Integrating dispersion modelling and experimental approach to optimize indoor/outdoor detection of airborne agents : mimicking the exposure of an infrastructure.
Date: 2022-09-02
Institutions: Joint Research Centre
Subjects: building, chemical product, critical infrastructure security, environmental monitoring, report, simulation

Integrating  dispersion  modelling  and 
to  optimize 
experimental  approach 
indoor/outdoor  detection  of  airborne 
agents 

Mimicking the exposure of an infrastructure 

ERNCIP  Detection  of  Indoor 
Airborne 
and 
Biological  Agents  Thematic 
Group 

Chemical 

Karakitsios, S. 
Maggos, T. 
Busker, R. 
Armand, P.  
Bartzis, J.G. 

Cardarilli, M. (Ed.) 

2022 

The research leading to these results has received funding from the European Union as part of 
the European Reference Network for Critical Infrastructure Protection (ERNCIP) project 

EUR 31172 EN 

This  publication  is  a  technical  report  by  the  Joint  Research  Centre  (JRC),  the  European  Commission’s  science 
and  knowledge  service.  It  aims  to  provide  evidence-based  scientific  support  to  the  European  policymaking 
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Contact information  
Name: Rainer JUNGWIRTH 
Address: Via E. Fermi 2749, 21027, Ispra (VA), Italy 
Email: rainer.jungwirth@ec.europa.eu 
Tel.: +39 0332785648 

EU Science Hub 
https://ec.europa.eu/jrc 

JRC129051 

EUR 31172 EN 

PDF 

ISBN 978-92-76-55599-5 

ISSN 1831-9424 

doi:10.2760/48622 

Luxembourg: Publications Office of the European Union, 2022 

© European Union, 2022 

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How  to  cite  this  report:  Karakitsios,  S.,  Maggos,  T.,  Busker,  R.,  Armand,  P.  and  Bartzis,  J.G., 
Integrating  dispersion  modelling  and  experimental  approach  to  optimize  indoor/outdoor  detection  of 
airborne  agents,  Cardarilli,  M.,  ed.,  EUR  31172  EN,  Publications  Office  of  the  European  Union, 
Luxembourg,  2022,  ISBN 978-92-76-55599-5, doi:10.2760/48622, JRC129051.

Contents 

Acknowledgements .......................................................................................................... 1 

Abstract ........................................................................................................................... 2 

1.  Introduction ................................................................................................................. 3 

1.1. Context ................................................................................................................. 3 

1.2. Purpose of the Piloting Activity ................................................................................. 4 

1.3. Content and structure of this document .................................................................... 4 

2.  Experimental Activity ................................................................................................... 5 

2.1. The Building Mock-Up ............................................................................................. 5 

2.1.1 Layout Design ............................................................................................... 5 

2.1.2 Construction .................................................................................................. 5 

2.1.3 Shipping ....................................................................................................... 6 

2.2. Detection Experiments ............................................................................................ 7 

2.2.1 Experimental set-up ....................................................................................... 8 

2.2.2 Scenario Design ........................................................................................... 12 

2.2.3 Analysis of results ........................................................................................ 18 

2.3. Results of Detection Experiments ........................................................................... 32 

2.3.1 Conclusions of experimental trials .................................................................. 33 

3.  Modelling Activity ...................................................................................................... 34 

3.1. Computation Approach (Code_Saturne) .................................................................. 34 

3.1.1 The Pre-Experimental Phase .......................................................................... 34 

3.1.1.1 Implementation of the aeraulic simulations ............................................. 36 
3.1.1.2 Dispersion simulation of chemical releases .............................................. 43 

3.1.2 The Post-Experimental Phase ........................................................................ 51 

3.1.2.1 Implementation of aeraulic simulations .................................................. 52 
3.1.2.2 Simulation of the experimental releases ................................................. 61 

3.1.3 Conclusions of Code_Saturne simulations........................................................ 77 

3.2. Computation Approach (ADREA-HF) ....................................................................... 79 

3.2.1 The Pre-Experimental Phase .......................................................................... 81 

3.2.1.1 The external source ............................................................................. 81 
3.2.1.2 The internal source .............................................................................. 85 

3.2.2 The Post-Experimental Phase ........................................................................ 87 

3.2.2.1 The external source ............................................................................. 88 
3.2.2.2 The internal source .............................................................................. 91 

3.2.3 Conclusions of ADREA-HF simulations ............................................................. 93 

3.2.4 Conclusions of CFD simulations ...................................................................... 94 

4.  Conclusions ................................................................................................................ 98 

References ................................................................................................................... 100 

List of abbreviations ..................................................................................................... 101 

List of figures ............................................................................................................... 102 

List of tables................................................................................................................. 105 

i 

Acknowledgements 

This  work  has  received  funding  from  the  European  Union’s  Horizon  2020  research  and 
innovation  programme  under  grant  agreement  No  775989,  as  part  of  the  European 
Reference Network for Critical Infrastructure Protection project. 

Authorship credit 

T. MAGGOS - Experimental activity, The building mock-up; general review;

R. BUSKER, J. VAN DER MEER, A. VAN WUIJCKHUIJSE - Experimental Activity, Detection
experiments;

P. ARMAND - Modelling activity, Computational approach with Code_Saturne;

J. BARTZIS - Modelling activity, Computational approach with ADREA-HF;

T. TJÄRNHAGE  -  Experimental  Activity,  Detection  experiments  (provision  of  Photo
Ionization Detector);

S. KARAKITSIOS  –  Group  coordination,  integration  of  chapters,  contribution  to  drafting,
general review.

Authors 

Spyros KARAKITSIOS, Aristotle University of Thessaloniki, Greece 

Thomas MAGGOS, National Center for Scientific Research Demokritos (NCSRD), Greece 

Ruud BUSKER, Netherlands Organisation for Applied Scientific Research (TNO), The 
Netherland 

Patrick ARMAND, Atomic and alternative Energies Commission (CEA), France 

John BARTZIS, University of Western Macedonia, Greece 

Contributors 

Jeroen VAN DER MEER, Netherlands Organisation for Applied Scientific Research (TNO), 
The Netherland 

Arjan VAN WUIJCKHUIJSE, Netherlands Organisation for Applied Scientific Research 
(TNO), The Netherland 

Tjorbörn TJÄRNHAGE, Swedish Defence Research Agency (FOI), Sweden 

Editor 

Monica CARDARILLI, European Commission, Joint Research Centre, Italy 

1 

Abstract 

The  overarching  objective  of  the  ERNCIP  “Detection  of  Indoor  Airborne  Chemical  & 
Biological  Agents”  Thematic  Group  (TG)  is  to  strengthen  resilience  of  critical 
infrastructure against airborne CB threats by improvement of detection capabilities. The 
subsidiary aim of the research described in this technical report is to develop knowledge 
on the behaviour of chemical releases, given some likely attack scenarios, and to propose 
ways  to  optimally  detect  those  inside  buildings.  Making  use  of  a  combined  approach 
consisting  of  experiments  and  dispersion  modelling  is  considered  a  strong  endeavour 
towards this aim. 

To provide input for dispersion model development a series of medium-scale experiments 
with  representative  simulant  chemical  has  been  designed.  The  study  made  use  of 
controlled release of simulant chemicals in an ambient breeze tunnel (AMB). In the AMB 
a mock-up of a simple building was installed (scale 1:5). The experiments involved three 
mimicked  attack  scenarios:  i)  focal  release  from  within  the  mimicked  building  and 
external release of agent approaching the mock-up from the outside from a ii) short and 
iii) a  larger  distance.  Tri-ethylphosphate  (TEP)  was  selected  as  simulant  (for  nerve
gases). The detector used was an ion mobility spectrometry (IMS) instrument.

Release of TEP was successful in all three exposure scenarios, it resulted in reproducible 
detection signals from 6 IMS instrument placed in 6 rooms of the building mock-up. The 
speed and magnitude in which TEP levels were recorded in the respective rooms showed 
a  clear  and  reproducible  picture  in  the  sense  that  some  of  the  rooms  appeared  to  be 
better  reachable  for  the  externally  released  TEP  than  others.  Subsequent  2  different 
dispersion  modelling  approaches  were  undertaken  to  understand  and  describe  those 
differences: 

I. the  Code_Saturne1  has  been  used  to  perform  the  simulations  both  before  and  after
the  experiments.  Pre-trials  computations  illustrate  the  differences  in  the  dispersion
pattern  when  an  internal  versus  an  external  released  is  considered.  Post-trials
computations  are  fully  consistent  with  the  pre-trials  computations  and  the  physical
phenomena enlightened in the different trials;

II. The  ADREA-HF  code2  has  been  used  to  perform  the  simulations  utilizing  the  CFD
RANS  approximation  and  the  standard  k-ε  turbulence  model.  The  results  show  that
rooms concentrations time profiles can be quite different. The room specific location
with respect to the source and the associated ambient air pattern plays an important
role to this differentiation. The poorly ventilated rooms are associated with relatively
long residence and arrival times. The poorly ventilated rooms tend to lower the peak
concentrations.

1 https://www.code-saturne.org/cms/web/
2 http://www2.ipta.demokritos.gr/pages/ADREA-HF.html

2 

1. Introduction

1.1.  Context 

In case critical infrastructure is subject to a deliberate release of toxic chemical agents, it 
is of utmost importance to be able to provide early warning messages signalling presence 
of  hazardous  chemicals  serving  as  triggers  to  activate  protective  countermeasures 
(Karakitsios  S.,  2016).  In  reverse,  in  case  of  a  hoax  attack,  it  is  needed  to  be  able  to 
disprove  suspected  danger  as  soon  and  as  firm  as  possible.  In  both  cases,  to  generate 
corresponding  early  warning  signals,  detection  systems  should  be  in  place.  These  must 
respond fast enough to allow life-saving protective measures such as evacuation, seeking 
shelter and applying personal protective equipment.  
Moreover,  these  detection  systems  should  also  be  highly  reliable,  meaning  having  low 
false alarm rates. More specifically this means having a low false negative alarm rate in 
order to avoid missing actual attacks as well as having a low false positive alarm rate to 
classify an event as a hoax and then to avoid unnecessary discontinuation of the critical 
infrastructure  functionality.  The  forthcoming  sensor  requirements  thus  need  to  include 
high  response  rates,  high  sensitivity  and  coverage  of  a  broad  spectrum  of  chemical 
agents (Busker R., 2016).  

Selecting  adequate  detection  system  is  challenging.  First  of  all,  responsible  authorities 
need to have insight into the threat the critical infrastructure may be subject to. Next, is 
needed to know which sensor(s) respond to the critical chemicals. In addition, one must 
be  able  to  place  sensors  throughout  the  critical  infrastructure  in  an  effective  and 
affordable  manner.  This  requires  the  ability  to  identify  those  spots  in  critical 
infrastructures that are best equipped to deploy sensors at. These must be the locations 
that, depending on the way an agent is released, are most likely to become hot-spots in 
the  sense  of  agent  concentration.  This  now,  obviously,  requires  insight  into  agent 
distribution. This in turn is highly dependent on the way hazardous chemicals have been 
/ might be released, on the physical properties of the agents, on the functionality of the 
critical infrastructure and the geometry and air circulation of the building itself. 
On top of this complexity, the challenge is that little is known about indoor behaviour of a 
released toxic agent, let alone how to optimally detect it. In complex buildings, such as 
transportation  hubs,  concentration  levels  of  released  chemical  vary  dramatically 
depending on the agent properties, on local ventilation, on design of the building and so 
on.  Therefore, there is  an urgent need to gain more insight into behaviour of dispersed 
chemical inside buildings and into the interaction of detection equipment (Karakitsios S. 
et  al.,  2020).  Such  insight  would  be  used  to  develop  optimal  detection  deployment 
strategies.  

Ultimately  there  is  a  need  for  advanced  modelling  and  simulation  tools  that  are  able  to 
predict  chemical  agent  distribution  throughout  a  typical  building,  thereby  providing 
guidance to sensor deployment and detailed placement. This requires knowledge of agent 
characteristics,  of  agent  distribution  mechanisms,  of  response  properties  of  qualifying 
sensors, of feasible attack scenarios, of building geometry and of the resulting likelihood 
that  early  warning  signals  are  timely  and  justly  generated.  Although  a  substantial 
amount  of  knowledge  of  the  distinct  elements  of  this  complex  challenge  is  available, 
integration of these elements into applicable insight is lacking (Karakitsios S., 2019). The 
European  Commission  therefore  launched  a  combined  piloting  experimental  and 
computational  study,  as  part  of  the  European  Reference  Network 
for  Critical 
Infrastructure  Protection  program  (ERNCIP  -  more  particularly  the  Detection  of  Indoor 
Airborne  Chemical  and  Biological  Agents  Thematic  Group)  to  address  mentioned 
shortcomings. 

3 

1.2.  Purpose of the Piloting Activity 

The  piloting  research  activity  aimed  to  apply  knowledge  on  chemical  releases  and  to 
implement  ways  to  detect  contaminant  dispersions  inside  buildings,  including  the 
interaction  of  indoor/outdoor  environments  through  an  agent-based  dispersion  analysis. 
This activity included mid-scale experiments with controlled release of simulant chemicals 
to  analyse  the  impact  on  critical  infrastructure  as  well  as  secondary  effects  to 
surrounding open areas due to the release of the chemical agent.  

In  particular,  the  purpose  of  the  piloting  activity  was  to  develop  knowledge  on  the 
behaviour of chemical releases, given some likely attack scenarios, and to propose ways 
to optimally detect those inside buildings. Making use of a combined approach consisting 
of  experiments  and  dispersion  modelling  is  considered  a  strong  endeavour  towards  this 
aim. In that respect, this study was set up to let the modelling support the experimental 
design and subsequently have the  experimental data feed into the dispersion models to 
validate and refine those. 

The  pilot  activity  was  run  from  February  to  July  2021  under  pandemic  restrictions  and 
mobility  limitations.  All  organizational  issues  and  technical  details  were  planned, 
coordinated and managed among the TG members via remote meetings only. This study 
is  the  result  of  an  outstanding  effort,  unique  and  continuous  support  given  by  the  TG 
members committed to it. 

The  combined  and  scaled  approach  is  considered  to  be  a  good  compromise  between 
feasibility  and  realism  which  requires  mimicking  a  building  through  a  mock-up 
(Karakitsios S. et al. (2020); Karakitsios, S. e. (2016); COST Action ES1006 (2012)). 

1.3.  Content and structure of this document 

This  document  describes  the  experimental  and  modelling  activity  carried  out  by  the  TG 
during  the  pilot  activity.  In  particular,  it  is  structured  according  to  the  various  steps 
carried out, from mock-up design to comparison of modelling studies with experimental 
results, which in turn correspond to the timeline of the piloting activity. 

Chapter 2 — Experimental activity 

Chapter 3 — Modelling activity 

Chapter 4 — Conclusions 

Each  chapter  can  be  considered  complementary  in  the  fields  of  application  and  for 
scenario release conditions. Chapters narrative has a modular structure which can adapt 
to different operational and technical needs as well as task objectives. 

This  document  moves  the  TG  work  towards  a  broader  investigation  on  how  the 
knowledge  gained  during  the  previous  years  can  be  applied  in  practice.  It  also 
contributes  to  strengthening  the  resilience  of  critical  infrastructures  against  airborne 
threats  by  improving  detection  capabilities  of  chemical-biological  release,  feeding  into 
recommendations for sensors placement and policy-making. 

4 

2. Experimental Activity

2.1.  The Building Mock-Up 

To  enable  experiments  that  provide  input  for  dispersion  model  development,  within 
constraints  of  time,  budget  and  experimental  safety,  it  was  proposed  to  perform  mid-
scale  experiments  with  representative  simulant  chemicals.  The  proposed  approach  was 
making use of controlled release of simulant chemicals in a mid-sized facility such as an 
ambient  breeze  tunnel  (AMB).  Within  the  project  constraints  this  was  considered  the 
optimal  compromise  between  feasibility  and  realism.  Release  of  smartly  selected 
chemicals  in  the  ABM  is  feasible  and  permitted.  This  approach  allows,  for  example, 
experimental  variations:  choice  of  agents,  controlled  wind  speed  and  direction,  distance 
and  angle  of  agent  release,  choice  and  tuning  of  detectors  and  design  of  the  target. 
Estimated  environmental  damage  and  experimental  risks  are  minimal  and  known  in 
advance.  Obviously,  this  approach  requires  mimicking  a  building.  For  that  purpose,  a 
scaled  (1:5)  mock-up  of  a  simple  building  was  delivered  in  this  project  and  the  design 
and construction phase are described in the following paragraphs. 

2.1.1 Layout Design 

A  detailed  design  of  a  simple  building  at  1:5 scale  making  an  approximate  volume  of  7 
m3 and a total surface of 36 m2 was delivered. The building has two levels each divided 
in two connected rooms with stairs in between. The building has inlet and outlet openings 
and it is designed to enable installing sensors inside the rooms. The design is such that 
the  physical  presence  of  the  sensors  including  accessory  cables  and  tubing  has  not 
influence on the dispersion behaviour of vaporous agent as the sensors are located in a 
‘closed  room’.  All  rooms  are  accessible  from  the  outside  of  the  building  to  facilitate  the 
installation  of  sensor  tubing  etc.  Also,  all  joints  are  sealed  to  prevent  leakage  to  other 
rooms.  To  minimize  interaction  of  chemical  agent  with  the  mock-up  material,  6  mm 
Plexiglas® was used for the construction.  

2.1.2 Construction 

The  mock-up  is  constructed  from  6  mm  Plexiglas®.  It  consists  of  13  rooms  which  are 
connected  through  internal  openings  (doors,  corridors,  staircases).  Only  room  7  is 
isolated from the rest as it is dedicated for sensors installation. All rooms are accessible 
from  the  outside  of  the  building  through  windows  which  are  closed  tightly  during  the 
experiments.  Pictures  from  the  construction  phase  of  the  mock-up  are  shown  below 
(Figures 1-3). 

Figure 1. Internal details and subdivision of the mock-up 

5 

Figure 2. External details and openings of the mock-up 

Figure 3. External view and levels of the mock-up 

2.1.3 Shipping 

The entire mock-up was constructed in Athens (Greece) and subsequently shipped to the 
experimental  site  (TNO,  The  Netherlands).  The  mock-up  was  sent  on  the  2nd  of  April 
2021  (Figures  4)  and  arrived  at  TNO  premises,  in  The  Netherlands,  on  the  8th  of  April 
2021 (Figure 5). 

6 

 
 
 
 
 
 
 
 
 
 
Figure 4. Details of the mock-up shipment in NCSR Demokritos, Greece 

Figure 5. The mock-up at TNO, The Netherlands 

2.2.  Detection Experiments  

Several options can be considered to design experiments that provide insight into indoor 
chemical agent behaviour and detection of agents. Large scale real-building experiments 
obviously  most  closely  mimic  realistic  scenarios,  but  take  a  very  long  time  to  prepare, 
they are costly and require a suitable building in which chemicals can be released without 
repercussions.  Furthermore,  experiments  using  real  toxic  agents  may  have  advantages 
from the perspective of sensor response prediction. In addition, it would in practice mean 
that experiments can only be done at small (laboratory fume-hood) scale. 
All  considered,  to  enable  experiments  that  provide  input  for  dispersion  model 
development, within constraints of time, budget and experimental safety, it was decided 

7 

 
 
 
 
 
 
 
to perform mid-scale experiments with representative simulant chemicals. This approach 
makes  use  of  controlled  release  of  simulant  chemicals  in  a  mid-sized  facility,  more 
particularly an ambient breeze tunnel (AMB). To mimic a critical infrastructure building a 
scaled  (roughly  1:5)  mock-up  of  a  simple  building  has  been  designed  and  delivered  as 
described in the previous paragraphs.  

The experimental design involves three simulated attack scenarios: 

1.  Focal  release  from  within  the  target  critical  infrastructure  building  (simulated  as 

point release from within the building mock-up); 

2.  External release of agent approaching the target building from the outside from a 
‘short distance’ (simulated as a point release of agent from distance of 3 m from 
mock-up); 

3.  External release from ‘far way’ as a semi-largescale attack (simulated by a release 

throughout the AMB). 

The  measurements  include  recording  sensor  responses  to  agents  released  according  to 
the three  scenarios. Sensors have been in several chambers of the mock-up to monitor 
agent  concentration  as  well  as  sensor  response  to  thereupon.  They  also  include  ground 
truth concentration level measurements by analytical chemical methods. Wind flows were 
measured  at  several  locations  within  the  AMB.  All  results  were  subsequently  used  as 
input parameters to modelling efforts. 

