as much information as possible about these conditions. The

results are presented elsewhere [11].

646
6. Approaches to new fire extinguishing methods and dedicated fire detection

Water mist as halon replacement in combination with nitrogen inerting is regarded as a promising alternative to the today's extinguishing system. The use of a water mist

system however implies several physico-chemical aspects which could have been

neglected with gaseous systems but now have to be checked and solved. Agent freezing,

short circuit prevention, weight, maintenance or smoke generation are points which

have to be considered.

For weight and efficiency reasons, the water mist suppression system must be associated

to a smart detection/activation system which is able to accurately detect and locate the

fire and activate the suppression in adequate on/off sequences. There are several

requirements for the detection system that are derived from a water mist based

extinguishing system.

In order to carry only a minimum amount of water in the aircraft due to weight reasons,

the extinguishing process has to be optimised. An extinguishing shall only be performed

where the fire is located. This implies that the fire detection system must be able to

provide a certain spatial resolution. At the moment, there is no need for such a zonal

detection system because the halon extinguishing system is based on a total flood

philosophy.

Furthermore, the detection system has to be waterproof because it has to monitor the fire

criticality status for the total remaining flight. The extinguishing efficiency of water,

even in combination with an inert gas is not comparable to the properties of halon and

there is a remaining risk that the fire will light up again. So, a fire monitoring function is

necessary.

The research concerning these items is being funded by the European Commission

within the 5th Framework Programme FireDetEx.

7. Aircraft integration of new Are detection technologies

After qualifying fire/smoke detectors for aircraft application, they have to be

implemented/integrated into the aircraft environment. Current integration tests for smoke

detectors are defined in the FAA Advisory Circular 25-9A [12]. The integration tests

mentioned herein can be performed with appropriate smoke generators, being selected out

647
of the following list, depending on the actual installation point of the sensor:

paper towel bum box

- Rosco Theatrical smoke generator

- Helium-injected Rosco Theatrical smoke generator

A pipe or cigar

- A Woodsman Bee Smoker

Any other acceptable smoke generator

The smoke emerging from one of those sources must be detected within one minute after

the start of the fire [13]. This time includes all the necessary signal processing and

transduction to display an alarm message in the cockpit.

Consequences for new technologies

The existing authority requirements concerning integration of smoke detectors restrict the

development of new approaches. An example are multicriteria/multisensor devices. Such a

system needs a certain time to process a certain internal signal evaluation out of the

various parameters that are recorded to come to an alarm decision. This alarm decision

will be of a higher reliability, but might take a little more time.

Furthermore, the event "start of a fire" is not clearly defined. The amount of smoke

produced for example by a smoke generator might be equal to the smoke emitted in a

rather advanced state of a real fire. Although other parameters that represent a real fire are

not reflected by an artificial smoke generator. This includes heat release in terms of

radiation and convection as well as gas development.

Current developments show that gas sensing technologies have a potential to be new or

additional fire detectors. At the moment, there is no integration test that is could be used

for certification of such a system. A real fire test as described in AC 25-9A cannot be

conducted during flight. But only a real fire has the gas constitution that is detected by gas

sensors.

Fig. 5 shows a test that has been conducted to compare the response of gas sensors to a

currently used smoke generator in Airbus (AX 1000) and a real fire of Kleenex tissue

towels. The test was carried out in a standard-layout lavatory. As sensing device a GSME

smouldering fire detector as it is used for lignite power plants was examined [14]. This

device comprises 3 semiconducting metal oxide gas sensors with optimized selectivity for

648
H2, CO and NOx. The GSME detector and its signal processing algorithm had not been

modified for this test. First, the smoke generator was switched on, producing an amount of

smoke labeled equal to 5 kleenex tissue paper towels. It can be seen that the gas sensor

device responds very poorly with all its 3 sensors and shows a slightly decreasing signal.

The aircraft optical smoke detector which was also installed, reacted after 35 seconds.

The GSME was positioned near the basin, which means it was not installed where the

current detector is installed. By burning 3 Kleenex, the detector showed a significant

signal and the internal processing algorithm predicted a certain "fire probability" which

can be used for defining an alarm threshold. Not being on its proper position yet and

burning 5 Kleenex resulted in a higher signal but a similar fire probability.

Afterwards, the sensor was installed into the position of the current detector and again, 5

Kleenex were burned. This time, the signal shape looked different due to changed airflow

conditions the sensor was exposed to and the fire probability had a higher value.

The final two peaks are two cycles of cigarette smoke, the first just normally smoking and

the second smoking and blowing at the detector. Cigarette smoke shows a different signal

shape than Kleenex towels and it can be seen that cigarette smoke does not result in any

value for the fire probability.

Gas Sensor smoke/ fire test

1 00000

0 500 1000 1500 2000 2500 3000 3500 4000 4500

Time (sec)

Fig. 5: Comparison of a smoke detector test with a real fire for a gas sensor basedfire

detection system ^

649
This example shows that the common smoke generator integration test is not suitable for

this kind of fire detector because these types of gas sensors will never respond to this

specific kind of smoke.

Only if the gas constitution of a characteristic fire is known, a gas generator might be

constructed for assuring a correct integration. But in this case, all the other fire parameters

will not be regarded. In this context it becomes clear, that new detection technologies need

dedicated specific-to-type aircraft integration flight tests after they have proven their fire

detection properties in ground tests.

8. Summary

New fire detection technologies bear the potential to improve the safety of aircraft by

making a fire warning more reliable and by reducing the false alarm rate. The risk of

unnecessary passenger evacuations and undue emergency landings can be minimized that

way. Approaches are the use of gas sensors or other multisensor/multicriteria devices as

well as visualisation tools like specific cameras with associated image processing.

However, the way to an aircraft integration coincides with the fulfillment of stringent

environmental and many other aircraft specific requirements.

The technology that is used for fire detection instruments strongly influences the kind of

testing that is necessary to validate a proper integration. For this reason the user of new

fire detection instruments, in this case the aircraft manufacturing industry, has to know

exactly what technology is used inside a fire detector in order to perform the right

verification for demonstration of compliance with the certification requirements.

650
9. Literature

[1] Freiling, A. Brandschutz in Airbus-Flugzeugen, S&S Report, April 2000

[2] Gottuk, D.T., Peatross, M.J., Roby, R.J., and Beyler, C.L., "Advanced Fire

Detection Using Multi-signature Alarm Algorithms," International Conference on Automatic Fire Detection "AUBE '99", 11th, March 16-18, 1999, Gerhard

Mercator University, Duisburg, Germany, Luck, H., Editor, pp. 237-246, 1999.

[3] Petig, H et al. History of Development Steps in Gas Sensor Fire Detection in

RWE Powerplants for Lignite and Waste Combustion, International Conference

on Automatic Fire Detection "AUBE '99", 11th, March 16-18, 1999, Gerhard

Mercator University, Duisburg, Germany, Luck, H., Editor, pp. 284-294, 1999.

