Full dissertation

Validation and application of Fire
Dynamics Simulator (FDS) in tunnel fires
Master of Science in Fire Safety Engineering
Jeroen Wiebes Kjos (B00624332)
Faculty of Arts, Design and Built Environment
School of the Built Environment
May 2015

I
Acknowledgements
I would like to thank a number of people who have helped me with this dissertation:
Dr. Jianping Zhang, my supervisor for this dissertation. He’s been a big help throughout this
dissertation. I want to thank him for all the support and input he has given me working on this
dissertation.
Haukur Ingason from SP Technical Research Institute of Sweden, Department of Fire
Technology. He has provided me with the experimental results from the Runehamar tunnel
fire experiments. This gave me the much needed experimental results from which I could do a
verification of FDS. Without these results there wouldn’t have been a dissertation.
Finally, I would like to thank my wife Christine. She has supported me throughout this whole
course and has been the main reason for where I am today. I know that I’m not always the
best person with words but I want to thank her for her patience and support throughout the
years we’ve known each other. She always supports me with every new plan I come up with
without any complaint and I am very thankful for that.

II
Abstract
The awareness to road tunnel fires has been greatly increased by a succession of large scale
accidental fires during the late 1990’s and early 2000’s. Studies have shown that road tunnel
design often is characterized by the assumption that large-scale accidents are too unlikely.
The origin of most harmful events are deficiencies in tunnel structures or in vehicles. The
largest focus so far has been to restrict access to tunnels for dangerous goods. But basic fire
loads like flour or margarine was the load of the truck that caused the fire in the Mont Blanc
tunnel fire.
Full-scale experiments give important information about the conditions during a fire, but these
experiments are very expensive to carry out and are often destructive to the tunnel. An
alternative to experiments is to perform computational studies about the tunnels performance
during a fire. In order for the fire safety engineer to use FDS for fire simulations in a tunnel, it
must be ensured that FDS is capable of modelling the real world which is studied in a
validation study.
The main objective of this dissertation is to validate Fire Dynamics Simulator (FDS) for
tunnel fires. FDS will be validated for the following parameters:
 Temperature
 Oxygen concentration
 CO concentration
 CO2 concentration
 Gas velocity
 Radiation
The validation study is performed by comparing computational results with the experimental
results using the Runahamar tunnel fire tests performed in 2003. When this is performed FDS
is used to investigate different fire safety strategies and assess their effect on the conditions in
the tunnel during a fire.
Results showed that:
 Temperature was underestimated near the fire (0-100 m downstream, 244 °C or 33 %)
and overestimated further away from the fire (350-458 m downstream, 24 °C or 33 %)
 Radiation was underestimated by an average of 110 kW/m2
or 82 %
 Minimum oxygen concentration was overestimated by an average of 0.02 Vol % or 15 %
 Maximum CO concentration was underestimated by an average of 618 ppm or 70 %
 Maximum CO2 concentration was underestimated by an average of 0.02 Vol % or 59 %
 Maximum air velocity was overestimated by an average of 2.55 m/s or 75 %
The design study concluded with that adding a sprinkler system, increasing ventilation
velocity and using a transverse ventilation system improved conditions in the tunnel when
comparing with the original setup during the Runahamar tunnel fire tests.

III
Declaration
I hereby declare that for 2 years following the date on which the thesis is deposited in
Research
Student Administration of Ulster University, the thesis shall remain confidential with access
or copying prohibited. Following expiry of this period I permit;
the Librarian of the University to allow the thesis to be copied in whole or in part without
reference to me on the understanding that such authority applies to the provision of single
copies made for study purposes or for inclusion within the stock of another library.
IT IS A CONDITION OF USE OF THIS THESIS THAT ANYONE WHO CONSULTS IT
MUST RECOGNISE THAT THE COPYRIGHT RESTS WITH THE UNIVERSITY AND
THEN SUBSEQUENTLY TO THE AUTHOR ON THE EXPIRY OF THIS PERIOD AND
THAT NO QUOTATION FROM THE THESIS AND NO INFORMATION DERIVED
FROM IT MAY BE PUBLISHED UNLESS THE SOURCE IS PROPERLY
ACKNOWLEDGED.

IV
TABLE OF CONTENTS
Acknowledgements................................................................................................................................................................................ I
Abstract.................................................................................................................................................................................................II
Declaration.......................................................................................................................................................................................... III
1 Introduction..................................................................................................................................................................................1
1.1 Background and rationale .....................................................................................................................................................1
1.2 Aim and objectives ...............................................................................................................................................................2
1.3 Organization of the thesis .....................................................................................................................................................2
2 Literature review..........................................................................................................................................................................3
2.1 Importance of tunnel fire safety............................................................................................................................................3
2.2 History of incidents in tunnels ..............................................................................................................................................5
2.3 Previous research on tunnel fire safety .................................................................................................................................8
2.3.1 Tunnel fire experiments ...........................................................................................................................................8
2.3.2 CFD simulations tunnel fires .................................................................................................................................11
2.4 Fire modelling.....................................................................................................................................................................14
2.4.1 Theoretical basis of CFD .......................................................................................................................................15
2.4.2 Turbulence modelling............................................................................................................................................16
2.4.3 Brief description of FDS........................................................................................................................................17
3 Description of experiment .........................................................................................................................................................20
3.1 The tests..............................................................................................................................................................................20
3.2 The tunnel...........................................................................................................................................................................21
3.3 Fire placement ....................................................................................................................................................................22
3.4 Measurements.....................................................................................................................................................................22
3.5 Meteorological conditions ..................................................................................................................................................23
4 Description of FDS input and simulations ...............................................................................................................................24
4.1 Fire......................................................................................................................................................................................24
4.2 Grid size..............................................................................................................................................................................26
4.3 Structure and properties......................................................................................................................................................27
4.4 Placement of measurements................................................................................................................................................29
4.5 Parametric study .................................................................................................................................................................29
4.6 Design study .......................................................................................................................................................................31
5 Results.........................................................................................................................................................................................34
5.1 Validation results................................................................................................................................................................34
5.3 Design study .......................................................................................................................................................................49
6 Discussion/Conclusion................................................................................................................................................................55
6.1 Validation of FDS for tunnel fires ......................................................................................................................................55
6.3 Design study .......................................................................................................................................................................60
6.4 Implications and future research.........................................................................................................................................62
7 References...................................................................................................................................................................................63
8 Appendices..................................................................................................................................................................................66
8.1 Results grid sensitivity analysis ..........................................................................................................................................66
8.2 Results parametric study.....................................................................................................................................................71
8.3 Example FDS input file ......................................................................................................................................................73
Picture on title page: (Ingason, et al., 2011)

V
FIGURES
Figure 1 Modelling of eddies using the different turbulence models ................................................16
Figure 2 Time dependence of a velocity component at a point .........................................................16
Figure 3: Runehamar test tunnel (Opstad & Wighus, 2003)..............................................................21
Figure 4: Fire protection boards (Ingason, et al., 2011).....................................................................21
Figure 5: Tunnel cross-section (Ingason, et al., 2011).......................................................................21
Figure 6: The mobile fan unit (Ingason, et al., 2011) ........................................................................22
Figure 7: Measuring station 458 m downstream of the fire (Ingason, et al., 2011)...........................23
Figure 8: Heat release rate during experiment and simulation ..........................................................24
Figure 9: Tunnel cross-section...........................................................................................................27
Figure 10: Fire input used in the validation case ...............................................................................30
Figure 11: Fire input used in the parametric study ............................................................................30
Figure 12: Categorization of tunnels (Chiyoda Engineering Consultants Co., Ltd., 2001)...............31
Figure 13: Setup of transverse ventilation case .................................................................................33
Figure 14: Experimental measurements 30 cm below ceiling ...........................................................34
Figure 15: Simulation measurements 30 cm below ceiling ...............................................................34
Figure 16: Difference between simulated temperatures and experimental results downstream of the
fire......................................................................................................................................................35
Figure 17: Temperatures 100 m downstream at different heights .....................................................35
Figure 18: Temperatures 250 m downstream at different heights .....................................................36
Figure 19: Radiation at target towards the fire 20 m downstream.....................................................38
Figure 20: Radiation on the ceiling 40 m downstream ......................................................................38
Figure 21: Oxygen concentration 458 m downstream 5.1 m above road surface..............................39
Figure 22: Oxygen concentration 458 m downstream 2.9 m above road surface..............................39
Figure 23: CO concentration 458 m downstream 5.1 m above road surface.....................................40
Figure 25: CO2 concentration 458 m downstream 5.1 m above road surface...................................41
Figure 27: Air velocity 458 m downstream 4.1 m above road surface..............................................42
Figure 28: Air velocity 458 m downstream 1.8 m above road surface..............................................42
Figure 29: Temperature 40 m downstream 30 cm below the ceiling.................................................43
Figure 30: Temperature 250 m downstream 1.8 m above road surface.............................................43
Figure 31: Radiation 0 m downstream on the ceiling........................................................................44
Figure 32: Radiation 40 m downstream on the ceiling ......................................................................44
Figure 33: CO concentration 458 m downfield 2.9 m above road surface ........................................45
Figure 36: Temperature 0 m downstream..........................................................................................47
Figure 37: Radiation 0 m downstream...............................................................................................48
Figure 38: Heat release rate - original vs sprinkler case....................................................................49
Figure 39: Temperature 20 m downstream 0.3 m below ceiling .......................................................49
Figure 40: Radiation 40 m downstream at ceiling height ..................................................................50
Figure 41: Averaged temperature difference downstream of the fire 30 cm below the ceiling ........51
Figure 42: Smoke being extracted above the ceiling .........................................................................52
Figure 43: Tunnel filled with smoke with longitudinal ventilation ...................................................53
Figure 44: Visibility inside the tunnel with longitudinal ventilation.................................................53
Figure 45: Temperatures in the tunnel for the original ventilation strategy ......................................54
Figure 46: Temperatures in the tunnel for the changed ventilation strategy .....................................54
Figure 47: Temperature measured 0 m downstream 30 cm below the ceiling ..................................59
Figure 48: HRR during grid sensitivity analysis................................................................................66
Figure 49: Temperature 0 m downstream - grid sensitivity analysis.................................................66
Figure 50: Temperature 10 m downstream - grid sensitivity analysis...............................................67