2.2.1 Experimental set-up  

The experimental set-up obviously is, highly simplified, downscaled, restricted by safety-
considerations and in some aspects inevitably artificial. The need to install chemical and 
physical  sensors  and  forthcoming  tubing  and  cabling  to  enable  measurements  definitely 
disturbs  agent  distribution  to  some  extent.  Several  precautions  have  been  taken  to 
minimize  the  influence  of  the  experimental  necessities  on  the  level  of  realism  of  the 
experiments.  

Essentially the experimental set-up involved the following step-wise approach: 

  Endorsing the most relevant and experimentally achievable exposure scenarios; 
 
  Establishing  a  facility  in  which  the  experiments  can  be  performed  in  an  almost 

Installing the building mock-up built; 

realistic and controlled manner; 

  Selecting  relevant  simulant  agents  that  have  physical  chemical  properties 

resembling real agents; 

  Selecting commercially available sensors that reasons to the agents of choice; 
  Providing  a  detailed  description  of  the  facility’s  geometry  and  the  position  of  the 

building mock-up therein; 

  Generating  volatile  chemicals  according  to  the  three  attack  scenarios  thereby 

exposing the building mock-up; 

  Describing and measuring the parameters that influence agent distribution such as 
wind  speed,  ambient  temperature,  ground  truth  level  of  chemical  agent,  agent 
generation rate; 

  Recording of sensor response to agent release inside the mock-up. 

The following sections describe the subsequent experimental details.  

Ambient Breeze Tunnel 

The TNO Ambient Breeze Tunnel is a rectangular building which measures 6 meters wide, 
6 meters high and 20 meters long (Figures 6-7). The back side is open, allowing vapor to 
freely  flow  out.  The  front  side  is  equipped  with  a  series  of  25  (distributed  as  5x5)  1  m 
diameter ventilators to generate the required wind speed. 

8 

 
 
 
 
 
 
 
 
 
 
Figure 6. AMB front side view 

Figure 7. AMB back side view 

Immediately  in  front  of  the  ventilators  on  the  AMB  inside  a  system  of  6  horizontal 
aluminium  tubes  is  mounted  to  enable  uniform  vapor  distribution  (Figure  8).  Each  of 
those tubes has many perforations through which vapor is released. The facility enables 
to generate chemical clouds in a range of 0.01-10 mg/m3 in windspeeds of 0-5 m/s. 

9 

 
 
 
 
 
 
Figure 8. View from inside the ABM showing the 6 tubes that distribute vapor 

Sensors and chemicals 

Commercially  available  and  actually  in-service  chemical  sensors  have  been  selected  to 
ensure a desired level of realism.  
The  first  detector  used  was  the  LCD  3.3  of  Smiths  Detection  Company,  UK  (Figure  9); 
this is based on ion mobility spectroscopy. The LCD 3.3 is able to detect relevant agents 
in the concentration range of 10 μg/m3 Ion Mobility Spectrometry (IMS). Data collection 
rate  1  Hz.  The  sensor  has  a  sample  flow  of  100  mL/min.  The  other  a  Photo  Ionization 
Detector (PID) designed and supplied by FOI3 (Sweden). 

Figure 9. LCD 3.3 detector (left) and Young Anemometer (right) 

3 https://www.foi.se/en/foi.html 

10 

 
 
 
 
 
 
 
                                                 
Sensors  have  been  placed  such  that  they  sample  from  spots  inside  the  mock-up  (one 
sensor in a room) while influencing the air flows and thus the agent dispersion inside the 
mock-up as little as possible. This holds for the physical presence of sensors, cables and 
tubing as well as for the sampling flow of the sensors. It should however be realized that 
a  certain  minimal  sampling  flow  is  needed  for  the  sensors  (0.1-1  L/min  for  a  typical 
detector)  to  sample  levels  of  agents  exceeding  the  limit  of  detection.  To  prevent  active 
pumping of air through the mock-up the detectors were installed in the rooms. 

Before,  during  and  shortly  after  each  release  of  agent  the  mounted  sensors  have  been 
read-out  and  recorded.  For  ground  truth  agent  level  measurements,  the  widely  used 
Tenax sampling with subsequent chemical analysis has been applied. 

Wind  speed  inside  the  AMB  as  well  as  inside  (selected  spots  of)  the  mock-up  has  been 
monitored using anemometers. Two different brands have been applied. The first sensor 
was  a  Young  anemometer  (see  Figure  9).  This  sensor  has  an  acquisition  rate  of  30  Hz. 
The  second  sensor  was  a  Gill  anemometer  and  has  an  acquisition  rate  of  1  Hz.  The 
datafiles have been converted into files (.txt), containing three columns. Those columns 
describe  respectively  the  windspeed  in  xyz-directions  in  m/s.  Ambient  temperature  and 
humidity were recorded.  

Characteristics of chemicals 

The  chemicals  (simulants  for  toxic  threat  agents)  selected  for  use  in  the  experiments 
were: 

•  Tri-ethylphosphate (TEP); simulant for nerve agents (Figure 10):  

-  boiling point: 215 °C;  
-  density: 1,07 g/cm³. 

•  Toluene simulant for a volatile TIC: 

-  boiling point: 111 °C;  
-  density: 0,867 g/cm³. 

Figure 10. Tri-ethylphosphate (TEP) 

To make the  experiments as  realistic as possible, a number of related  parameters were 
taken into consideration. First of all, the amount of toxic agent - a perpetrator is likely to 
be able to apply - was taken into consideration. This obviously depends on many factors, 
such as availability of toxic agent in a relevant amount, the aggregation form (in scope of 
the project: limited to more or less volatile liquids only), the inherent toxicity, the agent 
dispersion,  the  size,  geometry  and  function  of  the  target  building,  the  attackers’ 
intentions etc.  
In short: to achieve an ‘effective’ dosage, the agent needs to be delivered at the target 
location  at  least  at  a  concentration  (C)  for  a  given  duration  of  time  (t)  such  that  their 
product.  The  exposure  dosage  (concentration  x  time)  and  the  inherent  toxicity  are  the 
dominant  factors  determining  whether  or  not  a  realistic  attack  is  at  stake.  When  the 
‘desired’  concentration  for  a  given  time  has  been  established,  the  agent  release  rate  at 

11 

 
 
 
 
 
 
 
 
 
the  source  is  then  automatically  determined.  The  experimental  phase  has  been 
performed at the TNO  premises. The following (combinations of) parameters have been 
be varied: 

•  Release type Scenario 1, 2 or 3; 
•  Exposure dosage.  

2.2.2 Scenario Design 

The  mock-up  was  assembled  and  installed  in  the  Breeze  Tunnel  facility  at  TNO,  in  the 
Netherlands.  The  exact  location  of  the  mock-up  is  schematically  displayed  in  Figure  11. 
Prior  to  assembly  some  transport  damage  had  to  be  repaired  in  order  to  enable 
installation. The mock-up was installed in the Breeze tunnel on its own transport platform 
(Figure 12). 
All LCD 3.3 detectors were placed inside the mock-up on the bottom with its inlet head in 
the middle of the room (Figures 13-14). 

All cables were taped into the corners of the rooms as much as possible. Some openings 
had  to  be  drilled  in  order  to  feed  cables  through  walls.  the  aim  was  always  to  use  the 
least intrusive route towards the ‘sensor’ room where the laptop for data acquisition was 
placed. Only a single UTP and laptop power cable were exiting through the bottom of this 
room  to  enable  charging  of  the  laptop  and  viewing  of  the  live  signals  on  an  external 
screen  positioned  in  the  control  room.  The  location  of  the  wind  speed  sensors  is 
described for the individual experimental configurations.  

All  25  wind  fans  were  frequency-controlled  during  all  experiments  and  kept  at  28Hz 
(resulting in a wind-speed of approximately 3 m/s). The tunnel was emptied to such level 
that  only  the  most  essential  parts  of  the  test  facility  were  present  to  avoid  unwanted 
disturbance of the air stream.  

For sampling a series of 4 sampling lines (tubes) were positioned in front of the mock-up 
for  reference  of  the  source  concentration  and  inside  the  mock-up  for  quantifying  TEP 
concentration in the mock-up. A sampling point was positioned between rooms 11 and 13 
as a reference for the incoming vapor, in room 1 as reference for the exiting vapor and 
room  8  as  reference  for  the  lowest  concentration  in  the  ‘building’.  All  sampling  points 
were positioned next to the detector sampling head in the middle of the room. Sampling 
was  performed  using  a  vacuum  pump  from  outside  the  Breeze  Tunnel  preventing  the 
need  to  enter  the  facility  during  an  experiment.  The  whole  setup  was  controlled  from  a 
control room enabling remote control of wind speed and source conditions. 

12 

 
 
 
 
 
 
 
 
 
Figure 11. Schematic position of mock-up in AMB 

Figure 12. Actual mock-up positioned in AMB 

13 

 
 
 
 
 
 
Figure 13. Arrangement of the rooms in the mock-up 

Figure 14. Six LCD 3.3 detectors installed in the mock-up rooms indicated with numbers 

In total three release scenarios have been used for the experiments. In particular: 

•  The first scenario executed (Scenario 3) was a series of three experiments used 
to simulate a distant cloud (“low cloud”) passing the building. These experiments 
were  mainly  used  to  determine  the  release  amounts  needed  for  the  upcoming 
scenarios  and  as  a  means  of  testing  the  complete  setup  with  all  equipment 
installed.  A  series  of  low  hanging  clouds  were  created  across  the  whole  width  of 
the tunnel where the maximum hight of the cloud was approximately 2 meters. 

•  The  second  scenario  (Scenario  2)  was  an  external  release  at  short  distance. 

14 

 
 
 
 
 
 
 
The  aim  was  to  create  a  point  source  cloud  that  would  pass  the  inlets  of  the 
building.  The  release  location  was  tested  prior  to  the  actual  experiments  with  a 
smoke  generator.  It  was  clear  that  smoke  entered  the  mock-up  and  passed 
through  rooms.  The  release  location  was  then  fitted  with  a  long  piece  of  tubing 
leading to the vapor generator setup placed at remote location to not disturb the 
air  flow  towards  the  mock-up.  The  long  tubing  proved  to  promote  condensation 
during  the  first  experiments  which  resulted  in  trying  another  approach.  It  was 
decided  to  place  the  vapor  source  inside  the  tunnel  despite  the  influence  on  the 
air  flow  towards  the  mock-up.  With  this  setup  a  total  of  4  experiments  were 
performed. 

•  The  last  scenario  (Scenario  1)  was  the  simulation  of  an  internal  release  in 
room  ‘8’.  This  room  was  chosen  because  it  was  the  most  ‘remote’  room  in  the 
building  that  had  very  little  air  circulation.  In  room  8  a  release  point  was 
positioned  by  fitting  a  piece  of  tubing  in  the  middle  of  the  room  close  to  floor 
level. Also, a series of PID sensors were placed in a number of rooms to measure 
Toluene distribution. Testing with Toluene resulted in the conclusion that the PID 
sensors did not function as expected. Contact with the supplier did not result in a 
quick fix. Therefore, it was decided to switch to TEP that could be measured with 
the  already  installed  LCD  3.3  detectors.  Using  an  external  evaporator  TEP  was 
evaporated  and  fed  into  room  8  via  the  tubing.  In  total  three  experiments  were 
performed  in  this  configuration  varying  both  source  strength  and  duration  of 
release. 

Figure 15. Ground Floor level detector/sampling positions 

15 

 
 
 
 
 
Figure 16. Middle Floor level detector/sampling positions 

Figure 17. Top Floor level detector /sampling positions 

Tables 1 and 2 provide an overview of all experiments executed and exposure scenarios, 
respectively: 

Table 1. General overview of experiments performed 

Scenario number 

Experiment number 

3 

2 

1 
2 
3 
4 
5 
6 
7 
8 
9 

Experiments 
description 

Confidence  test  with 
cloud  over  full  width 
of the tunnel 

Comments 

distant release 

External  point  release 
close to mock-up with 
evaporator in tunnel 

replicate 
short intense release 
long 

low 

16 

 
 
 
 
 
 
 
 
 
 
1 

10 
11 
12 

13 

Internal point release 

concentration release 

TEP in room 8 

room 

in 
TEP 
replicate 
preconditioning 
syringe 

8, 
with 
of 

Experiments  4,5,  and  10  were  try-outs  that  did  not  result  in  follow-up  therefore  no 
results have been reported. 

Table 2. Detailed overview of experiments performed (agents and releases) 

Experiment 
number 

Agent 

Scenario; Rationale  Remark 

Release rate 
(gram / hour) 

Release 
duration 
(min) 

1 

2 

3 

4 

5 

6 

7 

8 

9 

10 

11 

12 

13 

TEP neat 

Scenario 3; Low cloud 
from distance 

TEP neat 

Scenario 3; Low cloud 
from distance 

TEP neat 

Scenario 3; Low cloud 
from distance 

TEP   4% 
in H2O 

TEP   4% 
in H2O 

TEP   4% 
in H2O 

TEP   4% 
in H2O 

TEP   4% 
in H2O 

TEP   4% 
in H2O 

Scenario 2; External 
release at short 
distance (3 m) 
Scenario 2; External 
release at short 
distance (3 m) 
Scenario 2; External 
release at short 
distance (3 m) 
Scenario 2; External 
release at short 
distance (3 m) 
Scenario 2; External 
release at short 
distance (3 m) 
Scenario 2; External 
release at short 
distance (3 m) 

70 

35 

35 

10 

10 

10 

Try-out failed due to condensation in 
tubing; results not presented 

Try-out failed due to condensation in 
tubing; results not presented 

140 

140 

420 

47 

10 

10 

210 

30 

Toluene 

Scenario 1; Release 
from within (room 8) 

Try-out failed due to lacking PID response; 
no further attempts; results not presented 

TEP neat 

Scenario 1; Release 
from within (room 8) 

TEP neat 

Scenario 1; Release 
from within (room 8) 

TEP neat 

Scenario 1; Release 
from within (room 8) 

2 

2 

4 

17 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
2.2.3 Analysis of results 

Scenario 3 

Figure 18. Scenario 3 - schematic overview of position of windspeed sensors and 
reference sample location around mock-up 

Experiment #1: Confidence test with cloud over full width of the tunnel (distant 
release) 

Experiment Time:  

•  Date: 19-04-2021  
•  Source start: 14:23:00  
•  Sampling start: 14:28:00  
•  Source Stop: 14:33:00  

Experimental parameters: 

•  Chemical released: TEP (neat). 
•  Dimensions cloud: approximately 2-meter-high, 6-meter-wide (only bottom two 
release tubes of tunnel are used). These are not exact dimensions since the vapor 
is  released  from  two  horizontal  tubes  in  front  of  the  wind  fans  in  a  turbulent  air 
stream. 

•  Windspeed: 28 Hz resulting in ±3 m/s (measured with anemometers in tunnel). 
•  Target  concentration  in  cloud:  0.5  mg/m3  (calculated  by  a  cloud  front  (~2  m 

high, ~6 meterwide) 12 m2 x 3 m/s) = 36 m3/s. 

•  Release amount 70 g/h (duration 10 minutes) = 11.7 gram total (calculated by 

70 gram/(36m3*3600s) = 0.5 mg/m3). 

•  Chemical  is  evaporated  using  a  Controlled  Evaporator  Mixer  that  uses  heat  (180 
°C) and nitrogen gas (80 L/min) to evaporate TEP liquid. This vapor is injected in 
a  tubular  transport  system  that  transports  the  mixed  vapor  to  the  inlet  tubes  of 
the tunnel using a small amount of additional transport air (10.000 m3/hour). This 
airstream leaves the horizontal inlet tubes via small holes that result in a mixing 
effect with the wind stream through the tunnel (bottom tube: 55 cm (center), 2nd 
tube 125 cm (center) both measured from floor level). 

•  LCD Sensor data is recorded using the central laptop in the mock-up. 

Results: 
Data recorded with the LCD’s was plotted in a graph displaying the response-time graph 
(Figure  19).  The  response  is  an  arbitrary  scale.  Reference  concentration  measurements 
drawn by a number of Tenax failed due to a problem with the sampling. 

18 

 
 
 
 
 
 
 
 
Figure 19. Experiment #1 - LCD 3.3 signals plotted against time 

Experiment #2: Confidence test with cloud over full width of the tunnel (distant 
release) 

Experiment Time:  

•  Date: 19-04-2021  
•  Source start: 15:10:00  
•  Sampling start: 15:15:00  
•  Source Stop: 15:20:00  

Experimental parameters: 

•  Chemical released: TEP (neat). 
•  Dimensions  cloud:  approximately  1-2  meters  high,  6-meter-wide  (only  bottom 
release tube of tunnel is used) (bottom tube: 125 cm (distance centerline tube to 
floor level, diameter tube 32 cm). These are not exact dimensions since the vapor 
is  released  from  one  horizontal  tube  in  front  of  the  wind  fans  in  a  turbulent  air 
stream. 

•  Windspeed: 28 Hz resulting in ±3 m/s (measured with anemometers in tunnel). 
•  Target concentration in cloud: 0.5 mg/m3 (calculated by a cloud front (~1 m 
high, ~6-meter-wide) 6 m2 x 3 m/s) = 18 m3/s. (Lower concentration anticipated 
due to use of a single release tube instead of all release tubes at the entrance of 
the tunnel). 

•  Release amount 35 g/h (duration 10 minutes) = 5.85-gram total (calculated by 

35 gram/(18m3*3600s) = 0.5 mg/m3). 

•  Chemical  is  evaporated  using  a  Controlled  Evaporator  Mixer  that  uses  heat  (180 
°C) and nitrogen gas (80L/min) to evaporate TEP liquid. This vapor is injected in a 
tubular transport system that transports the mixed vapor to the inlet tubes of the 
tunnel  using  a  small  amount  of  additional  transport  air  (7.000  m3/hour).  This 
airstream  leaves  the  horizontal  inlet  tube  via  small  holes  that  result  in  a  mixing 
effect  with  the  wind  stream  through  the  tunnel  (bottom  tube:  55  cm  (distance 
centerline tube to floor level, diameter tube 32 cm). 

•  LCD Sensor data is recorded using the central laptop in the mock-up. 

Results: 
Data recorded with the LCD’s was plotted in a graph displaying the response-time graph 

19 

 
 
 
 
 
 
 
(Figure 20). The response is an arbitrary scale. The concentration was determined by a 
series  of  reference  samples.  Each  sample  was  drawn  by  pumping  100mL  of  air  onto  a 
Tenax  tube  during  approximately  20  seconds  with  a  manual  air  pump.  The 
concentrations are listed in Table 3. 

Table 3. Experiment #2 - Sampling start time  

Date 

Location 

Time 

Result 
(mg/m3) 

Comments 

19-4-2021 

Reference 

15:15:30 

0,097 

19-4-2021 

Corridor 

15:16:12 

0,211 

19-4-2021 

Room 1 

14:29:44 

19-4-2021 

Room 8 

14:30:20 

nd 

nd 

sample tube 
broken 

problem with 
sampling 

Figure 20. Experiment #2 - LCD 3.3 signals plotted against time 

Experiment #3: Confidence test with cloud over full width of the tunnel (distant 
release) 

Experiment Time:  

•  Date: 19-04-2021  
•  Source start: 15:50:00  
•  Sampling start: 15:55:00  
•  Source Stop: 16:00:00  

Experimental parameters: 

•  Chemical released: TEP (neat). 
•  Dimensions  cloud:  approximately  1-2  meters  high,  6  meter  wide  (only  bottom 

20 

 
 
 
 
 
 
 
 
 
 
 
release tube of tunnel is used). These are not exact dimensions since the vapor is 
released  from  one  horizontal  tube  in  front  of  the  wind  fans  in  a  turbulent  air 
stream. 

•  Windspeed: 28 Hz resulting in ±3 m/s (measured with anemometers in tunnel). 
•  Target  concentration  in  cloud:  0.5  mg/m3  (calculated  by  a  cloud  front  (~1  m 

high, ~6 meter wide) 6 m2 x 3 m/s) = 18 m3/s. 

•  Release amount 35 g/h (duration 10 minutes) = 5.85 gram total (calculated by 

35 gram/(18m3*3600s) = 0.5 mg/m3). 