[4] Miiller, H.C. Rule based Detection Algorithms, Proceedings of the VdS

Conference "Gas Sensors for Fire Detection", Nov. 15^*^ and 16'*^ 2000, Cologne,

Germany, Chairing: Michael Schnell, VdS Schadenverhiitung

[5] Siebel, R. Strategies for the development of detection algorithms. Proceedings of

the VdS Conference "Gas Sensors for Fire Detection", Nov. 15'^ and 16'*' 2000,

Cologne, Germany, Chairing: Michael Schnell, VdS Schadenverhiitung

[6] Blake, D. Aircraft Cargo Compartment Fire Detection, International Halon

Replacement Working Group Meeting, May 2000, Atlantic Hotel, Bremen,

Germany, hosted by EADS Airbus GmbH

[7] Grosshandler, W.L. (Editor), Nuisance Alarms in Aircraft Cargo Areas and

Critical Telecommunication Systems: Proceedings of the Third NIST Fire

Detector Workshop, December 4-5, 1997, NISTIR 6146, National Institute of

Standards and Technology, Gaithersburg, MD, March 1998

[8] Freiling, A. Requirements for Fire Detection in Civil Aviation, Proceedings of the

VdS Conference "Gas Sensors for Fire Detection", Nov. 15"" and 16'*" 2000,

Cologne, Germany, Chairing: Michael Schnell, VdS Schadenverhutung

[9] Schmotzer, K, Aircraft Fire Detection Requirements, see see this book

of conference

651
[10] Freiling, A, Improving Safety for Aircraft, Proceedings of the Deutscher Luft- und

Raumfahrtkongress 2000, ref. DGLR-JT2000-156, Sept. 16-20 2000, Leipzig,

Germany, Deutsche Gesellschaft fiir Luft- und Raumfahrt

[11] Mangon, P. Fire Detection for aircraft cargo compartments, reduction of false

alarms, see this book of conference

[12] Federal Aviation Administration, Advisory Circular 25-9A, Smoke Detection,

Penetration and Evacuation Tests and related Flight Manual Emergency

Procedures, 1/6/94, Federal Aviation Administration, Washington DC, USA

[13] Joint Aviation Requirements, JAR-25, Large Aeroplanes, Change 14, 27May

1994, JAA, Hoofddorp, The Netherlands

[14] Kohl, D, Kelleter, J. Gassensor-Melder fiir Schwelbrande, in: Sicherstellung der

Schutzfunktion von Gassensoren, A. Freiling, R. Fromm, Edts. pp. 154-165,

Beuth-Verlag, Berlin, ISBN 3-410-79234-1, Dec. 1997

652
Philippe MANGON

CERBERUS S.A, BUG, France

FIRE DETECTION FOR AIRCRAFT CARGO COMPARTMENTS . REDUCTION OF FALSE ALARMS

Abstract

In order to better understand the reasons for the current high false fire

alarm ratio in Aeronautic Applications, an analysis of actual fire and false

alarm events has been conducted using different database.

This research (funded by the European Gommission within the 5th

Framework Programme FireDetEx) included the following analysis :

1. Analysis of false alarm cases

A review of false fire alarm cases extracted from different data bases

will be presented, For some t5^ical cases, it will be analysed whether

the alarm was triggered by a system malfunction, particular

environmental conditions or by the detection of aerosol particles.

2. Analysis of fire alarm cases

Real fire alarm cases will also be considered, it will be determined what

was the probable fire source, which phenomena has likely caused the

ignition and what should have been the best fire sensor under these

conditions.

3. Definition of fire and non-fire scenario

The fire detection system can only be improved on the basis of clear

performance objectives, fire and non-fire scenario will be presented

against which the performance of new fire detection concept can be

measured and evaluated.

653
Introduction

Among the various aircraft zones for which a fire protection is required,

the cargo compartments are specific in this sense that their characteristics

are very variable in terms of dimensions and topologies as well as

environmental conditions and fire threats.

Fire sources and their combustion mechanisms and products are

diversified, therefore there is no single physical parameter that would

allow the detection of this wide fire spectrum with an evenly distributed

sensitivity.

Under these conditions, in the currently used systems, the smoke

detectors have to be adjusted so as to early detect the fire type for which

their sensitivity is basically the worst (and to meet the certification

requirements [1]); making them also more sensitive to environmental

conditions.

Basically, a combination of several criteria to trigger a fire alarm would

bring a significant benefit in terms of discrimination capabilities, provided

of course that the fire and non-fire situations are well known.

Therefore in order to improve significantly the fire detection reliability, it

is necessary to better understand, under this environment, the physical

parameters that distinguish the start of a fire from those that are due to

non-dangerous phenomenon.

654
Analysis principle

Fire and false alarm events in operation were extracted from different

data base [2] [3] and compiled as shown below:

FAA

NTSB

FAA

CAA

Other

Data

Redundancy elimination

and

Common -setting of

Definition and

development of

Research Engines

Elimination of

Separation between FIRE events and FALSE

Classifie

d

Figure 1 : Fire and false alarm events analysis

The following classification logic was applied :

FIRE FALSE AL/RM

Aircraft

Flight Phase

Specification

Environment Conditions

Atmospheric Conditions

Airline

Place

Date

Hazards

Probable Cause

Freight

Ventilation

Source

Ignition

Propagation

Non-classified

\

/

System misbehaviour - Aerosol presence

Human error

Non-classified

Environment => Physical

\^ parameters

Figure 2 : Events classification logic

It is to be noted that at the time of the event, most of the here-above

information was not recorded (and practically impossible to retrieve after).

655
General outcomes

False

Fires

Figure 3 : Ratio fire/false alarm

In this graphic, regional aircraft are very few represented, the overall ratio

(90% of false fire warnings) would be higher if this aircraft category was

totally included [4].

Human

Physical

Non dangerous

Unexplain

System

misbehavio

Figure 4 : False alarm analysis

In most of the cases, the conditions in the cargo compartment at the time

of the alarm were not exactly known by the pilots or the crew, therefore

false alarm events were often considered unexplainable or attributed to a

system malfunction whereas a better knowledge of these conditions would

have led to a different classification.

However, system misbehaviours under degraded situations (wiring

failures, power supply failures, management of redundancies in case of

656
internal failures, ..) take probably a significant part in the overall ratio

and have to be considered as an improvement axis.

Analysis of typical false alarm cases

Event 1: Description

Date : 21/11/1985

Source : CAA (extract)

Aircraft Make : BOEING B-747

Carrier : NOT REPORTED

Phase Flight : CRUISE

Narrative : Lower aft cargo hold fire warning. A/c diverted emergency

evacuation. False fire warning. Following a lower aft cargo

hold fire warning a/c diverted to lajes where an emergency

evacuation was effected. Some difficulty due to excessive force

needed to open fully doors 2&41&2&5r. Several

passengers sustained minor injuries. The cause of the fire

warning was attributed to condensation emanating from a

considerable quantity of 'warm' fruit. The two detectors were

slightly oversensitive but this is considered a very minor

contributory factor. A mod has been initiated to fit a dual loop

smoke detector system.

Probable environmental conditions at the time of event 1

Physical Temp Humid/ Radiat Combu Other Comb Other

parameter Conden st Aerosol gases gases

s Aerosol

s

s

Probable Low High Low Low Mediu Low High

level m

657
Event 2: Description

Date: 23/10/1998

Source : AIRBUS (extract)

Aircraft Make : AIRBUS A-340

Carrier : SABENA

Phase Flight : TAKE OFF

Narrative : At 4000 ft with configuration 1 forward cargo smoke red

alarm came on. According to ec procedures the fwd cargo

cooUng was switched off. The switch was pre in max. One

minute later the alarm went out. Visual check performed and

confirmed neither smoke nor fire in the fwd compartment.

Flight was continued. During cruise at flight level 290

lavatory sm warning came on. Toilet gl triggered this alarm a

lot of times. Visual confirmed nobody inside the toilet and no

smoke evidence. Action: maintenance inspected fwd cargo and

lavatory and did not find any indi of fire or smoke.

Investigation related to oil smell in cabin revealed 3 oil

quantity lower than on other engines. Suspected oil suction to

air system. Deactivated engine 3 bleed system switch SDCU

and smoke detector test were satisfactory. The next flights

were also performed with engine 3 bleed off and oil

consumption was monitored and found within limits. On

ground in bru when switching APU bleed on smoke appeared

in cabin cockpit. Smoke disappeared after switching off pack

2. Smoke did not with pack 2 on afterwards. Problems

suspected to come from APU pneumatic duct. Maintenance

found oil leak on filter bowl. O'ring replaced and leak check

performed. Engine 3 bleed system was reactivated.

658
Probable environmental conditions at the time of event 2

Physical Temp Humid/ Radiat Combu Other Comb Other

parameter Conden

s

St

Aerosol

s

Aerosol

s

gases gases

Probable Low Low Low Mediu Low Low or Low

level m or High

mediu

m

Analysis of fire alarm cases

Event 3: Description

Date : 20/03/1991

Source : FAA INCIDENT DATA SYSTEM

Aircraft Make : LKHEED L-188-C

Carrier : REEVE ALEUTIAN AIRWAYS INC

Phase Flight : FCD/PREC LDG FROM CRUISE

Narrative : Dense fumes in cargo compartment. Diverted and landed.

