## 3 Results and Discussion
### Discussion  
The optimal total error ratios and the related prediction accuracy changes of the six NACA0012 models are illustrated in Figure 37. For the trailing edge shape, except for the designed sharp trailing edge based on the definition formula adopting 264 points, the blunt trailing edge performs worse than the sharp trailing edge and the related accuracy declines are 11.41%, 2.14%, 31.73%, and 9.88% in order. The inappropriate selection of the trailing edge shape would cause a fairly big error and the maximum prediction accuracy decrease degree could reach 31.73%. Next, considering the three modeling methods, the optimal result of the Airfoil tools method is 2.7%, and those of the NACA4 generator and definition formula are 2.42% and 2.05% in order. Moreover, the definition formula could provide the best performance, the accuracy improvements of which compared with the other two methods are 24.08% and 15.42%. It is worth noting that though the definition formula could obtain the minimum total error ratio, except for the condition of the definition formula adopting 200 points, NACA4 behaves better. So, the choice between NACA4 and the definition formula is determined by the adopted data points. Further, considering the influences of data point source adopted in the definition formula and the number on the simulation accuracy, for the 132 data points sourced from the Airfoil tools, with the number doubled to 264, the prediction accuracy drops and the accuracy decrement is 4.55%. For the 200 data points sourced from the NACA4 generator, the prediction accuracy also drops and the decline degree is 19.71% with the number doubled to 400. Hence, the increase of the data point number actually decreases the prediction accuracy instead of improving it. Among the applied four numbers of data points, the optimal results of 200 and 400 data points are 2.05% and 2.454%, which are better than those of 132 and 264 data points. Compared with the other three, the corresponding improvements of 200 data points are calculated and the values are 22.46%, 25.79%, and 16.58%, respectively. As a result, data points from NACA4 have a better performance than that of the data points from Airfoil tools and the addition of the data point number is not directly correlated with the calculation accuracy. In conclusion, we should give priority to choosing the definition formula to design the sharp trailing edge and adopting the 200 data points supplied by NACA4. Figure 36: Comparison of the simulation performances of AUSM flux type under different simula-tion configurations. The optimal total error ratios of far-field distances, turbulence models, flux types, and the related accuracy improvements are presented in Figure 38. Considering the far-field distance, the prediction accuracy is increased as the far-field distance increases from 12\(L\) to 16\(L\), and the accuracy improvement is 15.42%, while the accuracy drops as the further increase from 16\(L\) to 20\(L\), and the decline degree is 19.88%. We could conclude that the performance of 16 m far-field distance is preferred and the related accuracy improvements are 15.42% and 16.58%, in turn. Therefore, the boosts of the far-field distance may not undoubtedly result in a rise in simulation accuracy but result in a decline, and the far-field distance of 16\(L\) is recommended. Further, considering the turbulence model, at the far-field distances of 12\(L\) and 20\(L\), SST behaves better than SA and the related accuracy improvements are 14.30% and 17.54%. But at a far-field distance of 16\(L\), the superior result is obtained by the SA and this is the finest value of the turbulence model. Hence, the turbulence model choice is related to the far-field distance, and the SA turbulence model should be preferentially considered. As to the flux type, as shown in Figure 38, ROE could achieve a better performance than that of AUSM, as the minimum improvement is 22.68% and the maximum improvement is 37.3%. Therefore, the ROE flux type is suggested.