# A Parametric Study of Wind Pressure Distribution on Facades Using Computational Fluid Dynamics
## 2 Description of the CFD Model
### Turbulence Model  
A number of studies have shown that in industrial flow applications, the LES approach addresses the most critical characteristics of the fluid flow by directly solving a significant portion of the turbulence spectrum [15; 16]. Fluid particles are considered to bunch in groups or clusters exhibiting random fluctuations in velocities that may be described as haphazard or random, with the particles transported by predominately turbulent diffusion. These clusters exhibit a rotational structure, with the larger eddies interacting with and extracting energy from the mean flow [17]. Their anisotropic behavior is dictated by the geometry of the problem domain, the boundary conditions and body forces [18]. In tandem with this resolution, the effect of somewhat isotropic small-scale eddies was adequately accounted for in sub-grid scale (SGS) modeling [19]. Notably this method is considered by the industry to have reached a point of accepted maturity, with significant promise on current operational demands [4].  
Figure 2: Computational domain.  
The accuracy of a LES model is dependent on the modeling of SGS stresses, and therefore, the SGS model was chosen with the aim of accurate transfer of energy between unresolved and resolved turbulent scales [19]. In this work, a Hybrid RANS-LES strategy was used to account for near wall fine resolution demand at wall surfaces. RANS equations are solved in the inner logarithmic layer, while the filtered Reynolds-averaged equations are solved away from the wall across the computational domain by LES [20]. This sub-grid scale model is termed Wall-modeled LES (WMLES).  
The WMLES sub-grid scale model was selected due to its suitability for resolving turbulent industrial flows. This approach differs from the usual case of wall resolved LES by employing the RANS method at physical wall boundaries. Therefore, a coarser mesh is sufficient at these locations than would otherwise be the case. As a result, a larger time step was utilized. The basis of spatial discretization in LES was carried out through the Momentum Bounded Central Differencing upwind scheme while making use of Green-Gauss Cell Based gradients and derivatives. An upwind scheme calculates quantities that are updated based on the cell upstream in the normal direction of the velocity. Node based schemes are more suited to skewed and unstructured meshes, while also being more computationally demanding. Therefore, through the application of orthogonal, structured meshes, the cell-based approach is more efficient without loss of accuracy. For pressure, a second order resolution was specified instead of a first order resolution as in Ansys Fluent because a higher-order scheme may result in greater accuracy. The maximum numerical error tolerance was set to be equal to \(1\times 10^{-4}\), as recommended by [6]. As the model encounters no issues satisfying this limit, over all time steps, the more accurate solution for pressure in the second order is justified. Finally, temporal discretization was calculated through the Bounded Second Order Implicit transient formulation.