•  Chemical  is  evaporated  using  a  Controlled  Evaporator  Mixer  that  uses  heat  (180 
°C) and nitrogen gas (80L/min) to evaporate TEP liquid. This vapor is injected in a 
tubular transport system that transports the mixed vapor to the inlet tubes of the 
tunnel  using  a  small  amount  of  additional  transport  air  (7.000  m3/hour).  This 
airstream  leaves  the  horizontal  inlet  tube  via  small  holes  that  result  in  a  mixing 
effect  with  the  wind  stream  through  the  tunnel  (bottom  tube:  125  cm  (distance 
centerline tube to floor level, diameter tube 32 cm). 

•  LCD Sensor data is recorded using the central laptop in the mock-up. 

Results: 
Data recorded with the LCD’s was plotted in a graph displaying the response-time graph 
(Figure 21). The response is an arbitrary scale. The concentration was determined by a 
series  of  reference  samples.  Each  sample  was  drawn  by  pumping  100  mL  of  air  onto  a 
Tenax  tube  during  approximately  20  seconds  with  a  manual  air  pump.  The 
concentrations are listed in Table 4. 

Table 4. Experiment #3 - Sampling start time 

Date 

Location 

Time 

Result 
(mg/m3) 

Comments 

19-4-2021 

Reference 

15:55:32 

0,196 

19-4-2021 

Corridor 

15:56:13 

0,182 

19-4-2021 

Room 1 

15:56:55 

0,192 

19-4-2021 

Room 8 

15:57:45 

nd 

Below detection 
limit 

21 

 
 
 
 
 
 
 
Figure 21. Experiment #3 - LCD 3.3 signals plotted against time 

Scenario 2 

Figure 22. Scenario 2 - schematic overview of position of windspeed sensors, reference 
sample and source location around mock-up 

22 

 
 
 
 
 
 
 
Figure 23. Scenario 2 - Release apparatus used as point source (right) in front of mock-
up (left) 

Experiment  #6:  External  point  release  close  to  mock-up  with  evaporator  in 
tunnel 

Experiment Time:  

•  Date: 22-04-2021  
•  Source start: 09:58:00  
•  Sampling start: 10:03:00  
•  Source Stop: 10:08:00  

Experimental parameters: 

•  Chemical released: TEP (4% solution in mq water). 
•  Dimensions  cloud:  the  source  was  a  CEM  (description  below)  that  was 
positioned  at  50cm  above  floor  level.  From  this  point  the  vapor  spreads  towards 
the mock-up. 

•  Windspeed: 28 Hz resulting in ±3 m/s (measured with anemometers in tunnel). 
•  Target concentration in cloud: 0.5 mg/m3 (calculated by a cone shaped cloud 
front  (length  3  m,  radius  0.57  m  at  front  of  building)  1.02m2  x  3  m/s)  =  3.1 
m3/se x 0.5 mg/m3 = 1.7 mg/s = 5.6 gram/h (neat). 

•  Release amount 140 g/h, 4 % solution (duration 10 minutes) = 0.93 gram total 

TEP. 

•  Chemical  is  evaporated  using  a  Controlled  Evaporator  Mixer,  positioned  in  the 
tunnel,  that  uses  heat  (180  °C)  and  nitrogen  gas  (80  L/min)  to  evaporate  TEP 
liquid.  This  vapor  released  in  the  tunnel  using  a  horizontal  outlet  tube  pointing 
towards  the  mock-up.  The  outer  diameter  of  the  tube  is  4.1  cm  and  the  inner 
diameter is 3.9 cm. The height of the tube is 50 cm, the length is 25 cm. 

•  LCD Sensor data is recorded using the central laptop in the mock-up. 

Results: 
Data recorded with the LCD’s was plotted in a graph displaying the response-time graph 
(Figure 24). The response is an arbitrary scale. The concentration was determined by a 
series  of  reference  samples.  Each  sample  was  drawn  by  pumping  100  mL  of  air  onto  a 
Tenax  tube  during  approximately  20  seconds  with  a  manual  air  pump.  The 

23 

 
 
 
 
 
 
concentrations are listed in Table 5. 

Table 5. Experiment #6 - Sampling start time  

Date 

Location 

Time 

Result 
(mg/m3) 

Comments 

22-4-2021 

Reference 

10:03:18 

0,86 

22-4-2021 

Corridor 

10:03:48 

0,49 

22-4-2021 

Room 1 

10:04:15 

0,53 

22-4-2021 

Room 8 

10:04:46 

0,062 

Figure 24. Experiment #6 - LCD 3.3 signals plotted against time 

Experiment  #7:  External  point  release  close  to  mock-up  with  evaporator  in 
tunnel (replicate) 

Experiment Time:  

•  Date: 22-04-2021  
•  Source start: 10:38:50  
•  Sampling start: 10:43:00  
•  Source Stop: 10:49:00  

Experimental parameters: 

•  Chemical released: TEP (4% solution in mq water). 
•  Dimensions  cloud:  the  source  was  a  CEM  (description  below)  that  was 
positioned  at  50cm  above  floor  level.  From  this  point  the  vapor  spreads  towards 
the mock-up. 

•  Windspeed: 28 Hz resulting in ±3 m/s (measured with anemometers in tunnel). 
•  Target concentration in cloud: 0.5 mg/m3 (calculated by a cone shaped cloud 

24 

 
 
 
 
 
 
 
 
 
 
 
front (length 3 m, radius 0.57 m at front of building) 1.02 m2 x 3 m/s) = 3.1 m3/s 
x 0.5 mg/m3 = 1.7 mg/s = 5.6 gram/h (neat). 

•  Release amount 140 g/h, 4 % solution (duration 10 minutes) = 0.93 gram total 

TEP. 

•  Chemical  is  evaporated  using  a  Controlled  Evaporator  Mixer,  positioned  in  the 
tunnel,  that  uses  heat  (180  °C)  and  nitrogen  gas  (80  L/min)  to  evaporate  TEP 
liquid.  This  vapor  released  in  the  tunnel  using  a  horizontal  outlet  tube  pointing 
towards  the  mock-up.  The  outer  diameter  of  the  tube  is  4.1  cm  and  the  inner 
diameter is 3.9 cm. The height of the tube is 50 cm, the length is 25 cm. 

•  LCD Sensor data is recorded using the central laptop in the mock-up. 

Results: 
Data recorded with the LCD’s was plotted in a graph displaying the response-time graph 
(Figure 25). The response is an arbitrary scale. The concentration was determined by a 
series  of  reference  samples.  Each  sample  was  drawn  by  pumping  100mL  of  air  onto  a 
Tenax  tube  during  approximately  20  seconds  with  a  manual  air  pump.  The 
concentrations are listed in Table 6. 

Table 6. Experiment #7 - Sampling start time 

Date 

Location 

Time 

Result 
(mg/m3) 

Comments 

22-4-2021 

Reference 

10:44:33 

0,77 

22-4-2021 

Corridor 

10:45:35 

0,5 

22-4-2021 

Room 1 

10:46:16 

0,49 

22-4-2021 

Room 8 

10:47:00 

0,18 

Figure 25. Experiment #7 - LCD 3.3 signals plotted against time 

25 

 
 
 
 
 
 
 
 
 
 
Experiment  #8:  External  point  release  close  to  mock-up  with  evaporator  in 
tunnel (short intense release) 

Experiment Time:  

•  Date: 22-04-2021  
•  Source start: 14:52:25  
•  Sampling start: 10:53:25 (only reference short release)  
•  Source Stop: 14:55:45  

Experimental parameters: 

•  Chemical released: TEP (4% solution in mq water). 
•  Dimensions  cloud:  the  source  was  a  CEM  (description  below)  that  was 
positioned  at  50cm  above  floor  level.  From  this  point  the  vapor  spreads  towards 
the mock-up. 

•  Windspeed: 28 Hz resulting in ±3 m/s (measured with anemometers in tunnel). 
•  Target concentration in cloud: 1.5 mg/m3 (calculated by a cone shaped cloud 
front (length 3 m, radius 0.57 m at front of building) 1.02 m2 x 3 m/s) = 3.1 m3/s 
x  1.5  mg/m3  =  4.6  mg/s  =  16.5  gram/h  (neat).  The  concentration  is  3  times 
higher and the  release  time is 3 times shorter than experiment 7 resulting in an 
equal released amount. 

•  Release amount 140 g/h, 4 % solution (duration 3:20 minutes) = 0.93 gram total 

TEP. 

•  Chemical  is  evaporated  using  a  Controlled  Evaporator  Mixer,  positioned  in  the 
tunnel,  that  uses  heat  (180  °C)  and  nitrogen  gas  (80  L/min)  to  evaporate  TEP 
liquid.  This  vapor  released  in  the  tunnel  using  a  horizontal  outlet  tube  pointing 
towards  the  mock-up.  The  outer  diameter  of  the  tube  is  4.1  cm  and  the  inner 
diameter is 3.9 cm. The height of the tube is 50 cm, the length is 25 cm. 

•  LCD Sensor data is recorded using the central laptop in the mock-up. 

Results: 
Data recorded with the LCD’s was plotted in a graph displaying the response-time graph 
(Figure 26). The response is an arbitrary scale. The concentration was determined by a 
series  of  reference  samples.  Each  sample  was  drawn  by  pumping  50  mL  of  air  onto  a 
Tenax tube during approximately 10 seconds with a manual air pump. The concentration 
is given in Table 7. 

Table 7. Experiment #8 - Sampling start time  

Date 

Location 

Time 

Result 
(mg/m3) 

22-4-2021 

Reference 

14:53:26 

2,22 

Comments 

only reference 
sampled (short 
release) 

26 

 
 
 
 
 
 
 
Figure 26. Experiment #8 - LCD 3.3 signals plotted against time 

Experiment  #9:  External  point  release  close  to  mock-up  with  evaporator  in 
tunnel (long low concentration release) 

Experiment Time:  

•  Date: 22-04-2021  
•  Source start: 15:25:00  
•  Sampling start: 15:35:00  
•  Source Stop: 15:55:00  

Experimental parameters: 

•  Chemical released: TEP (4% solution in mq water). 
•  Dimensions  cloud:  the  source  was  a  CEM  (description  below)  that  was 
positioned  at  50cm  above  floor  level.  From  this  point  the  vapor  spreads  towards 
the mock-up. 

•  Windspeed: 28 Hz resulting in ±3 m/s (measured with anemometers in tunnel). 
•  Target  concentration  in  cloud:  0.167  mg/m3  (calculated  by  a  cone  shaped 
cloud  front  (length  3  m,  radius  0.57  m  at  front  of  building)  1.02  m2  x  3  m/s)  = 
3.1 m3/s x 0.167 mg/m3 = 0.5 mg/s = 1.86 gram/h (neat). The concentration is 3 
times lower and the release time is 3 times longer than experiment 7 resulting in 
an equal released amount. 

•  Release amount 140 g/h, 4 % solution (duration 30 minutes) = 0.93 gram total 

TEP. 

•  Chemical  is  evaporated  using  a  Controlled  Evaporator  Mixer,  positioned  in  the 
tunnel,  that  uses  heat  (180  °C)  and  nitrogen  gas  (80  L/min)  to  evaporate  TEP 
liquid.  This  vapor  released  in  the  tunnel  using  a  horizontal  outlet  tube  pointing 
towards  the  mock-up.  The  outer  diameter  of  the  tube  is  4.1  cm  and  the  inner 
diameter is 3.9 cm. The height of the tube is 50 cm, the length is 25 cm. 

•  LCD Sensor data is recorded using the central laptop in the mock-up. 

Results: 
Data recorded with the LCD’s was plotted in a graph displaying the response-time graph 
(Figure 27). The response is an arbitrary scale. The concentration was determined by a 
series of reference samples. Each sample was drawn by pumping 50-100 mL of air onto a 
Tenax tube during approximately 10 seconds with a manual air pump. The concentration 
is listed in Table 8. 

27 

 
     
 
 
 
 
 
 
Table 8. Experiment #9 - Sampling start time 

Date 

Location 

Time 

Result 
(mg/m3) 

Comments 

22-4-2021 

Reference 

15:35:14 

0,67 

22-4-2021 

Corridor 

15:35:44 

0,24 

22-4-2021 

Room 1 

15:36:12 

0,23 

22-4-2021 

Room 8 

15:36:50 

0,062 

Figure 27. Experiment #9 - LCD 3.3 signals plotted against time 

28 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Scenario 1 

Figure 28. Scenario 1 - Schematic overview of position of windspeed sensors and source 
location around mock-up 

Figure 29. Scenario 1 - Evaporator positioned behind the mock-up 

Experiment #11: Internal point release (TEP in room 8) 

Experiment Time:  

•  Date: 29-04-2021  
•  Source start: 15:07:00  
•  Sampling start: no reference sample  

29 

 
 
 
 
 
 
 
 
•  Source Stop: 15:09:00  
•  All windows open: 15:20:00 (venting mock-up)  

Experimental parameters: 

•  Chemical released: TEP (neat). 
•  TEP was released in room ‘8’ in the center of the room close to ground level (7 cm 

above floor). 

•  Windspeed: 28 Hz resulting in ±3 m/s (measured with anemometers in tunnel). 
•  Target concentration 1.5 mg/m3. Vapor was created by evaporating 235 nL/min 
(duration  2  minutes)  of  TEP  and  transporting  it  to  room  8  with  a  volume  of 
approximately 0.4 m3. 

•  Chemical  is  evaporated  using  a  Controlled  Evaporator  Mixer  that  uses  heat  (150 
°C) and nitrogen gas (1.3 L/min) to evaporate TEP liquid. This vapor released in 
using  a  piece  of  tubing  that  end  in  room  8  in  the  center  of  the  room  at  7  cm 
above floor level. The tubing dimension is 6 mm outside, 4 mm inside and points 
upward. 

•  LCD Sensor data is recorded using the central laptop in the mock-up. 
•  No  reference  samples  were  taken  due  to  high  concentration.  The  high 
concentration  measurement  would  result  in  a  saturation  of  the  sampling  devices 
and thereby underestimation of the actual concentration. 

•  After release all windows were opened (see time table for exact time) to vent the 

rooms. 

Results: 
The response of the LCD in room 8 is saturated due to high concentration. 

Figure 30. Experiment #11 - LCD signals plotted against time 

Experiment #12: Internal point release (TEP in room 8) 

Experiment Time:  

•  Date: 29-04-2021  
•  Source start: 15:39:00  
•  Sampling start: no reference sample  
•  Source Stop: 15:41:00  
•  All windows open: 15:50:00 (venting mock-up)  

30 

 
 
 
 
 
 
 
Experimental parameters: 

•  Chemical released: TEP (neat). 
•  TEP was released in room ‘8’ in the center of the room close to ground level (7 cm 

above floor). 

•  Windspeed: 28 Hz resulting in ±3 m/s (measured with anemometers in tunnel). 
•  Target  concentration  6  mg/m3.  Vapor  was  created  by  evaporating  1  μL/min 
(duration  2  minutes)  of  TEP  and  transporting  it  to  room  8  with  a  volume  of 
approximately 0.4 m3. 

•  Chemical  is  evaporated  using  a  Controlled  Evaporator  Mixer  that  uses  heat  (150 
°C)  and  nitrogen  gas  (1.3L/min)  to  evaporate  TEP  liquid.  This  vapor  released  in 
using  a  piece  of  tubing  that  end  in  room  8  in  the  center  of  the  room  at  7  cm 
above floor level. The tubing dimension is 6 mm outside, 4 mm inside and points 
upward. 

•  LCD Sensor data is recorded using the central laptop in the mock-up.  
•  No reference samples were taken due to high concentration. 
•  After release all windows were opened to vent the rooms. 

Results: 
The response of the LCD in room 8 is saturated due to high concentration. 

Figure 31. Experiment #12 - LCD signals plotted against time 

Experiment  #13:  Internal  point  release  (TEP  in  room  8,  replicate  with 
preconditioning of syringe) 

Experiment Time:  

•  Date: 29-04-2021  
•  Source start: 16:15:00  
•  Sampling start: no reference sample  
•  Source Stop: 16:19:00  
•  All windows open: 16:24:00 (venting mock-up)  

Experimental parameters: 

•  Chemical released: TEP (neat). 
•  TEP was released in room ‘8’ in the center of the room close to ground level (7 cm 

above floor). 

31 

 
 
 
 
     
 
 
 
 
 
•  Windspeed: 28 Hz resulting in ±3 m/s (measured with anemometers in tunnel). 
•  Target  concentration  6  mg/m3.  Vapor  was  created  by  evaporating  1  μL/min 
(duration  2  minutes)  of  TEP  and  transporting  it  to  room  8  with  a  volume  of 
approximately 0.4 m3. 

•  Chemical  is  evaporated  using  a  Controlled  Evaporator  Mixer  that  uses  heat  (150 
°C) and nitrogen gas (1.3 L/min) to evaporate TEP liquid. The vapor was released 
in the center of room 8 at 7 cm above floor level by using a piece of tubing. The 
tubing  dimension  is  6  mm  outside,  4  mm  inside  and  points  upward.  Prior  to 
release the syringe used was preconditioned. This is why the release took longer 
to start (less control). 

•  LCD Sensor data is recorded using the central laptop in the mock-up. 
•  No reference samples were taken due to high concentration. 
•  After  release  all  windows  were  opened  (see  ’Experiment  time’  for  exact  time)  to 

vent the rooms. 

Results: 
The response of the LCD in room 8 is saturated due to high concentration. 

Figure 32. Experiment #13 - LCD signals plotted against time 

2.3.  Results of Detection Experiments  

As shown by the analysis of results, the experimental study only had a small number of 
parameters  that  varied.  Only  one  chemical  has  bene  used,  TEP;  only  one  detector  has 
been applied (LCD 3.3) and only one windspeed setting was used (3 m/s). Reflecting on 
the experimental approach many parameters could have been chosen to allow a broader 
variation of relevant circumstances.  
As stated in the previous chapters, three attack scenarios have been mimicked: release 
of  agent  from  within  the  building  mock-up;  release  of  agents  at  short  distance  (3  m) 
from outside the mock-up; release of a cloud of agents from ‘far away’ by exposing the 
entire AMB and the mock-up therein.  

Numerous  simulants for toxic agents could have been selected. Obviously, prerequisites 
were  detectability  by  the  sensors  at  hand,  favourable  physical  chemical  properties  and 

32 

 
 
 
 
 
    
 
 
 
 
low  toxicity.  Due  to  the  limited  resources  in  budget  and  time,  only  TEP  was  used.  This 
might be regarded as a shortcoming, but for achieving the study objective, it turned that 
the  substantial  amount  of  data  obtained  from  the  TEP  studies  have  been  useful  and 
fulfilling. 
Many  chemical  sensors  could  have  been  used.  The  attempts  to  apply  PID  detectors 
(kindly made available by FOI) for detecting toluene as a model Toxic Industrial Chemical 
sadly failed. Then it was decided to continue (Scenario 1) experiments just with TEP and 
the LCD 3.3 detectors which proved excellently able to detect that substance. 
Range of TEP concentrations might have been studied, however, the most essential part 
of the range finding efforts was to achieve levels that resulted in actual LCD signals with 
successful TEP generation and some level of realism. 

Due to the high demands regarding information on the local turbulence and wind speed 
within  the  AMB,  it  was  decided  to  limit  to  wind  speed  applied  in  the  AMB  to  just  one 
setting (3 m/s). 
The actual time to execute the experiments (including time needed to repair the damage 
to the mock-up arisen during transport; range finding and facility cleaning) was limited to 
2 working weeks. Still, the short duration combined with the variety of prerequisites and 
the high level of uncertainty of the particular experimental approach did not prohibit the 
delivery of an impressive data set and interesting findings. 

2.3.1 Conclusions of experimental trials 

The  experiments, the  results of which have been described in the previous section, had 
as  essential  objective  to  serve  as  input  for  subsequent  modelling  of  agent  dispersion. 
Overarching conclusions need to draw on the basis of the combination and integration of 
experiments  and  modelling.  A  more  comprehensive  overview  of  conclusions  is  given 
based  on  the  modelling  executed  before  and  after  the  experiments.  However,  some 
observations can be made: 

•  Release of TEP was successful in all three exposure scenarios: 

o  Scenario 1 resulted in substantial TEP concentration in the room 8 it was 

released in; 

o  Scenario 2 (after some failed attempts) resulted in clear and reproducible 

TEP releases; 

o  Scenario 3 resulted in clear and reproducible TEP releases. 

