Design of Tunnel Ventilations Systems for Fire Emergencies Using Multiscale Modelling

Fourth International Symposium on Tunnel Safety and Security, Frankfurt am Main, Germany, March 17-19, 2010

427

Design of Tunnel Ventilations Systems for Fire Emergencies

Using Multiscale Modelling

F. Colella11 Politecnico di Torino, Dipartimento di Energetica, Italy.

,2 *, G. Rein2, V. Verda1, R. Borchiellini1, R. Carvel2, T. Steinhaus2 J. L. Torero2

2 University of Edinburgh, BRE Centre for Fire Safety Engineering, UK * corresponding author: e-mail address: [email protected]

ABSTRACT This paper presents a novel and fast modelling approach to simulate tunnel ventilation flows during fire emergencies. The complexity and high cost of full CFD models and the inaccuracies of simplistic zone or analytical models are avoided by efficiently combining mono-dimensional (1D) and CFD (3D) modelling techniques. A simple 1D network approach is used to model tunnel regions where the flow is fully developed (far field), and a detailed CFD representation is used where flow conditions require 3D resolution (near field). This multi-scale method has previously been applied to simulate tunnel ventilation systems including jet fans, vertical shafts and portals (Colella et al 2009, Build. Environ. 44(12): 2357-2367) and it is applied here to include the effect of fire both in steady state and transient situations. The methodology has been applied to a modern tunnel of 7 m diameter section and 1.2 km in length. Different fire scenarios ranging from 10 MW to 100 MW are investigated with a variable number of operating jet fans. Emphasis has been given to the discussion of the different coupling procedures for steady state and transient calculations. An accurate discussion on the computational cost reduction as well as on the control of the numerical error is also presented. Compared to the full CFD solution, the maximum flow field error can be reduced below 2%, but providing a reduction of two orders of magnitude in computational time. The much lower computational cost is of great engineering value, especially for parametric and sensitivity studies required in the design or assessment of ventilation and fire safety systems. KEYWORDS: multiscale modelling, jet fans, longitudinal ventilation system INTRODUCTION The response of the ventilation system during a fire is a complex problem. The resulting air flow within a tunnel is dependent on the combination of the fire-induced flows and the active ventilation devices (jet fans, axial fans), tunnel layout, atmospheric conditions at the portals and the presence of vehicles. Depending on the accuracy required and the resources available, a solution to the problem can be reached using different numerical tools. The overall behaviour of the ventilation system can be approximated using 1D fluid dynamics models under the assumptions that all the fluid-dynamic quantities are uniform in each tunnel cross section and gradients are only present in the longitudinal direction. 1D models have low computational requirements and are specially attractive for parametric studies where a large number of simulations have to be conducted. In the last two decades several contributions on the application of 1D models to tunnel flows have been published [1-5]. All the contributions underline the strength of 1D models to investigate ventilation scenarios within tunnels. However, this modelling approach cannot predict the characteristic of complex three-dimensional (3D) flow regions typically encountered close to operating ventilation devices (jet fans), intersection of galleries (shafts) or close to the fire, where air entrainment, plume formation and thermal stratification dominate the flow movement. Thus, in order to account for these important elements, 1D models must rely on approximated overall aerodynamics coefficients calculated somewhere else [5]. Computational fluid dynamics (CFD) remains the most powerful method to predict the flow behaviour due to ventilation devices, large obstructions or fire but its use requires much larger computational resources


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than simpler 1D models. In the last two decades, the application of CFD as a predictive tool for fire safety engineering is becoming widespread and great achievements have been made. CFD simulations of full and small scale tunnel fires have confirmed the capability of CFD tools as instruments to predict critical velocity or temperature fields with a certain degree of accuracy [6-10]. Only a limited number of CFD studies directly focus on the performance of tunnel ventilation systems in normal operating conditions or in case of fire [11-15]. This kind of analysis usually requires the adoption of a very large computational domain including the tunnel regions where the operating ventilation devices are located. CFD analysis of fire phenomena within tunnels suffers from the limitations set by the size of the computational domains. The high aspect ratio between longitudinal and transversal length scales leads to very large meshes. The number of grid points escalates with the tunnel length and often becomes impractical for engineering purposes, even for short tunnels domains less than 500 m long. An assessment of the mesh requirements for tunnel flows is made by Colella et al. [16-17] for active ventilation devices and for fire-induced flows. Grid independent solutions could be achieved only for mesh density larger that 4000 cells/m and 2500 cells/m for ventilation and fire induced flow respectively. The high computational cost leads to the practical problem that arises when the CFD model has to consider boundary conditions or flow characteristics in locations far away from the region of interest. This is the case of tunnel portals, ventilation stations or jet fan series located long distances away from the fire. In these cases, even if only a limited region of the tunnel has to be investigated for the fire, an accurate solution of the flow movement requires that the numerical model includes all the active ventilation devices and the whole tunnel layout. For typical tunnels, this could mean that the computational domain is several kilometres long. MULTISCALE MODEL The study of ventilation and fire-induced flows in tunnels [8-10, 13, 15, 16] provides the evidence that in the vicinity of operating jet fans or close to the fire source the flow field has a complex 3D behaviour with large transversal and longitudinal temperature and velocity gradients. The flow in these regions needs to be calculated using CFD tools since any other simpler approach would only lead to inaccurate results. These regions are hereafter named as the near field [16]. However, it has been demonstrated for cold flow scenarios [16] and for fire scenarios [10,17] that some distance downstream of these regions, the temperature and velocity gradients in the transversal direction tend to disappear and the flow becomes 1D. In this portion of the domain the transversal components of the flow velocity can be up to two orders of magnitude smaller than the longitudinal components. These regions are hereafter named as the far field [16]. The use of CFD models to simulate the fluid behaviour in the far field leads to large increases in the computational requirements but very small improvements in the accuracy of the results. The adoption of multiscale models represents a way to avoid the large computational cost of the full CFD and the inaccuracies of simplistic assumptions of 1D models. A multiscale method uses different levels of detail when describing the fluid dynamic behaviour of near field and far field. The behaviour of far field and near field regions is modelled by using a 1D model and CFD model respectively. The 1D and the CFD models exchange information at the 1D-3D interfaces and thus run in parallel. This approach allows a significant reduction of the computational time. At the moment multiscale modelling techniques have been applied to design tunnel ventilation systems in no-fire operating conditions [16]. The application of multiscale techniques coupling the fire to the ventilations systems is the subject of this paper. In this work, the prediction capabilities of full CFD and multiscale models are compared in terms of accuracy and computational time using a modern tunnel as case study. 1D model of the far field The general methodology of the 1D network model for tunnels is presented in [1 and 16] and only a brief overview is given here. The developed 1D model is based on a generalized Bernoulli formulation. It is designed to account for buoyancy effects, transient fluid-dynamic and thermal phenomena, piston effects


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and transport of pollutant species. It is developed to handle complex layouts typical of modern tunnel ventilation system (especially true for transverse ventilated tunnel) on the basis of a topological representation of the tunnel network. The fluid dynamic model needs the mass and momentum conservation equations to be solved in the whole domain (Eq. 1 and Eq. 2).