VI
Figure 53: Radiation on the ceiling 0 m downstream - grid sensitivity analysis...............................68
Figure 54: Radiation on the ceiling 10 m downstream - grid sensitivity analysis.............................69

VII
TABLES
Table 1: History of fires in tunnels between 1999 and 2009 (Beard & Carvel, 2012) ........................5
Table 2: Overview of selected tunnel fire experiments (Lönnermark, 2005)....................................10
Table 4: Results validation study U.S. Nuclear regulatory Commission (2007)...............................13
Table 5: Different test commodities (Ingason, et al., 2011) ..............................................................20
Table 6: Properties of fuels used in experiment.................................................................................25
Table 7: Properties of fuel used as input to FDS ...............................................................................25
Table 8: Material properties used in the simulations .........................................................................28
Table 9: Safety facilities for each tunnel category (Chiyoda Engineering Consultants Co., Ltd.,
2001) ..................................................................................................................................................32
Table 10: Accuracy estimating temperature at different points.........................................................37
Table 11: Accuracy estimating radiation at different points..............................................................39
Table 12: Accuracy estimating oxygen concentration at different points .........................................40
Table 13: Accuracy estimating CO concentration at different points................................................41
Table 14: Accuracy estimating CO2 concentration at different points..............................................42
Table 15: Accuracy estimating air velocity at different points..........................................................43
Table 16: Results of changes to the HRRPUA ..................................................................................44
Table 17: Results of changes to the soot-yield ..................................................................................45
Table 18: Results of changes to the CO-yield....................................................................................46
Table 19: Results of changes to the heat of combustion....................................................................47
Table 20: Results of changes to the fire setup ...................................................................................48
Table 21: Results with added sprinkler system..................................................................................50
Table 22: Results with changed ventilation velocity .........................................................................51
Table 23: Changes to the temperature 0-150 m downstream - parametric study ..............................71
Table 24: Changes to the temperature 350-458 m downstream - parametric study ..........................71
Table 25: Changes to the measured radiation - parametric study......................................................71
Table 26: Changes to the oxygen concentration - parametric study..................................................71
Table 27: Changes to the CO concentration - parametric study ........................................................71
Table 28: Changes to the CO2 concentration - parametric study ......................................................71
Table 29: Changes to the air velocity - parametric study ..................................................................72

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1 Introduction
1.1 Background and rationale
My personal interest in tunnel fires started when I started on my B.Sc. dissertation. Norway is one
of the countries in the world that constructs most road tunnels (Amundsen & Ranes, 1997). To get
updated on the latest research on tunnel fires and egress from road tunnels Multiconsult AS (my
current employer) wanted me to write a dissertation on egress from a bi-directional road tunnel
(Wiebes, 2012). When writing this dissertation I became aware of the special characteristics of a
tunnel fire. With tunnels getting longer and more complex, the use of fire simulations is more often
used. This tool creates great possibilities for the planning of a tunnel because a lot of different
scenarios can be simulated without damaging the tunnel. This tool is often used to choose between
different fire safety strategies. During this dissertation Fire Dynamics Simulator (FDS) will be
validated for the use in tunnel fires. The programs validation guide (McGrattan, et al., 2015)
describes all the experiments that were used for model validation. This list of experiments only
contains one tunnel fire experiment with fires up to 5.3 MW. Beard & Carvel (2012) have shown
that investigations estimate fire sizes much larger than this value (350 MW, The Channel Tunnel
fire 18th November 1996).
Research into tunnel fires started after several large fires occurred in Europe: The Mont Blanc
tunnel (9 deaths, 1999), the Tauern tunnel (12 deaths, 1999) and the St. Gotthard tunnel (11 deaths,
2001). Every year in the ten year span from 1994 to 2004 at least one fatal accidental fire has
occurred (Beard & Carvel, 2012). In 2001 26 different full-scale fire tests were conducted in a new
tunnel in Holland to both look at heat and smoke spread, tunnel ventilation strategies, accuracy of
CFD modelling tools and other objectives (Bouwdienst Rijkswaterstaat - Steunpunt
Tunnelveiligheid, 2002). The UPTUN project was a research program on upgrading the fire safety
in existing tunnels which lasted from 2002 until 2006 with 19 EU members participating in the
program. As a part of this program several reports were written on both human behaviour, tunnel
fire dynamics, experimental results and more. Ingason, et al. (2011) performed four large-scale tests
in an abandoned tunnel in Norway and found that the highest temperature can be up to 1365 °C and
peak heat release rate was 203 MW. They also measured gas concentrations, velocities, radiation
and smoke movement. Vianello, et al. (2012) compared results from both laboratory scaled
experiments and real scale tunnel fires to quantitatively assess the scaling effect. This experimental
data allowed a complete characterization of toxic gases from car model fires. Both experimental and
numerical tests have been performed in a midscale tunnel by Blanchard, et al. (2012). These tests
looked at temperatures, velocities and radiative fluxes upstream and downstream of a fire location.
Efforts have been done to verify the validity of FDS v5.4. A sensitivity study has also been
performed to improve understanding of tunnel fires. Guo and Zhang (2014) used both FDS and
Fluent to compare numerical results with analytical and experimental solutions on backlayering.

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1.2 Aim and objectives
The aim of this dissertation is to validate Fire Dynamics Simulator (FDS) for tunnel fires and then
to use it as a tool to investigate different fire safety strategies in a tunnel. In this dissertation FDS
version 6.2.0 will be used. All default values will be used unless specified otherwise. This
dissertation will use the data from the Runehamar Tunnel Fire Tests (Test 4) performed by SP Fire
Technology in 2003. During these tests the heat release rate, temperatures, gas concentrations (O2,
CO2 and CO), air velocities and radiation levels were measured and multiple reports were written on
these tests. This data will be compared with simulation results in order to validate the use of FDS
for tunnel fires and the accuracy of predicting these quantities.
1.3 Organization of the thesis
In chapter 1 the dissertation is introduced. The background, aims and objectives and organization of
the thesis are introduced in this chapter.
In chapter 2 the literature review is presented. First the importance of tunnel fire safety in tunnels is
discussed and why this is so important. The main purpose of this chapter is to gain knowledge of
tunnel fire safety and why this dissertation is looking into this area. Then the history of relevant
incidents in tunnels is reviewed. The purpose of this chapter is to gain a broader understanding of
the importance of a proper safety strategy and to show the consequences a fire in a tunnel can have
on tunnel users. After this a review of both previous experimental work and CFD simulations on
fires in tunnels is presented. The literature review is finished with a presentation of the theory
behind CFD and a brief description of FDS.
In chapter 3 the experiment used in the validation case is described in detail. While a detailed
description of all the choices and simplifications made to simulate the experiment are presented in
chapter 4. All input will be documented in this chapter. It was chosen to do a validation case,
parametric study and a design study. A description is given behind all simulations.
Chapter 5 runs through all the results from the validation study, parametric study and design study.
In chapter 6 the dissertation is reviewed and results are interpreted. Implications from results are
discussed and recommendations for future studies are given.

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2 Literature review
2.1 Importance of tunnel fire safety
The awareness to road tunnel fires have been greatly increased by a succession of large scale
accidental fires in underground traffic systems:
 The King’s Cross underground station fire in London (1987)
 The Mont Blanc tunnel (March 1999)
 The Tauern tunnel (May 1999)
 The Saint Gotthard tunnel(October 2001)
But also some more recent deliberately caused or triggered accidents in underground traffic systems
like:
 Daegu (February 2003)
 Madrid (March 2004)
 London (July 2005)
Even though the cause and all circumstances are not know yet, these events have pointed out some
areas which needed more research or areas of weakness in the design.
Studies (Organisation for Economic Cooperation and Development (OECD), 2006) have shown that
road tunnel design often is characterized by the assumption that large-scale accidents are too
unlikely. This can either be because of:
 the absence of intersections
 no ‘soft’ road users or
 bad weather doesn’t affect conditions inside a tunnel too much
 often a lower speed limit
 etc.
While these factors reduce the probability of a fire in a tunnel, the origin of most harmful events are
deficiencies in tunnel structures or in vehicles. The largest focus so far has been to restrict access to
tunnels for dangerous goods. But basic fire loads like flour or margarine was the load of the truck
that caused the fire in the Mont Blanc tunnel (which lasted for 53 hours).
There are two main issues to be solved when assessing the fire safety designing of a tunnel:
1. The safe egress of the tunnel users inside the tunnel, where the hot smoke in the tunnel is of
great concern
2. The structural safety during a fire, where the temperature is important.
An extensive understanding of the fire development, how the fire spreads and how smoke moves in
an enclosure is valuable information for the engineers that produce the safety design of a tunnel
(Beard & Carvel, 2012). Important information about the conditions during a fire can be learned
when performing full-scale experiments, but these experiments are often very expensive to carry
out. Experiments provide valuable observations and measurements, while theoretical models
describe physical phenomena using mathematics and the input of experimental data (Yeoh & Yuen,
2009). Field modelling (also called computational fluid dynamics/CFD simulations) is being used
more frequently to predict the conditions in tunnels and corridors during a fire. The advantage of
this tool is that it can predict multiple variables like the air flow, local temperatures and smoke
movement (Xiaojun, 2008).

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In order to use CFD simulation in fire safety engineering several parameters need to be validated for
their prediction of real life fires. These parameters are how the model treats turbulence and models
combustion, radiation, soot production and solid pyrolysis. Beard (1997) describes that errors in
models are based upon the following limitations:
 Numerical assumptions in a model can only ever be an approximation to the real world
 Results depend on several assumptions made by the user like the numerical techniques adopted,
resolution of the grid and the boundary conditions that are assumed
 The possibility of computer software error requires assessment on the methods and procedures
that have been developed and applied. With regards to computer hardware error, users should be
mindful of plausible faults that may exist in the hardware
 Human errors are sometimes unavoidable. Some of the most common mistakes are made while
inserting input or in the analysis of output.
In order to evaluate a model ASTM E 1355 (American Society for Testing and Materials, 2012)
defines this process as "The process of quantifying the accuracy of chosen results from a model
when applied for a specific use." This evaluation process consists of two main components:
verification and validation. Verification is a process to check how correct the model solves the
governing equations. Validation is a process to determine how appropriate the use of the model (and
how the governing equations are solved) is as a mathematical model of the physical phenomena of
interest. Verification only looks at of the equations are solved correctly, while validation usually
compares model results with experimental measurements. This process is important to establish
both the range of use of the model and its limitations.

