Abstract

In the aerodynamic shape design, the drag prediction has always been an extremely challenging mission for the exploration of a configuration. As for the more complex configurations, it is especially desired to the availability of a highly accurate and reliable aerodynamic numerical solution. For improving the drag prediction accuracy and promoting the aerodynamic shape designs, firstly, the characteristics of drag prediction based on far-field drag method and near-field drag method is analyzed and compared. Also, the merits and demerits of defining axial velocity defect with the current main far-field drag prediction approaches is summarized, which promotes the building of the improved method of axial velocity defect and the improved far-field drag prediction and decomposition approach. Moreover, during the establishment of the drag decomposition method, it is necessary to judge and decide on the selection of the drag region. Therefore, the discussions on the sensitivity of the relevant parameters are fulfilled. Furthermore, based on the far-field drag prediction and decomposition method constructed, the aerodynamic performance research of Common Research Model wing-body configuration is launched. The results show that it can effectively observe and analyze the changes in drag components, their impact on the total drag and the contribution percentage. Finally, combining the far-field drag prediction and decomposition method proposed in this paper with a gradient-based aerodynamic shape optimization design system, the aerodynamic shape optimization designs are studied with CRM wing-body configuration. The results can not only directly analyze the detailed change of the visualized drag region, but also can obtain the more accurate total drag and lift-to-drag ratio of the optimized configuration by removing the spurious drag.

Highlights

  • The results show that it can ef⁃ fectively observe and analyze the changes in drag components, their impact on the total drag and the contribution percentage

  • Æ1.School of Aeronautics, Northwestern Polytechnical University, Xi′an 710072, China; çç.Unmanned System Research Institute, Northwestern Polytechnical University, Xi′an 710072, China;÷÷

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Summary

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为了确定本文最终所采用的激波阻力区域扩展 层数,选用 NACA0012 翼型进行分析验证。 验证求 解的网格选取了 3 套计算网格, 分别是网格量为 129×129 的粗网格、257×257 的中等网格和 513×513 的密网格。 选择基于欧拉方程的数值求解方法,计 算状态为 Ma = 0.80,α = 0°。 图 3 给出了激波阻力随 扩增层数的变化,从图中可看出,随着扩展层数的增 长,激波阻力系数的预测结果趋向一致,当选择扩展 5 层就可取得较好的结果。 因此最终确定选取激波 阻力区域的扩增层数为 5。. 本文选用 RAE2822 翼型进行分析验证。 求解 的网 格 选 取 了 3 套计算网格, 分别是网格量为 25 600的粗网格、102 400 的中等网格和 409 600 的 密网格。 选择基于 RANS 方程的求解器,计算状态 为 Ma = 0.734,CL = 0.824,Re = 6.5×106。 图 6 给出了 黏性阻力随扩增层数的变化,从图中可看出,随着扩 展层数的增加,黏性阻力系数的预测结果趋于稳定, 当选择扩展 4 层就可取得较好的结果。 因此最终选 定黏性阻力区域的扩增层数为 4。. 取和分析[4,17] 。 仍选用 RAE2822 翼型进行了计算 验证。 求解的网格选取网格量为 25 600 的粗网格。 计算基于 RANS 方程,计算状态为 Ma = 0.734,CL = 0.824,Re = 6.5×106。 从图 7 中可看出黏性阻力和激 波阻力随阈值的变化,随着阈值的增加,激波阻力系 数和黏性阻力系数的预测结果均变化较小,取 Kbl = 1.1 就可取得较好的结果。 因此决定在本文中选取 Kbl = 1.1。. 力积分依赖于对飞机下游区边界 SD 的选取,因此需 要分析和确定诱导阻力的积分面选取。 为此,本文 选用椭圆平面形状机翼构型进行计算验证,其基本 几何信息为:平面形状采用椭圆公式生成,参考面积 为 3.06 m2。 验证求解的网格选取了 2 套计算网格, 分别是网格量为 192 512 的粗网格和1 540 096的密 网格,图 14 为构型的平面形状及粗网格分布示意 图。 而为了观察诱导阻力的变化情况,本文选择欧 拉方程在亚声速工况下进行求解,计算状态为 Ma = 0.50,α = 0°。.

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CRM 翼身组合体构型优化前后的气动特性对
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