MSxu

t=

+

(1)

=

++

++

MxM SgzpuxuS

tu 2

21

(2)

where, represents the fluid density, u the longitudinal velocity, SM the mass source term per unit volume, p the static pressure, and SMx the momentum source terms per unit volume acting along the longitudinal direction, z the vertical elevation and g the gravity acceleration; x and t represent the longitudinal and temporal coordinates respectively. The momentum source term contains all the terms related to wall friction, losses due to flow separation at the portals and after obstacles. Eventually, pressure rise due to fan operation and piston effect are also accounted for. The calculation of the temperature profile within the tunnel domain needs the energy conservation equation to be solved. A generic formulation for a tunnel portion of length dx states

( ) ve qTTSUxT

xTuc

tTc +

=

+

2

2

(3)

where is the fluid conductivity, U the global heat transfer coefficient between the fluid and the wall, is the surface perimeter, S the cross section, Te the external temperature, c the specific heat and v the fluid velocity along x. The term qv accounts for heat generation in the control volume (i.e due to fire). In general the first term, representing the heat conduction along the longitudinal coordinate can be neglected if compared to the other terms of Eq. 3. In the case of steady state computations all the temporal derivatives in the mass, momentum and energy conservation equations must be neglected. The problem can be solved only after discretizing the computational domain in control volumes which allows the integration of momentum, continuity and energy equations. The tunnel domain is discretized in oriented elements called branches, interconnected by nodes. An example of a general network layout is presented in Figure 1 where the nodes are numbered as i and branches as j.

Figure 1 Example of the network representation of a tunnel showing branches between nodes CFD model of the near field

The CFD modelling of the near field has been conducted using the commercial CFD code FLUENT. This code has been extensively used for simulating duct flows and it has demonstrated its capability to model ventilation flows within tunnels as well as fire induced flows [8-10, 15, 16].

The turbulent fluctuations of the fluid-dynamic quantities have been modelled by means of Reynolds-averaged Navier-stokes equations (RANS). Among all the RANS turbulence models, the k- model has been applied in this work. The production and the destruction of turbulence kinetic due to the buoyancy


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have been also accounted for. RANS models have been extensively used and largely validated by the scientific community to simulate fire induced flows [6-8, 9-10, 13, 15].

The fire has been modelled as a volumetric source of energy at a constant rate without using a dedicated combustion model. This simplified approach, previously used to model fire induced flows in tunnels [13, 15], is the most practical given its low computational cost. It avoids the burden and the complexity of combustion and radiation models and the large uncertainty associated to the burning of condensed-phase fuels. Greater details on the fire model are provided in [17].

The tunnel walls have been assumed as adiabatic. Other heat transfer boundary conditions to the walls could be used, but the adiabatic condition represents the worst case in terms of buoyancy strength, threat to people and damage to the structure.

The CFD model requires also a representation of the jet fans. The methodology used here simulates the real construction of the jet fans as a cylindrical fluid region delimitated by walls and containing an internal cross surface where a constant positive pressure jump is enforced

The simulations have been considered to be converged when the scaled residuals were lower than 10-5 with the exception of the energy equation where the maximum allowed value was 10-7. For time dependent calculation time steps ranging from 0.1 s to 1 s have been used. The effect of the interface location

One of the most important issues on the use of multiscale model for tunnel ventilation and fires is related to the location of the interfaces between 1D and CFD models. These boundaries must be located in regions of the domain where the temperature or velocity gradients are negligible and the flow behaves largely as 1D. These dictate the length of the CFD domain. This required length is case specific and in general depends on tunnel geometry, installation details of ventilation devices, presence of obstacle, etc. Only broad guidelines are given here.

In previous work [16], the multiscale model was used to study the discharge velocity cone generated by a jet fan installed in a modern tunnel and 1.5 km long. The analysis of the effect of the interface location showed that the multiscale solution was within 1% of the full CFD solution when the length of the CFD domain containing the jet fan was larger than 20 times the tunnel hydraulic diameter.

In another work [17], the multiscale model was used to simulate a several fire scenarios in the same tunnel whose sizes ranged from 10 MW to 100MW. The analysis of the effect of the interface location showed that the multiscale solution deviates from the full CFD solution by few percents when the length of the CFD domain containing the fire was larger than 13 times the tunnel hydraulic diameter.

The simulation of tunnel fire scenarios when the ventilation flow is below the critical velocity must be performed ensuring that there the back-layering does not extend upstream the inlet boundary. If these conditions do not apply, the hypothesis of 1D flow pattern at the interfaces would not be valid, leading to inaccuracies. Coupling strategies The solution to the multiscale problem requires the coupling of the 1D and CFD models. The iterative solver procedure requires a continuous exchange of information between the models at the interface during the computations. A three stage coupling has been adopted for the scope. A full 1D model of the system is solved during the first stage. A CFD model of the near field is solved during the second stage. The boundary conditions are provided by the full 1D model run at the first stage. The global multiscale convergence can only be reached during the third stage when the 1D model of the far field and CFD model of the near field are run sequentially k-times exchanging periodically the boundary conditions at the interfaces (see Fig. 3). In comparison to more traditional coupling approaches, a three stage coupling allows a significant reduction of the multiscale iterations needed to reach a global convergence. The complete sequence of operations to be conducted during the solving procedure is described hereafter.

stage 1. a. Run the full 1D model of the system until convergence is reached b. Pressure and temperature values at the nodes corresponding to the interfaces i and i+1

must be recorded (to be used as boundary conditions of the CFD model in the next stage)


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stage 2. a. Run the CFD model of the near field until a certain degree of convergence is reached b. Integrate the flow velocities at the interfaces i and i+1 to calculate the global mass flow

rate (to be used as boundary conditions for the 1D model of the far field in the next stage) c. Calculate average temperature values at the interfaces i and i+1(to be used as boundary

conditions for the 1D model of the far field in the next stage) stage 3.