Smoke from box marked fish that contained batteries.

Probable environmental conditions at the time of event 3

Physical Temp Humid/ Radiat Combu Other Comb Other

parameter Conden st Aerosol gases gases

s Aerosol

s

s

Probable Low Low Low or Mediu Mediu Mediu Mediu

level Mediu m m or m m or m High High

659
Event 4: Description

Date : 05/09/1996

Source : NTSB AVIATION ACCIDENT/INCIDENT DATABASE

Aircraft Make : DOUG DCIO-IOF

Carrier : NOT REPORTED

Phase Flight : CRUISE

Narrative : The airplane was at fl 330 when the flight crew determined

that there was smoke in the cabin cargo compartment. An

emergency was declared and the flight diverted to

newburgh/stewart international airport and landed. The

airplane was destroyed by fire after landing. The fire had

burned for about 4 hours after smoke was first detected.

Investigation revealed that the deepest and most severe heat

and fire damage occurred in and around container 6r which

contained a dna synthesiser containing flammable liquids.

More of 6r's structure was consumed than of any other

container and it was the only container that exhibited severe

floor damage. Further 6r was the only container to exhibit

heat damage on its bottom surface and the area below

container 6r showed the most extensive evidence of scorching

of the composite flooring material. However there was

insufficient reliable evidence to reach a conclusion as to where

the fire originated. The presence of flammable chemicals in

the dna synthesiser was wholly unintended and unknown to

the prepared of the package and shipper. The captain did not

adequately manage his crew resources when he failed to call

for checklists or to monitor and facilitate the accomplishment

of required checklist items. The department of transportation

hazardous materials regulations do not adequately address

the need for hazardous materials information on file at a

660
carrier to be quickly retrievable in a format useful to

emergency responders.

Probable environmental conditions at the time of event 4

Physical Temp Humid/ Radiat Combu Other Comb Other

parameter Conden st Aerosol gases gases

s Aerosol

s

s

Probable High Lov^ Mediu High Low or High Low

level m or Mediu

High m

Definition of fire and non-fire scenario

Some fire and non fire scenario are presented here-below as possible

development tests for fire detection systems.

661
Fire cases

• Open cellulosic fire (wood)

:

• Cardboard boxes : EN 54 - TFl [5] -Open cardboard fire

• Smouldering pyrolysis fire -Smouldering cardboard fire

(wood) : : EN 54 - TF2 • Textile :

• Glowing smouldering fire -60 % Wool / 40 % Acrylic

(cotton) : : EN 54 - TF3 (open)

• Open plastics fire -60 % Wool / 40 % Acrylic

(polyurethane) : : EN 54 - TF4 (smouldering)

• Liquid fire (n-heptane) : : EN -100 % cotton (open)

54 - TF5 -100 % cotton (smouldering)

• Liquid fire (methylated spirits) -100 % polyester (open)

: : EN 54 - TF6 -100 % polyester (smouldering)

• Paper (UL268)

:

-100 % wool (open)

-Paper towels (open) -100 % wool (smouldering)

-Scheduled newspapers (open) • Jet A fuel fire

-Normal newspapers (open) • Diesel fire

-Normal newspapers • Oil fire

(smouldering) • Cable fire

Nonfireeas^:

• Moisture • Fruit / Animals / Vegetables

• Condensation • Oil

• Fog • Exhaust gas

• Sand and Dust

Tableau 1 : Fire and non - fire scenario

662
Summary

The exploitation of actual fire alarm events is tricky because most of the

time, the parameters recorded at the time of the event do not allow to

determine the condition for which the alarms were triggered and can even

lead to wrong conclusions.

However this analysis has allowed us to clarify some t3TDical fire and nonfire situations and to outline performance tests accordingly.

Fire sources are extremely diversified and, in particular the materials

involved are most of the time unexpected or even normally forbidden as

cargo loads. As well their combustion products or effects are variable with,

according to the event, predominance of different physical parameters.

False alarm sources are also diversified, in some cases the corresponding

single physical parameters are very close to those that characterise the

start of a fire.

Under these conditions, the adjunction of several detection criterion can

increase considerably the discriminatory capabilities of the fire detection

systems.

The dynamic of the various signals has to be taken into account in the fire

alarm decision as an additional discriminatory factor, for this a minimum

analysis duration is necessary which is very often not compatible with the

current certification criteria (considering in particular the propagation

time of the combustion products).

Performance development or qualification tests must be on one hand

feasible under well controlled metrological conditions and on the other

663
hand representative of a large range of realistic fire and non - fire

situations.

References

[1] Schmoetzer, K . Aircraft Fire Detection: Requirements, Qualification

and Certification Aspects, see this book of conference.

[2] FAA In service events Data Base.

[3] NTSB In service events Data Base.

[4] Blake, D. FAA Technical Center, Fire Safety Section, Report No

DOT/FAA/AR-TN0029, June 2000.

[5] EN 54-9 Components of automatic fire detection systems

Part 9 Methods of test of sensitivity to fire".

664
T. Wittkopp, C. Hecker, D. Opitz

VEDAIR-AG, An der Eickesmuhle 22/23, 41238 Mdnchengladbach, Germany

The Cargo Fire Monitoring system (CFMS) for the visualisation of fire events in

aircraft cargo holds

1 Introduction

Fire on board of an aircraft represents one of the most dangerous in-flight situations.

Following a fire alarm from the freight compartment, the pilot is obliged to immediately

activate the fire suppression system before proceeding with an emergency landing.

Up to now, detection of fire has most always meant the detection of smoke by threshold

devices like photoelectric or ionisation smoke detectors. These devices are troubleprone and lead to a reported rate of up to 200:1 false to genuine alarms [I]. Biake surveys fire alarms in aircraft cargo compartments from the last 25 years and finds that

both the number of false alarms and the ratio of false to real alarms is steadily increasing

[1]. This is also due to the conversion of class D to class C compartments, resulting m

an increased overall number of fire detection units. This high rate of false alarms is unacceptable, as an alarm, followed by a diverting and landing at nearest suitable airport,

causes high costs of approximately $50,000 [1] and is possibly connected with a higher

safety risk due to a variety of factors such as unfamiliar aiiports, less effective navigational aids, shorter runways, inferior fire fighting and the loss of the cargo load. Fire

alarm may entice the pilot to an inadequately riskily landing, for example ditching on

water during an over-water flight, and generally limits the credibility of fire alarm systems. Nevertheless, an unnecessary use of the fire suppression system often results, for

the moment, still in an unwanted release of Halon 1301, which is identified as one of the

substances contributing to stratospheric ozone depletion.

Additionally, with common threshold detectors used in "air tight"' cargo areas, the

smoke alarm equipment will continue to report an alarm condition, even if the fire has

been extinguished by the suppression system. Thus, the pilot has no chance to know, if a

fire has been extinguished or if it is even continuing to grow. This situation is found to

665
be unsatisfying and it is stated, that the pilot should have better and more reliable information about the cargo compartment status, especially about the fire growth and the effectiveness of fire suppression actions [2].

Cleary and Grosshandler [3] provide a broad survey on fire detection in aircraft cargo

compartments, especially concentrating on the more common fire detection methodologies.

Digital imaging more and more becomes a means for the detection of fires [4-8]. Very

early, Goedeke et al. [5] describe a detection system which is capable to detect open fire

sources. Ultraviolet and infrared detectors produce event signals, an image processor

then evaluates images from a colour video camera to determine bright area objects to

confirm the fire event. In a detailed approach, Foo [6] applies a rule-based machine vision approach to detect and categorize hydrocarbon fires in aircraft dry bays and engine

compartments. A set of heuristics based on statistical measures derived from the histogram and image subtraction analyses of successive image frames is used to differentiate

between the fire and the non-fire status. Cheng et al. [7] propose a new video fire detection system and examine the underlying principles of a video based fire detection system

in a more general way. They mainly conclude that fire detection could be carried out

with the same video unit as the regular video observation and that is well suited for the

fire detection in large spaces like warehouses. Another approach uses a colour video

camera to monitor temperature and species sensitive sensors which change colour at a

prescribed temperature or carbon monoxide concentration [8].

