不同RANS/LES混合方法在汽车标模外流场非定常数值模拟中的对比  

Comparison of the Different Hybrid RANS/LES Methods for Unsteady Numerical Simulation of the Flow around an Automotive Generic Model

作　　者：杨志刚[1,2,3] 陶悦 夏超 史芳琳 YANG Zhigang;TAO Yue;XIA Chao;SHI Fanglin(School of Automotive Studies,Tongji University,Shanghai 201804,China;Shanghai Automotive Wind Tunnel Center,Tongji University,Shanghai 201804,China;Beijing Aeronautical Science&Technology Research Institute,Beijing 102211,China;Shanghai Aerospace Equipments Manufacturer Co.,Ltd.,Shanghai 200245)

机构地区：[1]同济大学汽车学院,上海201804 [2]同济大学上海地面交通工具风洞中心,上海201804 [3]北京民用飞机技术研究中心,北京1022111 [4]上海航天设备制造总厂有限公司,上海200245

出　　处：《同济大学学报（自然科学版）》2024年第S01期76-87,共12页Journal of Tongji University:Natural Science

基　　金：国家自然基金项目(52372360);国家重点研发计划(2022YFE0208000);上海市地面交通工具空气动力与热环境模拟重点实验室项目(23DZ2229029);中央高校基本科研业务费专项资金资助。

摘　　要：针对MIRA汽车标模外流场,采用RANS/LES混合方法开展非定常数值模拟,通过与风洞实验测力、测压结果进行详细对比分析,探究不同RANS/LES混合方法(DDES、IDDES、SBES、SDES)以及基于不同RANS模型(RKE、SA、SST k-ω、GEKO k-ω)的DDES方法在汽车外流场计算中的适用性。研究表明:对于气动力系数,不同混合方法的预测结果都偏高,其中DDES-GEKO模型的相对误差最小;对于表面压力系数,不同混合方法对垂直中截线的压力预测结果与实验值吻合程度较高,其中SBES-GEKO模型的结果更优;DDES模型内嵌不同RANS模型对后风窗的压力预测差别明显,其中SA模型较优;而不同混合方法对车底的压力预测偏差较大,对车底前部压力预测都小于实验值,其中对车底后部,SBES-GEKO模型的结果较优;此外,SBESGEKO模型能较好的识别出尾迹区的非定常流动结构。Unsteady numerical simulations were carried out using different RANS/LES hybrid methods for the outflow field of the MIRA generic model.The applicability of RANS/LES hybrid methods(DDES,IDDES,SBES,SDES)and DDES based on different RANS models(RKE,SA,SST k-ω,GEKO k-ω)in calculating the automotive outflow field was explored through detailed comparative analyses with the results of the wind tunnel experimental measurements of aerodynamic forces and pressures.The study shows that the results of different hybrid methods are all on the high side in predicting aerodynamic coefficients,with the DDES-GEKO model having the smallest relative error.For surface pressure coefficients,the predictive results of different hybrid methods for the vertical center line are in good agreement with the experimental values,among which the SBES-GEKO model performs better.The embedded RANS models in the DDES show significant differences in predicting pressure on the rear windshield,with the SA model being better.The pressure predictions for the underbody are deviated by different hybrid methods,with the pressure predictions for the front part being smaller than experimental values,while the SBES-GEKO model gives better results for the rear part.Additionally,the SBES-GEKO model is able to identify the unsteady flow structures in the wake region well.

关 键 词：MIRA汽车标模 非定常数值模拟 RANS/LES混合方法 

分 类 号：U461[机械工程—车辆工程]