a. Run the 1D model of the far field until convergence is reached b. Pressure and temperature values at the nodes corresponding to the interfaces i and i+1

must be recorded and used as boundary conditions of the CFD model c. Run the CFD model of the near field until a certain degree of convergence is reached d. Integrate the flow velocities at the interfaces i and i+1 to calculate the global mass flow

rate (to be used as boundary conditions for the 1D model of the far field in the next stage) e. Calculate average temperature values at the interfaces i and i+1(to be used as boundary

conditions for the 1D model of the far field in the next stage) f. Check global convergence

i. If global convergence is not reached go back to point a (eventually a relaxation step can be added)

ii. If global convergence is reached quit the calculation or proceed to the next time step for time dependent calculation

=iA

iext dAvG '

,

itotp

iT 1+itotp 1+iT1 2 i-1

i i+2 N-1 N

i+1

CFD domain 1D domain

=

i

i

A

Ai

dAv

dAvTT

=iA

iext dAvG '

,

+

+

=+

1

11

i

i

A

Ai

dAv

dAvTT

Figure 2 Coupling procedure between 1D and CFD models during stage 3

This way of coupling is called direct coupling. It allows a significant reduction in the computational time if compared to the full CFD calculation of the same scenario. However the timescale of the direct coupling calculations is limited by the computational speed solving the CFD portion of model, the near field. This can take from some minutes to up to many hours to solve depending on the complexity of the scenario. The global convergence check is performed by monitoring the evolution of any fluid-dynamic quantity at the 1D-CFD interfaces during the inner k-iterations performed during step 3. In particular, the model checks whether or not the deviation of a certain fluid-dynamic quantity computed in two sequential multiscale iterations is lower than a fixed tolerance. Fig. 3 shows the evolution of total pressure and mass flow rate computed at a 1D-CFD interface during a multiscale calculation. The maximum deviation allowed was 10-6 which was reached after around 20 multiscale iterations. It is worth to note that, given the high uncertainty characterizing tunnel ventilation flow calculations, lower accuracy (i.e. 10-3) can be used shortening significantly the computing time (~10 multiscale iterations).


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-0.08

-0.07

-0.06

-0.05

-0.04

-0.03

-0.02

-0.01

0.001 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

Multiscale iteration - kM

ass

flow

rat

e [k

g/s]

0.00

0.50

1.00

1.50

2.00

2.50

3.00

3.50

Tota

l pre

ssur

e [P

a]

mass flow rate

total pressure

1.E-081.E-07

1.E-06

1.E-05

1.E-04

1.E-03

1.E-02

1.E-01

1.E+001 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Multiscale iteration - k

Dev

iatio

n [k

g/s

or P

a] mass flow ratetotal pressure

Figure 3 a). Evolution of total pressure and mass flow rate at a 1D-3D interface during a multiscale

calculation. The maximum deviation allowed was 10-6. b). Deviation of the mass flow rate and total pressure at a 1D-CFD interface during a multiscale calculation

CASE STUDY The previously discussed multiscale model has been used to simulate a 1200 m long tunnel longitudinally ventilated. This layout is realistic and typical of a modern generic uni-directional road tunnel. A schematic of the tunnel layout is presented in Fig. 4. The tunnel is 6.5 m high with a standard horseshoe cross section of around 53 m2. The tunnel is equipped with two groups of 5 jet fans pairs spaced 50 m, each group installed near a tunnel portal. The jet fans are rated by the manufacturer at the volumetric flow rate of 8.9 m3/s with a discharge flow velocity of 34 m/s.

Fire source

Northportal

Southportal

125m

north portal jet fan pairs #1 - #5(approximate position)

50m

1200m

600m

south portal jet fan pairs #6 - #10(approximate position)

#1 #2 #3 #4 #5 #6 #7 #8 #9 #10

Figure 4 Layout of the tunnel used as case study showing the relative position of the fire, jet fans

and portals (not to scale). The fires are located in middle of the tunnel and 4 different sizes ranging from 10 MW to 100 MW are considered. For sake of simplicity, only results relative to 30 MW fires will be presented, while a more detailed list of results is available in [17]. The emergency ventilation strategy, as for most longitudinally ventilated tunnels, requires the ventilation system to push all the smoke downstream from the incident region in the same direction as the road traffic flow, thus avoiding the smoke spreading against the ventilation flow (back-layering effect). The vehicles downstream the fire zone are assumed to leave the tunnel safely. All the studies on back layering show that the maximum critical velocity is in the range from 2.5 m/s to 3 m/s [8-9, 18-19]. Thus, an adequate ventilation system has to provide air velocities higher than this range in the region of the fire incident. Steady state simulation of fires The HRR is assumed to be constant and that steady state conditions are reached within the tunnel. In this section only 7 different ventilation scenarios (see Table 1) were considered. The number of operating jet fans has been chosen in order to guarantee supercritical ventilation velocities in each fire scenario.


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Scenario 1 30 MW x x - - -Scenario 2 30 MW x x x x -Scenario 3 30 MW x x x x xScenario 4 30 MW - - - - xScenario 5 10 MW x - - - -Scenario 6 50 MW x x x - -Scenario 7 100 MW x x x x -

Fire SizeJet fan

pairs #1- 2Jet fan pair #3

Jet fan pair #4

Jet fan pair #5

Jet fan pairs #6 - 10

Table 1 Settings of the ventilation system for the four fire scenarios considered The calculations have been performed by using direct coupling with a 400 m long CFD model of the near field. The 1D representation of the rest of the tunnel included the 10 pairs of jet fans. A sketch of the coupling is presented in Fig. 5.

North portal

Far field Near field Far field

South portal K+3

K 1

L3D

K+1

K+2 K+4

K+5

i

t+3

t

t+1

t+2 t+4

t+5

N

i+1 340

380 38

0

420

460

460

500

500

540

580

620

660

660

700

Figure 5 Multiscale coupling procedure to model the fire region and the rest of the tunnel

Table 2 presents a comparison of the bulk flow rate computed in the multiscale, full CFD and full 1D predictions for the 7 scenarios under investigation. The multiscale results show a good agreement when compared to full CFD predictions. The deviations range between 0.01% and 7%. On the other hand, it can be seen that simple 1D model does not provide highly accurate bulk flow results since the deviations from full CFD data range from around 12 % to 67% worsening for higher HRR.