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2.2 History of incidents in tunnels
Both French (Perard, 1996), German (Baubehörde Highway Department, 1992) and Swiss
(Ruckstuhl, 1990) statistics show that accidents occur less frequently in road tunnels than on the
open road. French statistics show that there will only be one or two car fires for every hundred
million cars that pass through a tunnel (per kilometre of tunnel). This number is about eight for
heavy goods vehicles (per kilometre of tunnel). Of these eight fires there will be only one serious
enough to cause any damage to the tunnel (per kilometre of tunnel). Statistics show that there will
be between one and three serious fires (i.e. involving multiple vehicles and fatalities) out of every
thousand million heavy goods vehicles (per kilometre of tunnel). This might sound like an accident
is very unlikely but there are over 15.000 operational road, rail and underground railway/metro
tunnels in Europe alone, there are many road tunnels with very high traffic densities and a lot of
tunnels are many kilometres long (Beard & Carvel, 2012). Significant and fatal accidental fires in
tunnel seem to occur on an annual basis. This problem has the potential to become worse in the
future because more and longer tunnels are constructed and traffic densities increase everywhere.
Because of this, it is important to look at the past and see what can be learned from fires that
already happened in order to prevent them from happening in the future. In this chapter a list is
given of fatal or significant road tunnel fires between 1999-2009. Also a more detailed description
is given about some of the most significant tunnel fires in recent history.
Fires in tunnels between 1999 and 2009
Fires in tunnels have obtained special attention after several large fires in the Alps. These records of
tunnel fire incidents are a summary of information given in the Handbook of Tunnel Fire Safety
(Beard & Carvel, 2012) of the years after those fires. There were multiple large fires prior to these
accidents as well, but it is chosen to focus on these fires due to the availability of information.
Table 1: History of fires in tunnels between 1999 and 2009 (Beard & Carvel, 2012)
Where Vehicles
involved
Injuries What happened
Eiksund tunnel,
7.7 km long,
Norway, 2009
1 truck and
1 car
5 died A small truck and van collided in the middle of the tunnel
and caught fire shortly after the collision. The fire
department could not reach the fire due to heat and dense
smoke. At the time this was the deepest underwater tunnel
in the world (267 m below sea level).
Newhall Pass tunnel,
166 m long,
Interstate 5,
California, USA,
2007
30
commercial
vehicles
and 1
passenger
vehicle
3 died
23 injured
One truck lost control and struck the barrier at the side of
the carriageway. Other trucks collided with the first and a
fire started at the front of the crash. The fire then spread
backwards to other vehicles due to wind conditions. It took
24 hours to control the fire. The fire caused major
structural damage to the tunnel.
San Martino tunnel,
4.8 km long, near
Lecco, Italy, 2007
1 truck 2 died
10 injured
A lorry crashed into the tunnel wall and caught fire. This
caused a large pile-up behind the accident and cause the
rescue services to use 45 minutes to reach the accident.
Burnley tunnel,
3.5 km long,
Melbourne,
Australia, 2007
3 trucks
and
4 cars
3 died A rear-end collision caused a pile-up of 3 lorries and 4
cars. This resulted in explosions and a fire. The tunnels
deluge system managed to contain the fire. 400 tunnel
users were able to escape the tunnel safely.
Eidsvoll tunnel on
E6, 1.2 km long, near
Oslo, Norway, 2006
1 fuel
tanker and
1 car
1 died A head-on collision between a car and a fuel tanker. The
tanker caught fire immediately. The tanker driver was able
to escape but the car driver died.
Viamala tunnel,
0.7 km long,
Switzerland, 2006
1 bus and
4 cars
9 died
5 injured
A crash involving a bus and two cars. This resulted in a
fire which spread to two other cars.

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Where Vehicles
involved
Highway tunnel on
B31, 0.2 km long,
near Eriskirch,
Germany, 2005
2 cars 5 died
4 injured
A car skidded, crashed into an oncoming car and hit the
tunnel wall. The car caught fire and four people died in the
fire. A fifth person was thrown from the car.
Fréjus tunnel,
12.9 km long,
France/Italy, 2005
4 trucks 2 died
21 injured
A HGV caught fire and stopped in the tunnel. Because of
ventilation conditions three other HGVs caught fire on the
opposite carriageway. It appeared that the two that died
waited too long before escaping.
Baregg tunnel,
1.1 km long, near
Baden, Switzerland,
2004
1 truck and
1 car
1 died
5 injured
A truck collided with a car and two other trucks, which
had stopped in the tunnel because of an earlier collision.
The car caught fire and spread to one of the trucks.
Dullin tunnel,
1.5 km long, near
Chambéry, France,
2004
1 bus No
injuries
A fire started in the engine compartment at the rear end of
a bus. The bus carried 37 tourists. Rather than stopping,
the driver decided to continue driving out of the tunnel
despite flames spreading into the passenger compartment.
Outside of the tunnel everyone managed to escape the bus
without any injuries.
Fløyfjell tunnel,
3.1 km long, Bergen,
Norway, 2003
1 car 1 died A car crashed into the left-hand wall before moving across
the carriageway into an emergency telephone box on the
right. The car caught fire immediately and spread to the
tunnel lining. The tunnel was equipped with a sprinkler
system which managed to quickly extinguish the burning
tunnel lining but not the car. The driver was trapped in his
vehicle because of the crash and died.
St. Gotthard tunnel,
16.9 km long, near
Airolo, Switzerland,
2001
23 vehicles 11 died A head-on collision between 2 HGVs resulted in a very
large fire which lasted for over 2 days. It was found that
the death toll would have been much larger if the tunnel
was not equipped with a parallel service/escape tunnel.
Gleinalm tunnel,
8 km long, near
Graz, Austria, 2001
2 cars 5 died
4 injured
A head-on collision resulted in a fire near the middle of the
tunnel. Firefighters extinguished the fire quickly after
arrival.
Seljestad tunnel,
1.3 km long,
Norway, 2000
1 truck and
5 cars
20 injuries Two trucks slowed down to pass each other in this narrow
tunnel. 5 cars behind one of these trucks stopped. Another
oncoming truck did not see these stopping cars and
crashed into these stationary cars crushing them into each
other. The resulting fire spread to all cars and the
oncoming truck. Four people were trapped in the tunnel
because of the thick smoke for over 90 minutes but still
survived because they laid down on the road where the
smoke was thinner.
Tauern tunnel,
6.4 km long, south
east of Salzburg,
Austria, 1999
16 trucks
and
24 cars
12 died
42 injured
A HGV collided with a queue of stationary traffic. Eight
people died because of this crash. The HGV quickly
caught fire as well as another HGV and the four-car pile-
up between them. Four people died because of the fire.
Two passengers of a car died because they did not leave
there vehicle. One HGV driver died because he returned
from safety to collect some documents. Despite the deaths,
the ventilation system was reported to work very well.

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Where Vehicles
involved
Mont Blanc tunnel,
11.6 km long,
France/Italy, 1999
34 vehicles 39 died A few kilometres into the tunnel an HGV began emitting
some smoke. After a while the HGV stopped and was
quickly engulfed in flames. One motorcycle, nine cars, 18
HGVs and a van entered the tunnel behind the burning
HGV before the tunnel was closed (downstream). Of these
29 vehicles only four managed to pass the burning HGV to
safety. Nobody in the other 25 vehicles survived. Eight
HGVs and several cars entered the tunnel from the other
side (upstream).Nobody who entered the tunnel from the
upstream side got injured. The fire lasted for 53 hours.
The uncontrolled spread of toxic smoke (which led to the
majority of deaths) has been blamed on poor operation of
the ventilation system and a lack of communication
between the French and the Italian operators.
Human behaviour (including human error) seems to be a large factor contributing to deaths in road
tunnel fires:
 The fires in the Nihonzaka and Caldecott tunnels both started because of a collision.
 Many people who died in the Mont Blanc tunnel may have survived if they evacuated their
vehicles earlier and ran away from the smoke.
 Conditions during the Mont Blanc fire may also have been less severe if the operators used
another ventilation strategy.
Of the listed road tunnel fires, about one third started as a result of human behaviour and half of the
incidents started due to mechanical or electrical failure. The listed road tunnel fires also show that
any type of vehicle (car, HGV, bus, tanker, etc.) may be involved in a fire either as the first object
ignited or due to fire spread. Those incidents which have led to multiple deaths usually involve one
or more HGVs. This can be because of the large fire load or dangerous cargo. The involvement of
HGVs has meant that fire-fighting has been difficult and rescue operations have been hindered.
One of the main lessons learned from these fires is that tunnel users need to be informed
appropriately in case of an emergency, especially in long road tunnels. Taking human behaviour
into account in the design phase of a tunnel is very difficult because this is only recently being
researched and results are usually dependant of many different factors which can’t be taken into
account in the design phase (tiredness, intoxication, etc.). Informing tunnel users is one solution.
Checking conditions during a fire and if tunnel users get out safely is another way to insure egress
safety. Numerical simulations can be used to predict conditions during a fire. This way the design
can be checked and changed if needed. The only other reliable way to check this is by large-scale
fire tests. But to do this the tunnel needs to be ready built and changes to the design would be very
costly. Numerical simulations are a valuable tool but can only be reliable if the used model is
validated for tunnel fires. This dissertation will try to contribute to this work by both looking at
validation work done by others and by performing a validation study for Fire Dynamics Simulator
version 6.

