## 2 Background
### CFD Simulations
(2018) showed that the standard k-\(\varepsilon\) model and its variants can overestimate the reattachment length behind the bluff body compared to experimental results, which is considered a common drawback of using the k-\(\varepsilon\) model. Spalart-Allmaras models are another group of models favoured in engineering applications because only one transport equation is introduced. However, the limitation of this model is significant. [PERSON] (2018) indicates the model is designed for specific airfoil simulations and not suitable as a generalised turbulence model. This is also shown by [PERSON] and [PERSON] (2018) who suggest that the Spalart-Allmaras model overpredicts the wind pressure in a channel-flow simulation. In engineering practice, it is essential to calibrate the Spalart-Allmaras models before their implementation, which is normally based on experimental data. More recently, there have been some attempts to use machine learning techniques to calibrate parameters used in the model ([PERSON], 2018; [PERSON], 2018). Despite the large progress made in eddy-viscosity turbulence models, there are still some limitations. [PERSON] (2018) suggests that for cases with large streamline curvature, strong fluctuations or large flow circulations, eddy-viscosity models might deliver unphysical results in the vorticity. Instead, Reynolds stress turbulence models, which directly resolve transport equations of Reynolds stress without the Boussinesq hypothesis, will perform better with strong turbulent flow. Many models have been developed based on two fundamental models, the LRR model by [PERSON] et al. (2018) \begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{2}{c}{**Eddy-Viscosity Turbulence Model**} & \multicolumn{2}{c}{**Reference**} \\ \hline \multirow{3}{*}{k-\(\varepsilon\) two-equation models} & Standard k-\(\varepsilon\) model & [PERSON] and [PERSON] (2018) \\  & Low-Reynolds Number k-\(\varepsilon\) model & [PERSON] (2018) \\  & Renormalisation Group (RNG) k-\(\varepsilon\) model & [PERSON] et al. (2018) \\  & Realizable k-\(\varepsilon\) model & [PERSON] et al. (2018) \\ \hline \multirow{2}{*}{k-\(\omega\) two-equation models} & Standard k-\(\omega\) model & [PERSON] (2018) \\  & Latest standard k-\(\omega\) model & [PERSON] (2018) \\ \hline \multirow{2}{*}{Hybrid two-equation models} & Standard k-\(\omega\) SST model & [PERSON] (2018) \\  & k-\(\omega\) SST model with controlled decay & [PERSON] and [PERSON] (2018) \\ \hline \multirow{2}{*}{Spalart-Allmaras one-equation models} & Standard Spalart–Allmaras model & [PERSON] and [PERSON] (2018) \\  & Low-Reynolds Number Spalart–Allmaras model & [PERSON] and [PERSON] (2018) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: A list of eddy-viscosity turbulence models. and the SSG model by [PERSON] et al. [37]. Interested readers are referred to a review paper by [PERSON] and [PERSON] [38]. However, Reynolds stress models normally contain more transport equations than eddy-viscosity models. For instance, a recent blended model of LRR and SSG by [PERSON] et al. [39] contains six transport equations and one additional equation to close the solution, which makes the computation significantly slower than using two-equation eddy-viscosity models. Therefore, it is necessary to balance between the efficiency and the accuracy in the selection of turbulence closure for the RANS formulation. RANS simulations with appropriate turbulence models can provide sufficiently accurate results in the study of time-averaged wind effects. However, for cases with large flow separations and vortex shedding, transient simulations that reflect the unsteady nature of turbulence should be adopted. Although the RANS formulation can be used to perform transient simulations, i.e., the unsteady RANS (URANS) simulations, [PERSON] et al. [40] suggest that URANS might smooth the fluctuation caused by vortices as the velocity field is time-averaged.