Full Scale CFDmass flow rate [kg/s]

mass flow rate [kg/s] deviation

mass flow rate [kg/s] deviation

Scenario 1 216 221 2.1% 277 28.2%Scenario 2 301 301 0.01% 356 18.1%Scenario 3 435 435 0.1% 490 12.6%Scenario 4 299 296 1.1% 351 17.3%Scenario 5 205 204 3.1% 236 15.4%Scenario 6 227 234 0.02% 310 36.5%Scenario 7 194 209 7.4% 325 67.3%

Multiscale direct 1D model

Table 2 Accuracy of the multiscale simulation for the four fire scenarios considered. Comparison of full

CFD, multiscale (direct and indireact coupling) and 1D model results The results are also presented in Fig. 6 as temperature and horizontal velocity fields on the tunnel longitudinal plane. For sake of simplicity, only the results for the first scenario are presented. The rest of scenarios can be found in [17]. The multiscale predictions are shown to be in excellent agreement with the full CFD predictions. In particular, no important differences are observed the temperature fields. Some local deviations are observed in the horizontal velocity fields. These are due to flow perturbations from the operating jet fans.


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CFD: Tunnel Longitudinal section:

325

375

375

350

400

425

580 590 600 610 620 630 640

temperature: 325 375 425 500 600 [K]

Longitudinal coordinate [m]

MULTISCALE: Tunnel Longitudinal section:

325325

400350

425

425375

180 190 200 210 220 230 240

temperature: 325 375 425 500 600 [K]


CFD: Tunnel Longitudinal section:

3

3 4

4

5

4

5580 590 600 610 620 630 640

x-velocity: -3 -1 1 3 5 7 [m/s]


MULTISCALE: Tunnel Longitudinal section:

44

34

3

5

180 190 200 210 220 230 240

x-velocity: -3 -1 1 3 5 7 [m/s]


Figure 5 Comparison of results near the fire for the multiscale and the full CFD simulations for a fire

of 30 MW and ventilation scenario 1. Velocity and temperature values are expressed in m/s and K respectively. The longitudinal coordinates start at the upstream boundary of the corresponding CFD domain

Table 1 and 2 also show that the number of operating jet fans required to achieve critical ventilation velocity in the fire region varies with the fire size. In particular 2, 3, 4 and 5 jet fan pairs must be activated to provide super-critical ventilation velocity for a 10 MW, 30 MW, 50 MW and 100 MW fire respectively. These results show that throttling effect of the fire is large as it was already proved experimentally in 1979 [20]. In [17] it has been showed that a 100MW fire in a 1.2 km long tunnel can produce a decrease in the tunnel bulk velocity as high as 30%. Time dependent simulation of fire emergency scenarios Two ventilation scenarios have been considered in the time dependent simulations corresponding to scenario 2 and 3 of Table 1. The fire growth curve has been designed following the recommendations presented in [21]. It is constituted by an incipient phase (4 minutes long) characterized by a slow growth rate (0.5 MW/min) followed by a second phase characterized by a higher fire growth rate (15 MW/min). The maximum HRR is reached after around 350 s (see Fig. 6.Left). The detection time is assumed to be 2 minutes. The ventilation system is supposed to be activated once the fire has been detected. The temporal duration of the simulated fire scenarios was 10 min when approximately steady state conditions were achieved. The multiscale calculations have been performed by using direct coupling with a 300 m long CFD model of the near field. The 1D representation of the rest of the tunnel included the 10 pairs of jet fans (see Fig. 5). No time dependent full CFD calculation could be performed given the unreasonable computing time required to conduct such analysis.


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0

5

10

15

20

25

30

35

40

45

50

0 100 200 300 400 500 600

time [s]

HRR

[MW

]

`

Tim

e to

det

ectio

n

15 MW/min

Incipient phase

0

50

100

150

200

250

300

350

400

450

500

0 100 200 300 400 500 600

time [s]

Mas

s flo

w r

ate

[kg/

s]

0

1

2

3

4

5

6

7

8

Velo

city

[m/s

]

`

Tim

e to

det

ectio

n

Scenario 2

Scenario 3

Figure 6 Left). Fire growth curve Right). Time dependent evolution of the mass flow rate through the tunnel for scenario 2 and

3. The time to detection is 2 minute. Supercritical conditions (vair> 3m/s) are reached after 190 s and 160 s for scenario 2 and 3 respectively.

Figure 6.Right shows the temporal evolution of the mass flow rate through the tunnel as computed by the multiscale model. Supercritical ventilation conditions are reached after 190 s and 160 s for scenario 3 and 2 respectively when the fire is still in its incipient phase and its HRR is lower than 2MW.

330320 310

elev

ation[m

]

50 100 150 200 2500246

temperature: 300 320 340 360 380 400

-0.6 -0.4 0.6

0

0

0.4 1


elev

ation[m

]

50 100 150 200 2500246

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

Figure 7 Multiscale results in the vicinity of the fire computed 2 min after the fire outbreak for

scenario 2 and 3. The ventilation system is about to be started. Velocity and temperature values are expressed in m/s and K respectively. The longitudinal coordinates start at the upstream boundary of the corresponding CFD domain

Figure 7 shows the conditions within the tunnel 2 min after the fire outbreak. As it can be seen velocity and temperature profiles are still symmetric as the ventilation system has not been activated yet. The smoke fronts are located around 110 m far away from the fire source.


436

310

320310

330

elev

ation[m

]

50 100 150 200 2500

5

temperature: 300 320 340 360 380 400

310

310310320


elev

ation[m

]

50 100 150 200 2500

5

p


scenario 2(top) and 3(bottom). Temperature values are expressed in K. The longitudinal coordinates start at the upstream boundary of the corresponding CFD domain

Fig. 8 shows the multiscale results calculated for the fire near field 3 min after the fire outbreak. As in can be seen the ventilation velocity is high enough to counteract against the smoke spread in both the cases. The back layering distance has been reduced from 110 m to 70 m and ~0 m for scenario 2 and 3 respectively. Further analysis of the results have demonstrated that the upstream part of the tunnel is completely cleared from the smoke after around 220 s and 180 s for scenario 2 and 3 respectively. Furthermore it can be seen that, given the relatively low ventilation velocity, smoke stratification is maintained both in the upstream and downstream regions.

