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采用一种多层多块隐式嵌套重叠网格技术,对美国国家航空航天局通用化研究模型(NASA-CRM)翼身平尾(WBT)组合体进行了数值模拟与分析。多层多块隐式嵌套重叠网格技术是结合多层多块嵌套重叠网格处理策略和隐式切割方法,在建立重叠网格之间的流场信息传递关系时,基于网格单元切割准则选择“最优”重叠单元而无需人工设定插值边界。对美国AIAA委员会召开的第4届阻力预测研讨会(DPW-4)提供的CRM WBT组合体生成4种不同密度的结构化多层多块嵌套重叠网格,并采用计算流体力学(CFD)方法进行数值计算和阻力预测,计算结果与CFL3D和OVERFLOW的结果进行了对比。数值模拟结果表明:计算得到的压力分布和极曲线与CFL3D和OVERFLOW的结果几乎相同,说明了隐式嵌套重叠网格技术的有效性,同时也验证了流场求解方法与程序的可靠性。当迎角增大到3°左右时,在机身与机翼、尾翼连接处出现明显的分离涡,影响CRM WBT组合体的气动特性。在阻力预测方面,增加网格密度能够提高阻力预测的精度。采用不同的湍流模型会导致升、阻力系数的计算结果存在一定的差异,因此,湍流模型的选择也是阻力预测需要考虑的因素。
A multi-layer, multi-block implicit nested overlapping grid technique was used to numerically simulate and analyze the NASA-CRM wing-tail horizontal tail assembly (WBT). Multi-layer multi-block implicit nested overlay grid technology is a combination of multi-layer multi-block nested overlapping grid processing strategy and implicit cutting method. When establishing the flow field information transfer relationship between overlapping grids, Cutting Guidelines Select “Optimal ” Overlapping Cells without having to manually set interpolation boundaries. The CRM WBT assembly provided by the 4th AIAA Committee on Resistance Prediction (DPW-4) at the American AIAA Committee generated four kinds of structured multi-layer and multi-block nested overlapping grids of different densities and used computational fluid dynamics (CFD) Methods The numerical calculation and the resistance prediction are carried out. The calculated results are compared with the results of CFL3D and OVERFLOW. Numerical simulation results show that the calculated pressure distribution and polar curve are almost the same as those of CFL3D and OVERFLOW, which shows the effectiveness of the implicit nested overlapping grid technique and the reliability of the solution method and procedure of the flow field. When the angle of attack increases to about 3 °, a distinct separation vortex occurs at the connection between the fuselage and the wing and tail, which affects the aerodynamic characteristics of the CRM WBT assembly. In terms of resistance prediction, increasing the grid density can improve the accuracy of the resistance prediction. The use of different turbulence models results in some differences in the calculated results of drag coefficient and drag coefficient. Therefore, the choice of turbulence model is also a factor to be considered in drag prediction.