multiscale modelling of tunnel fires (plenary, valencia, 2011)

Multiscale Modelling

of Tunnel Fires

Dr Guillermo Rein

School of Engineering

University of Edinburgh

Contributions from F Colella, R Borchiellini,

V Verda, R Carvel and J Torero

Valencia, June 1, 2011

www.sp.se

�This work is based on the PhD Thesis of

Francesco Colella (2010)

�It is a joint effort between Politecnico di

Torino and University of Edinburgh

“A case can be made for fire being, next to the life processes,

the most complex of phenomena to understand”

Prof. Hoyt C. Hottel, MIT, 1984

1050650450110010005003302420TOTAL

200202002705501560Underground

10037030180701402101160Roads

7502602206503803601051200Railways

SpainNorwayUKFranceGermanySwissAustriaItaly

Lengths of EU Tunnels (km)

Tunnels are key infrastructure

Tunnel Fires are rare but costly

Some important examples:

�Great Belt (Denmark, 1994)

�Channel (UK-France, 1996)

�Mont Blanc (Italy-France, 1999)

�Kaprun (Austria, 2000)

�Gotthard (Italy-Swiss, 2001)

�Channel (2006)

�Channel (2008)

�…

0

20

40

60

80

100

120

1940 - 1969 1970 - 1979 1980 - 1989 1990 - 1999 2000 - 2007

accidents injuredscasualties

> 400

# o

f e

ve

nts

period

Tunnel Fires are rare but costly

Colella, Multiscale Modelling of Tunnel Ventilation Flows and Fires, PhD thesis, Politecnico di Torino, 2010. http://hdl.handle.net/1842/3528

� Avoid any harm to occupants and rescue teams

�Minimize disruption of transport and economic

costs (=minimize damage to infrastructure)

Tunnel fire emergencies must be managed by a

global safety system and strategies capable of

integrating:

1. Detection

2. Evacuation

3. Ventilation - Smoke management

4. Suppression and Fire fighting

Tunnel Safety Strategy

Ventilation System

�Objective: maintaining tenable conditions to

allow safe evacuation and rescue procedures

as well as fire fighting

� Plays a crucial role within the safety strategy

� Most widespread safety system in tunnels

� Normal operating conditions: small part of the system

use for maintaining visibility and pollutants at

acceptable levels

Wu and Bakar 2000, FSJ

Tunnel Safety Strategy

Vehicle Traffic

Smoke evacuation

Forced ventilation

Safe evacuation and rescue path

Back-layering

� Back-layering: reverse flow

of smoke against the

longitudinal ventilation

� Critical velocity: minimum

longitudinal air flow

preventing back-layering

Oka and Atkinson, 1995, FSJ

Grant et al, Phil. Trans R Soc Lond A 1998

Ventilations Systems

�Longitudinal: forced airflow pushes smoke

along the main tunnel gallery to end

shaft/portal

�Transversal: forced airflows push/pull smoke

out of gallery at distributed location to

auxiliary duct system

�Hybrid: some combination of the two

Generating Ventilation Flows

�The response of the ventilation system during a fire is a complex problem

�Calculation of airflows is a must designers, owners, operators and manufacturers

�Resulting airflow depends on combination of:

�Active ventilation devices (jet fans, axial fans)

�Tunnel layout (long domain, section, shafts)

�Fire-induced flows

�Portal atmospheric conditions (wind, rain)

�Large obstacles (stopped vehicles, wreck)

Generating Ventilation Flows

� Domain split in branches and nodes

� Mass conservation in each node

� Momentum conservation in each branch (Bernoulli)

� Fans are source of momentum (pv curves by

manufacturer?)

� Fire is a source of heat

1D Network model

Fan Characteristic Curve

1D Network model

� Fast simulations (within seconds in a desktop PC)

� Predict the global behaviour

� Industry uses it to explore wide design options

BUT

� Need calibration constants (requires full scale testing)

� ballpark figures (does not predict accurate behaviour)

� Assumes flow is 1D (not valid for fire or fans)

CFD model

� Continuity:

� Momentum:

� Energy:

� + Turbulence

0̀=⋅∇+∂∂

uρρt

( ) ∑+⋅∇+−∇=⋅∇+∂

∂Sτuu

up

tρρ

[ ] ( )( ) ( ) ∑+⋅+∇⋅∇=+⋅∇+∂∂

Eeff STkpEEt

uτu effρρ

�Fire modelled as rectangular obstruction releasing hot combustion products from the top and extracting cold air from the sides