330

390

320 380

310

elev

ation[m

]

50 100 150 200 2500

5

temperature: 300 320 340 360 380 400

310

320

330

330

340


elev

ation[m

]

50 100 150 200 2500

5


scenario 2(top) and 3(bottom). Temperature values are expressed in K. The longitudinal coordinates start at the upstream boundary of the corresponding CFD domain

For sake of simplicity only the conditions established in the tunnel 10 min after the fire outbreak are presented (see Fig. 9). As it can be seen the ventilation velocity is high enough to avoid back-layering in both the scenarios. Average higher temperatures are recorded for scenario 2 given the lower bulk flow velocity achieved. Smoke stratification is completely destroyed in the fire downstream region in both the scenarios. It has to be asserted that the assumption of 1D flow at the upstream boundary must be maintained during


437

the whole calculation in order to achieve highly accurate results. This condition is always guaranteed for supercritical ventilation scenarios. For subcritical ventilation conditions, the location of the upstream boundary must be properly chosen to ensure that all the back-layering is captured within the CFD domain. As it can be seen from time dependent calculations, the fire effluents formed a long back-layering (~ 110 m) at the early stages but it was largely contained in the CFD domain. A rough estimation of the back-layering distance to properly design the dimension of the CFD zone for sub-critical ventilation scenarios can be obtained using the experimental correlation presented in [22]. For time dependent calculation a rough estimation of the smoke front velocity and the consequent travelled distance can be based on the correlation presented in [23]. However, a posteriori post-processing of the CFD results must always be conducted to clarify this matter. FINAL REMARKS The simulation of tunnel ventilation flows and fires by using multiscale techniques is a relatively new computational approach. The multiscale model, built by coupling 1D and CFD representations of the flow phenomena, allows a significant reduction in the computational time, as the more time consuming tool is only used to simulate a small part of the domain (near field). This region is characterized by high velocity and temperature gradients. The regions within the domain where the flow behaves as a fully-developed channel flow are modelled using a 1D model which is computationally effective and still able to provide sufficiently accurate results for this type of flow. The numerical coupling of the model takes place at the 1D-CFD interfaces where the two models provide the required boundary conditions to each-other. The reduction of the computational complexity is significant. In the presented examples for a modern 1.2 km long tunnel, the computational time was reduced by two orders of magnitude. Direct coupling calculation required less than 2 hr and around 150 hr for steady state and time dependent simulations respectively. However, the full CFD steady state simulations required between 48 and 72 hr to converge. No time dependent full CFD calculation could be performed due to the untreatable amount of computing time required. The full coupling achieved between ventilation system and fire allowed the evaluation of the fire throttling effect. It is pointed out that it can be as high as 30% for a 100MW fire in 1.2 km long tunnel. The adoption of this novel approach requires care on the location of the 1D-CFD interfaces. Previous studies [16,17] have showed that their position have a significant impact on the accuracy of the solution. Better predictions are achieved when the interfaces are located in regions where the flow behaves in a 1D fashion. The attainment of this flow pattern depends on the specific tunnel layout, jet fan characteristics, jet fan installation details and the presence of any other obstacles. Simulations of tunnel ventilation flow have some degree of uncertainty due to the real flow conditions at the portals (atmospheric pressure and wind), wall roughness, fire load, fire geometry, throttling effects of vehicles, and various other perturbing factors. For these reasons, the error introduced by using the multiscale model is well within the uncertainty range of the calculations and is acceptable for engineering purposes. On the other hand, the significantly lower computational time required by the multiscale models offers the advantage that many ventilation scenarios can be considered. Furthermore, it is asserted that the multiscale modelling approach represents the most practical way to perform accurate simulations of coupled ventilation flow and fire in tunnels longer than a few kilometres, when the limitation of the computational times becomes seriously restrictive REFERENCE LIST

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6. Woodburn PJ, Britter RE, CFD simulation of tunnel fire part I, Fire Safety Journal, 26, 35-62, 1996.

7. Woodburn PJ, Britter RE, CFD simulation of tunnel fire part II, Fire Safety Journal, 26, 63-90, 1996.

8. Wu Y, Bakar MZA, Control of smoke flow in tunnel fires using longitudinal ventilation systems - a study of the critical velocity, Fire Safety Journal, 35, 363-390, 2000.

9. Vauquelin O, Wu Y, Influence of tunnel width on longitudinal smoke control, Fire Safety Journal, 41, 420426, 2006.

10. Van Maele K, Merci B., Application of RANS and LES field simulations to predict the critical ventilation velocity in longitudinally ventilated horizontal tunnels, Fire Safety Journal, 43, 598609, 2008.

11. Armstrong J, The ventilation of vehicle tunnels by jet fans the axisymmetric case, Proceedings of the seminar on installation effects in fan systems, Mechanical Engineering Press, London, 1993.

12. Tabarra M, Optimizing jet fan performance in longitudinally ventilated rectangular tunnels, Separated and complex flows, ASME FED 217, 3542, 1995.

13. Karki KC, Patankar SV, CFD model for jet fan ventilation systems, Proceedings of Aerodynamics and Ventilation of vehicle tunnels conference, 2000

14. Massachusetts Highway Department, Memorial tunnel fire ventilation test program comprehensive test report, 1995.

15. Galdo Vega M, Arguelles Diaz KM, Fernandez Oro JM, Ballesteros Tajadura R, Santolaria Morros C, Numerical 3D simulation of a longitudinal ventilation system: memorial tunnel case, Tunnelling and Underground Space Technology, 23(5), 53951, 2008.

16. Colella F, Rein G, Borchiellini R, Carvel R, Torero JL, V. Verda V, Calculation and Design of Tunnel Ventilation Systems using a Two-scale Modelling Approach, Building and Environment, 44, 2357-2367, 2009.

17. Colella F., Rein G., Borchiellini R., Torero J.L., A Novel Multiscale Methodology for Simulating Tunnel Ventilation Flows during Fires, Fire Technology, (in press).

18. Kunsch JP, Simple model for control of fire gases in a ventilated tunnel, Fire Safety Journa,l 37(1), 6781,2002.

19. Oka Y, Atkinson GT, Control of smoke flow in tunnel fires, Fire Safety Journal, 25(4), 30522, 1995.

20. Lee CK, Chaiken RF, J.M. Singer JM, Interaction between duct fires and ventilation flow: an experimental study, Combustion Science & Technology, 20, 59-72, 1979.

21. Carvel, R., Design fires for tunnel water mist suppression systems, Proceedings of 3rd International Symposium on Tunnel Safety and Security, Stockholm, 12-14 March, 2008

22. Ingason, H., Fire Dynamics in Tunnels. In The Handbook of Tunnel Fire Safety (R. O. Carvel and A. N. Beard, Eds.), Thomas Telford Publishing, 231-266, London, 2005.

23. Heselden, A.J.M., Studies of Fire and Smoke Behavior Relevant to Tunnels, Current Paper CP66/78, Building Research Establishment, 1978.

