Robert P. Garner

Federal Aviation Administration

Civil Aerospace Medical Institute

Oklahoma City, Oklahoma 73125

Kwai L. Wong

Shawn C. Ericson

UT Joint Institute for Computational Sciences

Oak Ridge National Laboratory

Oak Ridge, Tennessee 37831

A.J. Baker

Joseph A. Orzechowski

University of Tennessee

Mechanical, Aerospace, and Engineering Sciences Department

Knoxville, Tennessee 37996

April 2004

Final Report

This document is available to the public

through the National Technical Information

Service, Springfield, Virginia 22161.

Office of Aerospace Medicine

Washington, DC 20591

CFD Validation for Contaminant 

Transport in Aircraft Cabin 

Ventilation Flow Fields

DOT/FAA/AM-04/7
NOTICE

This document is disseminated under the sponsorship of

the U.S. Department of Transportation in the interest of

information exchange. The United States Government

assumes no liability for the contents thereof.
i

Technical Report Documentation Page 

1. Report No. 2. Government Accession No. 3. Recipient's Catalog No. 

DOT/FAA/AM-04/7 

4. Title and Subtitle 5. Report Date 

 

April 2004 

 

CFD Validation for Contaminant Transport in Aircraft Cabin Ventilation 

Flow Fields 6. Performing Organization Code 

 7. Author(s) 8. Performing Organization Report No. Garner RP1

, Wong KL2, Ericson SC2, Baker AJ3, Orzechowski JA3

 

9. Performing Organization Name and Address 10. Work Unit No. (TRAIS) 

 

 

 

FAA Civil Aerospace Medical Institute 

P.O. Box 25082 

Oklahoma City, OK 73125 11. Contract or Grant No. 

 12. Sponsoring Agency name and Address 

 

 

13. Type of Report and Period Covered 

 14. Sponsoring Agency Code 

1

Office of Aerospace Medicine, Federal Aviation Administration 

800 Independence Ave, S.W., Washington, DC 20591 

2

UT Joint Institute for Computational Sciences 

Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831 

3

University of Tennessee Computational Fluid Dynamics Laboratory 

Mechanical, Aerospace, and Engineering Sciences Department 

Knoxville, Tennessee 37996

 

15. Supplemental Notes 

16. Abstract 

Civil transport aircraft have clearly been demonstrated as a preferred target of terrorist organizations through the 

years. The threat of the release of a noxious chemical or biological agent into the passenger cabin is real. 

Protection of occupants is critical for maintaining public confidence in air travel. Therefore, the Federal Aviation 

Administration (FAA) Civil Aerospace Medical Institute (CAMI) is directing a project for quantitative evaluation 

of the distribution of contaminants released during mid-flight in commercial airliner passenger cabins. The effort 

uses the CAMI 747 Aircraft Environmental Research Facility (AERF) to collect airflow data to be used to validate 

computational fluid dynamics (CFD) algorithms that can predict the potential distribution of a variety of 

particles in the aircraft. The cabin velocity vector field is measured at a number of points within the cabin using 

3-dimensional sonic and conventional hot-wire anemometers. These data serve as a comparison basis to 

simulation via CFD that predicts the 3-dimensional ventilation flow field. This paper presents the first results of 

validation of CFD prediction of the cabin flow field in a segment of a 747 AERF. Agreement between the data 

and the simulation is good, hence represents the first steps towards development of a CFD system to 

quantitatively study contaminant distribution and assist in testing optimal responses to an attack on various 

airframes. The release of a chemical weapon has been the focus of work to date, but CFD is ultimately applicable 

to predicting the distribution of other entities through the aircraft environmental control system (ECS) such as 

biological, nuclear, or other toxic agents. Concurrently, the approach is applicable to generic cabin air quality 

issues where an understanding of flow fields is of critical importance in terms of the role that fresh air and 

contaminant levels play in passenger comfort, health, and safety. 

