翼型流动分离的EnKF非稳态数据同化  

EnKF Unsteady Data Assimilation of the Flow Separation Around an Aerofoil

作　　者：张禹瑶 何创新 刘应征[1,2] ZHANG Yuyao;HE Chuangxin;LIU Yingzheng(Key Laboratory of Education Ministry for Power Machinery and Engineering,School of Mechanical Engineering,Shanghai Jiao Tong University,Shanghai 200240,China;Gas Turbine Research Institute,Shanghai Jiao Tong University,Shanghai 200240,China)

机构地区：[1]上海交通大学机械与动力工程学院动力机械与工程教育部重点实验室,上海200240 [2]上海交通大学燃气轮机研究院,上海200240

出　　处：《气体物理》2024年第3期35-45,共11页Physics of Gases

基　　金：国家自然科学基金(12272231,92152301)。

摘　　要：为了改善RANS模型对流动分离现象的预测性能,针对NACA0012翼型绕流,采用基于非稳态计算的集合Kalman滤波(ensemble Kalman filter,EnKF)数据同化方法,结合粒子图像测速(particle image velocimetry,PIV)数据对SST湍流模型的模型常数进行了修正,对比分析了不同模型常数扰动幅度、不同集合样本数目下稳态数据同化和非稳态数据同化的预测结果差异。研究结果表明,相对于稳态计算,非稳态计算能够增强数值模拟的稳健性,从而可以在较大的扰动幅度下,改善RANS模型的初始预测分布。稳态数据同化在模型常数扰动幅度较大或者样本数目较少时存在明显缺陷,非稳态的数据同化具有更好的鲁棒性,能够在更大扰动幅度和更少样本数下得到最优的湍流模型常数,对流场的预测更加准确。To improve the prediction performance of the RANS model for flow separation,the model constants of the SST turbulence model were recalibrated using the unsteady ensemble Kalman filter(EnKF)data assimilation(DA)combined with the particle image velocimetry(PIV)data of the flow around a NACA0012 aerofoil.The differences in prediction between steady DA and unsteady DA with different model constant perturbations and ensemble sizes were compared and analyzed.The results show that the unsteady simulation can enhance the robustness of the numerical simulation and improve the initial prediction distribution of the RANS model compared to the steady simulation.The steady DA has obvious defects for large model constant perturbation or small ensemble size.The unsteady DA is more robust and can obtain the optimal turbulence model constants with larger perturbation and smaller ensemble size,resulting in more accurate prediction of the flow fields.

关 键 词：非稳态数据同化 集合KALMAN滤波 流动分离 模型常数优化 翼型 

分 类 号：V211[航空宇航科学与技术—航空宇航推进理论与工程]