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Fire Protection of Underground Transportation
Systems: A Decision Support Tool for Designers and
Rescue Services
Karl Fridolf
Researcher, SP Fire Research
Post graduate student, Lund university
TuFT: Tunnel
Fire Tools
Fire
Text
based
input
TuFT
Comma
separated
output
Evacuation
Tunnel
Simulation
in TuFT
Rescue
Operation
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Fire dynamics
•  Underlying equations based on research by
Ingason (2012)
•  Empirically derived hand calculation models
•  One-dimensional bulk flow of gases
•  Assumptions
•  Wind direction
•  Completely mixted
Fire dynamics
•  Gas temperatures, concentrations and visibility
assessed as cross-sectional averaged values x m
from the fire at t s into the fire development
•  Consequences?
• 
• 
Far from as exact as, for example, any CFD model
Computational time TuFT << CFD model
Ingason, H. (2012). Fire dynamics in tunnels. In A. Beard & R. Carvel (Eds.), Handbook of Tunnel Fire Safety
(Second ed., pp. 273-307). London, UK: ICE Publishing.
Fire dynamics
Fire dynamics
1.  Tunnel
Start
Tunnel type, length, width, height, wind speed and
direction, ambient temperature and distances
between emergency exits
2.  Fire
• 
Growth/Decay type and rates, maximum HRR and
burning time, heat of combustion, burning efficiency,
mass optical density, yields (CO, CO2 and HCN),
position in the tunnel
3.  Prediction position(s)
• 
YES
t=t+1s
• 
Is t(sim) <
t(tot)
NO
Evaluate gas
temperature, gas
concentrations and
visibility at x m for
time t s
Print information in
all results vectors
to comma
separated textfile
Save evaluations
in corresponding
results vector
Stop
Position(s) in the tunnel
HRR
Backlayering
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Gas temperature
Evacuation
• 
• 
• 
• 
• 
Assessment based on FD calculations
One dimensional movement
Incapacitation/Death predicted with FED
Walking speed dependent on visibility
Evaluations done every s of the specified scenario
Start
Evacuation
NO
1.  Individuals (road tunnel)
• 
Start position, perception and preparation times, time before
leaving vehicle, evacuation to emergency exit or portal, type
of walking speed estimation
Same as for individuals, but parameters defined in intervals
YES
Is t < t(preevacuation)?
NO
Is agent
incapacitated?
2.  Groups (road tunnel)
• 
YES
Start position, length, number of available doors, door width,
number of passengers, perception and preparation times, safe
place, type of walking speed estimation
NO
Update and save
position (same as in
previous time step).
NO
Is t < t(leave
vehicle)?
YES
Is agent
incapacitated?
Update and save
position (same as in
previous time step).
NO
Determine walking
speed.
Is t < t(leave
vehicle)?
YES
Evaluate heat transfer at
position x m for time t s
and save in
corresponding vector.
Update position based
on distance moved for
time step t s.
YES
Update position based
on distance moved for
time step t s.
Calculate FED of heat
and save in
corresponding results
vector.
NO
Evaluate gas temperature,
heat transfer, gas
concentrations and visibility at
position x m for time t s and
save in corresponding results
vector.
Calculate FED of toxicants
and heat and save in
corresponding results vector.
YES
Is agent safe/
dead?
t=t+1s
Position in the tunnel
YES
Is t < t(preevacuation)?
NO
Determine walking
speed.
3.  Train (rail tunnel)
• 
Evacuation
YES
Is agent down
stream fire?
NO
Stop and print
evaluations and results
to comma separated
textfiles.
NO
YES
Is agent safe/
dead?
t=t+1s
Visibility
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• 
• 
• 
• 
• 
FID (asphyxiants)
FID (heat)
Rescue operation
Rescue operation
Assessment based on FD calculations
One dimensional movement
2 fire fighters at a time approaching the fire
Evaluation of increased body temperature
Start
Evaluations done every s of the specified scenario
Is t <
t(preparation)?
1.  Rescue operation
• 
Preparation time, if operation be done up or
downstream fire, if operation be done from portal or
emergency exit, number of fire fighters, availability to
thermal imaging camera, size of air bottles, length of long
and short hoses, times to connect branches and hoses
YES
t=t+1s
NO
Any fire fighters
available?
NO
YES
NO
Is t(active) <
t(available) for current
fire fighters?
YES
NO
Is increased body
temperature < 2.5°C?
Rescue operation
Request
instruction.
Rescue operation
Connect hose/
branch
Move toward fire.
Determine walking
speed.
Evaluate increased body
temperature at position
x m for time t s and save
in corresponding vector.
Update position based
on distance moved for
time step t s.
NO
t=t+1s
Is fire source
reached?
Update position (same
as in previous time
step).
YES
Stop and print results to
comma separated
textfiles.
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Concluding TuFT
Simple tool for quick estimations
Fire dynamics
Evacuation
Rescue operation
Coarse sub models
•  Predictive capability good, see Fridolf & Wahlqvist
(2014)
•  Focus on calculation technique
•  Model requires “perfect” user
• 
• 
• 
• 
• 
brand.lth.se/TuFT
• 
• 
• 
• 
Latest version of TuFT
Technical documentation (Swedish)
User manual (Swedish and English)
TuFT Macro
Available for download November 24!
Fridolf, K., & Wahlqvist, J. (2014). Predictive Capabilities of Computer Models for
Simulation of Tunnel Fires. Lund: Lund University.
Thank you!
Karl Fridolf
Researcher, SP Fire Research
Post graduate student, Lund university
karl.fridolf@sp.se
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