 

17. Key Words 18. Distribution Statement 

 

 

Cabin Airflow, Computational Fluid Dynamic, Model 

Validation, Aerosol Distribution, Coanda Effect 

Document is available to the public through the 

National Technical Information Service; 

Springfield, VA 22161 19. Security Classif. (of this report) 20. Security Classif. (of this page) 21. No. of Pages 22. Price 

Unclassified Unclassified 9 

Form DOT F 1700.7 (8-72) Reproduction of completed page authorized

1

CFD VALIDATION FOR CONTAMINANT TRANSPORT IN

AIRCRAFT CABIN VENTILATION FLOW FIELDS

INTRODUCTION

The risk of release of a noxious material into a commercial aircraft cabin during flight is significant due to 

the worldwide terrorist threat targeting civilians. Reaction management of this potential disaster must be based 

on quantitative analyses of potential scenarios affecting 

the cabin environment. As opposed to in-flight testing, 

which is prohibitively expensive and logistically awkward, 

a mathematical modeling with validation component has 

been proposed. 

Development of an accurate simulation of the flow field 

in a cabin space utilizing computational fluid dynamics 

(CFD) is a significant challenge. First, the numerical 

analysis requires tremendous computing power to solve 

the sets of equations in a reasonable time frame. Second, 

the complexity of the environment must be handled. Any 

obstructions have to be taken into consideration since they 

can influence the flow field and add to the computational 

task. For these reasons initial steps towards the development of a transport aircraft cabin model approach were 

made using the 747 Aircraft Environmental Research 

Facility (AERF, Figure 1) at the FAA Civil Aerospace 

Medical Institute.

METHODS

The CFD approach is potentially viable for predicting 

the 3-dimensional, very low (2-5) air-change per hour 

(ACH) flow fields associated with aircraft environmental 

control system (ECS) operation in commercial transports. 

A CFD model has been identified describing the unsteady, 

time-accurate, buoyant ventilation flow field in an aircraft 

cabin at cruise conditions, capable of execution using 

an augmented laminar Taylor-stabilized finite turbulent 

kinetic energy (TKE) model. CFD simulations were conducted using a finite element theory implementation of 

this model, via the University of Tennessee CFD Lab 

PICMSS (Parallel Interoperable Computational Mechanics Simulation System) code. Comparison simulations 

have used an available commercial code (Fluent).

The comparative validation data were collected utilizing 

principally 3D sonic anemometers (Campbell Scientific, 

CSAT3) in an isolated cabin segment of the 747-AERF, 

which was devoid of seats. Figure 2 provides an overview of the data collection approach. The selected sonic 

anemometers simultaneously measured the 3 Cartesian 

components of the velocity field and were sensitive to 

velocity magnitude as low as 0.1 cm/sec. The experimental 

protocol was established to guarantee freedom from drift 

over the lengthy data collection period. Additional hot 

wire data were taken in the immediate vicinity of the 

supply jet entry into the cabin. Flow uni-directionality 

and speed in the immediate vicinity of the inlet made 

this experimental methodology viable.

Data were collected in parallel sections that ran the 

length of the segment utilized. At each transverse plane 

station, the data acquisition period was 7 minutes with 

recording rate of 30 samples/second. Within the 3-5 minute window, the sampled data were resolved into mean 

and fluctuating components on 1-second intervals. The 

mean data were subsequently averaged over four 30-second 

periods to generate the essential flow field picture.

RESULTS

Figure 3 shows the totally averaged transverse plane 

velocity vector field superposed on an outline generated 

from a CFD mesh. Several z-station data sets confirm 

existence of the Coanda effect, with the supply jet remaining attached to the underside of the luggage bin, 

hence clearly penetrating the cabin to the center luggage 

carrier. The resulting circulating flow field encompasses 

the entire cabin half span, with a smaller counter-rotating flow field generated in the upper region between 

luggage carriers. The character of this flow pattern does 

not exhibit significant z-dependence, as expected, based 

Figure 1. Boeing 747-100 modified for use as 

Aircraft Environmental Research Facility (ERF). The 

facility can be modified to meet a variety of research 

and educational requirements.
2 

on the geometric simplicity of the empty cabin and its 

associated symmetries. The following comments summarize the acquired cabin airflow data:

1. The velocity field is definitely time varying and 

unsteady, with a periodic unsteadiness of the order 

3-4 minutes.

2. The mean velocity extrema within the Coanda jet 

region are ~1 m/s, while an average speed in the cabin 

proper is less than ~0.2 m/s. 

3. The rms data level holds steady at ~0.006–0.01 m/s, 

hence the average turbulence kinetic energy k is ~ 

0.0006 m2/s2.

4. For average mean flow magnitude of ~0.15 m/s, the 

average turbulence level is of the order ~6 %.