Fire detection by digital imaging is fast, can cover wide areas of observation and additionally allows visual inspection, e.g. in the case of an alarm. Most often, only the visible or infra-red radiation of a fire is monitored, which limits the detection to open fire

sources. This is unsatisfactorily, because smouldering fires might remain undetected for

long periods of time, containing a high risk of a sudden turning to a large open fire. To partly overcome these restrictions, a new fire detection system based on digital imaging (CFMS) is introduced which creates the basis for an in-flight cockpit video sur666
veillance system, combined with fire detection capabilities. It allows the fast detection

of both open and smouldering fires and additionally the verification of fire alarms given

by other, standard fire detection systems and the monitoring e.g. of fire growth and fire

suppression actions in closed spaces like in aircraft cargo compartments Deviating from

most approaches, a fire can be detected and monitored even when the flame itself is not

visible. This is done by a combination of a special illumination technique and a digital

imaging algonthm which is capable to cleariy emphasise the relevant fire signatures.

These signatures include smoke and its characteristic properties, the visible light emitted

from the fire and reflected from the walls and characteristic periodic phenomena like

fire flickering.

The underlying concepts of this novel video-based fire detection system are described in

detail and the advantages and limitations are discussed. To evaluate its efficiency, results are presented from experiments that were conducted in a mock-up of a typical aircraft cargo compartment.

If a fire is detected, it is very useful to have the opportunity to visually inspect the compartment. Unfortunately, it is almost not possible to perceive relatively small amounts of

smoke. This is especially true if the scene is observed through a small gap as it is the

case with containers loaded into the compartment (Fig. 8a) or the smoke develops

slowly because human vision adapts on the slowly changing scene. Therefore, an algorithm is presented to drastically enhance the visibility of smoke or fire respectively and

is discussed.

2 Experimental

Experiments are performed in a compartment as figured in Fig. 1. Various configurations, varying in the test fires used and the underlying set-up of lamps, cameras and the

fire places were implemented from which a small selection is presented here (Table 1). The cargo compartment is monitored with a camera and the video stream is captured

both real-time with a computer device and on tape.

667
2.1 Test location

The tests are performed in a mock-up of an Airbus A340 cargo compartment (Fig. 1),

both in an empty (unloaded) cargo room and with two containers loaded into the compartment which confine the camera's field of view to a narrow band of 7 cm height. The

difference between the two implementations is that in the case of the unloaded cargo

freight, the fire, which is located on the floor, lies within the camera's field of view. In

the other case, the camera peers through the gap between the compartment ceiling and

the container's top, which makes the proper detection of fire more complicated because

the fire can only be observed indirectly by the ascending smoke and the reflections of

the fire glow.

4.2 m

Camera •

Field of viewContainer

,o

Fire Halogen

lamps

Height: 1 .7 m

Fig. 1: Cargo bay mock-up (Airbus A340) as used within the framework of the fire tests

668
2.2 Test fires

Three test cases are defined in Table 1. The two mentioned test fires are described below.

2.2.1 Smouldering wood fire

About 25 pieces of birch wood (Dimensions 3.5 x 1 x 2 cm^) are placed on a heating

plate which is provided with concentric ribs. Smoke of bright colour is produced after

approx.. 3-5 min of heating

2.2.2 Open polyurethane foam fire

Two layers of polyurethane foam (Dimensions 25 x 25 x 2 cm^) are stacked one above

the other and are ignited. A bright flame develops which produces large amounts of dark

smoke.

Test Test fire, according Fire-type Material Container

case to EN-54 [9]

1 TF4 open plastic fire polyurethane foam no

2 TF4 open plastic fire polyurethane foam yes

3 TF2 smouldering pyrolysis wood yes

Table 1: Definition of test cases 1-3

2.3 Imaging system, cameras and illumination

Regular BAS-video cameras in combination with a computer are used to record the

scenes in digital forni. Halogen lamps illuminate the scene. The set-up is shown in Fig.

1. It is noticeable that the lamps illuminate the scene indirectly in the sense of a darkfield illumination. This permits the visualisation not only of the open fire but of the

smoke too, which would be invisible without lighting.

669
2.4 Image processing

Foo [6] describes the basis for a fire detection system based on digital imaging and statistical analysis of the frames.

Among other, the mean g and the standard deviation o of the pixel values of one image

are identified as parameters that determine whether a fire is likely or is not. The mean j is defined as follows:

n The standard deviation is defined as:

(2)

i=l n-1

A fire is likely if the mean g of the image pixels is high, according to a very well lit

room. A large standard deviation a, which means that there are bright spots on dark

background, then verifies the fire. A fire is unlikely if the mean g and the standard deviation are both low. Thus, to detect

a fire, the time-dependent mean and the standard variation is monitored and a fire alarm

is released if the change exceeds the pre-determined thresholds.

For the enhancement of smoke visibility, it is necessary to have a reference image. This

can be calculated as the mean of the time series of undisturbed images, recorded in the

non-fire-case. The actual image is first smoothed and then subtracted from the reference

image, yielding after suitable thresholding the smoky areas as bright regions, which can

be superimposed with the actual image to result in an image which can be easily interpreted (Fig. 8).

670
Fig. 2: Fire according to Test-case 1, 180 s after beginning ofexperiment

a)

b)

Fig. 3: Fire according to Test-case 2, viewed through the gap between container and

ceiling. The fire itself is hidden.

a) Before fire is ignited.

b) 100 s after beginning ofexperiment, fire is fully developed.

3 Results and Discussion

For various test cases (cf. Table 1), time-dependent sets of the mean f and the standard

deviation o are computed. The results are shown in Fig. 4-Fig. 6.

For test case 1 (polyurethane fire without container, Fig. 2), there is a good response of

both the mean g and the standard deviation o (Fig. 4) to the fire. The mean approximately doubles and the standard variation rises by a factor of 3.

For test case 2 (polyurethane foam fire with container. Fig. 3), the response is much

smaller, what could be expected due to the smaller relative image region where the fire

itself or its luminosity is visible (Fig. 5).

671
In test case 3, the mean is approximately constant while the standard variation rises

about 20% (Fig. 6). This is quite small compared with the test cases 1 and 2 but significant in relation to the statistical fluctuations of the values o(t). Nevertheless, it has to be

considered the relatively small amount of smoke produced in that experiment.

The response function can be improved if the image region is divided into separate, nonoverlapping windows and the calculation of the mean and the standard variation is done

for each individual window. To demonstrate, for test case 3 the image is divided into 4

sub-windows, 2 in the horizontal direction and 2 in vertical direction and statistical data

is calculated for each sub-window. In Fig. 7, the calculated mean for the 4 single windows is presented.

It can be recognized, that the mean of the windows in the image's upper half (window

(1.1) and (2,1)) remain constant because the regions mapped by two windows are not

affected by changes induced by the developing fire. The mean in the lower left window

(1.2) grows around 4% and the mean in the lower right window (2,2) lowers about 3%,

which is significant compared to the variation range of the undisturbed values g(t) . The

increase is due to the illumination of the smoke by the halogen lamps which are located

on the left hand side of the image. The decrease in the lower right window can be explained with the light attenuation from the left to the right.

In Fig. 8 a comparison between a source image and the processed image is presented,

where the smoke is emphasized to improve its visibility. From this example it is obvious

that this emphasis leads to a better observability of the fire situation, allowing a better

estimation whether a fire alarm is real or is not and, if the alarm is real whether there's a

large open fire or a less dangerous smouldering fire.

672
open polyurethane foam fire, without containers

160-1

140 .

