## 3 Numerical Simulation
### Boundary Conditions and RANS
In this work, the RNG \(k\)-\(\varepsilon\) turbulence model, as suggested in [32], was chosen for CFD simulations of the wind field over the Taizhou area. The transport equations of the RNG k-\(\varepsilon\) model are given as follows:  
\[\frac{\partial k}{\partial t}+\frac{\partial\overline{k}\overline{u_{i}}}{ \partial x_{i}}=\frac{\partial}{\partial x_{j}}[(v+\frac{\mu_{t}}{\sigma_{k}} )\frac{\partial k}{\partial x_{j}}]+P_{k}-\varepsilon \tag{4}\] \begin{table}
\begin{tabular}{c c} \hline
**Location** & **Type** \\ \hline Inlet & Velocity inlet \\ Outlet & Pressure outlet \\ Side surface & Symmetry \\ Top surface & Symmetry \\ Bottom surface & Wall \\ \hline \end{tabular}
\end{table} 
Table 6: Boundary conditions. \begin{table}
\begin{tabular}{c c c c} \hline
**Topographic Condition** & **Approximate Length/m** & **Power Exponent \(a\)** &
\begin{tabular}{c} **Zero Plane** \\ **Displacement \(d\)/m** \\ \end{tabular} \\ \hline Sea, mudflat, snow plain, etc. & 0.000–0.003 & 0.1–0.13 & 0 \\ \hline Open country, open country with & & \\ crops, fences, and a few trees & 0.003–0.2 & 0.14–0.2 & 0.1 \\ \hline Dense forests, homes, suburbs & 0.2–1 & 0.2–0.25 & 5 \\ \hline Cities & 1–2 & 0.25–0.3 & 10 \\ \hline Big city center & 2–4 & 0.3–0.5 & 10 \\ \hline \end{tabular}
\end{table} 
Table 5: Parameters of the mean wind speed profile. \[\frac{\partial\epsilon}{\partial t}+\frac{\partial\overline{\epsilon u_{i}}}{ \partial x_{i}}=\frac{\partial}{\partial x_{j}}[(\
u+\frac{\mu_{t}}{\sigma_{ \epsilon RNG}})\frac{\partial\epsilon}{\partial x_{j}}]+\frac{\epsilon}{k}(C_{ 1\epsilon RNG}P_{k}-C_{2\epsilon RNG}\epsilon) \tag{5}\]  
where \(k\) is the turbulent kinetic energy and \(\epsilon\) is the turbulence dissipation rate; \(P_{k}\) represents the generation of \(k\) due to the mean velocity gradients; \(\
u\) and \(\mu_{t}\) are the kinematic molecular viscosity and the dynamic turbulent viscosity, respectively; \(\sigma_{k}\) and \(\sigma_{\epsilon RNG}\) are turbulent Prandtl numbers for k and \(\epsilon\), respectively; \(C_{1\epsilon RNG}\) and \(C_{2\epsilon RNG}\) denote two empirical constants in the RNG k-\(\epsilon\) turbulence model. The steady-state of the flow field was solved by the SIMPLE algorithm. At the same time, the residual error of each solved variable was monitored to judge whether the calculation converged or not. The numerical iteration converged when the continuous residual error was less than 0.001, or the results did not change with the increase in the iteration times. Five cases of CFD simulation were carried out, as listed in Table 7. By adjusting the boundary inlet wind conditions, the wind speed and wind direction were solved and obtained at the five reference stations during the simulation. If the simulated wind speed and wind direction at one of five reference stations were the same as the observation result of extreme wind, the corresponding simulation case was regarded as successful. The simulated wind speed data at 44 streets and 85 towns in Taizhou was then collected from the CFD simulation case and could be used as the extreme wind speed data to determine the basic wind pressure.