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2.3 Previous research on tunnel fire safety
2.3.1 Tunnel fire experiments
Before the 1960s fire research was mainly focused on fire safety in mining tunnels. Fire tests in
road tunnels became more relevant in the early 1960s when many tunnels were being constructed,
especially in the Alps. Before these tests fire loads were mainly mine related like coal, wooden
structures and conveyor belts. Research into tunnel fires started after several large fires occurred in
Europe. These tests were performed in order to understand more on what might happen during
vehicle fires in tunnels (Beard & Carvel, 2012).
This chapter will focus on the conclusions or findings of some of the larger tests carried out.
Over the years several different tests in tunnels have been carried out for several reasons. The three
main reasons being:
 To gain knowledge of the fire dynamics in tunnels (incl. validation of simulation software)
 To test and commission tunnel installations like ventilation systems, sprinkler systems and
tunnel lining
 To gain knowledge on human behaviour during tunnel fires
One of the first road tunnel fire experiments was the Ofenegg tunnel fire experiments in 1965. An
important question is what would happen if a fuel tanker would have an accident in one of the new
tunnels in the Alps. In order to investigate this, a series of tests were set up in an abandoned railway
tunnel (Haerter, 1994). There were carried out 12 tests and the main observations were:
 Natural and semi-transversely ventilated fires burned slower than equivalent fires in the open
air, due to oxygen depletion. This effect was greater with larger fires.
 Longitudinal ventilation can cause an increase in burning rate (compared with other fires in
tunnels, not compared with burning in the open air).
 The velocity and thickness of the smoke layer was greater for larger fires (up to 11 m/s and 4 m
for semi-transverse ventilation).
 Longitudinal ventilation can cause the smoke layer to fill the whole tunnel (loss of
stratification).
 Maximum temperatures (of pool fires) were achieved within 1-2 minutes from ignition.
 Survival isn’t possible until 30-40 m from a large pool fire (with any ventilation configuration),
and the chances of survival downstream of the fire are substantially reduced with longitudinal
ventilation.
 Sprinklers can extinguish the fire, but fuel vapours will remain and re-ignite, with devastating
effects (airflow above 30 m/s and damage to the tunnel facilities).

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The EUREKA EU-499 'FIRETUN' test series (1990-1992) was the first extensive large-scale test
series where the HRR and gas temperatures from various vehicles were measured. Researchers from
9 different countries carried out the majority of the 21 tests in an abandoned tunnel in Hammerfest,
Norway. The main objectives of the tests were to investigate the fire behaviour of different type of
fuels including real road and rail vehicles (Ingason, et al., 2015). Other objectives were to provide
information on escape, rescue and fire-fighting possibilities, the effect of the surrounding structure
on the fire, reusing the structure (damage done, time required for redevelopment, etc.),
accumulation of theory (improving the understanding of fire, modifying models, etc.) and the
formation, distribution and precipitation of contaminants (Beard & Carvel, 2012). Many
conclusions have been drawn from this test series. Some of the mayor conclusions regarding road
tunnels were (Beard & Carvel, 2012):
 The temperatures during most of the vehicle fires reaches 800-900 °C. Temperatures during
HGV tests reached 1,300 °C. Temperatures decreased substantially within a short distance from
each fire location. Temperatures were greater downwind than upwind (Haack, 1995).
 Growth fire rates of vehicles vary from 'medium' to 'ultra-fast' (Ingason, 1995).
 The fire growth and burning pattern was strongly influenced by ventilation conditions
(Malhotra, 1995).
 The maximum concentration of polycyclic aromatic hydrocarbons (PAHs) and other pollutants
was found at about 70-80 m downwind of each fire location (Bahadir, et al., 1995).
 Longitudinal ventilation destroyed stratification downwind of the HGV fire (Malhotra, 1995).
In terms of actual scale the memorial tunnel fire ventilation test program is the largest tunnel fire
test series to date. In total 98 pool fire tests between 10-100 MW were carried out. As a part of the
test program several tunnel ventilation systems and configurations systems were assessed to
evaluate their respective smoke and temperature management capabilities. The main conclusions
include (Beard & Carvel, 2012):
 Longitudinal ventilation:
o Longitudinal ventilation using jet fans was highly effective in controlling smoke spread
for fires up to 100 MW. It is however only appropriate for unidirectional tunnels.
o The configuration of the fans did not affect the air velocity. The number of active fans
and the thrust were mostly the important factors.
o A 10 MW fire tended to reduce the longitudinal airflow by 10 %, and a 100 MW fire
reduced it by 50-60 %.
o Airflow velocities of 2.5-3 m/s were sufficient to prevent backlayering of smoke from
100 MW pool fires.
 Transverse ventilation:
o Supplying air is not enough in a tunnel fire situation, extraction is also necessary.
o Longitudinal airflow is a major factor in smoke control for transversely ventilated
tunnels.
o Multiple-zone ventilation systems are better than single-zone ventilation systems at
controlling smoke.
o Single-point extraction openings and oversized exhaust ports significantly enhance the
ability of a ventilation system to control and extract smoke.
 Smoke and heat movement:
o The time taken for smoke to enter the 'occupied zone' at positions distant from the fire
location was dependent on the height and geometry of the tunnel ceiling.
o A significant reduction in visibility was reached more quickly than was debilitating heat.

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Following the fires in the Mont Blanc tunnel, Tauern tunnel and St. Gotthard tunnel a series of 26
fire tests were carried out in the second Benelux tunnel as part of the Project safety test in 2001. The
main objectives of the tests were to assess tenability conditions for escaping motorists in case tunnel
fire and to assess the efficiency of detection systems, ventilation systems and sprinkler systems
(Ingason, et al., 2015). The main conclusions from the test series included (Beard & Carvel, 2012):
 Radiation levels were lethal within 6 m of a fully developed passenger-vehicle fire. For small
HGVs this distance increased to 12 m. Carbon monoxide was not a threat at these locations due
to convections and stratification.
 With and without longitudinal ventilation there was poor visibility due to smoke at 100-200 m
from the fire location. Toxicity limits were not exceeded at these locations.
 Sprinklers substantially reduced the air temperature and the temperature of vehicles in the
vicinity of the fires. Lethal temperatures were not observed and fire spread was controller for
the range of vehicles tested.
 Linear fire detectors, in general, activated an alarm between 3-5 minutes after the start of
ventilation conditions.
 Escape-route signage became invisible very quickly in smoke when hung at normal height
above doors.
An overview of selected tunnel fire experiments are shown in Table 2.
Table 2: Overview of selected tunnel fire experiments (Lönnermark, 2005)
Location Nr. Of
tests
Length
[m]
Height
[m]
Cross
section
[m2
]
Objects Measurements Comments
Ofenegg, CH,
1965
11 190 6 23 Petrol (6.6,
47.5, 95 m2
)
T, u, CO, O2,
smoke spread, 𝑚̇ 𝑓
(estimated)
Single track rail
tunnel, dead end,
sprinkler
Glasgow, UK,
1970
5 620 5.2 39.5 Kerosine
(1.44, 2.88,
5.76 m2
)
T, smoke spread Disused railway
tunnel
Zwenberg, AT,
1975
30 390 3.9 20 Petrol (6.8,
13.6 m2
),
wood, rubber
T, u, CO, CO2,
O2, NOx, THC,
visibility
Disused railway
tunnel
TNO, NL,
1979-80
2 8 2 4 Petrol (~3
m2
)
T, humidity Experimental
tunnel
P.W.R.I,
Japan, 1980
16 700 ~6.8 57.3 Petrol (4, 6
m2
),
passenger
car, bus
T, u, CO, OD, 𝑚̇ 𝑓,
radiation
Special test
tunnel, sprinkler
Kakeihigasi
Tunnel,
P.W.R.I,
Japan, 1980
8 3277 ~6.8 58 Petrol (4 m2
),
bus
T, u, CO, O2, OD,
𝑚̇ 𝑓, radiation
In use road
tunnel, sprinkler
TUB-VTT,
Finland, 1985
2 140 5 24-31 Wood cribs
(simulating
subway
coach and
collision of 2
cars)
T, u, CO, CO2,
O2, 𝑚̇ 𝑓, visibility,
smoke height
Disued cavern
system
Repparfjord,
NO, 1990-92
21 2300 4.8-5.5 25-35 Wood cribs,
cars, metro
car, rail cars,
heptane,
HGV
HRR, T, U, CO,
CO2, O2, SO2,
CxHy, NO, OD,
visibility, soot,
smoke spread,
PCDD/F, PAH,
PBDD/F, 𝑚̇ 𝑓
Disued
transportation
tunnel

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Location Nr. Of
tests
Length
[m]
Height
[m]
Cross
section
[m2
]
Objects Measurements Comments
Memorial,
USA, 1993-95
98 853 4.4 and
7.9
36 and 60 Fuel oil (4.5-
45 m2
T, u, CO, CO2,
CH4, THC, 𝑚̇ 𝑓,
visibility,
stratification
853 m, 8.8x4.3,
foam
Shimizu No. 3,
Japan, 2001
10 1120 8.5 115 Petrol (1, 4, 9
m2
), cars, bus
T, u, OD,
radiation
New road tunnel,
sprinkler
2nd
Benelux,
NL, 2002
14 872 5.1 50 n-heptane +
toluene, car,
van, wood
pallets (HGV
mock-up)
T, U, CO, OD,
𝑚̇ 𝑓, radiation,
smoke front,
visibility, fire
detection
New road tunnel,
sprinkler
Runehamar,
NO, 2003
4 1600 5-6 32-47 Cellulose,
plastic,
furniture
HRR, T, PT, u,
CO, CO2, O2,
HCN, H2O,
isocyanates, OD,
radiation
Disused road
tunnel
2.3.2 CFD simulations tunnel fires
Validation of FDS
In the FDS validation guide (McGrattan, et al., 2015) there are included 11 different reports and one
validation experiment on tunnel fires. In the mentioned reports 3 are based on the Memorial Tunnel
fire ventilation test program which all used version 2 of FDS. This was one of the largest tunnel fire
tests to date in terms of actual scale. All these reports were fires without a fixed firefighting system.
Other reports mentioned in the validation guide were on fires in mines or fire in tunnels with fixed
firefighting systems. FDS versions used in these cases were version 3, 4 and 5. The one experiment
mentioned in the validation guide was without any reference.
Cochard (2003) used test case 321 A of the memorial Tunnel fire ventilation test program to
validate FDS mostly for calculating the maximum gas temperature. Maximum temperatures were
mostly well estimated and were within 20-50 °C for far field while near field temperatures were
mostly overestimated before ventilation started and underestimated after ventilation started.
Maximum temperature was estimated within by 60-250 °C. Most measurements were within 100 °C
(about 12 %) but some temperatures were off by 250 °C or 50 %.
Another report based on the Memorial Tunnel fire ventilation test program was by McGrattan &
Hamins (2002). The main topic of the report was a fire in the Howard Street Tunnel. But as a part of
this report the Memorial Tunnel fire ventilation test program was used to validate FDS for tunnel
fires. The test with fire sizes of 20 and 50 MW with natural ventilation were used. For both the 20
and 50 MW fire peak temperatures were within 50 °C. Temperatures further away from the fire
were less accurate, but this was due to the coarse grid which was for parts of the tunnel more than
100 m from the fire.
The third report based on the Memorial Tunnel fire ventilation test program was by Hwang &
Edwards (2005). This report looked specifically at the critical velocity which was sufficient to
prevent back-layering in longitudinally ventilated tunnels. Specifically it was looked at if a steady
value for the critical ventilation velocity would be reached for large fires. Simulation results were
compared with both small and large scale tests. Results show that the velocity profile is predicted
quite well close to the fire. Upstream of the fire the velocity in the ceiling layer is under-predicted
and the velocity in the lower layer is over predicted. Overall the temperatures agreed well with the
experiments. The simulation overpredicted the ceiling layer velocity and layer thickness.