120

100

80

6040

20

0

standard deviation

0.000 50.000 100.000 150.000 200.000 250.000 300.000 350.000

time [s]

Fig. 4: Time-dependent mean and standard deviation for an open polyurethane foam

fire, without containers (Test case 1).

open polyurethane foam fire, with containers

90

o> 30

20

10

standard deviation

-1 r "T 1 0.000 50.000 100.000 150.000 200.000 250.000 300.000

time [s]

Fig. 5: Time-dependent mean and standard deviation for an open polyurethane foam

fire, with containers (Test case 2).

673
80

70

60

S 50

u> 30

20

10

000

smouldering wood, with containers

mean

standard deviation

100.000 200.000 300.000

time [s]

400.000 500.000 600.000

Fig. 6: Time-dependent mean and standard deviation for a smouldering wood fire, with

containers (Test case 3).

Smouldering wood, with containers

Image is split into 4 windows -—

Mean of Window (2.2)

(1.1) (2,1)

(1.2) (2.2)

Mean of Window (2. 1

)

V[ / Mean of Window (1,2) -->.,^

Mean of Window (1,1)

1 1 1 1 1 1 0.000 100.000 200.000 300.000 400.000 500.000 600.000

time [s]

Fig. 7: Time-dependent mean for a smouldering wood fire, with containers (Test case

3). The image is split into 4 Window, each covering one quadrant of the image region.

Thus, Window (1,1) is the upper left, Window (2,2) the lower right.

674
b)

Fig. 8: Fire according to test-case 3 (smouldering wood fire, t = 400 s after start of

the experiment), seen through the gap between the container's top edge and the ceiling.

a) Original image.

b) The produced smoke is reliably detected and is masked bright white.

4 Summar>' and Outlook

The novel CFMS concept is capable to detect fires by means of a computer-based imaging system. It is able to detect both smouldering and open fire sources, even when the

fire source is hidden, e.g. behind containers and if the fire produces only small amounts

of smoke. From these first experiments it can be expected that the CFMS system, can become a reliable fire detection tool, which provides the possibility to visually inspect

the monitored compartment as an add-on, both to verify a fire alarm or, in the non-firecase, to serve e.g. as monitoring equipment. The conducted experiments clearly demonstrate that a "naked eye" detection of fire signatures, e.g. smoke, from the captured sequences is very failure prone.

This new concept will lead to a significant improvement in the detection and observation of cargo compartment fires and the results encourage further studies.

5 Symbols

1 Mean of pixel values

gi Pixel value

n Number of pixels in an image

o Standard deviation

675
6 References

[1] Blake,D., Aircraft Cargo Compartment Smoke Detector Alarm Incidents on U.S.-

Registered Aircraft, 1974-1999, DOT/FAA/AR-TNOO/29, Federal Aviation Administration, Atlantic City, NJ

[2] Phillips,T., Cabin & Fire Safety: A Perspective From the Cockpit, International

Aircraft Fire and Cabin Safety Research Conference, Atlantic City, NJ (1998)

[3] Cleary,T., Grosshandler,W., Survey of Fire Detection Technologies and System

Evaluation/Certification Methodologies and Their Suitability for Aircraft Cargo

Compartments, NISTIR 6356, NIST, Gaithersburg, MD (1999)

[4] Audoin,L., Kolb,G., Torero,J.L., Most,J.M., Fire Safety Journal 1995; 24:167-187

[5] Goedeke,A.D., Droa,B., Viglione,S., Gross,H.G., Fire detection systems, USPatent 5' 153'722 (1992)

[6] Foo,S.Y., Knowledge-based systems 1996, 9:531-540

[7] Cheng,X., Wu,J., Yuan,X., Zhou,H., Fire Safety Journal 1999; 33:57-69

[8] Plumb,O.A., Richards,R.F., Development of an Economical Video Based Fire

Detection and Location System, NIST GCR 96-695, NIST, Gaithersburg, MD

(1996)

[9] EN54: Components of Automatic Fire Detection Systems, European Comitee for

Standardisation, July 1982

676
G.BOUCOURT

LATECOERE 135 rue de Periole 31079 Toulouse Cedex

05/France gerard.boucourt@latecoere.fr

Two dimensional multi detection fire sensor, system architecture

& performances

The problem of today's fire sensor systems in aircraft is the fact that false

alarms occur, and that there is no possibility to monitor the efficiency of fire

extinguishing nor the cargo compartment itself.

Today, in most cases one dimensional or spot-type smoke detectors are used. The only

fire parameter that is detected by these sensors is smoke. A fire can be seen as a hot

point (smoldering fire), a flame or smoke. In some cases, depending on the kind of fire,

smoke comes later than a certain degree of radiation. One dimensional smoke detectors

need to be exposed to high energetic phenomenon to reach the sensor. In other cases

detection comes later again.

False fire alarms in aircraft cargo compartments can be caused by

interferences detected as a fire like mist, dust, environmental conditions,

particles.

Time to detection could be too long due to the fact that the technology

applied covers only some aspects of a fire and the detector covers a small

area of the cargo bay and not a volume.

A real status of the cargo after or while extinguishing is not yet available to

the pilot. Only a binary information correlated with a remaining probability

of uncertainty is transmitted to the cockpit.

677
In order to improve overall fire detection system efficiency in a/c while

decreasing pilot stress during fire alarms procedures, we propose a new

sensor technology, associated to measurement and detection signal

processing to be part of a confirmation system.

Part of the research has been funded by the European Commission within the 5th

Framework Programme FireDetEx.

1. Sensor technology & architecture

The purpose of the sensor is to identify all of the phases of a fire and

therefore, consequentially, hot spots, fiames and smoke.

The different phases of a fire are as follow:

Smoldering fire: period during which heating begins and gasification occurs.

Start of chemical reaction: period during which complete development of

P5a*olysis occurs. The ignition point sets off the beginning of combustion.

Smoke emission and moderate convection are observed.

Flame: fast exothermic reaction covering the beginning of the flame and the

completely developed fire:

- radiant energy generated around the flame,

- thermal convective energy,

smoke emission (important for hydrocarbons).

For the detection of hot spots and measurement of radiant energy emitted

by the flame, the use of infrared detectors is required. In addition, fire

resistance of materials used in bays (lining) is 850°C over 15s. The device

must therefore be capable of detecting temperatures well below the

resistance of the materials.

678
Space coverage for detection must be close to 100%. This necessitates the

use of two-dimensional sensors providing coverage of a significant solid

angle.

The detector chosen will therefore be a 2D sensor.

The entire principle of thermo graphic cameras is based on the emission of

the black body emission. The spectral response of the black body has been

determined by Planck and is expressed in the following equation:

^^•^ ^--<'-^>°

A'[exp(C2M.r)-l]

Cl=3.742x 10' W nmVm'

C2=1.439x 10' ^m.K

The response of the thermo graphic camera is the superposition of the spectral response of the

black body with the spectral response of its sensor.

Transmission through particles:

Monochromatic transmission through spherical particles can be

expressed by the following equation:

Hypothesis:

- Monodisperse distribution (the particles are all the same size).

- the particles are nonabsorbent (ex: water).

2 Eq.2 T=e

with:

- N number of particles

- a particle rays

679
- Q effectiveness factor

- X path

- X size indicator

Eq.3 ;c=2^

The effectiveness factor is determined in the graph below.

Fig.3

5 10 15 20 25 30

For particles of this size, transmission is significantly degraded with the

wavelength. Attenuation will be increasingly greater as particles are

bigger, keeping in mind that particle sizes are from 0.01 to lOO/xm.

Selection of a technology for the FDVS program:

The choice of a technology is influenced by:

- The environment : On-board 2D sensors, due to the necessity of protecting

them from the outside environment (moisture, pressure, temperature), look

through a window (the 2D sensor is placed in a heated, pressurized zone or

in a wathertight case fitted with a viewing window.

680
- The precision of measurement at temperature : For measurements of

temperatures in range of fire , 2D sensor working in VSW (Very Short Wave

0.4-2.7 pLm) spectrum yield more precise results than do traditional infrared

cameras. The uncertainty on the emissivity affects results of VSW 2D sensor

measurements less.