•  Ground  truth  measurements  of  TEP  by  Tenax  sampling  and  subsequent  GC-MS 

analysis were (apart from some missing data) successful; 

•  The LCD 3.3 detector was well able to detect offered levels of TEP; 
•  Efforts to apply the PID system to detect toluene failed; 
•  The detectors inside the mock-up responded well; 
•  Clear, significant and reproducible differences of sensor response between sensors 

placed in different mock-up rooms have been recorded: 

o  Scenario 1 (experiments 11, 12, 13): 

-  Apart from room 8 itself TEP was detected in room 9. 

o  Scenario 2 (experiments 6, 7, 8, 9):  

-  Ranking room that showed to fastest response: 4=10>1>11>9>8;  
-  Ranking room that showed highest level: 4>10>9>1>11>8. 

o  Scenario 3 (experiments 1, 2, 3):  

-  Ranking room that showed to fastest response: 4=10>11=1>9>8;  
-  Ranking room that showed highest level: 4>11>9>10=1>8. 

Release of TEP was successful in all three exposure scenarios, it resulted in reproducible 
detection signals from 6 IMS instrument placed in 6 rooms of the building mock-up. The 
speed and magnitude in which TEP levels were recorded in the respective rooms showed 
a  clear  and  reproducible  picture  in  the  sense  that  some  of  the  rooms  appeared  to  be 
better  reachable  for  the  externally  released  TEP  than  others.  Subsequent  dispersion 
modelling will be undertaken to understand and describe those differences. 

33 

 
 
 
 
 
3.  Modelling Activity 

3D simulations were performed with Code_Saturne and ADREA-HF CFD models with the 
aim  to  firstly  pre-compute,  and  secondly  reproduce,  the  flow  and  dispersion  of  the 
chemical in the above-described mock-up. 

3.1.  Computation Approach (Code_Saturne) 

A  Computational  Fluid  Dynamics  (CFD)  work  was  applied  on  the  spatial  and  temporal 
distribution  of  chemicals  inside  a  mock-up  placed  in  a  wind  tunnel  where  vapours  are 
transported and dispersed by a turbulent flow generated by large fans. 
The  objective  of  the  experimental  trials  was  to  mimic  the  exposure  of  a  building 
undergoing  an  attack  from  the  outside  in  a  near-by  release  location.  The  situation  of  a 
release occurring inside the mock-up is also considered. 
In general, as in this particular project, 3D modelling and simulation based on CFD have 
a double use regarding experiments: 

•  The first one relates to the design of the trials before they are performed;  
• 

the second one relates to the replication of the trials after they are carried out.  

Thus,  simulations  have  been  carried  out  with  Code_Saturne  both  before  and  after  the 
experiments.  Pre-trials  computations  illustrate  the  differences  in  the  dispersion 
pattern  when  an  internal  versus  an  external  released  is  considered.  Post-trials 
computations  are  fully  consistent  with  the  pre-trials  computations  and  the  physical 
phenomena enlightened in the different trials. 
Preparatory CFD simulations have the great interest to predictionally and reliably inform 
about  what  will  be  encountered  in  the  experiments.  CFD  simulations  for  replicating 
experimental  trials  generally  provide  a  good  estimate  of  the  concentrations  where 
sensors were set up in the wind tunnel and building mock-up. Moreover, they give a full 
space  and  time  knowledge  of  the  flow  and  dispersion  patterns,  while  experiments  only 
exploit a very limited number of sensors measuring at a limited number of instants.  

3.1.1 The Pre-Experimental Phase  

As  a  first  step,  the  preparatory  simulation  work  based  on  the  trials  conditions  is 
presented hereafter. 
Figure  33  shows  the  principle  of  the  experimental  assembly.  While  the  flow  in  the  wind 
tunnel should be essentially one-directional, it is anticipated that a complex 3D flow was 
established in and around the mock-up placed in the wind tunnel (Figure 34).  
Indeed,  there  are  three  openings  in  the  mock-up  wall  facing  the  incident  air  flow  from 
the  blower  fans  (these  openings  are  at  the  second  floor  of  the  lower  part  of  the  mock-
up). There are also three openings for air extraction located on one of the mock-up side 
walls (these openings are at the second floor of the upper part of the mock-up). 

Two  successive  releases  were  planned,  one  upstream  of  the  flow  outside  the  mock-up 
(Figure 35), the other in the mock-up on the ground floor of the lower part (Figure 36). 

34 

 
 
 
 
 
 
 
 
Figure 33. Principle of the experiment 

Figure 34. Positioning of the mock-up in the wind tunnel 

35 

 
 
 
 
 
 
Figure 35. Positioning of the external release in the wind tunnel 

Figure 36. Positioning of the internal release 

3.1.1.1 Implementation of the aeraulic simulations 

As mentioned above, the CFD simulations have been implemented using the open-source 
software  Code_Saturne  that  is  developed  by  the  R&D  of  EDF  (the  French  electricity 
board)  and  the  CEREA  at  the  “École  des  Ponts-et-Chaussées”  (Paris,  France).  The 
elaboration of the geometry and mesh has been performed with Ansa4 software and the 

4 https://www.ansa-usa.com/software/ansa/ 

36 

 
 
 
 
 
 
 
                                                 
post-processing of the results with Paraview5 software. 
In  order to  evaluate the sensitivity of the flow numerical solution to the mesh type and 
the  spatial  discretization,  several  meshes  were  tested  and  compared.  Then,  the  mesh 
considered as the most suitable for the problem was selected and used in the rest of the 
numerical study. 

Generation of the 3D digital mock-up 

The  3D  geometry  of  the  mock-up  was  elaborated  from  the  2D  plans  provided  to  the 
modelers.  It  is  conformed  to  the  Plexiglas®  mock-up.  The  small  building  mock-up 
consists of an upper part and a lower part (Figure 37). 
In  the  low-rise  building,  there  is  a  ground  floor  and  a  second  floor  with  four  rooms  per 
level, one of  which, on  the ground floor, is condemned to place sensor  equipment. One 
corridor per level serves the different rooms. The two levels communicate by a staircase 
represented  by  a  hole  in  the  floor  in  the  digital  mock-up  as  in  the  Plexiglas®  mock-up. 
The  air  inlets  are  located  in  the  two  upstairs  rooms  facing  the  incident  flow  and  in  the 
central corridor. 
In  the  high-rise  building,  the  rooms  are  twice  as  high  as  in  the low-rise  building.  There 
are also two levels communicating by a staircase represented by a hole in the floor in in 
the digital mock-up as in the Plexiglas® mock-up. On the ground floor, there is a lobby 
and a room. Upstairs, two large rooms on either side of the stairwell and a much smaller 
central room take up the space. 

Figure 37. Geometry of the mock-up consisting in a low-rise building and a high-rise 
building 

Boundary conditions, solvers and meshing options 

Without  preliminary  knowledge  on  the  part  of  the  experimenters  of  the  air  flow  in  the 
wind tunnel, it was decided not to consider it and to perform simulations of the flow only 
inside the mock-up. Thus, it was necessary to set the boundary conditions for the entry 

5 https://www.paraview.org/ 

37 

 
 
 
 
 
                                                 
of  air  into  the  mock-up.  The  outlets  were  considered  to  be  in  direct  contact  with  the 
outside atmosphere. 

For the simulations presented below, the following boundary conditions were defined: 

•  The air velocity at the inlets is Vinlet = U0 = 1.0 m.s-1; 
•  The static pressure at the outlets is Poutlet = Patm. 

First  of  all,  it  has  to  be  pointed  out  that  3D  flow  in  the  mock-up  (as  the  flow  in  a  full-
scale  building)  may  not  admit  a  stationary  solution.  It  is  even  more  likely  that  the  3D 
flow is unsteady. Yet, a steady simulation is easier to handle and its results are easier to 
present  and  compare  with  measurements.  It  is  also  anticipated  that  even  if  the  “real” 
flow in the mock-up is unsteady, the stationary solution should be representative of the 
flow pattern in the mock-up. Thus, for the sake of simplicity, it was decided to seek for a 
stationary flow solution. 

A Computational Fluid Dynamics problem implies a mathematical solver for the numerical 
resolution of the Navier-Stokes equations. It implies an iterative process that requires a 
stop criterion when the converged state of the solution is reached. In case of a stationary 
flow,  the  velocity  and  pressure  fields  no  longer  change  with  additional  iterations  of  the 
solver. In the mock-up study, two stationary solvers of Code_Saturne were implemented 
and their parameters adapted. 
The  turbulence  was  evaluated  using  a  RANS  k-ε  model  as  it  generally  quickly  produces 
correct  results  for  fluid  mechanics  problems  in  confined  environments.  Yet,  it  is  worth 
noticing that more precise and informative turbulence models could have been used for a 
higher computational cost. 

Four  meshes  (Figure  38)  were  generated  with  different  cell  types  and  sizes  (Table  9). 
The  study  related  to  the  meshes  had  the  main  objective  to  calculate  a  converged 
stationary flow solution with a mesh having a moderate number of cells. 

Figure 38. View of the four tested meshes 

Table 9. Characteristics of the four tested meshes 

Name of the 
mesh 

Type of the 
cells 

Fine_tetra 

Coarse_tetra 

Hexa 

Hexcore 

Tetrahedra 

Tetrahedra 

Hexahedra 

Tetra / 
hexahedra 

38 

 
 
 
 
 
 
 
 
 
Average cell 
size 

Total number 
of cells 

1-4 cm 

2-6 cm 

3 cm 

2-5 cm 

2,146,518 

457,853 

252,080 

71,259 

Thereafter,  the  velocity  fields  computed  on  the  different  meshes  are  shown  in  a  2D 
vertical section plane of the mock-up (Figure 39). In first intention, the stationary solver 
“by  default”  of  Code_Saturne  was  used  with  a  relaxation  coefficient  of  the  equations 
equal  to  0.7.  The  velocity  in  the  2D  vertical  section  plane  is  presented  for  the  four 
meshes (Figure 40). 
It  is  noticeable  in  Figure  40  that  the  overall  results  are  similar  with  more  marked 
numerical diffusion effects on the air jets for the coarsest mesh (Hexcore). However, the 
results are relatively different in the lower velocity zones such as the recirculation zones 
created by the shears with the fast jets. 

Vertical section plane 

Figure 39. View of the vertical section plane across the mock-up 

Figure 40. 2D velocity fields in a vertical section plane. Results for the four meshes 

39 

 
 
 
 
 
 
 
When the velocity results are visualized dynamically along with the numerical iterations, 
it  becomes  apparent  that  the  chosen  solver  leads  to  an  unstable  solution  whatever  the 
mesh used, in particular in the corridor of the ground floor, which is the zone envisaged 
to perform the internal to the mock-up release. These instabilities are unsteady turbulent 
phenomena inherent in the geometry, which make the robust convergence of the flow to 
a  stationary  solution  difficult.  Thus,  to  stabilize  the  CFD  calculations  and  make  them 
converge  towards  a  mean  stationary  solution,  we  used  another  solver  of  Code_Saturne 
adapted to a “quasi-stationary” resolution. With this solver, the time step varies in time 
and  space  around  a  reference  (dtref  =  0.05  s)  while  respecting  a  criterion  of  numerical 
stability  (CFL  <  5).  Moreover,  the  time  step  is  relaxed  until  the  convergence  towards  a 
numerically stable solution. 
Using  the  quasi-stationary  solver,  an  overall  stabilization  of  the  air  flow  is  observed,  in 
particular in the previously unstable zone of the corridor on the ground floor (Figure 41). 

The four meshes produce very similar results, the two tetrahedral meshes still exhibiting 
instabilities with the chosen parameters. It is probable that the search for suitable sets of 
parameters would stabilize these two calculations as well. Two of the meshes, Hexa and 
Hexcore, converge towards almost stationary solutions in the sense that the velocity no 
longer  changes  in  almost  the  entire  domain.  The  two  solutions  are  quite  close  to  each 
other, the Hexcore mesh being more subject to numerical diffusion than the Hexa mesh. 
This  is  an  expected  effect  of  the  relatively  low  resolution  in  space,  in  particular  at  the 
level of the jets coming from the air inlets. 

Unstable flows not reaching steady solutions 

        Stable flows reaching steady solutions 

Figure 41. Influence of the four meshes on the convergence of the velocity field 

Finally, the hexahedral mesh (Hexa case) was kept and numerical parameters were fitted 
to  obtain  a  stable  stationary  flow  solution  in  view  of  the  dispersion  simulations  to  be 
carried out.  In these conditions, the velocity field and the velocity vectors are  shown in 
different  section planes in Figure 42, Figure 43 and Figure 44.  The streamlines are also 
presented  in  Figure  45.  It  is  interesting  to  note  that  the  air  jets  coming  from  the 
openings in the mock-up upstream wall generate high-velocity recirculation in the rooms 
and the corridor at the second floor of the low-rise building. The area on the ground floor 
of  the  low-rise  building  is  generally  much  less  ventilated  than  the  upper  floor  of  this 
building and the high-rise building. 

40 

 
 
 
 
 
 
 
Figure 42. Velocity field in two section planes across the mock-up 

Figure 43. Velocity field and velocity vectors in two section planes of the lower building 

41 

 
 
 
 
 
 
 
Figure 44. Velocity field and velocity vectors in a section plane of the higher building 

Top view 

Side view 

Side view 

Figure 45. Streamlines in the mock-up 

42 

 
 
 
 
 
3.1.1.2 Dispersion simulation of chemical releases  

Case-studies for the releases 

Thereafter,  two  test-cases  of  dispersion  simulations  internal  to  the  mock-up  are 
considered: 

•  The  first  test-case  is  associated  with  a  release  located  outside  the  mock-up 

upstream of the flow; 

•  the second test-case with a release located inside the mock-up.  

The  parameters  of  these  3D  simulations,  namely  the  air  inlet  velocity  and  the 
characteristics  of  the  release  (amplitude  and  duration)  could  be  changed  according  to 
the experimental conditions. 

Release from the exterior of the mock-up (First test-case) 

The first test-case corresponds to the dispersion of a chemical released out of the mock-
up upstream of the flow at a sufficient distance so that the concentration of the chemical 
entering the mock-up is supposed to be uniform. 

An  unsteady  transport  equation  is  solved  for  a  scalar  “C”  representing  the  volumetric 
concentration  of  the  chemical.  The  stationary  velocity  field  computed  previously  is  used 
for this simulation. 

The initial concentration of the chemical is zero in the whole simulation domain. At t = 0, 
it  becomes  non-zero  on  the  three  air  inlets  (Figure  46).  The  boundary  condition  for  the 
scalar C takes the value C0 = 1 for 60 seconds before returning to zero. The evolution of 
the concentration in the mock-up is computed for 1,400 seconds. 

C0 = 1 for one minute 
(t = 0 to 60 s) 

Figure 46. First test-case – Boundary condition for the concentration at the mock-up 
inlets C0 = 1 for one minute (t = 0 to 60 s) 

As  the  dispersion  simulation  is  unsteady  by  nature,  the  3D  results  must  be  analysed 
dynamically.  Two  successive  phases  in  the  simulation  may  be  highlighted:  the  actual 
dispersion for 60 seconds then the purging of the system. 

43 

 
 
 
 
 
 
 
 
 
 
 
The following Figures show the concentration of the chemical at different times: 

•  Figure 47 presents the chemical plume delimited by the concentration C = 0.1 at t 
=  8  s,  i.e.  at  the  start  of  the  dispersion  process;  the  upstairs  side  rooms  fill  up 
first as the plume moves faster down the hall; 

•  Figure  48  presents  the  chemical  concentration  in  a  vertical  section  plane  across 
the mock-up at t = 53.2 s; the filling process and the preferential path taken by 
the chemical are clearly visualized; 

•  Figure  49  presents  the  chemical  concentration  in  a  vertical  section  plane  across 
the mock-up at t = 74.2 s; the mock-up is purged by the injection of fresh air into 
the small building. 

Figure 47. First test-case – Chemical cloud delimited by the concentration C = 0.1 at t = 
8 s 

44 

 
 
 
Figure 48. First test-case – Chemical volume concentration in a vertical section plane at 
t = 53.2 s 

Figure 49. First test-case – Chemical volume concentration in a vertical section plane at 
t = 74,2 s 

Figure 50 shows the quantity of the chemical remaining in the mock-up (in blue) and the 
average concentration of the chemical in the mock-up (in orange, logarithmic scale) over 
time. 

It  can  be  observed  that  90%  of  the  chemical  is  evacuated  from  the  mock-up  in  400 
seconds, while there  are still some cells in the mesh with positive concentrations at the 
end of the simulation (t = 1,400 s). Likewise, the average concentration does not reach 
zero, even if it becomes very low at the end of the simulation. 

45 

 
 
 
 
 
 
Figure 50. First test-case – Quantity of the chemical in the mock-up (in blue) and 
average concentration of chemical in the mock-up (in orange, logarithmic scale) over 
time 

Release from inside the building (Second test-case) 

The second test-case corresponds to the dispersion of a chemical from inside the mock-
up, and more precisely from the corridor of the ground floor. 
As previously, an unsteady transport equation is solved for a scalar “C” representing the 
volumetric  concentration  of  the  chemical  emitted  from  a  volume  source.  Again,  the 
stationary velocity field is used for this simulation. 
The initial concentration of the chemical is zero in the whole simulation domain. At t = 0, 
it becomes non-zero in a cell of the mesh (Figure 51). A unit source term is considered: 
it could be a unit of mass (e.g. 1 mg). The source term is dispersed for 60 seconds, that 
is to say 1.67 10-2 unit per second. The evolution of the concentration in the mock-up is 
computed for 1,400 seconds. 

46 

 
 
 
 
Location of the source 

Figure 51. Second test-case – Location of the source inside the mock-up in the corridor 
of the ground floor 

As for the first test-case, the dispersion of the chemical plume is unsteady by nature and 
the results have to be analysed dynamically through animations. 

The following Figures show the concentration of the chemical at different times: 

•  Figure 52 presents the chemical plume delimited by the concentration C = 0.01 at 
t  =  43.0  s;  the  filling  of  the  corridor  of  the  ground  floor  in  the  low-rise  building 
precedes  the  transfer  of  the  chemical  to  the  lobby  of  the  high-rise  building;  the 
side rooms of the ground floor and the first floor of the low-rise building are only 
slightly or not impacted by dispersal; 

•  Figure  53  presents  the  chemical  concentration  in  a  vertical  section  plane  across 
the mock-up at t = 51.0 s; the plume rises above the  source and it is entrained 
into  the  recirculation  cell  created  in  the  corridor  by  the  shear  with  the  air  jet 
coming from the floor; this recirculation is represented by the arrow in Figure 43. 

47 

 
 
 
 
 
Figure 52. Second test-case – Chemical cloud delimited by the concentration C = 0.01 
at t = 43,0 s 

Figure 53. Second test-case – Chemical volume concentration in a vertical section plane 
at t = 51,0 s 

Figure 54 shows the quantity of the chemical remaining in the mock-up (in blue) and the 
average concentration of the chemical in the mock-up (in orange, logarithmic scale) over 
time. It can be observed that 80% of the chemical is evacuated from the mock-up in 550 
seconds,  while  a  significant  amount  of  the  chemical  remains  in  the  mock-up, 

48 

 
 
 
 
 
approximately 10% of the total released quantity, at the end of the simulation (t = 1,400 
s). 

Figure 54. Second test-case – Quantity of the chemical remaining in the mock-up (in 
blue) and average concentration of the chemical in the mock-up (in orange, logarithmic 
scale) over time 

Figure 55 and Figure 56 show the average concentrations over time in the different parts 
of the mock-up as they are identified in Figure 13. 
The  highest  average  concentrations  are  located  in  the  Corridor  1  and  Corridor  2  and  in 
the  stairwell  on  the  ground  floor  of  the  low-rise  building  since  Corridor  1  is  the  area 
where  the  release  takes  place.  This  area  has  a  small  volume,  what  results  in  a  high 
concentration at this location. The  other rooms of the low-rise building impacted by the 
chemical  are  Room  6,  Room  8  and  Room  9.  In  the  high-rise  building,  all  rooms  behave 
similarly and achieve comparable average concentrations. 

The  comments  are  identical  for  the  maximum  concentrations  reached  over  time  in  the 
different parts of the mock-up as presented in Figure 57 and Figure 58. 

