基于ResNet数据驱动的压气机叶型优化  

Optimization of compressor blade profile based on ResNet data drive

作　　者：杜周 马玉林[1] 徐全勇[2] 吴锋 冯旭栋 DU Zhou;MA Yulin;XU Quanyong;WU Feng;FENG Xudong(School of Mechanics and Engineering,Liaoning Technical University,Fuxin Liaoning 123000,China;Institute for Aero Engine,Tsinghua University,Beijing 100084,China;Sichuan Gas Turbine Establishment,Aero Engine Corporation of China,Mianyang Sichuan 621000,China)

机构地区：[1]辽宁工程技术大学力学与工程学院,辽宁阜新123000 [2]清华大学航空发动机研究院,北京100084 [3]中国航发四川燃气涡轮研究院,四川绵阳621000

出　　处：《航空动力学报》2023年第7期1592-1603,共12页Journal of Aerospace Power

基　　金：国家科技重大专项(J2019-Ⅴ-0001-0092,J2019-Ⅴ-0013-0108)。

摘　　要：为提高用于叶型优化设计ResNet深度学习模型的泛化性,对一种适用于亚声速和跨声速的压气机叶型进行了参数化设计。叶型基于Matlab进行几何模型构建,设计变量为叶型最大厚度、最大厚度位置、栅距和叶型尾缘与轴向的夹角,几何模型通过Pointwise软件进行批量网格划分,网格量级为30万,通过OpenFOAM流体仿真软件进行批量计算。最终通过4个设计变量参数化建模后进行仿真得到了叶型流场仿真数据集,该数据集包含22 331个叶型仿真算例,可为ResNet深度学习模型提供训练集和测试题,有助于提高模型的泛化性。To improve the generalizability of the ResNet deep learning model used for optimal design of the blade profile,a parametric design of a compressor blade profile suitable for subsonic and transonic speeds was carried out.The blade profile was constructed based on the geometric model in Matlab,and the design variables included the maximum thickness of the blade profile,the position of the maximum thickness,the mesh distance and the angle between the trailing edge of the blade profile and the axial direction,and the geometric model was meshed in batch by Pointwise software with a mesh size of 300000,and simulation was carried out by OpenFOAM fluid simulation.The final simulation was performed by parametric modeling of 4 design variables to obtain the fluid simulation dataset of blade profile,which contained 22331 simulation cases of blade profile and can provide training and test datasets for ResNet deep learning models,helping to improve the generalization of the model.

关 键 词：机器学习 压气机叶型 MATLAB 计算流体力学(CFD) OPENFOAM 

分 类 号：V231.3[航空宇航科学与技术—航空宇航推进理论与工程]