In addition, given that the emissivity of metallic materials tends to increase

as w^avelength decreases, this brings yet another advantage to measuring

the temperatures of these materials v^ith VSW 2D sensor when they are

operating at temperatures greater than 500°C.

- The transmission (taking the size of smoke or fog particles into account),

which is better in the VSW band and which therefore provides a better

smoke detection.

- The cost: the cost ratio between the three sub-bands is approximately the

following:

VSW= xl, SW=x50, LW=xl50

CVSU architecture

light

Fig.4

Power supply &

signal preconditioning

sensor

connector

window

housing

681
The sensor is composed of

:

1 two dimensional sensor and its lens,

- 1 power supply, signal pre-conditioning

1 sealed housing with front window & connector

2- System architecture

Schematic diagram of the system.

Fig.5

CVSU 1 CCIU

Power supply Interface to other

system

Not provide by the system

The FDVS system is composed of two different equipment, CVSU& CCIU:

- Cargo Viewer & Sensor Unit (CVSU) is the hot spot, fire & smoke sensor

but also provide images for the visualisation in the cockpit. The CVSU

682
makes detection, visualises the cargo (image capture), lights the cargo.

The CVSU includes a power supply, a 2D sensor and its signal preconditioning, a viewing window. The CVSU has an electrical interface to

the CCIU. The CVSU is interfaced to the power supply of the a/c and

mechanically with the lining of the cargo compartment & structure.

- Cargo Camera Interface Unit (CCIU) is the computation of the system.

It controls and interfaces with sensors and processes all signals delivered

from the sensors. It interfaces to other systems: primary fire/smoke

detection system, a/c flight warning system, power supply, maintenance

system, extinguishing system. The CCIU processes information's

(visualisation mode, detection mode), acquires information from sensors,

switches the sensors, processes hot spot, smoke & fire information's,

realises self control and reporting to maintenance system (BITE),

processes warning, exchange data with other systems ( primary

smoke/fire system, power supply, maintenance system, ventilation

system, flight warning system, extinguishing system).The CCIU realises

the Man Machine Interface, controls the image on relevant display unit,

controls the lights on its front panel. The CCIU is interfaced to the power

supply of the a/c and mechanically to electronic bay.

In case of warning from the primary system the FDVS system provides

automatically to the cockpit display, a cargo compartment view including

computation information.

Display Unit (DU) located in the cockpit performances

Maximum time response to hot spot event < 10 s Maximum time response to smoke event < 10 s Maximum time response to fire event < 60 s

683
AIRCRAFT INSTALLATION

An example of the aircraft installation could be:

Fig.6 top view

2CVSU 2CVSU 1 nVSTT

Fig.7

CARGO COMPARTMENT

Top view Side view

684
The position of the cargo compartment visuahsation sensors within the

cargo compartment is optimised:

- Any phenomenon in the field of view of the two sensors is

automatically detected and the detection coverage is 100%,

- Time of detection is always the same and it is very short (no need to

reach the sensor).

This configuration allows monitoring phenomenon between containers &

between containers and lining (ceiling).

3- Measurement & detection signal processing

HOT SPOT Principle:

The principle is that of a thermographic camera and not that of a simple

pyrometer. The camera provides a heat image associated to a temperature

scale, and the result obtained yields a thermogram.

Hot spot detection is based on analysis of the hot spot's luminance (energy)

in the dark.

FIRE Principle:

Fire detection is based on analysis of the fluctuation of the flame (energy,

frequency) in the dark

SMOKE Principle:

Smoke detection is based on reflection-transmission of a reflecting body.

The principle is the following:

685
Fig.8

The relation of the transmission is the following:

Eq.3 ^=^=10

10 reference signal (no smoke)

I signal received

686
Detection & tracking principle:

Fig.9

measurement

tracking nn

yes

wammg

no

yes

4- Lab test

The fire detection & visualisation system is able to recognize the fire tests

TFl through TF6 following EN 54 part 9.

Interferences like sun, rising sun, sun with chopper, do not affect (no

warning) the fire detection & visualisation system.

These tests are successful in :

- a certified EN54 test chamber.

- a downscaled cargo compartment volume in an empty cargo compartment.

- a downscaled cargo compartment volume in a partially loaded cargo

compartment.

- a full scale cargo compartment fully loaded.

Fog or condensation interferences do not produce a warning.

687
Environmental not correlated with a real fire conditions has no effect on

processed images.

Other trial

One trial was made on AIRBUS A330 a/c with 120g FAA powder and fiill

cargo compartment loaded: FDVS sensor detection time was 5s and optical

smoke did not detect. The test was stopped after 15mn.

Trials were made on AIRBUS A330 a/c with 240g FAA powder and full

cargo compartment loaded: FDVS sensor detection time was 3s and optical

smoke detector time was 2mn & 5s.

Trial were made on AIRBUS A300/600ST to monitor the upper and lower

cargo compartment in un-pressurised conditions.

Environment tests

Trial will be made in 2001 to mix temperature variation & pressure

variation with a real fire to make sure that technology has the same

potential on real environment.

688
Thomas Cleary and Michelle Donnelly

Building and Fire Research Laboratory, National Institute of Standards and Technology

Gaithersburg, MD, 20899 U.S.A.

Aircraft Cargo Compartment Fire and Nuisance Source Tests in the FE/DE

1. Introduction

Commercial transport aircraft cargo compartments require both fire detection and

suppression capabilities in order to meet regulatory requirements. Historically, while

there have been few fires reported in cargo compartments, false alarms are a much more

frequent event. A recent study places the false alarm to smoke detection ratio at 200:1

over the last five years [1]. A significant fraction of false alarms is thought to be due to

nuisance sources such as condensed water vapor, and other aerosol sources [2]. The

Federal Aviation Administration requires that detectors meet standards in SAE AS 8036

(wherein UL smoke box testing is referenced as appropriate to check alarm sensitivity)

which specifies that the alarm must fall between 60 %/ft to 96 %/ft light transmission

(extinction coefficients between 3.0 m'^ to 0.13 m'^) [3]. Each new cargo compartment

design must pass a system test on the ground and in-flight using "smoke" which may be

produced from aerosol generators, tobacco smoldering or other non-fire sources. An

alarm must be recorded within 60 s of the start of the aerosol source. The FAA is

developing standard flaming fire and smoldering fire sources that will be more

repeatable than the range of aerosol sources currently in use, and that will allow other

types of detectors besides smoke detectors to be qualified. In a recent survey of fire

detection technologies, gas and thermal sensing were identified as plausible additions to

particulate sensing in cargo compartments to improve detection [4]. Here, fire and

nuisance scenarios were emulated in the fire emulator/detector evaluator (FE/DE), and

gas, thermal and particulate sensor signals were gathered to determine potential sensor

combinations that would overcome various nuisance alarm events in cargo

compartments. The selection of the flaming, smoldering, and nuisance alarm scenarios

was guided by a desire to cover a range of potential fire and nuisance alarm scenarios

that would each progress to a point were current aircraft detectors would alarm; there

689
was no basis for these scenarios from statistical analysis of fire data, nor service

difficulty reports addressing false alarms.

2. Fire and Nuisance Alarm Scenarios

Two separate flaming, smoldering, and nuisance scenarios tailored for aircraft cargo

compartments were emulated in the FE/DE and are described here. Additional scenarios

are described in a NIST report [5]. The FE/DE is a 0.3 m high by 0.6 m wide crosssection flow tunnel designed to reproduce the time-varying speed, temperature and

concentration (gas and particulate) expected at detector locations in the early stages of

the fire (Figure 1). The FE/DE employs a variable speed blower and resistance heaters

to control velocity and temperature (ambient and higher) over ranges of 0.02 m/s to

greater than 1 m/s and 20 °C to 80 °C, respectively.

Exhaust

Variable

Speed

Fan

Test Section Honeycomb

Waterlnletfbr Dust

Spray Gases Feeder -11^

Propene

Smoke

Burner

Electrical Heaters

yr

Vertical Riser

Fire Emulator

Detector Evaluator

Gas

Sampling

Aspirated

Detector Sampling

Spot-type

Recessed

Detector Laser

Beams

Flow

Analog Output

Detector Test Section

Side View

Figure 1 . Schematic of fire emulator/detector evaluator.

