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Assessing the influence of the input variables employed by fire dynamics simulator (FDS) software to model numerically solid-phase pyrolysis of cardboard

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Abstract

Understanding a material’s fire behaviour implies to know the thermal decomposition processes. Thermal analysis techniques are widely employed to study thermal decomposition processes, especially to calculate the kinetic and thermal properties. Cardboard boxes are widely employed as rack-storage commodities in industrial buildings. Hence, the characterization of the cardboard is considered a key factor for fire safety engineering, because it enables the determination of its thermal behaviour at high temperatures. The employment of mathematical or computational models for modelling the thermal decomposition processes is commonly used in fire safety engineering (FSE). The fire dynamics simulator (FDS) software is one of the most commonly used computational fluid dynamics softwares in FSE to address thermal analysis. To properly set up FDS and obtain accurate results, the numerical values of the thermal and kinetic properties are needed as input data. Owing to the large number of variables to be determined, a preliminary study is bound to be helpful, which can well assess the influence of each variable over the pyrolysis model, discarding or restricting their influence. This study, based on the Monte Carlo method, presents a sensitivity analysis for the variables utilized as input data by the FDS software. The results show the conversion factor α, i.e. the mass involved in each reaction, and the triplet kinetics have a major impact on the reproduction of the thermal decomposition process in fire computer modelling.

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Abbreviations

A :

Pre-exponential factor (s−1)

E a :

Activation energy (kJ kmol−1)

\(n_{\text{j}}\) :

Reaction order of the reaction j (–)

H r :

Heat of reaction (kJ kg−1)

r :

Reaction rate at temperature \(T_{\text{s}}\)

\(T_{\text{s}}\) :

Surface temperature (°C)

ρ :

Density (kg m−3)

C p :

Specific heat (kJ kg−1 K−1)

k :

Conductivity (W m−1 K−1)

ε :

Emissivity (–)

η :

Absorption coefficient (m−1)

\(Y_{\text{s,i}}\) :

Quotient between the density of solid material i produced by the reaction (\(\rho_{\text{s,i}}\)) at surface temperature \(T_{\text{s}}\) divided by the initial density of the solid material (\(\rho_{\text{s}} (0)\)) (–)

\(r_{\text{ij}}\) :

Reaction rate (kg s−1)

\(v_{{{\text{si}}^{\prime } {\text{j}}}}\) :

Yield produced of the material i by the reaction j

\(r_{{{\text{i}}^{\prime } {\text{j}}}}\) :

Residue produced of the material i by the reaction j

α :

Coefficient the conversion factor of reactant (–)

\(\dot{q}_{\text{s,c}}^{\prime \prime \prime }\) :

Heat release rate per unit volume produced by the chemical reaction of the sample (kJ m−3)

\(m\) :

Sample mass (kg)

M :

Reacting material

P :

Submaterial generated as product of the reaction

F :

Fuel gas released by the reaction

G :

Non-burning gas released by the reaction

R :

Residue produced by the reaction

ν p :

Amount of submaterial produced

ν f :

Amount of fuel gas released

ν g :

Amount of non-burning gas released

ν r :

Amount of residue produced

\({\text{var}}_{\text{k}}^{0}\) :

Value of the variable k of the reference case

\({\text{var}}_{\text{k}}^{0}\) :

Value of the variable k of the reference case

\({\text{var}}_{\text{k}}^{\text{mu}}\) :

Value of the variable k of the mutated case

\({\text{par}}_{\text{s}}^{0}\) :

Value of the parameter s of the reference case

\({\text{par}}_{\text{s}}^{\text{mu}}\) :

Value of the parameter s of the mutated case

\({\text{in}}_{\text{k}}^{\text{s}}\) :

Value of influence of variable k over parameter s

TG:

Thermogravimetric analysis curve

DTG:

Derivative thermogravimetric analysis curve

MLR:

Mass loss rate

DSC:

Differential scanning calorimetry

STA:

Simultaneous thermal analysis

MSE:

Mean squared error

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Acknowledgements

Authors would like to thank to the Consejo de Seguridad Nuclear for the cooperation and co-financing the project “Simulation of fires in nuclear power plants” and to CAFESTO Project funded by FEDER/Ministerio de Ciencia, Innovación y Universidades – Agencia Estatal de Investigación/Proyecto RTC-2017-6066-8.

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  1. GIDAI, University of Cantabria, Avda. Los Castros, s/n, 39005, Santander, Spain

    Alain Alonso, Mariano Lázaro, Pedro Lázaro, David Lázaro & Daniel Alvear

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  1. Alain Alonso
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Alonso, A., Lázaro, M., Lázaro, P. et al. Assessing the influence of the input variables employed by fire dynamics simulator (FDS) software to model numerically solid-phase pyrolysis of cardboard. J Therm Anal Calorim 140, 263–273 (2020). https://doi.org/10.1007/s10973-019-08804-6

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