49 

 
 
 
 
 
 
Figure 55. Second test-case – Average concentrations in the different rooms of the 
upper part of the mock-up 

Figure 56. Second test-case – Average concentrations in the different rooms of the 
lower part of the mock-up 

50 

 
 
 
 
 
Figure 57. Second test-case – Maximum concentrations in the different rooms of the 
mock-up 

Figure 58. Second test-case – Maximum concentrations in the different rooms of the 
mock-up (on the left: upper part; on the right: lower part) 

3.1.2 The Post-Experimental Phase 

This  section  presents  the  second  part  of  the  3D  modelling  and  simulation  work  carried 
out  after  the  realization  of  the  experimental  trials  in  the  wind  tunnel  (by  TNO  in  April 
2021). As for the preparation of the experiments, the simulations of the actual trials were 
performed  with  Code_Saturne.  However,  there  is  a  difference  as  in  the  following  of  the 
report, the digital 3D mock-up includes both the wind tunnel and the small building, itself 
immersed in the wind tunnel used during the tests (Figure 59). 

51 

 
 
 
 
 
 
 
 
Figure 59. Installation of the small building mock-up in the wind tunnel 

3.1.2.1 Implementation of aeraulic simulations 

Generation of the 3D digital mock-up for the trials 

Both  geometries  (wind  tunnel  and  small  building)  were  accounted  for  in  the  same 
calculations, with the same overall mesh therefore. The geometry of the small building is 
shown in Figure 60. The mesh is the same as which was retained for the simulations of 
the flow and releases in the “indoor only” configuration.  

Unlike the simulations carried out to prepare for the experiments, the simulations of the 
trials couple the inside and outside of the  small building. Thus, the inlets and outlets of 
the  previous  calculations  (small  building  only)  are  replaced  by  openings  towards  the 
environment of the wind tunnel. 

52 

 
 
 
 
 
 
Openings 

Low-rise building 

High-
rise 
build
ing 

Openings 

Figure 60. Geometry of the small building in the coupled indoor/outdoor configuration 

Table 10. Characteristics of the small building in the coupled indoor/outdoor 
configuration 

Name of the mesh 

Type of the cells 

Average size of the cells 

Total number of cells cellules 

Hexa 

Hexahedra 

3 cm 

252,080 

Figure  61  shows  the  full  geometry  for  the  simulations,  in  which  the  small  building  is 
immersed  in  the  wind  tunnel.  The  latter  is  represented  by  a  parallelepiped  19.4  m  long 
and  6m  x  6m  section.  The  small  building  is  represented  on  its  platform,  which  is  also 
considered as in the trials. Air flow in the wind tunnel is provided by twenty large fans at 
the entrance to the wind tunnel. 

53 

 
 
 
 
Figure 61. Full geometry of the small building immersed in the wind tunnel in the 
coupled indoor/outdoor configuration 

Figure 62 and Figure 63 present the 3D mesh of the wind tunnel, which was generated 
with  the  Ansa  software.  This  is  a  hexahedral  mesh  whose  cells  have  a  reference  size 
(length  of  the  edges)  of  5  cm  and  3  cm  as  close  as  possible  to  the  building  model,  in 
particular at the level of the inlets and outlets. The mesh comprises 7,621,706 cells. 

Figure 62. Global view of the mesh of the wind tunnel 

54 

 
 
 
 
 
Figure 63. Zoom-in view of the mesh of the wind tunnel 

Boundary conditions and meshing 

The  boundary  conditions  at  the  inlet  of  the  wind  tunnel  are  deduced  from  the  data 
provided by TNO and presented in Figure 64: 

•  Constant inlet velocity: Vinlet = 3.5 m.s-1 (“plug” profile) 
•  Turbulent intensity deduced from the measurements (u’/U): Iturb = 15% 
•  Hydraulic diameter: Dh = 6 m 

Figure 64. Velocity measurements upstream of the small building model in the middle of 
the wind tunnel 

The boundary conditions at the outlet of the wind tunnel are those used typically for this 
kind  of  configuration,  namely  an  imposed  pressure  equal  to  the  atmospheric  pressure, 

55 

 
 
 
 
 
 
 
which  corresponds  to  a  free  outlet  in  contact  with  the  external  atmosphere  (called 
"outlet” in Code_Saturne). 

Velocity field 

The  resolution  of  the  stationary  velocity  field  by  Code_Saturne  is  performed  with  the 
“pseudo-steady” solver commented on the previous sub-section, the main parameters of 
which are presented below: 

•  Code_Saturne "pseudo-steady" solver; 
•  Pressure-velocity coupling: SIMPLEC; 
•  Reference time step: 0.05 s (from 0.005 s to 1.0 s as long as CFL < 5); 
•  Outputs / post-processing every 1 second; 
•  Convergence in 4000 iterations (18 hours on 60 cores of a work station). 

Figure 65 shows the velocity field in the horizontal section crossing the air inlets and in 
the  vertical  median  plane.  There  is  an  overall  deviation  of  the  air  flow  due  to  the 
presence of the small building in the tunnel. The air is forced to flow between the small 
building  and the  walls of  the  tunnel,  which  has  the  effect  of  accelerating it,  whether  on 
the sides or above the small building. At the rear of the small building, one can observe 
the recirculation of the air flow in a low-pressure zone. 

Figure 65. Velocity field in the horizontal section crossing the air inlets (top) and in the 
vertical median plane (bottom) 

Figure  66  represents  the  vertical  profile  of  the  air  velocity  3  m  upstream  of  the  small 
building.  The  velocity  is  almost  constant  (3.5  m.s-1).  The  velocity  profile  is  slightly 
irregular close to the top of the wind tunnel due to a spatial under-resolution of the upper 
boundary  layer.  The  lower  boundary  layer  is  more  finely  meshed  and  the  profile  is 
regular. 

56 

 
 
 
 
 
 
Figure 66. Vertical profile of the air velocity upstream the small building 

Figure 67 compares the velocity computed by Code_Saturne with the velocity measured 
by  an  anemo-meter  at  a  point located  3  m  upstream  the  small  building.  Both  velocities 
are  close,  what  demonstrates  that  the  air  flow  simulated  with  the  CFD  model  is 
representative of the air flow in the wind tunnel. 

Figure 67. Comparison of the measured and computed velocities at a point upstream 
the small building 

Figure  68  and  Figure  69  show  respectively  the  velocity  field  in  a  horizontal  section 
crossing  the  small  building  outlets  and  the  comparison  between  Code_Saturne 
computation  and  the  measurement  of  the  velocity  at  a  point  in  this  cross-section.  The 
simulated velocity is 4.0 m.s-1. Due to the acceleration of the flow between the building 
and the walls of the wind tunnel, this value is higher than upstream in the wind tunnel. 
One  can  notice  that  the  computed  velocity  slightly  overestimates  (ca.  0.5  m.s-1)  the 
measured velocity. The velocity measured at the point of consideration is identical to that 
measured upstream of the small building, whereas one should expect a slight over speed 
as explained before. 

57 

 
 
 
 
 
 
 
Figure 68. Velocity field in a horizontal section crossing the small building outlets 

Figure 69. Comparison of the measured and computed velocities at a point between the 
small building and the walls of the wind tunnel 

Figure  70  and  Figure  71  show  respectively  the  velocity  field  (in  magnitude)  and  the 
velocity  vectors  in  the  vertical  median  section  plane  of  the  small  building.  The  flow  is 
very  similar  to  that  calculated  in  the  building only  (indoor  calculation)  with,  however,  a 
higher  velocity  level  in  general  (2.1  m.s-1  in  the  inlets  of  the  building).  The  air  jets 
through the inlet openings are deflected upwards. This is an effect of the external air flow 
which  itself  is  deflected  by  the  platform  and  building  assembly.  Inside  the  building,  the 
configuration  of  the  air  flows  is  similar  to  the  one  obtained  in  the  “indoor  only” 
calculation. The velocity of the air flow between the two floors of the low-rise building is 
1.77  m.s-1.  Behind  this  air  jet,  there  is  a  very  low  speed  recirculation  zone  in  the 
downstairs corridor. 

58 

 
 
 
 
 
 
Figure 70. Velocity field restricted to the vertical median cross-section through the small 
building 

Figure 71. Velocity vectors restricted to the vertical median cross-section through the 
small building 

Figure  72  and  Figure  73  show  respectively  the  velocity  field  (in  magnitude)  and  the 
velocity vectors in the horizontal cross-section located 5 cm above the floor of the second 
level of the low-rise building, i.e. the approximate height where the sensors were placed 
during the trials. It is worth noting that some rooms (Room 10, Room 12 and Corridor) 
present quite high velocities while other rooms (Room 11 and Room 13) present almost 
zero velocities, a sign of less air renewal. 

59 

 
 
 
 
 
 
Figure 72. Velocity field in the horizontal cross-section located 5 cm above the floor of 
the second level of the low-rise building 

Figure 73. Velocity vectors in the horizontal cross-section located 5 cm above the floor 
of the second level of the low-rise building 

Figure  74  shows  the  velocity  field  and  velocity  vectors  in  the  horizontal  cross-section 
located  10  cm  above  the  floor  of  the  first  level  of  the  low-rise  building,  i.e.  the 
approximate  height  where  the  sensors  were  placed  during  the  trials.  One  can  observe 
that the velocities in the rooms on this ground floor are particularly low (Room 6, Room 8 
and Room 9). In these rooms, the air renewal is mainly due to the recirculation created 
by the shearing with the jet coming from the upper floor (see Figure 71). 

60 

 
 
 
 
 
Figure 74. Velocity field and velocity vectors in the horizontal cross-section located 10 
cm above the floor of the first level of the low-rise building 

Figure 75 shows the velocity field in a vertical section plane crossing the stairwell (on the 
right)  of  the  high-rise  building.  The  air  outlets  are  on  the  top  corner  left.  Upstairs,  the 
flow from the ground floor is distributed among the rooms and follows the floor, and then 
the back wall of the room (on the left), to the outlets. 

Figure 75. Velocity field in the vertical cross-section through the stairwell of the high 
rise 

3.1.2.2 Simulation of the experimental releases 

In  the  experimental  releases  under  consideration,  the  chemical  agent  is  the  tri-
ethylphosphate (TEP). Its density is 1.072 at 25°C. As it is close to 1, it can be assumed 
that this chemical is neither heavier nor lighter than air and behave as a passive tracer. 
As  for  the  preliminary  calculations  carried  out  before  the  trials,  the  dispersion  of  the 
chemical  is  simulated  by  solving  an  unsteady  transport  equation  for  a  passive  scalar  C 
representing the concentration of the chemical. This equation uses the stationary velocity 
field which is supposed not to change during the experimental releases. 

61 

 
 
 
 
 
 
 
 
Scenario 3 - Experiment #1 ("low cloud") 

In  the  so-called  "low  cloud"  experiment,  the  chemical  is  released  from  the  wind  tunnel 
entrance on a height of 2 m for 10 minutes. The target concentration downstream of the 
inlet openings of the small building is 0.5 mg.m-3. 
In the CFD simulation, a constant concentration is imposed equal to C0 = 0.5 mg.m-3 at 
the tunnel entrance on a height of 2 m from t = t0 and for 10 minutes (Figure 76). The 
distribution of the chemical in the tunnel and in the small building is calculated from t = 
t0. The total simulated time is 2,400 s. 

Figure 76. Experiment #1 – Initial condition (t = t0) at the entrance to the wind tunnel 

Figure 77 presents the concentration field in the median vertical plane of the wind tunnel 
at  different  times  between  t  =  t0  +  6  s  and  t  =  t0  +  2,000  s.  The  scale  of  the 
concentration is identical and varies between 0 and 0.5 mg.m-3. 
From  t  =  t0,  the  boundary  condition  for  the  concentration  at  the  entrance  to  the  wind 
tunnel  changes  instantly  (C0  =  0.5  mg.m-3).  One  can  observe  the  formation  of  a 
chemical plume upstream of the small building, which fills the wind tunnel to a height of 
2 m. The concentration field outside the building is static from about t = t0 + 20 s. The 
building  itself  quickly  fills  up  with  the  chemical  agent  penetrating  through  the  air  inlets 
facing the wind, first the upper floor of the low-rise building (up to t = t0 + 60 s), then 
gradually  the  high-rise  building  (t  =  t0  +  400  s).  From  t  =  t0  +  600  s,  the  boundary 
condition  for  the  concentration  at  the  entrance  to  the  tunnel  changes  again  (C0  =  0 
mg.m-3). Consequently, the small building is gradually purged by the supply of fresh air. 
At t = t0 + 660 s, the second level of the lower building is almost purged and the plume 
is progressively diluted in the other parts of the building. At t = t0 + 2,000 s, the whole 
building is almost purged. It is worth noticing that the purge is longer in the ground floor 
of the low-rise building as it is poorly ventilated. 

t = t0 + 6 s 

62 

 
 
 
 
 
 
t = t0 + 21 s 

t = t0 + 60 s 

t = t0 + 400 s 

t = t0 + 601 s 

t = t0 + 610 s 

63 

 
 
 
 
 
 
t = t0 + 660 s 

t = t0 + 2,000 
s 

Figure 77. Experiment #1 – Concentration field in the median vertical cross-section of 
the wind tunnel at different instants between t = t0 + 6 s and t = t0 + 2,000 s 

Figure 78 shows the sensors which were set up in the small building for the trials. The six 
sensors are of the LCD type (Figure 79) and are located in the center of the rooms at a 
height of 5 cm or 10 cm depending on the rooms. The cabling of the sensors to a laptop 
outside the small building was done in such a way as to interfere as little as possible with 
the flow inside the building. 

Figure 78. Location of the sensors inside the small building  

64 

 
 
 
 
 
Figure 79. LCD sensors 

Figure  80  compares  the  results  of  the  CFD  simulation  with  the  measurements  provided 
by  TNO.  For  the  results  of  the  calculations,  it  was  considered  the  evolution  of  the  local 
chemical  concentration  on  the  six  points  corresponding  to  the  positions  of  the  sensors 
(virtual sensors). 

It  must  be  pointed  out  that  it  is  very  difficult  to  compare  CFD  and  sensor  readings  for 
several reasons: 

•  First, the exact location of the sampling by the sensors is not clearly identified and 
the space occupied by the sensors themselves is far from negligible compared to 
the size of the rooms. 
In  addition,  the  numerical  results  are  computed  and  presented  in  mg.m-3  (scale 
on  the  left),  while  the  measurements  provided  by  TNO  are  in  an  arbitrary  unit 
(scale on the right) with no useable information to convert these values in mg.m-
3. 

• 

65 

 
 
 
 
 
 
Figure 80. Experiment #1 – Comparison of the concentrations obtained by the CFD and 
by the measurements at the six  

In  order  to  compare  more  efficiently  the  CFD  simulations  to  the  measurements,  both 
numerical  and  experimental  results  were  made  dimensionless  by  dividing  them  by  the 
maximum  concentration  Cmax.  It  was  assumed  that  each  sensor  was  calibrated 
independently  of  the  others  and  each  sensor  was  scaled  by  the  maximum  recorded 
concentration.  The  same  approach  was  used  for  the  virtual  sensors  in  order  to  be 
comparable with the measurements. 

Figure  81  shows  the  comparison  of  the  normalized  computed  and  measured 
concentrations. It can be observed: 

•  An  excellent  general  reproduction  of  the  dynamics  of  the  dispersion  in  most 

rooms; 

•  A  slightly  faster  filling  and  purging  for  the  simulations  compared  to  the 
measurements,  which  may  be  the  sign  of  a  slight  overestimation  of  the  velocity 
upstream of the small building; 

•  A  delay  of  the  simulation  in Room  9  compared  to  the  measurements  (about  300 

s); 

•  A  different  dynamic  in  Room  8,  but  this  remark  has  to  be  qualified  as  the 

concentration levels are very low in this room (see Figure 80). 

66 

 
 
 
 
 
 
Figure 81. Experiment #1 – Comparison of the normalized concentrations obtained by 
the CFD and by the measurements at the six sensors 

Scenario 2 - Experiment #6 ("external release") 

In this experiment called “external release”, the chemical agent is released from a device 
(presented in Figure 23) located upstream of the small building at a distance of 3 m and 
at  a  height  of  50  cm.  The  total  dispersed  mass  of  TEP  is  0.93  g  over  10  minutes.  The 
mean concentration Cmean of the plume at the entrance to the small building is estimated 
at approximately 0.5 mg.m-3. 

In the CFD simulation, the release is modelled by an intermittent volume source term in 
the  transport  equation  of  the  concentration.  At  t  =  t0,  it  is  assumed  that  the 
concentration  of  the  chemical  agent,  initially  zero  in  the  whole  domain,  becomes  non-
zero in a cell of the mesh (see Figure 82). The release rate from this source is defined by 
considering the total dispersed mass and the duration of dispersion, i.e. 0.93 g / 600 s = 
1.55.10-3 g.s-1. The evolution of the concentration in the tunnel and the small building is 
simulated for 2,400 seconds. 

67 

 
 
 
 
 
Figure 82. Experiment #6 – Location of the source for the external release 

Figure  83  presents  the  concentration  field  in  the  median  vertical  median  plane  of  the 
tunnel  at  different  times  between  t  =  t0  +  15  s  and  t  =  t0  +  1,000  s.  The  scale  of  the 
concentration is identical and varies between 0 and 0.5 mg.m-3. 
From  t  =  t0,  the  concentration  in  the  source  cell  upstream  of  the  building  changes 
instantly (C0 = 0.5 mg.m-3) and the generated plume is advected by the air flow in the 
wind  tunnel  towards  the  building.  The  chemical  agent  enters  the  small  building through 
the air inlets on the floor level of the low-rise building. The building fills up quickly, first 
the  upper  floor  of  the  low-rise  building  (t  =  t0  +  60  s),  then  gradually  the  high-rise 
building  (t  =  t0  +  400  s).  From  t  =  t0  +  600  s,  the  source  term  becomes  zero  and  the 
concentration of the chemical agent in the incident air also (C0 = 0 mg.m-3). The building 
is then progressively purged by the supply of fresh air. At t = t0 + 638 s, the floor level 
of  the  lower  building  is  almost  purged  and  the  plume  is  gradually  diluted  in  the  other 
parts  of  the  small  building.  At  t  =  t0  +  1000  s,  the  whole  building  is  almost  purged. 
Similarly,  to  experiment  #1,  it  is  worth  noticing  that  the  purge  is  longer  in  the  ground 
floor of the low-rise building as it is poorly ventilated. 

t = t0 + 15s 

68 

 
 
 
 
 
t = t0 + 60 s 

t = t0 + 150 s 

t = t0 + 400 s 

t = t0 + 638 s 

t = t0 + 1,000 
s 

69 

 
 
 
 
 
 
Figure 83. Experiment #6 – Concentration field in the median vertical cross-section of 
the wind tunnel at different instants between t = t0 + 15 s and t = t0 + 1,000 s 

Figure  84  compares  the  results  of  the  CFD  simulation  with  the  measurements  provided 
by  TNO.  For  the  results  of  the  calculations,  it  was  considered  the  evolution  of  the  local 
chemical  concentration  on  the  six  points  corresponding  to  the  positions  of  the  sensors 
(virtual sensors) shown in Figure 78. 

The  difficulties  described  for  experiment  #1  in  comparing  the  simulations  with  the 
experiments  are  still  present.  Likewise,  the  numerical  results  are  computed  and 
presented in mg.m-3 (scale on the left), while the measurements provided by TNO are in 
an arbitrary unit (scale on the right) with no useable information to convert these values 
in mg.m-3. 
It is important to note that the maximum concentration in the building (0.487 mg.m-3) is 
very close to the concentration targeted in the experiment (0.5 mg.m-3). 

Figure 84. Experiment #1 – Comparison of the concentrations obtained by the CFD and 
by the measurements at the six sensors 

Similarly,  to  experiment  #1,  both  numerical  and  experimental  results  were  made 
dimensionless  by  dividing  them  by  the  maximum  concentration  Cmax.  It  was  assumed 
that each sensor was calibrated independently of the others and each sensor was scaled 

70 

 
 
 
 
 
 
 
by  the  maximum  recorded  concentration.  The  same  approach  was  used  for  the  virtual 
sensors in order to be comparable with the measurements. 

Figure  85  shows  the  comparison  of  the  normalized  computed  and  measured 
concentrations.  The  observations  in  experiment  #6  are  similar  to  those  of  experiment 
#1: 

•  An  excellent  general  reproduction  of  the  dynamics  of  the  dispersion  in  most 

rooms; 

•  A  slightly  faster  filling  and  purging  for  the  simulations  compared  to  the 
measurements,  which  may  be  the  sign  of  a  slight  overestimation  of  the  velocity 
upstream of the small building; 

•  A  delay  of  the  simulation  in Room  9  compared  to  the  measurements  (about  300 

s), 

•  A  different  dynamics  in  Room  8,  but  this  remark  has  to  be  qualified  as  the 

concentration levels are very low in this room (see Figure 84). 