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There have been written several other reports on validation of FDS for tunnel fires than the reports
mentioned in the validation guide. Based on 4 different pool fires (1.8 MW and 3.2 MW at two
different surface heights each) Hu, et al. (2007) validated FDS 4 looking at ceiling jet temperature
distributions upstream and downstream. This was used to see how accurate FDS simulates the back-
layering length. Results were most accurate for the downstream area, where results closest to the
fire were within 2 °C (3 %) and further away within 5 °C (11 %). Upstream results where more
varied when looking at distance to the fire. But most results were within 7 °C (15 %). Results from
Kim, et al. (2008) showed other results however. Temperature difference in the upstream region
ranged from 0 (near floor) to 250 °C (1800 %, near ceiling). Gas temperatures in the downstream
area showed this trend as well, an increased difference between measured and simulated results
with the increase of height. Gas temperatures were underpredicted from 50 °C (20-30 %, near floor)
to 220 °C (50 %, near ceiling). The largest temperature difference was found in the region near the
fire. Gas temperatures were underpredicted between 80 °C (50 %) to 440 °C (100 %). This report
also compared simulated velocities and experimental measurements of gas velocities. The
difference ranged from 0.5 (20 %) to 5.5 m/s (520 %, near ceiling) in the upstream area.
Downstream results showed a difference of 1.5 m/s (36 %) to 1.65 m/s (57 %). Blanchard, et al.
(2012) performed tests in a 1/3 scale tunnel using heptane pool fires up to 4 MW, registering
temperatures, velocities and radiative fluxes both upstream and downstream of the fire. The test was
divided into two ventilation velocities; above and below the critical velocity (to avoid back-
layering).Below the critical velocity temperatures were predicted reasonably well (within 23 %
accuracy). The temperature is found to be more accurate away from the fire and underestimating the
temperatures in the flame region. Radiative flux measurements show that flame tilting wasn’t
captured well in FDS which led to an underestimation of radiative flux values downstream of the
fire. Upstream measurements show correct agreement between simulation and experiment. Test
results above the critical velocity also show that accuracy increases away from the fire.
Temperatures were within 19 % accuracy.
A report written by the U.S. Nuclear Regulatory Commission (2007) looked to validate FDS based
on 6 test series as part of a larger report on verification and validation of several fire models. This
report was primarily meant to be used for nuclear facilities, but the tests were usually performed in
normal buildings (high/low ceiling, etc.). No tests were performed in a tunnel but this validation
work is one of the more extensive works done up to date. In total 13 quantities were included in the
study:
 Hot gas layer (HGL) temperature and height
 Ceiling jet temperature
 Plume temperature
 Flame height
 Oxygen and carbon dioxide concentration
 Smoke concentration
 Compartment pressure
 Radiation heat flux, total heat flux and target temperature
 Wall heat flux and surface temperature

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The following formula was used to compare the experimental measurements with the model
predictions:
𝜀 =
∆𝑀 − ∆𝐸
∆𝐸
=
(𝑀 𝑝 − 𝑀0) − (𝐸 𝑝 − 𝐸0)
(𝐸 𝑝 − 𝐸0) (2.1)
Where:
∆𝑀: The difference between the peak value of the model prediction, 𝑀 𝑝, and its
original value, 𝑀0.
∆𝐸: The difference between the experimental measurement, 𝐸 𝑝,and its original
value, 𝐸0.
Each quantity is also assigned a colour rating to represent how well the model treats these
quantities. The green colour means that the conclusion was that the model accurately represents the
experimental conditions and that the difference between simulated results and the experimental
results are less than the combined experimental uncertainty.
The results of this study are represented in Table 3.
Table 3: Results validation study U.S. Nuclear regulatory Commission (2007)
Quantity Relative accuracy Colour rating
HGL temperature and depth +/-13 % Green
Ceiling jet temperature +/- 16 % Green
Plume temperature +/- 14 % Yellow
Oxygen and carbon dioxide
concentration
+/- 9 % Green
Should not be used for
CO, smoke or others
Smoke concentration +/- 33 % Yellow
Compartment pressure +/- 40 % Green
Radiation and total heat flux +/- 20 % Yellow
Target temperature +/- 14 % Yellow
Wall heat flux +/- 20 % Yellow
Surface temperature +/- 14 % Yellow

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2.4 Fire modelling
Energy released by a fire creates buoyant forces which drive complex three-dimensional flows.
These flows are affected by heat transfer into other constructions and turbulence around these
constructions. These flows are again affected by the geometry and the chosen ventilation system in
the tunnel (Rhodes, 2012). Because of the high costs involved with full scale tunnel fire
experiments, fire modelling has become a more popular choice to assess the fire safety strategy of a
tunnel during a fire.
There are two basic strategies within fire modelling:
 Zone modelling
 Field modelling
Zone modelling is a more basic strategy compared to field modelling. It requires less computational
resources and is primarily based on analytical and semi-analytical considerations. Zone modelling
divides a volume into two different zones, an upper zone/layer (smoke) and a lower zone/layer (air).
Each zone is described by a simple set of variables and semi-empirical laws. These variables
represent quantities (e.g. temperature, concentration, etc.) which are averaged over each zone. In
order to solve the system of equations boundary conditions need to be taken into account. These
boundary conditions between different zones, together with global conservation laws, lead to a
system of equations which determines the parameters of interest (Novozhilov, 2001).
Field modelling, or CFD modelling, present a more scientifically accurate approach to predicting
how a fire would behave. CFD modelling is based on the laws of conservation for physical
quantities such as mass, momentum, energy and species concentrations. These equations are solved
with the chosen resolution to yield distributions of the characteristics at any given point and time.
Field modelling is the most sophisticated tool available to fire safety engineers. This tool is usually
needed for tunnel fires because of the complex nature of tunnel fire dynamics.
Computational Fluid Dynamics (CFD) is the study of fluid systems that either is static or
dynamically changing in time and space (Yeoh & Yuen, 2009). High-technology industries like
aeronautics, astronautics and the automotive industry have a long-standing tradition integrating
CFD techniques into the design of vehicles, engines, spacecrafts and aircrafts. Because CFD models
become more available, many traditional engineering industries are now introducing CFD
modelling into their designing process. Mechanical, civil, chemical, electrical, electronic and
environmental engineering industries have benefited greatly of the use of CFD to understand and
resolve many problems that could not have been solved using other approaches. Lately CFD has
also been used in meteorology, hydrology and oceanography in order to understand the fluid flows
in rivers or oceans and give better weather forecasts.

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2.4.1 Theoretical basis of CFD
A brief summary of the theoretical basis of CFD is presented below. This summary is based on the
papers/books by McGrattan and Miles (2008), Novozhilov (2001) and Yeoh and Yuen (2009).
By using the Navier-Stokes equations, a set of partial differential equations, the motion of a fluid
can be described. These equations describe the conservation of mass, momentum and energy for a
flowing fluid.
Conservation of mass
The conservation equation for mass is:
𝜕𝜌
𝜕𝑡
+ ∇ ∙ 𝜌𝒖 = 0 (2.2)
This equation states that mass is neither created nor disappears. The change in density, ρ, at a given
point in the flow field is equal to the net mass flux, ρu, across the boundary of a small control
volume surrounding the point.
For fire simulations it is necessary to account for various gaseous species, 𝛶𝛼, like fuel or oxygen.
To account for this the mass conservation equation is changed:
𝜕(𝜌𝛶𝛼)
𝜕𝑡
+ ∇ ∙ (𝜌𝛶𝛼 𝒖) = ∇ ∙ 𝜌𝐷 𝛼∇𝛶𝛼 + 𝑚̇ 𝛼
′′′ (2.3)
When all species equations are added together, the mass diffusion and production terms on the
right-hand side sum to zero, leaving the original mass conservation equation.
Conservation of momentum
The conservation equation for momentum is:
𝜕(𝜌𝒖)
𝜕𝑡
+ ∇ ∙ (𝜌𝒖𝒖) = −∇𝑝 + 𝒇 + ∇ ∙ 𝝉 (2.4)
This is basically Newton's Second Law of Motion, 𝐹𝑜𝑟𝑐𝑒 = 𝑀𝑎𝑠𝑠 𝑥 𝐴𝑐𝑐𝑒𝑙𝑒𝑟𝑎𝑡𝑖𝑜𝑛. The force that
drive the fluid consist of the pressure gradient, ∇𝑝, friction (in the form of the viscous stress tensor,
τ), and external force terms, f, such as buoyancy.
Conservation of energy
The conservation equation for energy is:
𝜕(𝜌ℎ)
𝜕𝑡
+ ∇ ∙ (𝜌ℎ𝒖) =
𝐷𝑝
𝐷𝑡
+ 𝑞̇′′′
− ∇ ∙ 𝒒 + 𝜀 (2.5)
As in the mass conservation equation, the enthalpy, h, at a given point changes according to the net
energy flux across the boundary of a small control volume surrounding the point. Now, however,
there are additional source terms on the right-hand side of the equation related to the pressure,
combustion heat release rate, radiation and conduction and kinetic energy dissipation.