The fu-e sources chosen for this study cover a wide range of fire phenomena. They

consist of the following: (1) a flaming fire indicative of a plastic or liquid hydrocarbon

pool fire, (2) a "low-smoke" flaming fire that consists of ethanol-soaked cotton-polyester

blend fabric, (3) a smoldering cotton wick fire, and (4) a pyrolyzing mixed plastics

plaque obtained fi-om the FAA Technical Center. Two nuisance sources chosen to

represent potential source of false alarms due to environmental conditions in aircraft

cargo compartments were emulated. They consist of the following: (1) dust exposure.

690
and (2) an oil mist aerosol.

Measurements of laser light extinction, temperature, gas concentration and analog output

detectors at the test section were made to characterize the fire and nuisance sources. These measurements are indicative of sensor outputs that could be part of a multi-sensor

detector. Light extinction measurements are made across the tunnel duct at the test

section. The light transmission path across the 0.60 m wide duct is extended to 1.50 m

by reflecting the laser beam off two mirrors placed inside the duct. The beam is split

outside the tunnel resulting in two parallel beams, one at 4 cm and one at 15 cm (midheight) below the top of the duct. Photodetectors are positioned on the opposite side of

the duct to record the transmitted light intensity. In smoke-free air, the photodetector

output fluctuates randomly +/- 0.1 % about the mean intensity. The extinction

coefficient, k (m ') is obtained from the following equation:

-^ = exp-'^ [1]

/ 0 is the smoke-free light intensity, while / is the intensity when smoke is present, and L

is the path length (m). Process gas analyzers recorded CO, CO2, and water content in

the air from samples drawn from the test section. The instrument ranges are 0 to 5 xlO"^

volume fraction CO and 0 to 0.04 volume fraction CO2. The uncertainty of the

measurements are stated as 2.5 xlO"^ volume fraction CO and 2 xlO"^ volume fraction

CO2. The water analyzer range is 0 to 0.05 volume fraction with an expanded

uncertainty of 10 % of the measurement. The response time for these analyzers (90% of

ultimate response to a step change) is 15 s. An electrochemical CO sensor was placed in

the tunnel and its output recorded, then converted to CO volume fraction. Air

temperature was recorded by a type-K thermocouple constructed fi"om 0.08 mm bare

wire located 5 cm from the ceiling of the duct, and approximately 15 cm downstream

from the laser beam. An analog output photoelectric, ionization, and thermal detector

was located on the ceiling of the test section 30 cm downstream from the laser beam

The output of this detector was previously "calibrated" against propene soot, dust and

nebulized oil aerosol [6]. Two diflferent aircraft smoke detectors were installed to

indicate the smoke or nuisance source conditions that would cause existing detectors to

alarm. The alarm conditions are suggestive of existing performance, however, these

691
tests should not be considered detector performance tests. One detector {Dl} was a

spot-type photoelectric unit designed to be mounted on the ceiling of the cargo

compartment. It has a low profile housing and is covered by a large-opening protective

metal grate. It was installed at the ceiling location in the FE/DE test section, mounted as

it would be in a cargo compartment. The alarm point for this detector was set at an

extinction coefficient of approximately 0.13 m"' (4 %/ft, the most sensitive allowed by

SAE AS8036). The other detector {D2} was a draw-through type detector that is

designed to sample cargo compartment air through tubing with openings at the ceiling.

The alarm point for this detector was set at an extinction coefficient of approximately

0.31 m'' (9 %/ft). This detector was installed outside the test section with a 15 cm long

6 mm I.D. plastic tube extending 2.5 cm below the duct ceiling as the sample point. The

flow through the detector was set at approximately 4 L/min.

3. Experimental Results

Test fire 1, the flaming fire, emulates conditions developed as the result of a burgeoning

hydrocarbon pool or burning plastics fire. The FE/DE reproduced the flow conditions

by control of the fan speed to achieve the expected ceiling jet velocity. The heater set

point was controlled to provide the increasing temperature at the detector location.

Smoke was provided from the propene smoke generator attached to the FE/DE. The

smoke generator is an annular co-flowing, air/propene diffusion burner contained in a

steel duct with damper-controlled bypass and tunnel connections. The fan speed was set

at 10 Hz which yielded a mean core flow of 0.25 m/s at the test section. The damper

was opened at time = 0 to let smoke flow into the duct. Figures 2-4 show representative

values of smoke extinction, combustion gases and air temperature for this scenario. The

alarm time for the spot-type aircraft detector is marked on the extinction graph; the

draw-through detector did not record an alarm. In Figure 2, the extinction coefficient

began to rise at 30 s, (this delay represents the transport of the smoke from the smoke

generator to the test section). At about 40 s, both the photoelectric and ionization analog

signals began to rise. The photoelectric signal reached its maximum output at 60 s while

the ionization signal continued to climb until leveling off" at 120 s. The ionization signal

lagged the extinction value due to the smoke entry lag of the detector. Figure 3 shows

CO, CO2, and water volume fractions with background values subtracted.

692
Figure 4. Temperature rise for test fire 1 , flaming fire.

693
All gas concentrations began to rise at 45 s (time to transport gases to analyzers « 15 s).

The delayed response of the electrochemical cell is most likely due to diffusional

transport delay of CO into the cell itself The CO2 and water volume fractions are nearly

identical which would be expected from nearly complete combustion of propene. The

CO/CCh volume ratio is about 0.025, which is reasonable for the over-ventilated

diffusion flame in the smoke generator. The temperature rate of rise averaged 0.13 °C/s

from 40 s to 250 s (Figure 4). The thermistor output lagged the fast-response

thermocouple. This is a constant occurrence due to the relatively sluggish response

characteristics of the thermistor compared to the fast-response thermocouple.

Test fire 2, the low-smoke flaming fire, was designed to emulate conditions from a cargo

compartment fire that begins by ignition of alcohol-soaked baggage where the initial

effluent is very low smoke until the alcohol bums out and the fabric starts to bums. The

result is a temperature rise and gas signature that precedes the measurable smoke at the

test section. The fuel consisted of six 7 cm diameter cotton/polyester (50/50 blend by

weight) fabric circles stacked and held together with 3 staples, and wetted with 5 mL of

ethanol. The wetted fabric was placed over a 10 cm glass dish and supported by two

crisscrossed nichrome wires, then placed at the bottom of the vertical riser section of the

FE/DE prior to ignition. The fan speed was set to 10 Hz (mean flow velocity of 0.25

m/s), then the alcohol was ignited at time = 0. At first, only the alcohol burned as it was

wicked through the fabric or burned from excess in the dish below. During this phase,

no appreciable smoke was produced. As the alcohol burned out, the fabric circles

caught fire and bumed with considerable smoke production, then burned out. Figures 5-

7 show representative smoke, combustion gas production and temperature rise from this

scenario. In Figure 5, the ionization signal started to rise at 20 s while the extinction and

photoelectric signal started to rise at 60 s. The initial combustion produced an aerosol

that was not scattering much light, but was being sensed by the ionization detector. As

the fabric circles caught fire, the extinction coefficient and photoelectric signals began to

rise. The extinction coefficient decayed much faster than the photoelectric or ionization

signal due to hold-up of the heated smoke in the detector sensing volume. Figure 6

shows CO, CO2 ,and water volume fraction with background values subtracted. At 25 s

water and CO2 started to rise; initially, water was higher than the CO2 which is expected

694
i I—1—I—I—1—I—1—1—1—I—I—1—I—1—I—I—I—1—1—1—1 so _

Figure 6. Gas signatures for test fire 2, alcohol-soaked fabric fire.

695
as the ethanol was burning. The CO concentration began to rise as the fabric started to bum and remained elevated even as the extinction decreased to near zero. The output of

the electrochemical cell deviated from the CO analyzer and was most likely due to

temperature and humidity effects of the cell output. Air temperature rose at a rate of

nearly 1 °C/s, peaking at 27 °C above ambient (-20 °C) 30 s after the initial rise (Figure

7).

