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Numerical investigation of prediction performance of design fire curves for a tunnel fire

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Abstract

The prediction performance of design fire curves is numerically investigated for tunnel fire using the fire dynamics simulator (FDS). A large eddy simulation (LES) was adopted in the simulation of a previous 750 kW tunnel fire experiment. Based on the experimental heat release rate, t2-fire growth, quadratic and exponential design fire curves (DFCs) are mathematically constructed and adopted in the FDS simulation. The predictions of each DFCs are compared against the experimentally measured smoke temperature, smoke travel time, and carbon monoxide (CO) concentration. In addition, the prediction performance of the mixture fraction (MF) and mixing controlled fast chemistry (MCFC) combustion models, is compared. The simulation results of the MF and MCFC models are similar except for the CO concentration features. For the performance of the DFCs, t2-fire growth curve with the MF combustion model is the most effective combination, which demonstrated the most reasonable agreement with the experimental data.

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Abbreviations

E tot :

Total calorific value (kJ)

k :

Time width coefficient

n :

Retard index

\(\dot Q\) :

Heat release rate (kW)

\({\dot Q_{\max }}\) :

Maximum heat release rate (kW)

\({\dot Q_{{\rm{max}},av}}\) :

Average maximum heat release rate (kW)

r :

Amplitude coefficient

t :

Time (s)

t d :

Decay phase starting time (s) t2-fire growth curve

t D :

Decay phase starting time (s) quadratic curve

t end :

End time of fire (s)

t h :

Level-off time (s)

t o :

Ignition onset time (s)

α d :

Fire decay rate (kW/s2) t2-fire growth curve

α D,q :

Fire decay rate (kW/s2) quadratic curve

α g :

Fire growth rate (kW/s2) t2-fire growth curve

α gq :

Fire growth rate (kW/s2) quadratic curve

a,b,x,y, z :

Mole proportions

C :

Empirical constant

c p :

Specific Heat capacity (J/kg K)

\({D \over {Dt}}\) :

Material derivative

D α :

Diffusion coefficient (m2/s)

D LES :

Turbulent thermal diffusivity (kg /m s)

g :

Gravitational acceleration (m/s2)

h s :

Sensible enthalpy (kJ/kg)

h α :

Enthalpy of species α (kJ/kg)

k :

Thermal conductivity (W /m K)

k LES :

Turbulent thermal conductivity (W /m K)

k(T):

Reaction rate constant (cm3/mol s)

\(\dot m_\alpha ^{'''}\) :

Volumetric mass production rate (kg/m3 s)

\(\dot m_{\alpha ,1}^{'''}\) :

\(\dot m_\alpha ^{'''}\) in the first reaction step (kg/m3 s)

\(\dot m_{\alpha ,2}^{'''}\) :

\(\dot m_\alpha ^{'''}\) in the second reaction step (kg/m3 s)

p :

Thermodynamic pressure (Pa)

Pr t :

Turbulent Prandtl number

\({\dot q^{'''}}\) :

Heat release rate per unit volume (kW/m3)

\(\dot q_{\max }^{'''}\) :

Maximum \({\dot q^{'''}}\) (kw/m3)

\(\dot q_r^{'''}\) :

Net thermal radiation energy (kW/m3)

\(\dot q_r^{'''}\) :

Radiative heat flux (kW/m2)

R :

Universal gas constant (J/mol K)

s:

Mass stoichiometric coefficient of oxygen

Sc t :

Turbulent Schmidt number

sgs :

Sub-grid scale

t :

Time (s)

T :

Temperature (K)

u :

Velocity vector (m/s)

W:

Molecular weight of species (kg)

x − (1 − XH)Vs :

Carbon atoms that not converted to soot.

X H :

Hydrogen fraction in soot

Y α :

Species mass fraction

y CO :

CO yield

Y I F :

Mass fraction of fuel in the inlet stream

\(Y_{{N_2}}^\infty \) :

Ambient mass fraction of nitrogen

\(Y_{{O_2}}^\infty \) :

Ambient mass fraction of oxygen

y s :

Soot yield

Z :

Mixture fraction (kg/kg)

Z1, Z2, Z2 :

Mixture fraction variables

α:

General term to represent the gas species

ΔHF :

Heat of formation of fuel (kJ/mol)

ΔHCO :

Heat of formation of CO (kJ/mol)

Δh0f,a :

Heat of formation of species a (kJ/mol)

ρ :

Density (kg/m3)

Tij :

Viscous stress

T sgs ij :

Sub-grid scale (SGS) stress

T dev ij :

Total deviatoric stress

T :

Mixing time scale (s) MF model

T mix :

Mixing time scale (s) MCFC model

μ:

Dynamic viscosity (kg /m s)

v:

Stoichiometric coefficient

δ:

Kronecker delta

δx, δy, δz:

Filter width size along x, y, and z directions (m)

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Acknowledgments

This research was carried out with the support from the field-based firefighting activity-supporting technology development project of the National Fire Agency (MPSS-Fire Safety-2015-66), Republic of Korea.

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Authors and Affiliations

  1. Department of Safety Engineering, Pukyong National University, Busan, 48513, Korea

    Dinesh Myilsamy & Chang Bo Oh

  2. Department of Fire Protection Engineering, Pukyong National University, Busan, 48513, Korea

    Chi Young Lee

Authors
  1. Dinesh Myilsamy
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  2. Chang Bo Oh
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  3. Chi Young Lee
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Corresponding author

Correspondence to Chang Bo Oh.

Additional information

Dinesh Myilsamy is a Ph.D. student in the Department of Safety Engineering at Pukyong National University. He received his Master’s degree in Safety Engineering in the year of 2017. His research interest include computational fire dynamics, and combustion engineering.

Chang Bo Oh is a Professor in the Department of Safety Engineering at Pukyong National University. He received his Ph.D. degree in Mechanical Engineering from Inha University, Korea, in 2003. His research interests include computations of safety-related problems, such as toxic chemical spread, combustion, fire and explosion.

Chi Young Lee is an Associate Professor in the Department of Fire Protection Engineering at Pukyong National University. He received his Ph.D. degree in Mechanical Engineering from KAIST, Korea. His research interests include fire and thermal-fluids engineering.

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Myilsamy, D., Oh, C.B. & Lee, C.Y. Numerical investigation of prediction performance of design fire curves for a tunnel fire. J Mech Sci Technol 34, 3875–3887 (2020). https://doi.org/10.1007/s12206-020-0838-4

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  • DOI: https://doi.org/10.1007/s12206-020-0838-4

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