# Computational Fluid Dynamics of Compartent Fires: A Review of Methods and Applications
## 3 CFD Simulation in Compartment Fires
### Physical Models
#### 3.3.1 Turbulence Modeling
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Turbulence** & **Turbulence Modeling** & **Grid Resolution Requirement** & **Pros** & **Cons** \\ \hline RANS & Models all turbulent scales & Uses transport equations for turbulence & Relatively coarse grids & Low computational cost & Unable to capture unsteady/transient flow such as in compartment fires \\ \hline LES & Resolves the large eddies, models sub-grid scales & Sub-grid scale models & Finer than RANS, coarser than DNS & Captures transient and large-scale turbulent structures accurately & Requires a fine grid near walls (such as for compartment fires): increases computational cost \\ \hline DNS & Resolves all turbulent scales & No model needed & Extremely fine grids & Most reliable method & Very high computational cost and resources \\ \hline DES & RANS near walls and LES away from walls & A combination of RANS models and LES sub-grid models & Coarser near the walls and finer in other regions & Balanced computational cost and accuracy between RANS and LES & Accuracy depends on transition between RANS and LES (determining switching criteria between RANS and LES) \\ \hline VLES & Based on grid resolution, wide spectrum from RANS to DNS & Adapts with grid resolution, either modeled in coarse grids or resolved in fine grids & From course to very fine, depends different case & Adaptive approach, flexible for different case & May require local refinement strategies (e.g., adaptive mesh refinement). \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparative analysis of different turbulence modes in CFD. that cannot be captured by the computational grid. The SGS models play an important role in LES by representing the effect of these unresolved scales on the resolved flow. Different SGS models have been developed to simulate this influence with different levels of complexity and accuracy and are used in CFD tools. Constant Smagorinsky, Deardorff, Dynamic Smagorinsky, WALE, Vreman, K equation, and Dynamic K equation are presented by different authors [45; 56; 57; 58; 59; 60] and used by CFD tools based on their design. For example, Smagorinsky models dissipative processes that occur on length scales smaller than those resolved by the user-defined grid. One of the earliest and simplest SGS models used in LES which introduced the concept of SGS turbulent kinetic energy, was Deardorff. To overcome the Smagorinsky limits especially near the walls, [PERSON] and [PERSON] [57] developed a new model called WALE (Wall-Adapting Local Eddy-Viscosity) which depends on both the strain rate and the rate of rotation of the velocity gradients, providing better wall-bounded flow behavior. Moreover, in dynamic Smagorinsky, the basic idea is that the model adjusts the turbulence viscosity coefficient based on local flow properties at each point in the domain, improving flexibility and accuracy, while it is a pre-defined fixed value (typically between 0.1 and 0.2) in Constant Smagorinsky [12]. Vreman [58] is a more recent SGS model, designed to improve the treatment of SGS in complex flows, especially where the assumption of isotropic turbulence may fail. Vreman can be considered a more efficient method than Dynamic Smagorinsky and more accurate than Constant Smagorinsky [12]. K equation and Dynamic K equation were built based on the traditional Smagorinsky model by introducing a transport equation for the SGS kinetic energy, making it more flexible and capable of better representing complex flow dynamics. In general, K equation models aim to improve the estimation of turbulence in LES by using a transport equation for the sub-grid kinetic energy, rather than relying on a constant (e.g., constant Smagorinsky) or formula (e.g., Dynamic Smagorinsky) for the eddy viscosity. The Constant Smagorinsky, Deardorff, and K equation sub models consider fixed coefficients in their formulation (i.e., depends on the modeler choice: For example, 0.2 and 0.1 in FDS and 0.094 in OpenFOAM default setting, respectively [12; 61]).