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2.4.2 Turbulence modelling
Turbulence modelling is the construction and use of a model to predict the effects of turbulence. It
is a computational procedure to close the system of the flow equations. Within CFD modelling three
different turbulence models are available:
 Direct Numerical Simulation (DNS)
 Large Eddy Simulation (LES)
 Reynolds-Average Navier-Stokes (RANS)
Figure 1 Modelling of eddies using the
different turbulence models
Figure 2 Time dependence of a velocity
component at a point
Direct Numerical Simulations (DNS)
DNS means that the governing equations are solved numerically with no modifications. This
implies that all of the relevant temporal and spatial scales are resolved directly without modelling
the diffusive terms like viscosity, thermal conductivity and material diffusivity. For the simulation
to be valid, all the range of length scales including the smallest scales must be accommodated from
which the viscosity is active. Therefor it's important to capture the dissipating kinetic energy within
the turbulent flow. Because of the high demands of this technique on the spatial and temporal
resolution, it is limited to small laminar flames and sometimes small turbulent jets. Because of the
high computational costs this technique is still not practical for large-scale fire simulations.
Reynolds-Average Navier-Stokes (RANS)
In the RANS approach to turbulence, all of the unsteadiness in the flow is averaged out and
regarded as part of the turbulence. The flow variables (like velocity, enthalpy or species mass
fractions) are decomposed into time averaged components (denoted by overbar) and a fluctuating
component (denoted by prime):
∅(𝒙, 𝑡) = ∅̅(𝒙, 𝑡) + ∅′(𝒙, 𝑡) (2.6)
Substituting the decomposed primitive variables into the conservation equations and then applying
the same time-averaging process to the entire system of equations yields a set of equations that is
similar in form to the original equations, with the mass conservation equation remaining unchanged:
𝜕(𝜌𝒖̅)
𝜕𝑡
+ ∇ ∙ (𝜌𝒖̅𝒖̅) + ∇𝑝 = 𝒇 + ∇ ∙ 𝝉̅ − ∇ ∙ ρ𝒖′ ∙ 𝒖′̅̅̅̅̅̅̅̅ (2.7)
𝜕(𝜌ℎ̅)
𝜕𝑡
+ ∇ ∙ (𝜌ℎ̅ 𝒖̅) =
𝐷𝑝
𝐷𝑡
+ 𝑞̇′′′
− ∇ ∙ 𝒒̅ + 𝜀̅ − ∇ ∙ ρ𝒖′ ∙ ℎ′̅̅̅̅̅̅̅̅ (2.8)

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Because the Reynolds-averaging process increased the number of unknowns (additional terms on
right-hand side), the system of equations is no longer closed (more unknowns than equations). The
additional terms are referred to as the Reynolds stresses and the turbulent scalar flux. Most
commercial and fire-specific CFD models that use the RANS approach use an eddy viscosity
turbulence model to close the set of equations.
Large Eddy Simulation (LES)
The derivation of LES models is very similar to that of the RANS models, with subtle differences in
the interpretation of the decomposition of the primitive variables. RANS emphasises temporal
averaging, whereas LES emphasises spatial averaging, or filtering. The key difference between the
techniques lies in the magnitude of the diffusive coefficient, the eddy viscosity. The idea behind
LES is that the turbulent eddies that account for most of the mixing or large scale motion are large
enough to be calculated with sufficient accuracy from the equations of fluid dynamics. Small-scale
eddy motion is approximated by a model within the CFD software. In LES, the governing equations
are formally derived by applying a filtering operation, which proceeds according to
∅̅(𝑥𝑖
′
, 𝑡) = ∫ ∅(𝑥𝑖
′
, 𝑡) 𝐺(|𝑥𝑖 − 𝑥𝑖
′
|)𝑑𝑥𝑖
′
(2.9)
where G is a filter function. The most common localized functions are the Top Hat filter, Gaussian
filter and Fourier Cut-Off filter. Flow eddies larger that this filter width are considered to be large
eddies, while eddies smaller than this filter width are small eddies and require modelling.
2.4.3 Brief description of FDS
The first version of FDS was released in 2000, while the current version is 6.2.0. FDS is developed
to solve practical problems within the field of fire safety engineering and research. FDS uses both
the conservation equations (mass, momentum and energy), a set of boundary conditions and source
terms found in the governing equations describing:
 the low speed transport of heat and combustion products from fire
 the mass, momentum and energy exchange between hot gases and compartment walls
 the reaction of fuel with oxygen
 the redistribution of energy by thermal radiation
 the spray of water from sprinkler
 the activation of a smoke detector
 pyrolysis
 fire growth
 flame spread
The model has been mainly used for the design of smoke handling systems, sprinkler/detector
activation studies, residential and industrial fire reconstruction. Results can either be extracted from
the program by using measurement points or by using an integrated program for visual results,
Smokeview.

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FDS is designed to be used by practicing engineers for a variety of fire protection and other thermal
flow applications. Therefore, it must be relatively fast and robust, and it must be easy to describe
the scenario. This means that the user should only have to specify a small number of numerical
parameters, focusing instead on the physical description of the problem. Because the computational
domain usually encompasses a volume within a building, or the entire building itself, the most
obvious and simplest numerical grid is rectilinear. In fact, because FDS is a large eddy simulation
(LES) model, uniform meshing is preferred, and the only numerical parameters chosen by the end
user are the three dimensions of the grid. Once established, it is relatively simple to define
rectangular obstructions that define the geometry to the level of resolution determined by the grid.
These obstructions “snap” to the underlying grid, a very elementary form of an immersed boundary
method (IBM).
The governing equations are approximated using second-order accurate finite differences on a
collection of uniformly spaced three-dimensional grids. Multiple meshes can be processed in
parallel using Message Passing Interface (MPI) libraries. Scalar quantities are assigned to the centre
of each grid cell; vector components are assigned at the appropriate cell faces. This is what is
commonly referred to as a staggered grid (Harlow & Welch, 1965). Its main purpose is to avoid
“checker-boarding” in pressure-velocity coupling by naturally representing the pressure cell
velocity divergence, a very important thermodynamic quantity in the model.
For the prediction of flows FDS numerically solves a form of the Navier-Stokes equations
appropriate for low-speed, thermally-driven flow with an emphasis on smoke and heat transport
from fires. Turbulence is can be treated using either LES (Default), DNS or RANS.
For the modelling of combustion FDS mostly uses a combustion model based on that mixing is a
limited, infinitely fast reaction of lumped species. These lumped species are reacting scalar
quantities that represent a mixture of species (like air which is a mixture of nitrogen, oxygen, water
vapour and carbon dioxide). The reaction of fuel and oxygen is not necessarily instantaneous and
complete, and there are several optional schemes that are designed to predict the extent of
combustion in under-ventilated spaces. For an infinitely-fast reaction, reactant species in a given
grid cell are converted to product species at a rate determined by a characteristic mixing time, τmix.
For the modelling of radiative heat transfer a solution of the radiative transport equation for a gray
gas is included. A technique similar to finite volume methods used for convective transport is used
to solve this equation. By default 100 discrete angles are used. The RadCal narrow-band model is
used to compute the absorption coefficients of the gas-soot mixtures. When water mist suppression
systems and sprinkler systems are included in the simulation the fact that water droplets can absorb
and scatter thermal radiation is important to add to the model. This property of absorption and
scattering is added using a coefficient based on Mie theory. The scattering from gaseous species and
soot are considered negligible and are not included in the model.

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Model limitations
When using FDS it is important to know the more prominent limitations. Therefore they are listed
here. A list of more specific limitations can be found in the FDS technical reference guide.
Low-speed flow: FDS is not design for high speed flows (like explosions or detonations). The
model is limited to flows with speeds below 0.3 Mach number. During fires this is a reasonable
assumption.
Rectilinear geometry: The efficiency of FDS is due to the limitation of a rectangular grid cells. This
can limit the types of structures which are put into the model, but techniques are implemented to
reduce the effects. If the objective of the study is to investigate the boundary layer effect, good
results cannot be expected.
Fire growth and spread: Because of the large uncertainty of material properties (not known or hard
to obtain) and the complex nature of combustion FDS is most reliable when the heat release rate is
prescribes. This is also what FDS originally is designed for.
Combustion: FDS uses a mixture fraction combustion model. This model is based on the
assumption that the reaction of fuel and oxygen is infinitely fast and that the combustion is mixing-
controlled. For over-ventilated fires, this is a correct assumption. Combustion during under-
ventilated conditions and when a suppression agent is used, uncertainty increases due to the fact that
this is an area which needs more research.
Radiation: To solve radiation methods similar to those used for convection are applied, finite
volume methods. Because of simplifications used for combustion, the chosen chemical composition
of the fuel and the soot yield can affect the absorption and emission of thermal radiation. Another
simplification is that the radiative heat transport is discretized in 100 solid angles. This can affect
the distribution of radiant energy further away from the fire. This can be solved by increasing the
number of angles, but this increases the computational time as well.

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3 Description of experiment
The main purpose of this chapter is to describe the experiment used to validate FDS for tunnel fires.
This description will be the basis for the input files used in the simulations.
After a large number of catastrophic road tunnel fires occurred, several research projects (UPTUN-
Upgrading Existing Tunnels) and tunnel network (FIT-Fires in Tunnels) were established through
funding by the European Union (EU). The experiment described in this chapter was closely linked
to the UPTUN project.
The aim of the project was to obtain new knowledge about fire development and fire spread in
semi-trailer cargos and the heat exposure to the tunnel linings in the vicinity of the fire. At the time
this experiment was unique in that no data of this detail has been collected previously. At the time
there had only been performed two large-scale fire tests using semi-trailer fire loads. These tests
were in the EUREKA 499 test program.
3.1 The tests
This project consisted of five large-scale fire tests, including one pool fire test and four HGV mock-
up fire tests that were carried out in the Runahamar tunnel in Norway in year 2003.
The fire tests consisted of 5 different tests with the following fire loads:
Table 4: Different test commodities (Ingason, et al., 2011)
Test
number
Description of fire load
T0 200 L Diesel in a pool with a diameter of 2.27 m
T1 360 wood pallets measuring 1200 x 800 x 150 mm, 20 wood pallets
measuring 1200 x 1000 x 150 mm and 74 PE plastic pallets measuring
1200 x 800 x 150 mm; 122 m2
polyester tarpaulin
T2 216 wood pallets and 240 PUR mattresses measuring 1200 x 800 x 150
mm; 122 m2
polyester tarpaulin
T3 Furniture and fixtures (tightly packed plastic and wood cabinet doors,
upholstered PUR arm rests, upholstered sofas, stuffed animals, potted
plant (plastic), toy house of wood, plastic toys). 10 large rubber tyres
(800 kg); 122 m2
polyester tarpaulin
T4 600 corrugated paper cartons with interiors (600 x 400 x 500 mm) and
15 % of total mass of unexpanded polystyrene (PS) cups (18000 cups)
and 400 wood pallets (1200 x 1000 x 150 mm); 10 m2
polyester
tarpaulin
The test number used for validation purposes in this thesis is test number T4. The mass ratio of
cellulosic/plastic was 82/19 during this test (Ingason & Lönnermark, 2003).