�Dimensions scale with fire Froude scaling

( )( )∞−

−=TTc

Qm

GpG

λ1&&

CFD Fire Source

305

400

550550

700600

700900

9501000

1000

Longitudinal coordinate [m]

Ver

tical

Ele

vatio

n[m

]4.75 5 5.25 5.5 5.75

0

0.05

0.1

0.15

0.2

0.25

temperature: 300 350 450 550 650 750 850 950

30 kW Fire:Symmetry plane

350

325

350

350

325

450

325

375

600


Ver

tical

Ele

vatio

n[m

]

4.75 5 5.25 5.5 5.750

0.05

0.1

0.15

0.2

0.25

temperature: 300 350 400 450 500 550 600 650 700

3 kW Fire:Symmetry plane


Grid Independence Study


CFD model

� 3D flow field from first principles

� Accurate results

� Industry uses to verify a pre-approved design

BUT

� Slow simulations (~1.5 km takes 1 month to solve)

� Not affordable for long tunnels (solution time for 24 km?)

� Cannot study all ventilation strategies (only time for 1 or 2 cases)

rough1D

Co

mp

uta

tio

na

l ti

me

Tunnel Length

CFDaccurate

Is there

anything in

between?

CFD vs. 1D computing time

CFD model

1D model

1D model

Multiscale1D Regions:

� Low velocity/temperature gradients

� 1D models can be used

3D Regions:

� High velocity/temperature

gradients

� CFD models must used

L’

L’’

Tunnel Portal

CFD 3D jet fan module

1-dimensional network

1-dimensional network

Tunnel Portal

Multiscale – Jet fans

3D region

1D region

1D region

Multiscale – Fire source

3D region

1D region

1D region

Multiscale Modelling� Dramatic reduction of the computational time

by 2 orders of magnitude

� Full coupled ventilation/fire simulation of the whole domain takes a few hours

� Hundreds of simulations can be done in a week

� Fast/accurate tool allows to explore multiple scenarios and design question not possible before: � What is the best design for a wide range of conditions?� How many jet fans must be installed?

� What is different fire sizes are considered?

� What is the redundancy of the system?

� What is the impact of the wind conditions?

� Would a fire change the tunnel airflow resistance?

� How fast would the system response to activation?

� How much faster than fire growth?

L’

L’’

Jet fan pairs

Near fieldCFD model

1D model

1D model

L=?

1D/3D interface location?

L’

L’’

Correct 1D/3D interface location

Jet-fan discharge

L3D[m]

Mas

sflo

wra

te[k

g/s]

Err

or[%

]

0 100 200 3000

10

20

30

40

50

60

70

80

90

100

110

120

0

10

20

30

40

50

60

70

80

90

100

mass flow rateerror

L≈17 times the diameter L≈12 times the diameter

Fire source

Colella et al., A Novel Multiscale Methodology for Simulating Tunnel Ventilation Flows during Fires, Fire Technology 47, pp. 221-253, 2011. doi:10.1007/s10694-010-0144-2

Dartford Tunnels, London

� Under Thames river

� 1.5 km long

� Hybrid ventilation system

� Jet fans + supply & extraction shafts

� West Tunnel, built in 1963:

� 28 jet fans

� Diam 9.5 m

133m 1280m 157m

Cold Flow Validation

Cold Flow Validation

Colella et al., Calculation and design of tunnel ventilation systems using a two-scalemodelling approach, Building and Environment 44, 2009

System wide predictions

� Cold flow

� Multiple (x8) ventilation strategies studied

� Redundancy levels can be studied (how many jet fans can be disable while system provides safe conditions?)

Multiscale vs. CFD

� full CFD

� Multiscale

Scenario: 30 MW fire and 3 Jet fan pairs activated


Throttling Effect� Additional flow resistance imposed by the fire size (buoyancy, gas expansion and hot smoke aerodynamics)

� Reported experimentally in 1979 but hardly ever use

� eg, 100 MW fire decrease airflow by 30%

0

1

2

3

4

5

6

0 20 40 60 80 100 120

Fire size [MW]

# j

et

fan

pa

irs� Requires

coupling of fire and ventilation

� Can be significant for large fires and long tunnels

Jet fans needed to reach critical velocity


time

Detection/Activation:

Transient Problem

HRR

4 min

De

tect

ion

ra

ng

eVentilation #1?

