# Computational Fluid Dynamics of Compartent Fires: A Review of Methods and Applications
## 3 CFD Simulation in Compartment Fires
### Physical Models
#### 3.3.2 Pyrolysis
FDS uses single or multi-step Arrhenius models, OpenFOAM can adopt multi-step reactions, variable activation energies, or empirical laws defined by modeler, and ISIS uses a single-step Arrhenius model for pyrolysis of solid materials. [PERSON] [65] used a detailed pyrolysis model in wood fires, considering cellulose, active cellulose, hemicellulose, lignin, char, ash, moisture, and air as the wood components. Using FDS, they compared the results for three different methods: prescribing an experimental measurement of HRR as the input, and predictive pyrolysis models with and without the burn-away option (used to simulate the combustion and removal of solid materials as they burn and are consumed during a fire) [12]. As illustrated in Figure 5, the prescribed HRR was only able to reproduce the initial stage of the fire. The predictive pyrolysis model resulted in a delayed ignition and initial phase compared to the experimental measurement. However, the detailed pyrolysis model which was used could replicate the overall HRR curve. Besides that, the burn away option of FDS was not able to improve the HRR significantly as it caused the HRR curve to have some fluctuations since removing burned cells in the wood ribs exposed the unburned cell with an ambient temperature to the combustion gases with high temperature. [PERSON] and [PERSON] [66] used a pyrolysis kinetics model of the PUF (polyurethane foam) slab in numerical modeling of a compartment fire, assuming that PUF is converted directly to the gaseous fuel (C\({}_{6.3}\)H\({}_{7.1}\)NO\({}_{2.1}\)). They realized that by adopting the thermal decomposition of the PUF and the space within which the fire spread, the MLR of the solid fuel can be calculated and there is no need to prescribe the HRR in CFD modeling. Figure 5: Comparison of different methods in predicting HRR of wood cribs. Adopted from [PERSON] [65]. Considering multiple pyrolysis gases obtained from experimental tests for the non-charring solid fuel polymethyl methacrylate (PMMA) and importing them in a modified version of FireFOAM, [PERSON] et al. [67] validated their model against experimental data. The pyrolysis reaction occurred in a way that PMMA was converted to gas without any charring residue. Pyrolysis gases included the following: MMA, C\({}_{2}\)H\({}_{4}\), CO\({}_{2}\), CH\({}_{3}\)OH, and CO.  
[PERSON] et al. [68] developed a new model for pyrolysis of wet wood in FireFOAM. Their innovation was adopting a simple one-step nth-order Arrhenius expression for dry wood pyrolysis with a moisture drying reaction (i.e., conversion of moisture to vapor, as well as wood to char and Pyrolysate). After the model had been validated using the corresponding experimental data, the mass fluxes of water vapor and pyrolysate were examined in relation to the thickness of wet wood, considering various external radiation heat fluxes. [PERSON] et al. [69] studied the thermal pyrolysis model for simulating flame spread along the PMMA slabs in FDS. Compared to the finite rate pyrolysis model (Arrhenius model), the thermal pyrolysis model was not able to accurately replicate the experimental trend of the total HRR. To overcome this drawback, prescribed MLR should be increased, enabling the thermal pyrolysis model to produce more accurate HRR according to experimental measurements. * Liquid Fuels  
A fire induced by liquid fuels is generally known as pool fire and is defined by the turbulent diffusion of a flame above a liquid fuel which causes a volatile reaction of liquid evaporation [2; 12]. The volatiles are the result of pyrolysis in liquid fuels and depend on the fuel properties, temperature, radiative heat feedback, boiling temperature of the liquid fuel, etc. FDS, OpenFOAM, and ISIS differ significantly in their approach to model liquid fuel pyrolysis. FDS includes built-in evaporation models for liquid fuels, employing single or multi-step Arrhenius kinetics using parameters like boiling temperature and latent heat of vaporization.