Proceedings ISTSS 2010.pdfA4_Preface.pdfPREFACE

A5_Table of Contents.pdfTABLE OF CONTENTSKEYNOTE SPEAKERSRISKRISK AND SECURITYHUMAN BEHAVIOURPASSIVE FIRE PROTECTION AND CONSTRUCTIONACTIVE FIRE PROTECTIONACTIVE FIRE PROTECTION AND FIRE FIGHTINGVENTILATIONFIRE DYNAMICSPOSTERS

P3_Lnnermark.pdfabstractintroductionDifferent energy carriers and alternative fuelsRegulations and restrictionsFire incidentsDiscussionConclusionsAcknowledgementReferences

P4_Brinson.pdfABSTRACTINTRODUCTIONCETUNFPA 502

P5_ Ries.pdfThe city of Frankfurt am MainCity dataSpecific risks

Tunnels in Frankfurt am MainOverviewStreet tunnelsRailway tunnelsMetro system

Metro firesProblemsCausesObjectives

PreventionDetermination of the initial situationActions takenMeasures in the buildingConstructive measuresOrganisational measures

Measures for the Fire Department

Smoke managementObjectivesMobile ventilationMGVLuFMobile ventilators

Basic principles of mobile ventilation

List of references and further information

P6_Dix.pdfABSTRACT

P8_Diernhofer.pdfABSTRACTKEYWORDS: dangerous goods, road tunnel safety, risk analysis, risk assessment, expected valueEVALUATION OF RISKRisk acceptance

Legal Basis of tunnel safetyEU Directive 2004/54/EGAustrian Road Tunnel Safety LawADR 2007 / 2009

Development of RISK ANAlyEs in AustriaAustrian Tunnel Risk Model (TuRisMo)OECD/PIARC Model (DG-QRAM)

DEVELOPMENT OF A UNIFORM RISK ASSESSMENT PROCESS for DG TRANSPORTSData BasisDefinition of Assessment Process for Austrian Road TunnelsStage 1 Classification Matrix (Simplified Approach)Stage 2 Detailed Approach (DG-QRAM)Stage 3 Alternative Route

DefinitIon of risk CriteriaReference curve for risk assessmentAdjustment of the reference curve against the tunnel length

INTERNATIONAL Development TRENDSInternational OutlookREFERENCE LIST

P9_Cigolini.pdfINTRODUCTIONdecision making problems IN PERFORMANCE BASED FIRE DESIGNHAZARD ANALYSISSeNSITIVITY ANALYSISUNCERTAINTY AFFECTING THE ANALYSIS OUTCOMESADDRESSING UNCERTAINTYSTATISTICAL MODELINGSIMULATION TECHNIQUESCONSEQUENCE MATRIX AND MONTECARLO SIMULATIONConsequence matrixRESULTS AND COMMENTSREFERENCE LIST

P11_Jnsson.pdfAbstractIntroductionRisk AnalysisMethodologyHGV FiresConsequence analysisResults

CFD vs Hand calculationsCFD SimulationsHand calculationsAnalysis and Results

ConclusionNomenclatureReferences

P12_Chien.pdfAbstract1. INTRODUCTION2 Safety Assessment for an Existing Tunnel - Hsuehshan Tunnel2.1 Fire scenarios analysis2.2 Safety assessment2.2.1 Evacuation in tunnel fire Escape route signs2.2.2 Fire-fighting and rescue activities3 New Tunnel the East Coast Expressway Tunnels3.1 Fire scenarios analysis3.1.1 Assumption of Fire Scenario3.2 Analysis the Environment in Tunnel during Fires4. Conclusion and suggestions5. Reference

P13_Lemaire.pdfABSTRACTInTRoDUCTIONBACKGROUND TO THE TESTSTEST METHODEXPERIMENTAL RESULTSASSESSMENT OF BLEVE RISK FROM TEST RESULTSCONCLUSIONSREFERENCE LIST

P14_Wietek.pdfIntroductionERRA A5: Safety and Security of Underground Hubs with interconnected transportation services and shopping centersClassification of ERRAsObjectivesHow to achieve the ERRA objectivesApplication of the IRGC framework and the ERMF

ConclusionReference list

P15_De.pdfabstractintroductiongeotechnical centrifuge modelingcentrifuge modeling of explosionsBackgroundCentrifuge Model Tests on Tunnels in Dry SoilCentrifuge Model Tests on Submerged TunnelsInstrumentationCentrifuge Modeling of Crater FormationInfluence of Cover Thickness and Cover Material

results of centrifuge testsStrains Induced on Dry TunnelsStrains Induced on Submerged Tunnels

Conclusions

P16_Forsen.pdfAbstract1. Introduction2. CALCULATION OF PRESSURE LOAD from explosions inside the tunnel3. ResponseCross-sectional height, that is slab thickness, is assumed to be 1.2 m. Depth of reinforcing (distance from reinforcement to concrete surface at compressed side) is approximately 1.1 m. Reinforcing steel area is chosen to be 1 % of the cross sectiona...m 1.1 1.0 0.011 500 106 Nm = 6.05 MNm per meter.4. Pressure LOADING on a FaCade above a Tunnel Entrance5. Conclusion

P17_Frantzich.pdfReferences

P18_Tesson.pdfRecent research results on human factors and organizational aspects for road tunnels

P19_Haack.pdfEmergency Scenarios for Tunnels and Underground Stations in Public TransportUAlfred Haack1U, Joerg Schreyer21STUVA Inc., Cologne, Germany; former President of ITA 2STUVAtec GmbH, Cologne, Germany

ABSTRACTThe phase from the design to the operation of tunnels for railbound public transportation systems proves to be extremely protracted in practice. This is due to the necessary approval procedures and extremely different appraisals pertaining to safety f...

Keywords: smoke build-up time, evacuation time, walking speed, self rescue, assisted rescue, smoke protection measures1. EMERGENCY SITUATIONS IN TUNNELS AND CHOOSING A STANDARD EMERGENCY SCENARIO2. VEHICLE FIRE IN SCENARIO3. THE SPREADING OF SMOKE IN UNDERGROUND STATIONS3.1 Permissible smoke3.2 Measures designed to restrict smoke spreading3.2.1 General3.2.2 Smoke curtains3.2.3 Smoke removal shafts

3.3 Calculating smoke distribution

4. EVACUATING STATIONS4.1 General4.2 Reaction time and walking speed on the platform4.3 Congestion in front of the stairways4.4 Walking speeds on solid stairs4.5 Stair capacity4.6 Decisive number of persons for the evacuation4.7 Determining the evacuation times

5. COMPARISON OF EVACUATION AND SMOKE TIMES6. SUMMARY7. LITERATURE

P21_Clement.pdfDesign LifePrecast segmentsPrecast tunnel segments example:Railway link Bologna - Florence