CFD Predictions. CFD predictions for the cabin interior 

flow field were conducted using 2 different code-closure 

model systems. The computations were 3 dimensional; 

however, z-symmetry plane boundary conditions produced flow fields lacking z-dependence. Hence, CFD 

solutions on all z-planes are identical, the transverse plane 

discretization of which is shown in Figure 4 in a global 

view including magnifications to illustrate supply and 

exhaust ducts meshing details. These local geometries 

were directly measured for the CAMI 747 AERF. The 

highly non-uniform transverse mesh contained ~14,000 

node points per plane. The mesh was identical in every 

z-plane.

As stated, design of the 747 AERF ventilation supply 

relies on the Coanda effect to maintain supply jet attachment to the overhead luggage carrier until the flow reaches 

the first break in the carrier geometry. The experimental 

data clearly show this, and the supply jet penetrates to 

the center of the cabin until encountering the face of the 

central luggage carrier. 

The CFD prediction generated using the time-accurate 

CFD formulations stabilized with a modified Taylor finite 

element approximation (Williams and Baker, 1996), as 

implemented in PICMSS code (Wong et al., 2002), captures the flow phenomena for Re/L = 1000. The flow field 

does not come to a steady state after 2000s of simulation 

but oscillates about the essential velocity vector field as 

shown in Figure 5a. Figure 5b contains a magnification 

of the velocity field in the supply duct region exhausting 

into the cabin, and flow field attachment to the luggage 

carrier bottom is essentially steady. 

Predictions for comparison were made using the Fluent commercial code - finite volume implementation of 

a non-time accurate CFD theory. The Fluent simulation 

used the standard TKE closure model. The solution atFigure 2. Overview of the movement of the 3D 

anemometers utilized for data collection. Coordinate 

reference points in the segment utilized was X=0 in 

the center of the cabin, Y=0 at the floor of the cabin, 

and Z=0 at the bulkhead that had been installed in 

the rear of the segment.
3

Figure 3. The averaged transverse plane velocity vector field.

Figure 4. Cross section of the cabin discretization with close up views of the supply and exhaust ducts.
4 

Figure 5. a) PICMSS FE simulation at a Re/L = 1000. b) Velocity vectors in the area of the air inlet supply 

region illustrating Coanda effect flow attachment to the underside of the luggage bin.
5

tained steady state using the third-order stabilization 

scheme. The supply jet penetrates between the luggage 

carriers, Figure 6, and the recirculation region spanned 

the entire cabin region; hence this flow field resembles 

the averaged experimental data.

DISCUSSION

It would seem prudent that protective strategies against 

the threat of a chemical or biological weapons attack on a 

commercial transport aircraft should be developed. Due 

to the complexity of the cabin environment (airframe, 

flight altitude, etc.), judgments on any response to a given 

situation are subjective at best. Time accurate simulations will allow a variety of emergency circumstances 

to be evaluated in a quantitative manner. Insight into 

the best combination of actions to take in face of a particular challenge can be built. This approach also allows 

the response to any threat that may be anticipated to be 

integrated with current emergency protective equipment 

and procedures as closely as possible. This would assist 

in making protective systems aboard aircraft as efficient 

as possible in terms of both cost and function.

Figure 6. Steady flow field solution from the commercially available code Fluent.

Accurate simulations are also applicable to day-to-day 

operations. Cabin air quality is a very important issue for 

both the passengers and crewmembers. Advanced models 

have the potential to be used for optimizing aircraft environmental control system (ECS) parameters for both 

current and future aircraft. They could also provide the 

ability to study the specifics of a contamination accident 

after it has occurred. Both quantitative details and remedial 

steps could be examined.

CONCLUSIONS

A CFD model has been developed to predict the 3-

dimensional commercial aircraft cabin flow field characteristic of a Boeing 747. Validation data, as obtained in 

this configuration, confirm the time-accurate, optimally 

stabilized results predicted via the PICMSS implementation. The CFD algorithm designed for this application 

produced an unsteady prediction in agreement with data. 

Results obtained using a commercial code also produced 

an average flow field comparable to the experimental data 

and the PICMSS implementation.
6

REFERENCES

Williams, P.T., and Baker, A.J. (1996). Incompressible 

computational fluid dynamics and the continuity constraint method for the 3-D Navier-Stokes 

equations, J. Numerical Heat Transfer, Part B, 

29,137-273.

Wong, K.L., and Baker, A.J. (2002). A modular collaborative parallel CFD workbench, Journal of 

Supercomputing, 22, 45-53.