2 min

Ventilation #2?

Ventilation #3?

� Only after fire detection, ventilation is activated

� Acceleration of the mass of air in tunnel while fire is growing

� Race between fire growth and ventilation response

30 MW

15 MW/min

Ventilation Response in time

Colella et al., Time-dependent Multiscale Simulations of Fire Emergencies in Longitudinally Ventilated Tunnels, Proceedings 10th International Symposium on Fire Safety Science, 2011

1 min 59 s after ignition / 1 s from activation

70 m back –layering

330320 310


elev

atio

n[m

]

50 100 150 200 2500

2

4

6

temperature: 300 320 340 360 380 400

-0.6 -0.4 0.6

0

0

0.41


elev

atio

n[m

]

50 100 150 200 2500

2

4

6

x-velocity: -1 -0.8-0.6-0.4-0.2 0 0.2 0.4 0.6 0.8 1

Transient Results

400 500 600 7000

5


Scenario 1: 3 Jet fan pairs

Longitudinal coordinate [m]400 500 600 700

0

5


≈ 0 m back –layering


Ventilation scenario 2

400 500 600 7000

5



Transient Results

3 min after ignition / 1 min after activation

Ventilation scenario 1

400 500 600 700 800 900 1000 1100 12000

5

Transient Results


400 500 600 700 800 900 1000 11000

5


1200

400 500 600 700 800 900 1000 11000

5


1200

5 min after ignition / 2 min after activation

Detection vs. Ventilation Requirements

� Evaluation of the design options in terms of detection technology

� For a given required time to remove back-layering, options range from low tech detection + many jet fans , to high tech detection + few jet fans

0

50

100

150

200

250

300

350

400

2 3 4 5 6 7 8 9 10 11 12 13 14# active jet fan pairs

ela

pse

d t

ime

fro

m f

ire

ou

tbre

ak

[s] Scenarios 1, 2, 3; TD=2min

Scenarios 4, 5, 6; TD=2.5min

Scenarios 7, 8, 9; TD=1.5minDetection time 1.5 min

Detection time 2.0 min

Detection time 2.5 min

Colella et al., Time-dependent Multiscale Simulations of Fire Emergencies in Longitudinally Ventilated Tunnels, Proceedings 10th International Symposium on Fire Safety Science, 2011

Conclusions: efficient, accurate & fast

� Multiscale is as accurate as full CFD

� Dramatic reduction of the computational time (by 2

orders of magnitude)

� Hundreds of simulations can be done in a week

� Fast/accurate tool allows to explore multiple

scenarios and design question not possible

before

� Full coupling fire and ventilation

� Throttling effect is significant

� Allows for transient problems and full ventilations

strategies

Thanks!for more information


Colella et al., Analysis of the ventilation systems in the Dartford tunnels using a multi-scale modelling approach, Tunneling and Underground Space Technology 25, 2010 doi:10.1016/j.tust.2010.02.007

Colella et al., Calculation and design of tunnel ventilation systems using a two-scalemodelling approach, Building and Environment 44, 2009 doi:10.1016/j.buildenv.2009.03.020

Colella et al., One dimensional and multi-scale modelling of tunnel fires and ventilation flows, Chapter in: The Handbook of Tunnel Fire Safety, ICE Publishing (in press), 2011.


Colella et al., Time-dependent Multiscale Simulations of Fire Emergencies in Longitudinally Ventilated Tunnels, Proceedings 10th International Symposium on Fire Safety Science (in press), June 2011, Maryland.

Cold flow – Norfolk Tunnel AU

Prediction vs. Measurements

� Two one-directional tunnels in Sydney (AU)

� 460 m long

� 6 jet fan pairs (34 m/s discharge velocity) 80 m spaced

Multiscale – coupling procedure

-0.080

-0.070

-0.060

-0.050

-0.040

-0.030

-0.020

-0.010

0.000

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19

0.00

0.50

1.00

1.50

2.00

2.50

3.00

3.50

Total pressure at inlet

Mass flow rate at inlet

[kg/s] [Pa]

Multiscale iterations - K

multiscale modelling of tunnel fires (plenary, valencia, 2011)

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