Figure 85. Experiment #6 – Comparison of the normalized concentrations obtained by 
the CFD and by the measurements at the six sensors 

Regarding  the  Room  9,  the  numerical  results  exhibit  a  systematic  shift  of  about  300  s 
compared to the measurements. One possible explanation is the difference in positioning 

71 

 
 
 
 
 
 
 
between  the  virtual  sensors  (in  the  CFD)  and  the  real  sensors  (in  the  experiments).  In 
order to investigate the variability of the numerical results depending on the location of 
the sensor, four alternative positions of the virtual sensor close to its initial position (plus 
or minus 5 cm) in Room 9 were studied (see Figure 86). 

Figure 87 shows the results with the orange curve representing the concentration on the 
virtual  sensor  at  its  initial  position  and  the  other  curves  concentrations  obtained  by 
varying  the  position  of  the  virtual  sensor.  One  can  notice  that  the  results  vary 
significantly  when  the  position  of  the  sensor  is  modified.  For  example,  for  a  position 
closer to the door of Room 9, the concentration increases much earlier (pink curve) and 
is  more  synchronous  with  the  values  measured  by  the  sensor  during  the  experiment. 
Still,  it  seems  that  the  maximum  concentration  is  always  reached  at  about  the  same 
time, that is to say 300 s later than in the experiment. 

Figure 86. Experiment #6 – Alternative positions of the virtual sensor in Room 9 

72 

 
 
 
 
 
 
Figure 87. Experiment #6 – Comparison between the simulated concentrations on close 
virtual sensors in Room 9 and the measured concentrations in Room 9 

There are several potential explanations for this difference: 

•  The  shift  can  come  from  the  real  sensor.  Indeed,  it  is  quite  astonishing  that  the 
concentration  increases  instantaneously  at  t  =  t0,  whereas  a  certain  time  is 
necessary  for  the  plume  to  fill  in  progressively  the  different  rooms,  especially 
Room 8 and Room 9. 

•  There may be leakages in the small building mock-up or they may be differences 
between  the  geometry  of  the  building  in  the  experiment  and  the  geometry  and 
mesh  created  for  performing  the  simulations,  resulting  in  differences  in  the 
evaluation of the transfers within the building. 

•  The  flow  simulation  may  be  a  bit  less  accurate  in  Room  9  (and  also  in  Room  8) 
than  in  the  rest  of  the  small  building.  As  a  matter  of  fact,  these  two  rooms  are 
located in a “dead zone” of the flow. The ventilation is weak and only ensured by 
the  recirculation  which  is  generated  by  the  shear  of  the  air  jet  coming  from  the 
floor of the low-rise building. This is certainly difficult for our “k-epsilon” turbulent 
model  to  deal  with  this  configuration.  Moreover,  the  flow  in  this  part  of  the 
building  is  probably  unstable  and  intermittent,  contrarily  to  the  simplifying 
stationarity assumption made in the numerical study. Thus, the fluid dynamics in 
this part of the small building would probably need more sophisticated flow solver 
and turbulence modelling. 

Scenario 1 - Experiment #12 ("internal release") 

In the experiment called “internal release”, the chemical agent is released from a device 
(presented in Figures 28-29) located in the middle of Room 8 at a height of 7 cm. During 
the trial, the dispersal device is connected to an evaporation system located at the rear 
of  the  small  building  model  so  as  not  to  disturb  the  incident  air  flow  upstream  of  the 
building. 

According to TNO which conducted the trials, the internal dispersion experiment resulted 
in  several  successive  trials  which  were  unsuccessful  due  to  various  problems  with 
condensation  in  the  system,  adjustment  of  the  dispersed  amount  or  malfunction  of  the 
LCD  sensors.  In  the  end,  none  of  the  trials  carried  out  were  fully  satisfactory  because 

73 

 
 
 
 
 
 
even in the trial that was  chosen  for the  simulation work, the  LCD sensor placed in the 
same  room  as  the  source  (Room  8)  quickly  saturated  giving  no  usable  measurements, 
while for the other sensors placed in the building, the signal remained almost zero. 

In  the  trial  selected  for  this  report,  the  total  dispersed  quantity  of  TEP  is  2  μL 
(microliter),  i.e.  a  mass  of  approximately  2.35724.10-3  mg  for  2  minutes.  The 
concentration targeted in Room 8 is evaluated at approximately 6 mg.m-3. 
In the CFD simulation, the release is modelled using the same approach as in experiment 
#6 by an intermittent volume source term in the transport equation of the concentration. 
At t = t0, it is assumed that the concentration of the chemical agent, initially zero in the 
whole domain, becomes non-zero in a cell of the mesh (see Figure 88). The release rate 
is  defined  by  considering  the  total  dispersed  mass  and  the  duration  of  dispersion,  i.e. 
2.35724.10-3  mg  /  120  s  =  1.964.10-5  mg  /  s.  The  plume  is  advected  from  the  source 
into  the  small  building  by  the  air  flow  without  any  other  mechanism  to  disperse  the 
chemical  being  modelled.  The  evolution  of  the  concentration  in  the  small  building  is 
simulated for 1,300 seconds. 

Figure 88. Experiment #12 – Location of the source for the internal release (Room 8) 

Table  11  presents  the  maximum  concentrations  on  the  virtual  sensors  obtained  in  the 
simulation.  One  can  notice  a  ratio  of  at  least  10,000  between  the  sensor  in  Room  8 
where the source is located, and the other sensors. Obviously, there is an accumulation 
of the dispersed chemical only in Room 8. 

Table 11. Experiment #12 – Maximum concentration on the six virtual sensors 

Room 1 

Room 4  Room 8  Room 9 

Room 10 

Room 11 

Maximum 
concentration 
(mg.m-3) 

6.44.10-5 

2.05.10-4 

1.94 

2.53.10-4  6.29.10-27  1.31.10-21 

74 

 
 
 
 
 
 
Figure 89 shows the chemical plume defined by the volume whose concentration C is less 
than or equal to the Cmax maximum concentration at different times between t = t0 + 20 
s and t = t0 + 1,045 s. The scale of the concentration is logarithmic because the plume 
keeps very local in space and the gradients are very large in this configuration. 
It can be seen that after the initial phase of dispersion up to t = t0 + 120 s, the plume 
remains  mainly  contained  in  Room  8  throughout  the  simulation  without  spreading  into 
the  rest  of  the  small  building.  It  gradually  dilutes  in  the  room  and  ends  up  being 
evacuated slowly. Note that the visualization adopted does not allow to apprehending the 
extremely low concentrations during the purge of the building. 

t = t0 + 20 s 

t = t0 + 100 s 

t = t0 + 200 s 

75 

 
 
 
 
 
t = t0 + 480 s 

t = t0 + 680 s 

t = t0 + 1,045 
s 

Figure 89. Experiment #12 – Visualization of the chemical plume defined by the volume 
C ≤ Cmax inside the small building at different instants between t = t0 + 20 s and t = t0 
+ 1,045 s 

Figure 90 presents the concentrations on the virtual sensors in the CFD simulation. One 
can  observe  that  the  concentration  on  the  sensor  in  Room  8  is  much  greater  than  the 
concentrations  on  the  sensors  in  the  other  rooms.  During  the  experiment  conducted  by 
TNO, the response of the actual sensor in Room 8 was saturated making the comparison 
between the numerical results and the measurements impossible for this trial. 

76 

 
 
 
 
 
 
 
Figure 90. Experiment #12 – Concentrations on the virtual sensors in the CFD 
simulation 

3.1.3 Conclusions of Code_Saturne simulations 

Pre-experimental modelling 

The  first  set  of  simulations  was  done  before  the  experimental  trials  as  a  preparatory 
work. A 3D digital mock-up was generated which faithfully represents the small building. 
The  digital  mock-up  was  utilized  for  performing  aeraulic  CFD  simulations  with 
Code_Saturne. The simulations were carried out inside the building disregarding the wind 
tunnel (“indoor” simulations). A mesh sensitivity study was also performed to identify the 
best compromise in terms of stability and robustness of the air flow solution. 

It  should  be  noted  that  the  studied  fluid  dynamics  problem  has  intrinsic  unstable  low 
velocity flow zones with intermittency and unsteady turbulent perturbations. The location 
planned  for  the  internal  release  trials  is  one  of  those  unstable  flow  zones.  It  was 
immediately  identified  and  reported  during  the  first  set  of  simulations  that  the 
measurements and the comparison with the numerical results in this critical zone would 
therefore prove to be tricky. 

Two types  of dispersion simulations were  run with Code_Saturne depending on whether 
the source is outside or inside the small building. These computations relate only to the 
mock-up  disregarding  the  wind  tunnel.  They  have  been  compared  with  the  simulations 
carried out by using ADREA-HF code (see section 3.2.4).  

Comparison of modelling and experimental trials (Post-experimental modelling) 

The  second  set  of  simulations  was  done  after  the  experimental  trials  with  the  view  to 
replicating them. A 3D global digital model was generated including the building mock-up 
immerged  in  the  wind  tunnel.  The  digital  model  was  used  for  coupled  “indoor/outdoor” 
simulations of the air flow in the tunnel and the small building, then for three dispersion 
simulations, the sources of which were positioned differently according to the trials which 
one sought to reproduce. 
The 3D aeraulic simulations show velocity fields consistent with the air flow in the wind 
tunnel. The velocity measurements taken in the tunnel during the trials are close to the 
the  calculated  speed 
CFD  results  with,  however,  a  slight  overestimation  of 

77 

 
 
 
 
 
 
 
 
(approximately  0.5  m.s-1)  near  the  building  outlets.  This  deviation  may  be  due  to  an 
error in the positioning of the sensor or in the provided experimental data of the velocity 
profile generated by the fans at the entrance to the wind tunnel. 

The  flow  in  the  building  mock-up  is  consistent  and  similar  to  the  results  of  the  indoor 
simulation, with the exception that the velocity level is higher in the case of the coupled 
indoor/outdoor  simulation.  In  fact,  the  air  jets  at  the  building  inlets  are  deflected 
upwards. This is an effect of the external air flow which is itself deflected by the platform 
and building assembly. Inside the small building, the general configuration of the air flow 
is  similar  to  that  obtained  in  the  indoor  calculation.  On  the  ground  floor  of  the  low-rise 
part of the building, one can notice a low velocity recirculation zone in the corridor at the 
rear of the air jet between the two floors, and the poor ventilation of the side rooms in 
this zone, notably in Room 8, the targeted place for internal dispersion. This observation 
had already been made during the preparatory simulations. 

Dispersion  simulations  were  carried  out  to  reproduce  the  tree  types  of  release  trials  in 
the  wind  tunnel  (Scenarios  3,  2,  1).  TEP  was  the  chemical  agent  release  in  all 
experiments. In particular: 

• 

• 

• 

In Scenario 3 a “low cloud” of chemical with an almost uniform concentration of 
0.5  mg.m-3  is  generated  at  the  entrance  to  the  wind  tunnel  and  heads  to  the 
small building. The numerical results show a general excellent agreement with the 
trial in terms of dispersion dynamics in most of the rooms where sensors were set 
up.  The  filling  and  purging  of  the  rooms  seem  a  bit  faster  in  the  simulations 
compared  to  the  measurements.  This  could  be  due  to  a  slight  overestimation  of 
the flow velocity upstream of the mock-up building. Furthermore, there is a time 
shift of around 300 s (delay) in the rise of the numerical concentration in Room 9 
compared to the experimental concentration. There is also a different dynamics in 
Room  8  between  the  computation  and  the  trial,  but  this  has  to  be  considered  in 
the light of the very low concentrations in this room. 
In Scenario 2, an external release is arranged from a dispersal device installed 
at a distance of 3 m from the mock-up building. As shown by the simulations, this 
distance  is  such  that  the  chemical  concentration  at  the  small  building  inlets  is 
almost  equal  to  the  target  concentration  of  0.5  mg.m-3.  Very  logically,  the 
conclusions are thus identical to Scenario 3. 
In  Scenario  1,  an  internal  release  is  performed  from  a  dispersal  device 
installed in Room 8 of the mock-up building. A ratio of at least 10,000 is observed 
between the numerical concentration on the virtual sensor in Room 8, where the 
source is located, and the other sensors. This clearly signs an accumulation of the 
chemical agent only in Room 8. Moreover, the response of the sensor in Room 8 
reaches its saturation level, which makes any comparison with the CFD irrelevant. 

In the first two scenarios, a lag of the CFD results in comparison to the measurements in 
Room  9  has  been  noticed.  In  order  to  study  the  variability  of  the  results  depending  on 
the position of the virtual sensor in Room 9, we have considered alternative locations for 
this  sensor.  The  numerical  results  vary  significantly  when  the  position  of  the  sensor  is 
changed.  It  shows  that  there  are  sharp  concentration  gradients  in  the  room.  However, 
the maximum concentration is reached at about the same time, that is to say 300 s later 
in the computations than in the experiment. The explanations can be as follows: 

•  The  shift  can  originate  from  the  real  sensor.  Indeed,  it  is  surprising  that  the 
concentration  increases  instantaneously  in  Room  9,  while  a  certain  time  is 
necessary  for  the  plume  to  reach  gradually  the  different  rooms  of  the  mock-up 
building, especially Room 8 and Room 9. 

•  There may be leakages in the building mock-up or differences between the actual 
geometry  of  the  small  building  and  the  geometry  and  mesh  created  for 
performing the CFD simulations, resulting in differences in the transfers within the 

78 

 
 
 
 
 
 
building. 

•  The simulations may not accurately replicate the flow in Room 9 (and in Room 8) 
which  are  located  in  a  “dead  flow”  zone  of  the  small  building,  which  is  poorly 
ventilated. This zone is characterized by a recirculation generated by the shear of 
the  air  jet  coming  from  the  floor  of  the  low-rise  part  of  the  building.  The  fluid 
dynamics  in  this  zone  is  extremely  complex.  First,  the  flow  is  intermittent  and 
unstable,  contrarily  to  the  stationarity  assumption  made  to  simplify  the 
computations.  Then,  the  turbulence  is  difficult  to  model  and  would  perhaps 
deserve a more detailed approach than the one exploited in these simulations. 

3.2.  Computation Approach (ADREA-HF) 

A series of passive  releases with a specific time duration  ΔΤ have been simulated using 
CFD  RANS/LES  ADREA-HF.  Both  external  and  internal  sources  were  considered.  The 
positions of the sources are the experimental ones.  

For  the  pre-experimental  modelling  the  release  rate  is  unity  whereas  for  post 
experimental modelling the real experimental releases have been considered.  
In  order  to  study  the  dispersion  problem,  a  number  of  imaginary  and  real  sensors  are 
considered, one per each room and corridor to reveal the behaviour of the agents as it is 
spreading  through  the  various  compartments  of  the  building.  The  position  of  the 
imaginary  sensors  has  been  put  in  the  middle  of  the  room  at  a  height  of  0.26  m 
corresponding roughly to the human height in real scale. Additional positions of sensors 
have been added that correspond to the experimental ones.  In particular: 

1.  The  first  effort  concerns  pre-trials  computations.  Two  main  cases  were 

considered: 

o  Case I: The building is immersed into the tunnel, so that the setup is close 

to experimental conditions;  

o  Case II: The building is surrounded by free atmosphere, so that the setup is 

closer to reality. 

It is noticed that intercomparison between the two cases will enable us to get a feeling to 
what  degree  the  experimental  setup  represents  reality.  To  this  purpose,  this  pre-
experimental modelling study has considered: 

• 
• 
• 

Internal and external source releases at the experimental positions; 
In all cases the agent was considered passive; 
In each source there has been considered two release conditions:  

a) the release lasts 1 min; 
b) the release is continuous. 

•  These conditions: 

a) reveal the behaviour of the agent dispersion under relatively short release;  
b) enable to estimate dosages under any release time (Bartzis et al, 2021). 

2.  The  second  effort  concerns  post-trials  computations  where  internal  and 

external source releases are at the experimental positions. 

For  reason  of  simplicity  and  given  the  preliminary  character  of  the  experimental 
simulations it is assumed that the agent behaves as passive gas.   It  is noticed that the 
experimental  agent  was  TEP  with  a  Molecular  Weight  (MW=  182.15g/mol)  significantly 
higher  than  ambient  air  (MW=28.97g/mol).    This  assumption  could  be  considered  valid 
for low TEP concentrations in air. The reason is that, in this case, the density of the air 
mixture  could  be  comparable  with  the  density  of  normal  air.    It  is  noticed  that  the 
modelling  of  the  agent  dense  gas  behaviour  would  require  more  detailed  relevant 
measurements  at  the  release  exhaust  cross  section  that  goes  beyond  the  scope  of  this 

79 

 
 
 
 
 
 
 
 
 
study. 

The  experimental modelling considers both external and internal sources. The emphasis 
was given to the shortest time releases (External Experiment #8 (ΔΤ=200s) and Internal 
Experiment #12 (ΔΤ=120s)).  Those cases are  expected to be the most challenging due 
to  relatively  strong  concentrations  temporal  gradients  in  combination  with  the  spatial 
gradients  due  to  geometry  complexity.  The  other  experiments  have  also  been 
considered. 

ADREA-HF Methodology  

ADREA-HF code is a 3D finite volume compressible flow model treating passive as well as 
buoyant/multiphase  releases.  It  uses  the  Eulerian  approach  but,  in  the  passive  mode, 
can use the Langrangian approach as well. It can handle concentration statistics. It has 
been  widely  used  indoors  and  outdoors  for  air  quality  assessment  as  well  as  safety 
applications.  (Venetsanos  et  al,  2010).  ADREA-HF  code  is  used  in  Cartesian  grid. 
Geometry  is  reproduced  with  high  accuracy  (no  stair-case  effect)  with  the  use  of 
porosities, with the help of EDes pre-processor6. 

In  the  present  study  the  RANS  mode  with  k-ε  turbulence  model  has  been  used.  In 
particular: 

•  Domain  and  Grid:  The  computation  domain  includes  both  indoor  and  outdoor 

micro-environments.  

• 

• 

In Case I, the domain was extended up to the wind tunnel entrance and side 
and top walls. In the downstream direction it extends up to 10H where H is 
the building height (i.e H=2,05 m).  
In Case  II the domain was extended up 4H upstream, 15H downstream 6H 
vertically and 6H laterally. Concerning the grid size the minimum dx, dy, dz 
were  confined  by  the  building  wall  thickness  i.e.  0.005m.  211  x  134  x  114 
for Case I and 186 x 143 x 115 is the number of cells. 

•  Concentrations and Exposure: The agent spread into the air environment was 
quantified  at  specific  predetermined  locations  considering  a  real  or  fictitious 
sensor.  The  agent  burden  at  the  above  locations  was  expressed  with  two 
exposure  parameters:  (a)  the  ensemble  average  concentration  as  a  function  of 
time and (b) the ensemble average dosage.  
The  dosage  D  (kg  s/m3)  as  a  function  of  the  concentration  time  series  C(t) 
(kg/m3) at a specific sensor was defined as follows: 

(1) 

The modelled ensemble average C(t) was provided by the RANS CFD simulations. 
The prediction of ensemble average   was obtained by running RANS CFD at steady state 
mode  as  suggested  by  (Bartzis  et  al,  2021).  In  this  case  the  source  release  rate  was 
equal  to  the  source  release  rate  of  the  finite  release.  It  was  noticed  that  such  an 
approximation is as valid as RANS approximation itself. In this case: 

(2) 

Where: 

    is the ensemble average (mean) steady state concentration 

6 https://www.researchgate.net/profile/George-Efthimiou-
3/publication/320181386_CFD_SIMULATIONS_OF_POLLUTANT_SPATIAL_DISTRIBUTION_IN_A_LARGE_OFFICE/links/59d32e58
4585150177f92788/CFD-SIMULATIONS-OF-POLLUTANT-SPATIAL-DISTRIBUTION-IN-A-LARGE-
OFFICE.pdf?origin=publication_detail 

80 

 
  
 
 
 
 
 
 
 
              
 
 
 
 
  
 
 
 
 
 
 
 
                                                 
ΔΤS is the release time duration 

The above-mentioned modelled parameters have been presented in a normalized form as 
follows: 

Normalized Concentrations:                

Normalized Dosages:  

Where: 

UREF is the specified inlet velocity (m/s) 
Q is the source release rate (kg/s) 
QTS is the total mass released (kg) i.e. 