Concrete behavior under fireFire loadConcrete CompositionStructural propertiesApplication

Fire ProtectionCurrent Status of Tunnel Fire ProtectionStructural IssuesFire loads in reality

Protecting Structural Concrete from FirePassive verses active systemsSprayed mortarsPre-fabricated boardsPolypropylene Fibre Modified Concrete

REFERENCES

P24_Wickstrm.pdfABSTRACTintroductionBASIC HEAT TRANSFER THEORYThus the total heat flux to a surface isAlternatively the net radiation heat flux can be writtenThe convective heat transfer coefficient h depends mainly on flow conditions near the surface.Thus TAST is a weighted temperature always between Tr and Tg.CALCULATING INCIDENT RADIATION, ADIABATIC SURFACE TEMPERATURE AND HEAT TRANSFER BASED ON PLATE THERMOMETER MEASUREMENTSThe plate thermometer (PT) according to the international standard ISO 834 and the European standard EN 1363-1 consists of a metal (inconel) plate and insulation on its back. A thermocouple fixed to the metal plate registers its temperature. The PT ...Measurement of incident radiation in the CONE CALORIMETERUsing adiabatic surface temperature to predict heat flux to fire exposed structuresTo verify the theory of using the concept of adiabatic surface temperature three experiments7F were conducted in a small room with dimensions as given in the Room/Corner standard ISO 9705, i.e. 3.6 m by 2.4 m and 2.4 m high with a door opening 0.8 m ...Measurements on pool and spray fire tests at nearly steady state conditionsSummaryReferences

P25_Jnsson.pdfAbstractIntroductionSuppression systemsGeneralDeluge systemsMist systemsFoam systems

Design IssuesDesign fire size

Potential trade offsCost Benefit analysisConclusionReferences

P26_Arvidson.pdfabstractBackground and objectiveThe fire test set-upThe commodity

The fire suppression systemsfire test proceduresFire test resultsDiscussionFire tests without the roof of the trailer mock-upFire tests with the roof of the trailer mock-up

ConclusionsReferences

P27_ Mawhinney.pdfAbstractComputational DomainWater Mist Drop-Size CharacterizationModeling the Fuel Package and Heat Release RateREFERENCES

P28_Starke.pdfAbstractNomenclatureIntroduction

P29_Kratzmeir.pdfABSTRACTREFERENCESNational Fire Protection Association: NFPA 502 Standard for Road Tunnels, Bridges, and Other Limited Access Highways. Issue 2008

P30_Kivimki.pdfAbstractIntroductiontest procedureThe instrumentation arrangement was the same for both setups. The following parameters were measured during the tests:Gas temperature with 0.5 mm bare K-type thermocouples arranged as shown in Figure 4.Water pressure at the ceiling level close to the hydraulically most remote operating nozzle.Oxygen level 160 cm above the floor as shown in Figure 4.The fire damage was photographed after each test.experimental RESULTSSummaryACKNOWLEDGEMENTSreference List

P31_Ko.pdfabstractIntroductionDescription of the laboratory tunnelTunnel ventilation system

HRR measurement systemCALIBRATION TESTS

SUPPRESSION TESTSSprinkler systemInstrumentation

RESULTSDiscussionREFERENCES

P32_Harris.pdfAbstractIntroductionBackgroundFire Point TheoryComputational ModelingIntroductionModel details

Results and ImplementationSimulation ResultsImplementation

ConclusionsAcknowlegementsReferences

P33_English.pdfINCIDENT MANAGEMENT AND TUNNEL SYSTEMSINTRODUCTIONThe fire service in California cooperatively developed the Incident Management System (IMS) to resolve these conflicts. The National Incident Management System (USA) was adopted by the US Government under Homeland Security Presidential Directive 5, w...

P34_Stiegel.pdfFire Department of the city of Frankfurt am MainBasic Facts and equipmentFire and rescue response unit in Frankfurt am MainEquipment of the attack- teamBreathing apparatusRapid intervention teamHeavy rescue unitE- powered railway trolleyMass casualties unit

Operations at Tunnel incidentsDispatch policy, tactics and incident commandTactics on scene, modular conceptModule 1Module 2Module 3Module 4

TRAININGConcept and implementation

List of references and further information

P35_Kumm.pdfFire and Rescue Operations during Construction of TunnelsABSTRACTINTRODUCTIONFIRE AND RESCUE OPERATIONS DURING CONSTRUCTION OF TUNNELSEXPERIENCES FROM PERFORMED EXERCISESEXPERIENCES FROM REAL FIRES IN TUNNELS UNDER CONSTRUCTIONSCENARIOSFIRE AND RESCUE OPERATIONS FOR THE CHOSEN SCENARIOSDISCUSSION AND CONCLUSIONSACKNOWLEDGEMENTSREFERENCES

P36_Ingason Lnnermark.pdfIntroductionEArlier studiesWood crib porosity and ventilationIngason test seriesLnnermark and Ingason test seriesConclusionsReferences

P37_Wu.pdfABSTRACT

P38_Tarada.pdfabstractINTRODUCTION

analytical solutionsREFERENCEs

P38_Tarada.pdfabstractINTRODUCTION

analytical solutionsREFERENCEs

P39_Colella.pdfAbstractintroductionmultiscale model

P39_Colella.pdfAbstractintroductionmultiscale model

P40_Truchot.pdfABSTRACTGas behaviour in tunnels, an experimental approachExperimental apparatusTheoretical addExperimental designReference configurationReference case: Numerical approachImpact of the leak directionConsequences outside the tunnel: an illustration of the experimental configurationExperimental campaign conclusion

Consequences of gas leackage in tunnelChlorine leakage: Consequences of a highly toxic gasLiquid leakage: Pool evaporation consequences

ConclusionsREFERENCE LIST

P42_Carvel.pdfAbstractIntroductionThe incidents18th November 199621st August 2006

Comparison of the incidents

P43_Salvi.pdfAbstract1. Introduction2. Overview of the regulatory situation3. Risk assessment and management in tunnels, underground, metros and confined spacesSummary of the projectFurther development neededSummary of the projectFurther development neededSummary of the projectFurther development neededSummary of the projectFurther development needed

4. Security management in transport systemsSummary of the projectFurther development neededSummary of the projectSummary of the projectFurther development needed

5. Integrated risk management of new and emerging risksSummary of the projectFurther development needed

6. Innovative energy carriers for vehicles and their safetyFurther development needed

7. RESEARCH PERSPECTIVES8. ConclusionsREFERENCES

P44_Hansen.pdfIntroductionSTATISTICS AND FIRE CAUSESEXPERIMENTAL AND THEORETICAL WORK ON FUEL LOADSSMOKE SPREAD AND FIRE BEHAVIOURCALCULATIONS AND MODELLINGCONCLUSIONSReferences

P45_Cutonilli.pdfExampleRailcar Modeling ResultsComparison of Data

COnclusion

PO47_Ghiasi.pdfREFERENCE

PO48_Buonfiglioli.pdfAbstractIntroductionCASE STUDYIndustry standard approachBest practice approachComparison between the industry standard and best practice approaches.

