# 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  
Turbulence in fire dynamics is generally described as unsteady, chaotic, complex, swirling, and disordered motion of the fluid which causes velocity and temperature to fluctuate rapidly [42; 43]. This phenomenon is responsible for fire spread and flame shape in the compartment. While small or early-stage fires may initially exhibit laminar characteristics, as the fire grows in size and intensity, it quickly transitions into a turbulent regime due to increased flow velocities, buoyancy, and interactions with the enclosure walls and openings. As shown in Figure 1, compartment fires have two unsteady phases (i.e., from ignition to pre-development, and from decay to the cooling phase), one unsteady transition (flashover), and one steady phase (fully developed). In terms of CFD, different schemes are applied to estimate and model the turbulent flows behavior, differing in reliability, accuracy, and computational cost: Reynolds-Averaged Navier-Stokes (RANS) [44], LES [45], Direct Numerical Simulation (DNS). A combination of these methods is used in some cases such as in Detached Eddy Simulation (DES) by [PERSON] et al. [46], and Very Large Eddy Simulation (VLES) by [PERSON] [47]. The difference between these methods is based on the way they handle turbulence and the range of turbulent scales they resolve. In other words, their resolutions to turbulence and its modeling are different. Resolution in turbulence modes shows the level of detail modeling for turbulence eddies in each method. It means that in DNS, the Navier-Stokes equations are solved without any turbulence modeling, and it captures all scales of turbulence. On the other hand, in LES, the larger turbulent eddies are resolved directly, while the smaller sub-grid scale (SGS) eddies are modeled. The Navier-Stokes equations are time-averaged,and turbulence is modeled using empirical or semi-empirical turbulence models (e.g., k-\(\varepsilon\) and k-\(\omega\) models) in RANS. This can be further explained as follows:  
* RANS sees the flow properties (e.g., velocity, pressure, etc.) as the composition of mean and fluctuating components and solves the Navier-Stokes equations in a time-averaged manner. As a result, turbulence is modeled using transport equations. * LES resolves large turbulent eddies (which carry most of the turbulent energy) in the flow while modeling the smaller scales (turbulent eddies at the scales are smaller than the grid size, i.e., sub-grid scale). The method that is used to model sub-grid scale eddies will be presented in following section called sub-grid scale modeling. * The DNS method resolves all scales of turbulence without any modeling, solving the Navier-Stokes equations directly and completely. It requires extremely fine computational grids and small time-steps to capture the whole range of turbulent scales. * DES is based on benefiting from LES modeling while keeping the advantages of RANS mode where LES is computationally expensive. The main idea of DES is to use RANS adjacent to the surfaces (such as walls) to neglect the need for fine mesh resolution (as in LES) and switch to LES in other regions where larger scale eddies are present and resolving them is computationally feasible. * VLES is a flexible approach in turbulence modeling and has been designed to cover a wide range of turbulence modeling modes. It adapts its operation based on the grid resolution and required turbulence details: VLES works in RANS mode and most of the turbulent motions are not resolved when the grid size is very coarse. Finer grids can move it to a transitional approach between RANS and LES and more of the turbulence spectrum is resolved. VLES becomes pure LES when the grids are finer than the previous mode. And finally, if the computational grids are fine enough to resolve all turbulent scales, VLES works as DNS. This was first introduced by [PERSON] [47] and improved by [PERSON] et al. [48; 49].