 (3) 

        (4) 

                 (5) 

The convenience of the above-mentioned normalizations is justified from the fact that the 
relevant  numbers  produced  are  good  approximations  for  different  source  releases  and 
different inlet velocities. 

3.2.1 The Pre-Experimental Phase  

Pre-experimental  modelling  exercises  have  been  performed  mainly  focusing  on  short 
time (i.e., 1 min) releases internally and externally. The ADREA-HF code has been used 
to  perform  the  simulations  utilizing  the  CFD  RANS  approximation  and  the  standard  k-ε 
turbulence model. 

3.2.1.1 The external source 

The source was located at the experimental position, i.e., at the central vertical plane, 50 
cm above the ground and 5.25 m upward the mock-up. 

Steady state and dosage  

Steady  state  simulations  have  been  performed  for  Case  I  and  Case  II  as  explained 
above.  The  dosage  at  a  sensor  location  was  given  by  the  relationship  (Bartzis  et  al, 
2021): 

(D∙UREF)/QTS = (CS∙UREF)/Q                                          (6) 

In Figure 91, the normalized dosages at the building inlets are compared for Case I and 
Case II.  

81 

 
 
 
 
 
 
 
 
 
 
 
 
     
 
 
 
 
 
 
 
 
           
 
 
 
 
 
 
 
 
 
 
Figure 91. Building inlet dosages under in free and wind tunnel environment 

The dosages under the free atmosphere were almost half compared with the wind tunnel 
case. This was expected since the free atmosphere provided more space for the agent to 
escape  from  the  building.  In  other  words,  the  presence  of  the  tunnel  gives  a  more 
conservative picture compared with reality for the upwind external source.  

The 1- min short release 

In  this  section  only  the  wind  tunnel  case  was  considered  since  the  experiments  took 
place under this set up. It was noticed that the dosages here can be approximated by the 
relationship (Eq. 6) as this has been derived from the steady state simulations.  
Thus,  the  normalized  dosage  approximations  as  derived  by  the  corresponding  steady 
state simulations, are given in Figure 92. 

Figure 92. External source, 1-min release: estimation of the normalized dosages (m-2) 

The  normalized  dosages  at  the  outdoor  upstream  reference  point,  the  inlet  and  outlet 
centers  in  the  rooms  are  given.  As  expected,  the  steady  state  concentrations  and 

82 

 
 
 
 
 
 
 
consequently  dosages  become  uniform  indoors  as  a  result  of  the  long-lasting  incoming 
pollutant spreading everywhere. What makes the difference is the temporal behaviour as 
we  will  see  below.  It  was  noticed  that  the  dosage  indoors  was  substantially  lower  than 
the  dosage  upstream  outdoors  due  to  the  fact  that  a  significant  mass  of  the  agent 
escapes the building.  
The  agent  concentration  as  a  function  of  time,  in  each  sensor  considered,  is  given  in 
Figures 93-94.  

Figure 93. Low building normalized concentrations: top floor (left) and ground floor 
(right) 

Figure 94. High building normalized concentrations: top floor (left) and ground floor 
(right) 

It is clear that the rooms concentrations time profiles can be quite different. This can be 
explained  from  the  fact  that  rooms  did  not  have  the  same  ventilation  capabilities  and 
inflow pattern.  

The normalized peak concentrations in the various rooms and building inlets/outlets are 
shown in Figure 95. 

83 

 
 
 
 
 
 
 
 
Figure 95. External source, 1-min release: The normalized peak concentrations (m-2) 

Figure 96. External source, 1-min release: The residence times 

Figure 97. External source, 1-min release: The arrival times  

Figure 96 and Figure 97 concern arrival and residential times respectively. They refer to 
the time that the concentration reached half of its peak value (t_1/2 arrival) and the time 
that the concentration remained above half of its peak value (Δt_1/2 residence). 

84 

 
 
 
 
 
 
 
The rooms 6,8,9,11 and 13 were characterized by a relatively poor ventilation where the 
agent arrived relatively late, it kept low concentrations but its presence lasted for a long 
time. 
Higher  peak  concentrations  appear  in  the  low  building  top  floor  compared  with  peak 
concentrations in the high building which seems reasonable. 

3.2.1.2 The internal source 

The  source  was  located  at  the  experimental  position,  i.e.,  at  the  center  of  room  8  at  a 
distance 7 cm above the floor. 

Steady state and dosage  

Steady  state  simulations  have  been  performed  also  here,  for  Case  I  and  Case  II  as 
explained above. In Figure 98 the normalized dosages indoors are compared. 

Figure 98. Indoor Normalized Dosages under in free and wind tunnel environment  

For the indoor release, the dosages for the free and wind tunnel environment were rather 
close indicating that the wind tunnel results are more comparable with a real case.  

The 1-min short release  

In this section only the wind tunnel case was considered. It was noticed that the dosages 
here can be approximated by the relationship (Eq. 6) as this has been derived from the 
steady state simulations. Thus, no extra estimations were needed for the dosage.  

The  agent  concentration  as  a  function  of  time,  in  each  sensor  considered,  is  given  in 
Figures 99-100. 

85 

 
 
 
 
 
 
 
 
 
Figure 99. Low building ground floor normalized concentrations 

Figure 100. High building normalized concentrations: top floor (left) and ground floor 
(right)  

As explained before the rooms concentrations time profiles are quite different due to the 
fact that rooms do not have the same ventilation capabilities and inflow pattern.  
The normalized peak concentrations in the various rooms are shown in Figure 101. 

Figure 101. Internal source, 1-min release: The normalized peak concentrations (m-2) 

86 

 
 
 
 
 
 
 
 
Figure 102. Internal source, 1-min release: The residence times (seconds) 

Figure 103. Internal source, 1-min release: The arrival times (seconds) 

Figures 102 and 103 concern residential and arrival times respectively.  
The  rooms  6  and  9  are  characterized  by  a  relatively  high  residence  and  arrival  times 
mainly  due  to  poor  ventilation.  In  those  rooms  although  are  near  the  source  are 
experiencing modest peak concentrations.  
Due  to  the  specific  flow  patterns,  the  exposure  on  the  low  building  top  floor  is  almost 
non-existent.  

3.2.2 The Post-Experimental Phase 

The Inflow conditions 

After  setting  up  the  problem  geometry  and  the  sensors  position,  the  next  task  was  to 
define the inflow conditions. As shown in Figure 22, there are two  wind flow sensors:  a 
Gill  flow  meter  upstream  and  a  Young  flow  meter  close  to  one  side  of  the  building.    An 
initial remark is that the Gill flow meter is relatively far from the entrance i.e. 7.25 m.  

Looking  at  the  time  series  of  the  wind  data,  the  following  additional  remarks  can  be 
made: 

a)  In all experiments the Young wind velocities were lower than Gill wind velocities 
of the order of 4%. One would expect the opposite: the presence of the building 

87 

 
 
 
 
 
 
 
 
 
would  accelerate  the  flow  at  the  sides  of  the  building  and  decelerate  the  flow 
upstream, near the building; 

b)  There was a small but not negligible drift lateral velocity (i.e.  from -0.2m/s to -
0.4  m/s).  This  is  in  consistency  with  the  observed  lower  wind  speed  at  the  side 
where Young flow meter was installed. 

With  respect  to  model  inflow  conditions,  the  best  we  could  do  was  to  assume  uniform 
inflow  conditions  with  the  inlet  velocity  3.6  m/s  representing  the  measured  wind 
velocities  upstream.  It  was  noticed  that  the  precise  model  inlet  velocity  was  not  critical 
due  to  the  fact  that  the  passive  agent  concentrations  were  well  scaled  with  the  inflow 
velocity.  

Another  point  that  needs  to  be  noticed,  is  that  the  above-mentioned  remarks  on  the 
experimental flow behaviour were not consistent with the uniform inflow conditions which 
means  that  an  inherent  modelling  uncertainty  was  expected  due  to  ignorance  of  the 
precise inflow conditions. 

Concerning the inflow turbulent kinetic energy in both wind flow sensors, it measured: 

(7) 

Where k is the turbulent kinetic energy and VEL is the wind speed at the specific location. 
Adopting the same relationship for inflow conditions we specify k=0.22m2/s2 at the inlet. 

3.2.2.1 The external source 

There  have  been  performed  four  successful  experiments  with  ΔΤ  time  releases.  The 
following refer to Scenario 2:  

•  Experiment #6 (ΔΤ=600s),  
•  Experiment #7 (ΔΤ=610s),  
•  Experiment #8 (ΔΤ=200s), 
•  Experiment #9 (ΔΤ=1800s).  

For all four experiments the same total TEP mass was released: 930mg. 
Experiment #7 (Δτ=600s) is almost a repetition of Experiment #6 (Δτ=610s).  
Concentration measurements have been performed at specific locations by sampling and 
in the rooms 1,4,8,9,10 and 11 (Figure 13) by LCD Instrumentation.   

It  was  noticed  that  the  comparisons  with  LCD  measurements  was  not  straightforward. 
More specifically, the time series, with time resolution 2 s show:  

a)  significant ‘background’ mean values of the order of 0.08 mg/m3;  
b)  significant fluctuations with comparable intensity for the actual measurements and 

the ‘background’ measurements.  

For better interpretation, the time series have been smoothed by applying a 20 s moving 
average.  For  all  sensors  the  same  calibration  relationship  suggested  by  TNO  has  been 
used although this needs to be double checked. As explained above, the emphasis here 
was  given  on  Experiment  8  as  the  most  challenging  due  to  the  shortest  time  release: 
ΔΤ= 200s. 

Scenario 2 - Experiment #8 ("external release") 

The experiment layout outside the building is shown in Figure 22.  

88 

 
 
 
 
 
 
 
                 
 
       
 
 
 
 
 
 
 
Concentration measurements have been performed at the reference point (Figure 22) by 
sampling and in the rooms 1,4, 8,9,10 and 11 (Figure 13) by LCD Instrumentation.   

Figure  104  shows  the  concentration  comparison  at  the  reference  point.  There  is  a  10% 
model  underestimation.  Given  the  above-mentioned  inflow  uncertainties  those  results 
can  be  considered  quite  satisfactory.    It  was  noticed  that  since  the  reference  point  was 
located  upstream 
the  building 
contamination was expected.  

the  building,  some  model  underprediction  on 

Figure  105  shows  the  concentrations  comparison  in  the  rooms.  In  general,  there  is  a 
model  underestimation  with  exception  of  rooms  1  and  8.  50%  of  peak  concentration 
predictions  are  well  within  a  factor  of  2  which  despite  of  the  above-mentioned  inherent 
uncertainties, well within the state-of-the-art modelling.  

Figure  106  indicates  the  cloud  temporal  behaviour  indoors.  The  arrival  times  are 
comparable  except  for  the  room  8  showing  that  the  model  predicted  too  fast  cloud 
arrival. This is something that needs to be looked further. The model in general predicted 
lower residence times although both model and experiment follow the same trend except 
for room 11.   

Figure 104. Experiment #8 - reference point: Concentrations comparisons 

89 

 
 
 
 
  
 
 
 
Figure 105. Experiment #8 - Room concentrations comparisons 

Figure 106. Experiment #8 - Room residence and arrival times 

Scenario 2 - Experiments #6,7,9 ("external release") 

Modelling  of  the  other  external  source  experiments  (i.  e.  6,7,  and  9)  have  been 
successfully  performed.  A  summary  of  the  obtained  results  is  shown  in  Figure  107  in 
terms  of  room  peak  concentrations.  Concerning  the  peak  experimental  concentrations 
estimation:  

a)  The time series have been smoothed by applying a 20 s moving average;  
b)  The artificial spikes have been ignored;  
c)  A further smoothing is assumed based on visual inspection; 
d)  In the rooms where LCD measurements and sampling exist, the highest value has 

been taken.     

The  results  model  vs  experiment  shows  a  fairly  good  agreement  with  a  trend  towards 
underprediction.  The  estimated  uncertainty  within  a  factor  of  two  is  FAC2=  65%.  It  is 
noticed that, based on the state of the art of such models, the FAC2 needs to be greater 

90 

 
 
 
 
 
 
 
 
than  30%  (COST  ES1006,  2015).  The  obtained  results  show  that  the  uncertainties  in 
terms  of  model  vs  observation  discrepancies  are  well  within  the  above-mentioned 
margin. 

Figure 107. External source experiments: peak concentrations intercomparison 

3.2.2.2 The internal source 

There have been performed three successful experiments with ΔΤ time releases. Here the 
total TEP mass released per experiment was different. The following refer to Scenario 1:  

•  Experiment #11 (ΔΤ=120s) and (QTS=0.5mg), 
•  Experiment #12 (ΔΤ=120s) and (QTS=2.14mg),  
•  Experiment #13 (ΔΤ=240s) and (QTS=4.28mg). 

There were no concentration measurements by active sampling and there was the need 
to  rely  only  to  LCD  time  signals.  The  LCD  signal  analysis  has  shown  that  the 
measurements,  except  for  room  8,  were  relatively  too  low  and  therefore  the 
measurement part could not be easily distinguished from the ‘background’ part. The only 
exception  was  room  8  but  the  signal  was  experiencing  saturation  when  the 
concentrations became too high.   

In  Figure  108  the  experimental  concentration  in  the  room  8  is  shown.  Its  temporal 
behaviour is puzzling. 
In  experiment  #11  where  the  mass  release  was  the  smallest,  the  signal  did  not  show 
significant  disturbance  but  in  the  same  time  the  concentration  level  before  starting  the 
release was already elevated of the order of 0.3 mg/m3 well above the bias value: 0.075 
mg/m3.  A probable explanation might be agent prior existence unless there was problem 
with the instrument calibration.  
In experiments #12 and #13 there was no significant bias problem but the concentration 
started building up after a significant delay of the order of 150 s.  It was noticed that the 
release  time  was  of  this  order  of  magnitude.    The  peaks  came  much  later  (i.e.,  after 
300s).  

91 

 
 
 
 
 
 
 
 
It  is  important  to  understand  the  release  conditions  in  sufficient  detail.  This  behaviour 
might indicate ‘heavy gas’ behaviour. The rather stagnant flow conditions within room 8 
might  complicate  things  further.  Such  phenomena  cannot  be  modelled  reliably  without 
additional measurements at the release cross section and its neighbourhood. The release 
structure might also play a role and it needs to be described in more detail.  

Despite  the  above  difficulties,  the  experiments  modelling  with  the  passive  gas 
approximation has been performed as it has been planned for this study. The  emphasis 
was  given  to  experiment  #12  where  the  mass  release  rate  was  the  highest,  expecting 
more readable experimental concentrations. 

Figure 108. Internal source: Room 8 concentration measurements 

Scenario 1 - Experiment #12 ("internal release") 

The experiment layout outside the building is shown in Figure 28.  
Concentration  measurements  have  been  performed  in  the  rooms  1,4,8,9,10  and  11 
(Figure 13) by LCD Instrumentation. 

Figure 109 shows the concentration comparisons. The relatively slow concentration built 
up was visible in all rooms in consistence with the room 8 behaviour discussed above.   
In  the  rooms  10  and  11  both  predicted  and  modelled  concentrations  were  negligible  as 
expected.  

92 

 
 
 
 
 
 
 
Figure 109. Experiments #8 and #12 - Room concentrations comparisons 

3.2.3 Conclusions of ADREA-HF simulations 

Pre-experimental modelling 

Pre-experimental  modelling  exercises  have  been  performed  mainly  focusing  on  short 
time (i.e., 1 min) releases internally and externally. The ADREA-HF code has been used 
to  perform  the  simulations  utilizing  the  CFD  RANS  approximation  and  the  standard  k-ε 
turbulence model. 

The following concluding remarks of general interest, can be made at this stage. 
The wind tunnel set up is more likely to give a rather conservative picture compared with 
reality for the upwind external source. For the internal source the results were expected 
to be more realistic. 
For  the  external  source,  the  steady  state  concentrations  (and  consequently  finite  time 
dosages)  became  uniform  indoors  as  a  result  of  the  long-lasting  incoming  agent 
spreading everywhere indoors.   
For the internal source, dosages could become non-uniform with higher values near the 
source as expected.  

In  all  cases,  the  rooms  concentrations  time  profiles  can  be  quite  different.  The  room 
specific location with respect to the source and the associated ambient air pattern played 
an important role to this differentiation.  
The  poorly  ventilated  rooms  were  associated  with  relatively  long  residence  and  arrival 
times, and tended to lower the peak concentrations. 

93 

 
 
 
 
 
 
 
 
 
Comparison of modelling and experimental trials (Post-experimental modelling) 

The  first  attempt  of  experiments  vs  model  comparison  was  not  straightforward  due  to 
inherent  uncertainties  mainly  to  the  inflow  air  conditions,  the  release  conditions  at  the 
exhaust  duct  cross  section,  the  LCD  signals  calibration  and  possible  pre-existing  agent 
material in some experiments. 

For the external source, the concentration comparisons can be characterized satisfactory. 
The FAC2 parameter for peak concentrations is relatively high: 65%.  The model as it has 
been  applied,  shows  that  for  most  sensors  the  concentration  and  the  cloud  residence 
time was underpredicted especially in the rooms of low building ground floor.  

For the internal source the problem looks more problematic. The concentration temporal 
behaviour especially in the source room 8, a room with the lowest ventilation capacity, is 
puzzling.  In  addition,  the  specific  experiments  duration  seemed  to  be  not  long  enough. 
The experiment vs model discrepancy may be due to various reasons such as the lack of 
knowledge  of  the  precise  exit  duct  conditions,  the  validity  of  the  passive  gas 
approximation, pre-existing agent material, LCD signal measurement interpretation, etc.  
The limited number of measurements is also an additional element of model uncertainty.  

3.2.4 Conclusions of CFD simulations 

Comparison of modelling approaches (Code_Saturne & ADREA-HF code)  

It  is  important  to  notice  that  the  general  results  obtained  by  the  modelling  performed 
with Code_Saturne and ADREA-HF are remarkably comparable although some disparities 
remain,  perhaps  due  to  differences  in  the  geometric  representation  of  the  mock-up,  in 
particular the height of the doors. 
The  numerical  results  of  ADREA-HF  and  Code_Saturne  CFD  models  lead  to  the  same 
comments  on  the  dispersion  patterns  in  the  test-cases  with  either  an  external  or  an 
internal release. 

Figure  110  and  Figure  111  show  the  average  concentrations  over  time  in  the  different 
parts of the mock-up as they are identified in Figure 13. 
In  more  details,  concerning  the  high-rise  building  (Figure  110),  the  behaviours  are 
similar  for  the  rooms  marked  Lobby,  Room  1,  Room  2,  Room  5  and  Stairs  R  while  a 
slightly different behaviour is observed for the room marked Room 4. Regarding the low-
rise  building  (Figure  111),  similar  behaviours  are  noticed  for  almost  all  of  the  rooms 
except the rooms marked Room 9, Room 11, Room 13 and Stairs L. It is also visible that 
the purge of the rooms is longer with Code_Saturne compared to ADREA-HF. 

94 

 
 
  
 
 
 
 
 
 
Code_Saturne 

ADREA-HF 

Figure 110. Average concentrations in the different rooms of the upper part of the 
mock-up with release located outside the mock-up upstream of the flow. Top: 
Code_Saturne results. Bottom: ADREA-HF results  

95 

 
               
 
 
 
 
 
 
 
Code_Saturne 

ADREA-HF 

Figure 111. Average concentrations in the different rooms of the lower part of the 
mock-up with release located outside the mock-up upstream of the flow. Top: 
Code_Saturne results. Bottom: ADREA-HF results 

The  maximum  concentrations  reached  in  each  of  the  rooms  of  the  mock-up  during  the 
simulation are shown in Figure 112. As before, the results obtained by Code_Saturne are 
broadly  in  agreement  with  those  obtained  by  ADREA-HF.  As  already  mentioned,  the 
differences could be explained by different transfers due to more  or less large openings 
between the rooms. 

96 

 
              
 
 
 
 
 
 
Code_Saturne 

ADREA-HF 

Figure 112. Maximum concentrations in the different rooms of the mock-up with release 
located outside the mock-up upstream of the flow. Top: Code_Saturne results. Bottom: 
ADREA-HF results 

97 

 
 
 
4. Conclusions

The  subsidiary  aim  of  the  research  described  in  this  technical  report  was  to  develop 
knowledge on the behaviour of chemical releases, given some likely attack scenarios, and 
to propose ways to optimally detect those inside buildings.  