ConclusionsREFERENCES

PO49_Schneider.pdfAbstractintroductionREquired and available safe egress timevisibility of egress signs in smokeREFERENCE LIST

PO51_Kashef.pdfABSTRACTIntroductionExperimental StudyConclusions and RECOMMENDATIONS

PO52_Paris.pdfKEYWORDS: tunnel safety, operation, failure of equipment, minimum operating requirementsObjectiVEMEthodologyTECHNICAL AND HUMAN MEANS FAILURES DEFINITIONLINK WITH THE MAINTENANCECONCLUSIONREFERENCES

PO54_Aurin.pdfAIRPORT Tunnel Tegel Project describtonThe refurbishment and upgrade of the passive fire protectionRESUMEE

PO55_Belinsky.pdfABSTRACTREFERENCE LIST

PO56_Krawczyk.pdfABSTRACT:IntroductionMine ventilation network flow simulatormathematical model of the fire propagation modulegraphical inteRface for animated simulation of transientsEXAMPLES OF SIMULATIONCONCLussionReferences

PO58_Ruland.pdfIntroductionReference LIST

PO59_Rogner.pdfKEYWORDS: Fire detection, tunnel safety, line type heat detectors, smoke detection, video detection, redundancy

PO59_Rogner.pdfKEYWORDS: Fire detection, tunnel safety, line type heat detectors, smoke detection, video detection, redundancy

PO60_Capote.pdfabstractcONCLUSIONSREFERENCES

PO62_Buchmann.pdfIntroductionMeasurementMeasurement results / DiscussionREFERENCEs

PO66_Brons.pdfIntroductionCity and county of The HagueDiscussionReferences

PO67_Wang.pdfAn Integrated Safety/Security Video Image Detection (VID) System for Road Tunnel Protection

A4_Preface.pdfPREFACE

P3_Lnnermark.pdfabstractintroductionDifferent energy carriers and alternative fuelsRegulations and restrictionsFire incidentsDiscussionConclusionsAcknowledgementReferences

P6_Dix.pdfABSTRACT

P24_Wickstrm.pdfABSTRACTintroductionBASIC HEAT TRANSFER THEORYThus the total heat flux to a surface isAlternatively the net radiation heat flux can be writtenThe convective heat transfer coefficient h depends mainly on flow conditions near the surface.Thus TAST is a weighted temperature always between Tr and Tg.CALCULATING INCIDENT RADIATION, ADIABATIC SURFACE TEMPERATURE AND HEAT TRANSFER BASED ON PLATE THERMOMETER MEASUREMENTSThe plate thermometer (PT) according to the international standard ISO 834 and the European standard EN 1363-1 consists of a metal (inconel) plate and insulation on its back. A thermocouple fixed to the metal plate registers its temperature. The PT ...Measurement of incident radiation in the CONE CALORIMETERUsing adiabatic surface temperature to predict heat flux to fire exposed structuresTo verify the theory of using the concept of adiabatic surface temperature three experiments7F were conducted in a small room with dimensions as given in the Room/Corner standard ISO 9705, i.e. 3.6 m by 2.4 m and 2.4 m high with a door opening 0.8 m ...Measurements on pool and spray fire tests at nearly steady state conditionsSummaryReferences

P35_Kumm.pdfFire and Rescue Operations during Construction of TunnelsABSTRACTINTRODUCTIONFIRE AND RESCUE OPERATIONS DURING CONSTRUCTION OF TUNNELSEXPERIENCES FROM PERFORMED EXERCISESEXPERIENCES FROM REAL FIRES IN TUNNELS UNDER CONSTRUCTIONSCENARIOSFIRE AND RESCUE OPERATIONS FOR THE CHOSEN SCENARIOSDISCUSSION AND CONCLUSIONSACKNOWLEDGEMENTSREFERENCES

P40_Truchot.pdfABSTRACTGas behaviour in tunnels, an experimental approachExperimental apparatusTheoretical addExperimental designReference configurationReference case: Numerical approachImpact of the leak directionConsequences outside the tunnel: an illustration of the experimental configurationExperimental campaign conclusion

Consequences of gas leackage in tunnelChlorine leakage: Consequences of a highly toxic gasLiquid leakage: Pool evaporation consequences

ConclusionsREFERENCE LIST

P43_Salvi.pdfAbstract1. Introduction2. Overview of the regulatory situation3. Risk assessment and management in tunnels, underground, metros and confined spacesSummary of the projectFurther development neededSummary of the projectFurther development neededSummary of the projectFurther development neededSummary of the projectFurther development needed

4. Security management in transport systemsSummary of the projectFurther development neededSummary of the projectSummary of the projectFurther development needed

5. Integrated risk management of new and emerging risksSummary of the projectFurther development needed

6. Innovative energy carriers for vehicles and their safetyFurther development needed

7. RESEARCH PERSPECTIVES8. ConclusionsREFERENCES

P8_Diernhofer.pdfABSTRACTKEYWORDS: dangerous goods, road tunnel safety, risk analysis, risk assessment, expected valueEVALUATION OF RISKRisk acceptance

Legal Basis of tunnel safetyEU Directive 2004/54/EGAustrian Road Tunnel Safety LawADR 2007 / 2009

Development of RISK ANAlyEs in AustriaAustrian Tunnel Risk Model (TuRisMo)OECD/PIARC Model (DG-QRAM)

DEVELOPMENT OF A UNIFORM RISK ASSESSMENT PROCESS for DG TRANSPORTSData BasisDefinition of Assessment Process for Austrian Road TunnelsStage 1 Classification Matrix (Simplified Approach)Stage 2 Detailed Approach (DG-QRAM)Stage 3 Alternative Route

DefinitIon of risk CriteriaReference curve for risk assessmentAdjustment of the reference curve against the tunnel length

INTERNATIONAL Development TRENDSInternational OutlookREFERENCE LIST

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Design of Tunnel Ventilations Systems for Fire Emergencies Using Multiscale Modelling

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