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3.2 The tunnel
The tunnel is a 1,600 m long asphalted road tunnel which was constructed in the early sixties and
taken out of use for about 20 years ago. The tunnel is 6 m high and 9 m wide with a cross-section of
about 47 m2
. The slope is varying between 1-3 %. The tunnel had an uphill slope of 0,5 % up to 500
m from the east portal, followed by a 200 m plateau and the followed by a 900 m long downhill part
with an average slope of 1 %.
Figure 3: Runehamar test tunnel (Opstad & Wighus, 2003)
The tunnel is made in hard Gneiss type rock and has a concrete tunnel portal in each end.
During the tests the tunnel was protected using PROMATECT®-T fire protection boards. The
centre of the fire was 21.5 m from the east end of the protection (upstream) and about 53.5 m from
the west end (downstream).
Figure 4: Fire protection boards
(Ingason, et al., 2011)
Figure 5: Tunnel cross-section (Ingason,
et al., 2011)
The gap between the protection boards and the tunnel ceiling was closed using insulation boards at
the inlet of the protective inner tunnel. This was to protect the structure which was holding up the
protection boards if backlayering would occur.

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A mobile fan unit was used to produce a longitudinal air velocity throughout the tunnel to avoid
backlayering.
Figure 6: The mobile fan unit (Ingason, et al., 2011)
The measured air velocity before ignition was in the range of 2.9-3.4 m/s.
3.3 Fire placement
A HGV trailer mock-up was placed about 1037 m from the east portal (the centre of the fire). The
fire load was placed on a rack storage system to simulate a HGV measuring 10,450 mm by 2,900
mm. The height of the system was 4,500 mm, but the fire load didn't start before about 1,100 mm
above the asphalt.
3.4 Measurements
Temperature was measured at several positions along the tunnel. Unsheated thermocouples, 25 mm
type K, were used for measurements. Near the fire sheated 1 mm thermocouples were used.
Temperature was measured at several locations: 0 m, 20 m, 40 m, 70 m, 100 m, 150 m, 250 m, 350
m and 458 m (distance compared to centre of fire). At all these positions temperature was measured
0.3 m below the ceiling (4.8 m above the road in the region with fire protection boards and 5.7 m
above the road elsewhere). At both 100 m and 250 m temperature was also measured at 1.8 m above
the road surface. At 458 m there was a measurement station with thermocouples at 5 heights: 5.1 m,
4.1 m, 2.9 m, 1.8 m and 0.7 m above road surface.
Oxygen, CO and CO2 concentrations were measured at 458 m downstream at both 2.9 m and 5.1 m
above road surface.
Gas velocity was measured 458 m downstream at 5.1 m, 4.1 m, 2.9 m, 1.8 m and 0.7 m above road
surface.
Radiation was measured 0 and 40 m downstream on the ceiling and 20 m both up- and downstream
on the floor.

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Bi-directional pressure difference probes were used at the measuring station (458 m) to be able to
determine gas velocity (using corresponding gas temperature).
Figure 7: Measuring station 458 m downstream of the fire (Ingason, et al., 2011)
On the ceiling of the fire protective construction near the fire, plate thermometers were placed at 0
m, 10 m, 20 m and 40 m downstream of the fire centre. There were also placed a plate thermometer
on the floor, 20 m downstream, facing the fire load.
3.5 Meteorological conditions
Temperature inside the tunnel at the fire location varied between 10-11 °C before the tests. During
test T4 there was no measurable longitudinal air velocity inside the tunnel before the fans were
started.

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4 Description of FDS input and simulations
4.1 Fire
HRR-curve
The fire used in the simulations is created using a flat surface with a specified heat release rate,
HRR, and other properties. The HRR is a very important input parameter to simulate a fire correctly
because several other parameters depend on this input (like CO or soot production). Therefor it is
very important to input this correctly into the model. Figure 8 shows both the heat release rate of the
fire during the experiment and the simulated heat released. This figure shows that the fire growth
and decay are implemented into the model correctly.
Figure 8: Heat release rate during experiment and simulation
Fire area
In the description of the tests it was described that the fire load had the following dimensions:
 Width: 2.9 m,
 Length: 10.45 m
 Height: 3.3 m
 Fire load was standing 1.1 m above the road surface
Because of chosen cell sizes, the following sizes were chosen:
 Width: 3 m,
 Length: 10 m
 Height: 3.5 m
 Fire started 1 m above the road surface
The heat release rate per unit area, HRRPUA, was adjusted to maintain the correct HRR as shown
in Figure 8.
0
10000
20000
30000
40000
50000
60000
70000
HRR[kW]
Time [s]
HRR simulation
HRR experiment

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Heat of combustion, Soot- and CO-yield
The description in chapter 3 describes that the fire consisted mostly of wood and polystyrene and
that the mass ratio of cellulosic/plastic was 82/19 during test T4. In order to simulate this fire the
following material properties were used to calculate a correct value for heat of combustion, soot-
and CO-yield (Society of Fire Protection Engineers, 2008):
Table 5: Properties of fuels used in experiment
Parameter Value
Wood Heat of combustion 17.1 kJ/g
Soot-yield 0.015 g/g
CO-yield 0.004 g/g
Polystyrene Heat of combustion 38.1 kJ/g
Soot-yield 0.18 g/g
CO-yield 0.06 g/g
Because the fire was added in the model with a specified HRR, the value for heat of combustion,
soot- and CO-yield needed to be added as one value. From the mass ratio of cellulosic/plastic a
weighted value for each parameter was calculated and used in the model:
Table 6: Properties of fuel used as input to FDS
Parameter Value
Heat of combustion 21.05 kJ/g
Soot-yield 0.046 g/g
CO-yield 0.0145 g/g
Simulation time
Most of the received experimental data from the tests last for about 3,600 seconds, or 1 hour. That
is why all simulations last 3,600 seconds.

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4.2 Grid size
As a general rule the dimensionless parameter D*/dx is used to determine the grid size. This value
should be between 4 and 16 according to the FDS 6 user's guide (McGrattan, et al., 2015) where a
high number represents a fine grid and a low number represents a coarse grid size.
D* is the characteristic diameter of the fire and is calculated by looking at the fire size, gravitational
constant and the density, temperature and specific heat of air.
𝐷∗
= (
𝑄̇
𝜌∞ 𝑐 𝑝 𝑇∞√ 𝑔
)
2
5
With the fire size from experiment T4 (66.42 MW) this parameter will be 5.14. With a cell size of
0.5 m the parameter D*/dx will be 10.3 which is within the requirements (cell sizes in this thesis are
equal in all directions).
According to chapter 6.3.6 of the user's guide (McGrattan, et al., 2015; McGrattan, et al., 2015)
results from this grid must be tested with results from a finer grid. The chosen grid is satisfactory if
results are similar to the results of the finer grid. This process is called a grid sensitivity analysis.
The whole point of this is to reduce simulation time but not to lose accuracy. When grid size is
reduced, the number of cells increase. The smaller the cells are, the more accurate the results should
be. At a certain cell size results should be converging towards a certain value. When results
converge, smaller grid size doesn't increase accuracy but does increase simulation time. Therefor a
grid sensitivity analysis will contribute to an optimal simulation time with acceptable accuracy.
For the grid sensitivity analysis the simulation is reduced to only include the near field around the
fire. All other parameters are kept the same, except that the placement of thermocouples are
lowered to reduce the influence of heat transfer to surrounding constructions on the results.
Cell size is chosen to be 0.5 m. To perform a grid sensitivity analysis with a smaller cell size, a
simulation is completed with cell size of 0.4 m.
To check the accuracy it is chosen to look at the simulated heat release rate and measured
temperature at several locations:
 Temperature 4.5 m above road surface: 0 m, 10 m, 20 m and 40 m (downstream)
 Radiation on the ceiling: 0 m, 10 m, 20 m and 40 m (downstream)
Results from the grid sensitivity analysis are shown in annex 10.1. Graphs show that trends are
simulated equally and the resulting values for temperature and radiation are within reasonable
accuracy. These results show that the resulting values converge towards a result independent of cell
size and that a cell size of 0.5 m will give a satisfactory accuracy.

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4.3 Structure and properties
Structure in the model
The tunnel is made out of two concrete portals and rock in between. As recommended in the book
Tunnel Fire Dynamics (Ingason, et al., 2015), not the whole tunnel is put into the model to reduce
the volume that needs to be simulated (which reduces computational time). A section of 117 m
upstream from the centre of the fire and 55 m upstream from the latest measurement points are
included in the model (fire centre is at 1037 m into the tunnel, model includes 920-1550 m of the
tunnel). The ends of the tunnel are put in as vents where one end provides the airflow and the other
end is put in as an open vent.
Figure 9: Tunnel cross-section
Division into mesh
To maintain the highest accurate on the results, and not lose accuracy because of lost information
between mesh boundaries, it was chosen to simulate all cases with one mesh. A tunnel section of
920-1550 m into the tunnel was included in the model.