From  the  early  stages  of  the  Indoor  Airborne  TG,  it  has  been  highlighted  that  a  key 
component  of  the  efficient  detection  of  the  relevant  threats  is  the  optimization  of  the 
sensors  location,  and  this  can  only  be  obtained  by  making  use  of  a  combined  approach 
consisting  of  experiments  and  dispersion  modelling,  which  is  considered  a  strong 
endeavour towards this aim. This is clearly highlighted by the multi-disciplinarity and the 
expertise  of  the  respective  group  members,  that  resulted  in  the  comprehensive  study 
that has been carried out and described above.  

In  this  case,  a  high-end  facility  (ambient  breeze  tunnel  (AMB))  has  been  used  under 
controlled release of simulant chemicals in a mock-up of a simple building, of a scale 1:5. 
For  all  the  release  scenarios  of  the  TEP  that  has  been  selected  as  simulant  (for  nerve 
gases),  the  aim  was  (a)  the  successful  detection  (with  an  ion  mobility  spectrometry 
(IMS))  and  (b)  to  simulate  the  dispersion  process  and  (c)  to  evaluate  the  dispersion 
modelling against the measured concentration gradients in time and space.  

The  experiments  involved  three  mimicked  attack  scenarios:  i)  focal  release  from  within 
the mimicked building and external release  of agent approaching the mock-up from the 
outside from a ii) short and iii) a larger distance. TEP was selected as simulant (for nerve 
gases).  The  detector  used  was  an  ion  mobility  spectrometry  (IMS)  instrument. 
Subsequent 2 different dispersion modelling approaches were undertaken to understand 
and  describe  those  differences,  namely  (a)  the  ADREA-HF  code,  used  to  perform  the 
simulations utilizing the CFD RANS approximation and the standard k-ε turbulence model 
and  (b)  the  Code_Saturne,  used  to  perform  the  simulations  both  before  and  after  the 
experiments.  

Methodologically  speaking, 
it  was  shown  that  medium-scale  experiments  with 
representative  simulant  chemicals,  can  simulate  potential  threat  events  in  a  very 
sufficient  way,  while  computational  models  are  able  to  simulate  the  dispersion  of  a 
chemical  agent  very  sufficiently.    Release  of  TEP  was  successful  in  all  three  exposure 
scenarios, it resulted in reproducible detection signals from 6 IMS instrument placed in 6 
rooms  of  the  building  mock-up.  The  speed  and  magnitude  in  which  TEP  levels  were 
recorded  in  the  respective  rooms  showed  a  clear  and  reproducible  picture  in  the  sense 
that some of the rooms appeared to be better reachable for the externally released TEP 
than others. 

From  the  modelling  results,  it  has  been  illustrated  that  the  rooms  concentrations  time 
profiles can be quite different. The room specific location with respect to the source and 
the  associated  ambient  air  pattern  plays  an  important  role  to  this  differentiation.  The 
poorly  ventilated  rooms  are  associated  with  relatively  long  residence  and  arrival  times, 
and  tend  to  lower  the  peak  concentrations.  Pre-trials  computations  illustrate  the 
differences  in  the  dispersion  pattern  when  an  internal  versus  an  external  released  is 
considered. Post-trials computations are fully consistent with the pre-trials computations 
and  the  physical  phenomena  enlightened  in  the  different  trials.  At  the  same  time,  it  is 
worth  to  mention  that  the  two  independent  modelling  efforts  have  resulted  in  similar 
outcomes,  while  both  modelling  efforts  corroborate  excellent  with  the  measurements 
results.  

Overall,  this  successful  combined  experimental  and  modelling  exercise,  illustrates  that 
refined computational modelling can identify the significant differences in concentration-
time gradients after various release conditions. This has three major implications: 

98 

- Optimal  design  of  sensor  locations  in  a  critical  infrastructure,  where  sensors
should be located where the highest elevation of contamination levels is expected
to occur, ensuring the early identification;
Integrate  modelling  results  in  a  chemical  reconnaissance  system  that  relies  on
machine  learning,  modelling  algorithms,  as  well  as  the  contaminant  dispersion
model  to  combine  signals  from  different  sensors,  thus  ensure  early  identification
and reduce false alarm rates;

-

- Optimisation of evacuation plan in the case of an event.

This  extraordinary  experimental  effort  with  highly  complex  structures  with  rooms  of 
significant  ventilation  capability  variability,  apart  from  the  highly  important  results 
produced,  has  set  up  a  solid  ground  for  the  design  and  execution  of  new  targeted 
experiments  advancing  the  knowledge  in  understanding  the  behaviour  of  dispersed 
malicious  airborne  agents  inside  buildings  and  into  the  interaction  of  detection 
equipment.   

99 

References 

Busker,  R.  (2016).  Technology  Review  for  Detection  of  Airborne  Chemical  Agents  in 
Critical Infrastructures. Ispra: JRC [106323] 

Bartzis,  J.G.;  Efthimiou,  G.C.;  Andronopoulos,S.  “Modelling  Exposure  from  Airborne 
Hazardous  Short-Duration  Releases  in  Urban  Environments.”  Atmosphere,  Vol  12  No  2, 
2021, pp130. https://doi.org/10.3390/atmos12020130  

COST ES1006 - Model Evaluation Protocol, COST Action ES1006, April 2015 

COST  Action  ES1006  -  Evaluation,  improvement  and  guidance  for  the  use  of  local-scale 
emergency prediction and response tools for airborne hazards in built environments, April 
2012 

Karakitsios,  S.  e.  (2016).  Definition  of  relevant  scenarios  of  indoor  airborne  threats 
(chemical and biologial) in critical infrastructure. Ispra: JRC [102091] 

Karakitsios,  S.  (2019).  Report  on  gaps  for  research  and  standardization.  Ispra:  JRC 
[117096] 

Karakitsios,  S.  et  al.  (2020).  Challenges  on  detection,  identification  and  monitoring  of 
indoor airborne. Safety Sciences 129(5):104789 

Venetsanos A. G., Papanikolaou Ε. and Bartzis J. G., 2010: “The ADREA-HF CFD code for 
consequence assessment of hydrogen applications. Int. J. Hydrogen Energy, 35, 3908].  

100 

List of abbreviations 

 2/3 Dimensional 
 code 
 Ambient Breeze Tunnel  
 software 
 Concentration 
 Chemical and Biological  
 Atomic and alternative Energies Commission 
 Controlled Evaporator Mixer  
 Atmospheric Environment Center 
 Computational Fluid Dynamics  
 Courant–Friedrichs–Lewy 

2/3D    
ADREA HF 
AMB  
Ansa    
C  
CB  
CEA  
CEM  
CEREA  
CFD  
CFL  
Code_Saturne  code 
D  
EC  
EDF  
ERNCIP 
FAC2    
FOI  
Gill  
H  
IMS 
JRC  
LCD 
LES 
MQ  
MW  
N  
NCSRD  
Paraview 
PID  
QTS  
R&D  
RANS    
SIMPLEC 
TEP  
TG  
TIC  
TNO  
UK  
UREF    
UTP 
Young   
ΔΤS  

 Dosage 
 European Commission  
 the French electricity board  
 European Reference Network for Critical Infrastructure Protection  
 fraction of predictions within a factor of 2 observations 
 Swedish Defence Research Agency  
 anemometer  
 building height  
 Ion Mobility Spectrometry  
 Joint Research Centre  
 Lightweight Chemical Detector 
 Large Eddy Simulation 
 square metre 
 Molecular Weight 
 North 
 National Center for Scientific Research Demokritos  
 software 
 Photo Ionization Detector 
 Total Mass Released  
 Research and Development 
 Reynolds-Averaged Navier–Stokes 
 Semi-Implicit Method for Pressure Linked Equations-Consistent algorithm 
 Tri-EthylPhosphate  
 Thematic Group 
 Tentatively Identified Compounds 
 Netherlands Organisation for Applied Scientific Research  
 United Kingdom 
 specified inlet velocity  
 Unshielded Twisted Pair 
 anemometer 
 release time duration 

101 

List of figures 

Figure 1. Internal details and subdivision of the mock-up ........................................................ 5 
Figure 2. External details and openings of the mock-up .......................................................... 6 
Figure 3. External view and levels of the mock-up .................................................................. 6 
Figure 4. Details of the mock-up shipment in NCSR Demokritos, Greece ................................... 7 
Figure 5. The mock-up at TNO, The Netherlands .................................................................... 7 
Figure 6. AMB front side view .............................................................................................. 9 
Figure 7. AMB back side view .............................................................................................. 9 
Figure 8. View from inside the ABM showing the 6 tubes that distribute vapor ......................... 10 
Figure 9. LCD 3.3 detector (left) and Young Anemometer (right) ........................................... 10 
Figure 10. Tri-ethylphosphate (TEP) ................................................................................... 11 
Figure 11. Schematic position of mock-up in AMB ................................................................ 13 
Figure 12. Actual mock-up positioned in AMB ...................................................................... 13 
Figure 13. Arrangement of the rooms in the mock-up ........................................................... 14 
Figure 14. Six LCD 3.3 detectors installed in the mock-up rooms indicated with numbers ......... 14 
Figure 15. Ground Floor level detector/sampling positions ..................................................... 15 
Figure 16. Middle Floor level detector/sampling positions ...................................................... 16 
Figure 17. Top Floor level detector /sampling positions ......................................................... 16 
Figure 18. Scenario 3 - schematic overview of position of windspeed sensors and reference 
sample location around mock-up ......................................................................................... 18 
Figure 19. Experiment #1 - LCD 3.3 signals plotted against time ........................................... 19 
Figure 20. Experiment #2 - LCD 3.3 signals plotted against time ........................................... 20 
Figure 21. Experiment #3 - LCD 3.3 signals plotted against time ........................................... 22 
Figure 22. Scenario 2 - schematic overview of position of windspeed sensors, reference sample 
and source location around mock-up .................................................................................... 22 
Figure 23. Scenario 2 - Release apparatus used as point source (right) in front of mock-up (left)23 
Figure 24. Experiment #6 - LCD 3.3 signals plotted against time ........................................... 24 
Figure 25. Experiment #7 - LCD 3.3 signals plotted against time ........................................... 25 
Figure 26. Experiment #8 - LCD 3.3 signals plotted against time ........................................... 27 
Figure 27. Experiment #9 - LCD 3.3 signals plotted against time ........................................... 28 
Figure 28. Scenario 1 - Schematic overview of position of windspeed sensors and source location 
around mock-up ................................................................................................................ 29 
Figure 29. Scenario 1 - Evaporator positioned behind the mock-up ........................................ 29 
Figure 30. Experiment #11 - LCD signals plotted against time ............................................... 30 
Figure 31. Experiment #12 - LCD signals plotted against time ............................................... 31 
Figure 32. Experiment #13 - LCD signals plotted against time ............................................... 32 
Figure 33. Principle of the experiment ................................................................................ 35 
Figure 34. Positioning of the mock-up in the wind tunnel ...................................................... 35 
Figure 35. Positioning of the external release in the wind tunnel ............................................ 36 
Figure 36. Positioning of the internal release ....................................................................... 36 
Figure 37. Geometry of the mock-up consisting in a low-rise building and a high-rise building ... 37 
Figure 38. View of the four tested meshes .......................................................................... 38 
Figure 39. View of the vertical section plane across the mock-up ........................................... 39 
Figure 40. 2D velocity fields in a vertical section plane. Results for the four meshes ................. 39 
Figure 41. Influence of the four meshes on the convergence of the velocity field ..................... 40 
Figure 42. Velocity field in two section planes across the mock-up ......................................... 41 
Figure 43. Velocity field and velocity vectors in two section planes of the lower building ........... 41 
Figure 44. Velocity field and velocity vectors in a section plane of the higher building ............... 42 
Figure 45. Streamlines in the mock-up ............................................................................... 42 
Figure 46. First test-case – Boundary condition for the concentration at the mock-up inlets C0 = 1 
for one minute (t = 0 to 60 s) ............................................................................................. 43 
Figure 47. First test-case – Chemical cloud delimited by the concentration C = 0.1 at t = 8 s .... 44 
Figure 48. First test-case – Chemical volume concentration in a vertical section plane at t = 53.2 s
 ....................................................................................................................................... 45 
Figure 49. First test-case – Chemical volume concentration in a vertical section plane at t = 74,2 s
 ....................................................................................................................................... 45 
Figure 50. First test-case – Quantity of the chemical in the mock-up (in blue) and average 
concentration of chemical in the mock-up (in orange, logarithmic scale) over time .................... 46 
Figure 51. Second test-case – Location of the source inside the mock-up in the corridor of the 
ground floor ...................................................................................................................... 47 
Figure 52. Second test-case – Chemical cloud delimited by the concentration C = 0.01 at t = 43.0 
s ..................................................................................................................................... 48 

102 

Figure 53. Second test-case – Chemical volume concentration in a vertical section plane at t = 
51,0 s .............................................................................................................................. 48 
Figure 54. Second test-case – Quantity of the chemical remaining in the mock-up (in blue) and 
average concentration of the chemical in the mock-up (in orange, logarithmic scale) over time .. 49 
Figure 55. Second test-case – Average concentrations in the different rooms of the upper part of 
the mock-up ..................................................................................................................... 50 
Figure 56. Second test-case – Average concentrations in the different rooms of the lower part of 
the mock-up ..................................................................................................................... 50 
Figure 57. Second test-case – Maximum concentrations in the different rooms of the mock-up . 51 
Figure 58. Second test-case – Maximum concentrations in the different rooms of the mock-up (on 
the left: upper part; on the right: lower part) ....................................................................... 51 
Figure 59. Installation of the small building mock-up in the wind tunnel ................................. 52 
Figure 60. Geometry of the small building in the coupled indoor/outdoor configuration ............. 53 
Figure 61. Full geometry of the small building immersed in the wind tunnel in the coupled 
indoor/outdoor configuration............................................................................................... 54 
Figure 62. Global view of the mesh of the wind tunnel .......................................................... 54 
Figure 63. Zoom-in view of the mesh of the wind tunnel ....................................................... 55 
Figure 64. Velocity measurements upstream of the small building model in the middle of the wind 
tunnel .............................................................................................................................. 55 
Figure 65. Velocity field in the horizontal section crossing the air inlets (top) and in the vertical 
median plane (bottom) ...................................................................................................... 56 
Figure 66. Vertical profile of the air velocity upstream the small building ................................ 57 
Figure 67. Comparison of the measured and computed velocities at a point upstream the small 
building ............................................................................................................................ 57 
Figure 68. Velocity field in a horizontal section crossing the small building outlets .................... 58 
Figure 69. Comparison of the measured and computed velocities at a point between the small 
building and the walls of the wind tunnel .............................................................................. 58 
Figure 70. Velocity field restricted to the vertical median cross-section through the small building
 ....................................................................................................................................... 59 
Figure 71. Velocity vectors restricted to the vertical median cross-section through the small 
building ............................................................................................................................ 59 
Figure 72. Velocity field in the horizontal cross-section located 5 cm above the floor of the second 
level of the low-rise building ............................................................................................... 60 
Figure 73. Velocity vectors in the horizontal cross-section located 5 cm above the floor of the 
second level of the low-rise building .................................................................................... 60 
Figure 74. Velocity field and velocity vectors in the horizontal cross-section located 10 cm above 
the floor of the first level of the low-rise building ................................................................... 61 
Figure 75. Velocity field in the vertical cross-section through the stairwell of the high rise ........ 61 
Figure 76. Experiment #1 – Initial condition (t = t0) at the entrance to the wind tunnel ............ 62 
Figure 77. Experiment #1 – Concentration field in the median vertical cross-section of the wind 
tunnel at different instants between t = t0 + 6 s and t = t0 + 2,000 s ...................................... 64 
Figure 78. Location of the sensors inside the small building ................................................... 64 
Figure 79. LCD sensors ..................................................................................................... 65 
Figure 80. Experiment #1 – Comparison of the concentrations obtained by the CFD and by the 
measurements at the six .................................................................................................... 66 
Figure 81. Experiment #1 – Comparison of the normalized concentrations obtained by the CFD 
and by the measurements at the six sensors ........................................................................ 67 
Figure 82. Experiment #6 – Location of the source for the external release ............................. 68 
Figure 83. Experiment #6 – Concentration field in the median vertical cross-section of the wind 
tunnel at different instants between t = t0 + 15 s and t = t0 + 1,000 s .................................... 70 
Figure 84. Experiment #1 – Comparison of the concentrations obtained by the CFD and by the 
measurements at the six sensors ........................................................................................ 70 
Figure 85. Experiment #6 – Comparison of the normalized concentrations obtained by the CFD 
and by the measurements at the six sensors ........................................................................ 71 
Figure 86. Experiment #6 – Alternative positions of the virtual sensor in Room 9 .................... 72 
Figure 87. Experiment #6 – Comparison between the simulated concentrations on close virtual 
sensors in Room 9 and the measured concentrations in Room 9 .............................................. 73 
Figure 88. Experiment #12 – Location of the source for the internal release (Room 8) ............. 74 
Figure 89. Experiment #12 – Visualization of the chemical plume defined by the volume C ≤ 
Cmax inside the small building at different instants between t = t0 + 20 s and t = t0 + 1,045 s .. 76 
Figure 90. Experiment #12 – Concentrations on the virtual sensors in the CFD simulation ........ 77 
Figure 91. Building inlet dosages under in free and wind tunnel environment .......................... 82 
Figure 92. External source, 1-min release: estimation of the normalized dosages (m-2) ........... 82 
Figure 93. Low building normalized concentrations: top floor (left) and ground floor (right) ...... 83 

103 

Figure 94. High building normalized concentrations: top floor (left) and ground floor (right) ...... 83 
Figure 95. External source, 1-min release: The normalized peak concentrations (m-2) ............. 84 
Figure 96. External source, 1-min release: The residence times ............................................. 84 
Figure 97. External source, 1-min release: The arrival times ................................................. 84 
Figure 98. Indoor Normalized Dosages under in free and wind tunnel environment .................. 85 
Figure 99. Low building ground floor normalized concentrations ............................................. 86 
Figure 100. High building normalized concentrations: top floor (left) and ground floor (right) .... 86 
Figure 101. Internal source, 1-min release: The normalized peak concentrations (m-2) ........... 86 
Figure 102. Internal source, 1-min release: The residence times (sec) ................................... 87 
Figure 103. Internal source, 1-min release: The arrival times (sec) ........................................ 87 
Figure 104. Experiment #8 - reference point: Concentrations comparisons ............................. 89 
Figure 105. Experiment #8 - Room concentrations comparisons ............................................ 90 
Figure 106. Experiment #8 - Room residence and arrival times ............................................. 90 
Figure 107. External source experiments: peak concentrations intercomparison ...................... 91 
Figure 108. Internal source: Room 8 concentration measurements ........................................ 92 
Figure 109. Experiments #8 and #12 - Room concentrations comparisons ............................. 93 
Figure 110. Average concentrations in the different rooms of the upper part of the mock-up with 
release located outside the mock-up upstream of the flow. Top: Code_Saturne results. Bottom: 
ADREA-HF results. ............................................................................................................. 95 
Figure 111. Average concentrations in the different rooms of the lower part of the mock-up with 
release located outside the mock-up upstream of the flow. Top: Code_Saturne results. Bottom: 
ADREA-HF results. ............................................................................................................. 96 
Figure 112. Maximum concentrations in the different rooms of the mock-up with release located 
outside the mock-up upstream of the flow. Top: Code_Saturne results. Bottom: ADREA-HF results.
 ....................................................................................................................................... 97 

104 

List of tables 

Table 1. General overview of experiments performed ............................................................ 16 
Table 2. Detailed overview of experiments performed (agents and releases)............................ 17 
Table 3. Experiment #2 - Sampling start time ..................................................................... 20 
Table 4. Experiment #3 - Sampling start time ..................................................................... 21 
Table 5. Experiment #6 - Sampling start time ..................................................................... 24 
Table 6. Experiment #7 - Sampling start time ..................................................................... 25 
Table 7. Experiment #8 - Sampling start time ..................................................................... 26 
Table 8. Experiment #9 - Sampling start time ..................................................................... 28 
Table 9. Characteristics of the four tested meshes ................................................................ 38 
Table 10. Characteristics of the small building in the coupled indoor/outdoor configuration ....... 53 
Table 11. Experiment #12 – Maximum concentration on the six virtual sensors ....................... 74 

105 

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doi:10.2760/48622
ISBN 978-92-76-55599-5