Test fire 3, the smoldering cotton fire source, is a variant of a standard detector

sensitivity test fire EN 54 part 9 test fire 3. A staged-wick-ignition test fixture developed

for use in the FE/DE holds 32 cotton wicks 6 mm in diameter (typically 1 5 cm long) in a

vertical orientation around a circular frame. Unique to the test fixture developed here is

that each wick pair is spaced so that it is 5 cm away from adjacent pairs and the bottom

of each wick passes through a wound nichrome ignition wire. Opposing pairs are wired

in series producing a maximum of 8 independently ignited sets of 4 wicks. For the tests

here, the objective was to produce a steadily increasing smoke concentration at the test

section for 120 s. This was accomplished by igniting 8 sets of 4 wicks with a 12 s delay

between each set. The fan was set to 10 Hz, yielding a mean velocity of 0.25 m/s at the

test section, and the ignition sequence was started at time = 0. Figures 8 and 9 show

representative smoke and combustion gas production for this source. In Figure 8, the

extinction started to rise at 50 s followed by the ionization signal at 60 s and the

photoelectric signal at 70 s. The photoelectric signal reached its maximum output at 1 15

s and both of the aircraft detectors alarmed between 130 s and 135 s. The extinction

coefficient peaked at 140 s. In Figure 9, the gas concentrations began to rise between 60

s and 70 s, and peaked at about 170 s. The CO/CO2 volume ratio is about 1 :2.6, similar

to the 1 :3 ratio obtained in room tests with the same cotton smolder source [7]. The

C02/water volume ratio is approximately 1:3. Air temperature rise peaked at 3.5 °C

which is not uncharacteristic for low-energy-output smolder plumes

Test fire 4, pyrolyzing mixed plastics, uses the same smolder source being developed at

the FAA Technical Center. It consisted of a 10 cm by 10 cm by 0.5 cm plaque of

compressed plastic pellets, 2 mm to 5 mm in size with a nichrome wire embedded in it. The pellets were a mix of various plastics. The plaque was placed at the bottom of the

696
Figure 9. Gas signatures for test fire 3, smoldering cotton wicks.

vertical riser. The fan speed was set to 10 Hz yielding a mean flow velocity of 0.25 m/s

at the test section. At time = 0, 40 volts AC power was applied to the nichrome wire

which was sufficient to start the plastics to pyrolyze and emit smoke and gases. The

power was removed at 80 s and the pyrolyzing eventually stopped. Figures 10 and 1

1

show representative smoke and combustion gas production for this source. The

photoelectric, ionization signal, and extinction coefficient all began to rise at 30 s. The

photoelectric signal reached its maximum output at 50 s while the extinction coefficient

and ionization signal continued to rise peaking at 90 s and 105 s respectively. The spottype aircraft detector alarmed at 66 s, while the draw-through detector alarmed at 88 s. The combustion gas production levels were low compared to the other smolder sources.

697
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The CO volume fraction peaked at 6 x 10"^, while the CO2 volume fraction peaked at

5.0x10"^ % above the background level. No rise in the water concentration was

observed. Air temperature rise was about 1 above ambient temperature.

The dust aerosol was generated by injecting ISO 12103-1 fine grade Arizona test dust at

a constant rate from a powder screw feeder. With the fan speed set at 10 Hz, the dust

was fed into the duct at time = 0. Figure 12 shows the extinction coefficient and

detector output for this source. The extinction coefficient started to rise at about 12 s

followed by the photoelectric and ionization signals 10 s later. The extinction was above

2 m ' at 30 s and stayed between 2 m ' and 2.5 m'' until the dust flow was stopped at

698
100s and the remaining dust blown out of the duct. The photoelectric signal reached its maximum output at about 25 s, while the ionization continued to rise until the dust flow

was stopped. The spot-type aircraft detector alarmed at 59 s, while the draw-through

detector alarmed at 100 s.

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Figure 12. Particulate level for nuisance source 1, dust exposure.

300

The oil mist aerosol was generated by nebulizing cooking oil from a bank of 10 medical

inhalent nebulizers located at the bottom of the vertical riser of the FE/DE. This aerosol

is a surrogate for hydraulic oil or non-volatile mists introduced in a cargo compartment

intentionally (cargo treatment) or unintentionally. The fan speed was set at 7 Hz

yielding at mean flow velocity of 0. 1 5 m/s at the test section, and the nebulizers were

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Figure 13. Particulate level for nuisance source 2, oil mist exposure.

699
started at time = 0. Figure 1 3 shows the extinction coefficient and detector outputs for

this source. The extinction coefficient started to rise at 20 s and reached nearly steady

values between 0.6 m'^ and 0.7 m ' within 10 s. The photoelectric and ionization signals

began to rise at 40 s with the photoelectric signal saturating at about 60 s while the

ionization signal continued to rise until reaching a steady value at approximately 1 20 s. The spot-type aircraft detector alarmed at 120 s, while the draw-through detector

alarmed at 173 s.

4. Conclusions

Six plausible fire and nuisance alarm scenarios for aircraft cargo compartments were

reproduced in the FE/DE. The data gathered contains particulate, combustion gas and

temperature rise values that may be used to identify sensor combinations to discriminate

between fire and non-fire conditions. Background levels of particulate, combustion gas

and temperature fluctuations need to be included in any analysis leading to sensor

selection and alarm algorithm design.

5. References

[1] D., "Aircraft Cargo Compartment Smoke Detector Alarm Incidents on U.S.-

Registered Aircraft, 1974-1999," DOT/FAA/AR-TNOO/29, U.S. Dept. of Trans., Federal

Aviation Administration, 2000.

[2] Grosshandler, W., (editor), "Nuisance Alarms in Aircraft Cargo Areas and Critical

Telecommunications Systems: Proceedings of the 3rd NIST Fire Detector Workshop,"

December 4-5, 1997, NISTIR 6146, National Institute of Standards and Technology,

Gaithersburg, MD, March 1998.

[3] "Cargo Compartment Fire Detection Instruments," Aerospace Standard, AS 8036,

Society of Automotive Engineers, Inc., Warrendale, PA, 1985.

[4] Cleary, T., and Grosshandler, W., "Survey of Fire Detection Technologies and

System Evaluation/Certification Methodologies and their Suitability for Aircraft Cargo

Compartments," NISTIR 6356, National Institute of Standards and Technology,

Gaithersburg, MD, July 1999.

[5] Cleary, T., and Donnelly, M., "Improved Fire Detection in Aircraft Cargo

Compartments," NISTIR in preparation. National Institute of Standards and Technology,

Gaithersburg, MD, 2001.

[6] Cleary, T., Grosshandler, W., and Chemovsky, A., "Smoke Detector Response to

Nuisance Aerosols," Proceeding of the 1 1th International Conference on Automatic Fire

Detection "AUBE '99," March 16-18, 1999, Gerhard Mercator University, Duisburg,

Germany, Luck, H., Ed., pp 32-41, 1999.

[7] Cleary, T., Grosshandler, W., Nyden, M., and Rinkinen, W., "Signatures of

Smoldering/Pyrolyzing Fires for Multi-element Detector Evaluations," Proc. of the 7th

Inter. Conf on Fire Science and Eng., Interflam '96, Cambridge, England, March 1996.

700

NIST Special Publication 965

February 2001

U.S. DEPARTMENT OF COMMERCE

Donald L. Evans, Secretary

National Institute of Standards and Technology

Karen H. Brown, Acting Director

National Institute of Standards and Technology Special Publication 965

Natl. Inst. Stand. Technol. Spec. Publ. 965, 707ages (February 2001)

CODEN: NSPUE2

U.S. GOVERNMENT PRINTING OFFICE

WASHINGTON: 2001

Certain commercial entities, equipment, or materials may be identified in

this document in order to describe an experimental procedure or concept

adequately. Such identification is not intended to imply recommendation

or endorsement by the National Institute of Standards and Technology,

nor is it intended to imply that the entities, materials, or equipment are necessarily the best available for the purpose.