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Material properties used in the model
Use of materials and its parameters:
Table 7: Material properties used in the simulations
Parameter Value Unit Reference
Promat
(green colour in Figure 9)
Conductivity, k 0.212 W m ∙ K⁄ PROMATECT®-
T fire protection
boards data sheet
Specific heat,
cp
540 J kg ∙ K⁄ Value not given
on data sheet,
value assessed
appropriate
Density, ρ 900 kg m3⁄ PROMATECT®-
T fire protection
boards data sheet
Rock
(gray colour in Figure 9)
Conductivity, k 4.5 W m ∙ K⁄ (Engineering
Toolbox, 2015)
Rock, solid
Specific heat,
cp
840 J kg ∙ K⁄ (Engineering
Toolbox, 2015)
Stone
Density, ρ 2,550 kg m3⁄ (Engineering
Toolbox, 2015)
Stone

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4.4 Placement of measurements
Measurements were placed as described in chapter Error! Reference source not found..
Temperature was measured downstream at the following locations:
 0 m, 20 m, 40 m, 70 m, 100 m, 150 m, 250 m, 350 m and 458 m measured 0.3 m below the
ceiling
 100 m and 250 m measured 1.8 m above road surface
 458 m at several heights: 4.1 m, 2.9 m and 0.7 m above road surface
Oxygen, CO and CO2 concentrations were measured at 458 m downstream at both 2.9 m and 5.1 m
above road surface.
Gas velocity was measured 458 m downstream at 5.1 m, 4.1 m, 2.9 m, 1.8 m and 0.7 m above road
surface.
Radiation was measured 0 and 40 m downstream on the ceiling and 20 m both up- and downstream
on the floor.
4.5 Parametric study
To check how sensitive the results of this validation study are to changes to the input parameters
and to study what changes to the results when some of the input parameters were changed, a
parametric study is performed. The following parameters were included in this study:
 HRRPUA
 Soot-yield
 CO-yield
 Heat of combustion
 Fire setup
For the HRRPUA and the heat of combustion these values where changed ± 10 %. Because of the
small numbers for the soot- and CO-yield, these values are changed ± 100 %. For the fire setup it
was chosen to look at the results if the fire was included in the model as a block with the length,
width and height of the simulated truck with the fire being on all sides and top.
HRRPUA
Changing this parameter, and not the fire area, changes the HRR of the fire. It was chosen to change
this parameter ± 10 %.
Soot-yield
Changing this parameter changes the soot produced by the fire per mass unit of fuel combusted. It
was chosen to change this parameter ± 100 %.
CO-yield
Changing this parameter changes the CO produced by the fire per mass unit of fuel combusted. It
was chosen to change this parameter ± 100 %.
Heat of combustion
Changing this parameter changes the unit mass of fuel combusted per unit energy produced by the
fire. This parameter again changes the mass of soot and CO produced by the fire. It was chosen to
change this parameter ± 10 %.

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Fire setup
For the validation case it was chosen to simulate a truck fire using a flat surface which produced a
certain amount of energy per unit area as shown in Figure 10.
Figure 10: Fire input used in the validation case
Figure 11 shows the way the fire was used in the parametric study.
Figure 11: Fire input used in the parametric study
Instead of a flat surface with the heat released from the top, the fire was simulated using a block
with the dimensions of the truck used in the experiment with heat released from both the top and all
sides.

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4.6 Design study
As a part of this thesis a design study was performed. The main reason for this was to study the
changes to the results when the fire safety design was changed and to look at how these changes
affect the conditions in the tunnel during a fire. For this part of the thesis the following changes are
looked at separately:
 A sprinkler system is added to the tunnel
 Fan speed is reduced and increased by 50 %
 The ventilation system is changed to a transverse system
Sprinkler system
Japan is one of the few countries that have included sprinkler systems in their guidelines for road
tunnel safety facilities. In Japan tunnel safety facilities depend on traffic volume and tunnel length
(like many other countries).
Figure 12: Categorization of tunnels (Chiyoda Engineering Consultants Co., Ltd., 2001)
Sprinkler systems are only required for special tunnels (very long or high traffic volume). It's
important to mention that Japanese regulations don't regard a sprinkler system as equipment for fire
extinguishment but used it to control the fire size and spread.

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Table 8: Safety facilities for each tunnel category (Chiyoda Engineering Consultants Co., Ltd.,
2001)
Japanese regulations require the following for sprinklers (Chiyoda Engineering Consultants Co.,
Ltd., 2001):
 The spray section should be at least 50 m, controlled by an automatic valve
 The automatic valve is opened based on fire detection activation and reconfirmation of the fire
location by the tunnel operator with ITV cameras
 The operator usually activates two spray sections; fire section and the section upstream
 Water source should be able to supply for at least 40 minutes for two spray sections
 Standard volume should be 6 litre/minute/m2
 Distance between sprinkler heads should be between 2.5 and 5 m
 Sprinkler heads should be installed in the top corner at a height of about 5.7 m above the road
surface and 2.9 m from the centre of the tunnel arch.

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For typical cases in Japanese tunnels, required water volume for sprinklers is 4,800 litre/minute for
activation of two sections.
For the design case it is chosen to add sprinkler heads every 5 m starting 5 m into the tunnel. With
the fire placed at 1037 m into the tunnel section 1000-1050 and 1050-1100 m would have been
activated during a fire. This would activate 20 sprinkler heads. The water volume for each sprinkler
head would be 240 litre/minute
Fan speed changes
For the fan speed reduction of 50 % new air velocity was set to 1.575 m/s. For the 50 % increase of
fan speed new air velocity was set to 6.3 m/s. Fan speed affects the possibility of back layering and
the mixing of smoke with fresh air, thus reducing or increasing concentrations and temperatures. In
this design study it was looked at how much these parameters are being affected by fan speed.
Transverse ventilation system
The last design case looked at the changes a different ventilation strategy would give comparing
results with the original ventilation strategy. The original ventilation strategy was a longitudinal
ventilation system, while this design case looked at an exhaust semi-transverse ventilation system.
The original case was changed by adding a ceiling with holes around the fire. The boundary
conditions were set in a way that air was blown into the tunnel underneath the ceiling and air was
extracted above the ceiling. This should create a flow which transports smoke above the ceiling and
keep part of the tunnel used by tunnel users free of smoke and hot gasses when designed correctly.
Figure 13: Setup of transverse ventilation case
This case was used to assess how this ventilation system affects the environment in the tunnel,
where tunnel users are escaping from the fire.

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5 Results
5.1 Validation results
It was chosen to assess both how accurate extreme values were simulated (maximum or minimum
values where relevant) and how well values were simulated during the first 1,200 seconds (where
the fire grows and starts decaying). The averaged accuracy was calculated using 80 points during
this span.
Temperature
The following temperatures were measured during both the experiment and simulation 30 cm below
the ceiling:
Figure 14: Experimental measurements
30 cm below ceiling
Figure 15: Simulation measurements 30
cm below ceiling
Figure 14 and Figure 15 show that the temperatures were underestimated for most of the
measurement points close to the fire (0-100 m downstream). Temperatures further away, 250-350
m, were more accurately estimated. Temperature measured right above the fire were extremely
underestimated ( ~910 °C).
0
50
100
150
200
250
300
350
400
450
500
0 600 1200 1800 2400 3000 3600
Temperature[°C]
Time [s]
0 m downstream
20 m downstream
40 m downstream
70 m downstream
100 m downstream
250 m downstream
350 m downstream
0
50
100
150
200
250
300
350
400
450
500
0 600 1200 1800 2400 3000 3600
Temperature[°C]
Time [s]
0 m downstream
20 m downstream
40 m downstream
70 m downstream
100 m downstream
250 m downstream
350 m downstream

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Figure 16 shows a clearer trend, when excluding the measurement at 0 m downstream. The trend in
this figure is that the measurements closest to the fire were underestimated and the measurements
further away from the fire show an increasing accuracy with an increasing distance.
Figure 16: Difference between simulated temperatures and experimental results downstream of the
fire
There were also two measurement points which show the accuracy at different heights. The first one
was at 100 m downstream.
Figure 17: Temperatures 100 m downstream at different heights
Figure 17 shows that FDS couldn’t correctly simulate the smoke layer height. Temperatures at the
different heights had a relative equal value for both measurements in FDS. The experimental results
show a clearer temperature difference which again shows where the smoke height is. The
temperature difference between the upper and lower measurement point is about 70 °C during the
experiment and almost nothing during the simulation.
-300,00
-250,00
-200,00
-150,00
-100,00
-50,00
0,00
50,00
100,00
150,00
Temperaturedifference[°C]
Time [s]
20 m downstream
40 m downstream
70 m downstream
100 m downstream
250 m downstream
350 m downstream
0
50
100
150
200
250
300
350
Temperature[°C]
Time [s]
Height= 5.7
experiment
Height= 5.7 m
simulation
Height= 1.8 m
experiment
Height= 1.8 m
simulation

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The second measurement point was at 250 m downstream.
Figure 18: Temperatures 250 m downstream at different heights
This figure shows the same trend as in the last, but the difference is less obvious. The difference
between the top and bottom measurement point is about 20 °C during the experiment but the
difference is almost nothing during the simulation.
This shows for both for Figure 17 and Figure 18 that a uniform environment is achieved throughout
the whole cross-section instead of a division in two layers (a warmer smoke layer and a cooler layer
of air).
0
20
40
60
80
100
120
140
160
180Temperature[°C]
Time [s]
Height= 5.7 experiment
Height= 5.7 m
simulation
Height= 1.8 m
experiment
Height= 1.8 m
simulation

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Table 9 summarizes the accuracy of FDS at each measurement point estimating the maximum
temperature and an averaged accuracy of the first 1,200 seconds. A positive number means an
overestimation and a negative number means an underestimation.
Table 9: Accuracy estimating temperature at different points
Distance
from
fire [m]
Height
above
road
surface
[m]
Difference
estimating
maximum
temperature
Difference
estimating
temperature
averaged over first
1,200 seconds
°C % °C %
0 5.7 -910.40 -69.75 % -549.95 -61.59 %
20 5.7 -12.26 -3.08 % 29.27 28.53 %
40 5.7 -211.91 -38.11 % -118.62 -31.23 %
70 5.7 -195.02 -45.68 % -110.16 -38.34 %
100 5.7 -112.24 -34.10 % -62.58 -29.61 %
100 1.8 -26.09 -10.98 % -8.55 -0.72 %
150 5.7 -79.48 -29.89 % -40.27 -23.47 %
250 5.7 -18.63 -10.94 % -8.07 -9.65 %
250 1.8 7.18 5.06 % 2.34 0.59 %
350 5.7 7.97 6.82 % 4.95 13.63 %
458 5.1 21.66 25.96 % 12.97 21.46 %
458 4.1 23.35 28.68 % 14.14 24.67 %
458 2.9 25.71 33.71 % 15.27 27.87 %
458 1.8 32.09 47.96 % 19.10 43.94 %
458 0.7 32.54 55.78 % 19.25 47.04 %

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