## 4 Conclusions  
According to the reference data of Ref [17], under hypersonic speed, a detailed parameters influence of trailing edge shapes, far-field distances, turbulence models, and flux types on the simulation prediction accuracy of the compressible external flow field characterized by NACA0012 airfoil is discussed in this paper. The appropriate grid strategy and number method are confirmed in Section 2. Through comparisons among numerical mean error ratios, the internal relationships between the prediction accuracy and the abovementioned parameters are discussed in detail in Section 3. In particular, the trailing edge shape, the related modeling methods, the source, and quantity of the adopted data point dependencies are discussed in Section 3.5. Compared with the influence analysis discussions under incompressible external flow field at the AOA is \(0^{\circ}\) in Ref [3], the following conclusions could be drawn:  
* Unlike under incompressible external flow field, as to the trailing edge shape selection, the improper sharp trailing edge design could bring a larger error ratio than that of the blunt trailing edge shape. A similar situation exists for the modeling methods selection, in particular for the choice between the NACA4 and the definition formula. Except in a case where the definition formula adopts 200 points, NACA4 is preferred to establish the NACA0012 airfoil. Further, whether the adopted data points are from Airfoil tools or the NACA4 generator, the increase in the number of data points could not bring about an improvement in calculation accuracy. As to the far-field distance, the maximum far-field distance could not lead to the minimum simulation error ratio. Specifically, the numerical accuracy is promoted with the far-field distance increases from \(12L\) to \(16L\); then that is decreased with the further from \(16L\) to \(20L\) and a \(16L\) far-field distance is suggested. As to the turbulence model, the performance of SA adopting second-order upwind modified turbulent viscosity is better than that of SA adopting first-order. As for the flux type, the calculation accuracy of the ROE flux type is better than that of the AUSM flux type, and the unsuitable flux type selection could cause maximum accuracy loss. \begin{table}
\begin{tabular}{p{12.3 pt} p{12.3 pt} p{12.3 pt} p{12.3 pt} p{12.3 pt} p{12.3 pt} p{12.3 pt}} \hline \hline \multirow{2}{*}{**Parameters**} & \multirow{2}{*}{**Values**} & **Optimal Mean Error Ratios of P/P\({}_{\text{t}}\)** & **Optimal Mean Ratio of T/T\({}_{\text{t}}\)** & **Optimal Mean Ratio of P/U\({}_{\text{t}}\)** & **Optimal Mean Ratio of P/U\({}_{\text{t}}\)** & **Maximum Accuracy** \\ \hline \multirow{4}{*}{**Parameters**} & \multirow{2}{*}{**Values**} & 2.28\% & 3.96\% & 1.04\% & 2.42\% & \\ \cline{3-8}  & & \multicolumn{4}{c}{SST + NACA4 + ROE} & \\ \cline{3-8}  & & 2.54\% & 1.86\% & 1.74\% & 2.05\% & 19.88\% \\ \cline{3-8}  & & \multicolumn{4}{c}{SA + 200 points + ROE distances} & \\ \cline{3-8}  & & 20 m & 2.06\% & 4.22\% & 1.08\% & 2.45\% & \\ \cline{3-8}  & & \multicolumn{4}{c}{SST + 400 points + ROE distances} & \\ \hline \multirow{4}{*}{**Turbulence models**} & \multirow{2}{*}{SST} & 2.28\% & 3.96\% & 1.04\% & 2.42\% & \\ \cline{3-8}  & & \multicolumn{4}{c}{12 m + NACA4 + ROE distances} & \\ \cline{3-8}  & & \multicolumn{4}{c}{2.54\%} & 1.86\% & 1.74\% & 2.05\% & \\ \cline{3-8}  & & \multicolumn{4}{c}{16 m + 200 points + ROE distances} & \\ \hline \multirow{4}{*}{**Flux types**} & \multirow{2}{*}{ROE} & 2.54\% & 1.86\% & 1.74\% & 2.05\% & \\ \cline{3-8}  & & \multicolumn{4}{c}{SA + 200 points + 16 m} & 53.2\% \\ \cline{3-8}  & & \multicolumn{4}{c}{4.32\%} & 2.88\% & 2.21\% & 3.14\% & \\ \cline{3-8}  & & \multicolumn{4}{c}{SST + 132 points + 12 m} & \\ \hline \hline \end{tabular}
\end{table} 
Table 25: _Cont._* Despite the rise of the far-field distance, which would result in the aspect ratio addition on the condition it could be limited within a reasonable range, the simulation accuracy could be promoted as the far-field increases.