基于数据驱动的航空发动机风扇叶型气动性能优化设计OA

A flow feature embedding proxy model(embedding flow feature network,EFFN)was proposed,to improve the prediction accuracy of the proxy model by integrating the flow field information into the proxy model,and enable the proxy model to predict flow features.The requirement for the total number of training data samples in the EFFN is consistent or even less than that of traditional surrogate models used for aerody-namic optimization.It has higher prediction accuracy than traditional surrogate models with the same sample size,and can accurately predict flow characteristics,while to some extent solving the problem of poor physical interpretability of surrogate models.Meanwhile,due to the more reliable values predicted by the EFFN,it has better optimization results in aerodynamic optimization design.The results of optimizing the aerodynamic per-formances of the 2D blade profiles show that,the total pressure loss coefficient of the optimized blade profile based on the DBN model relatively decreases by 17.3%,while the total pressure loss coefficient of the opti-mized blade profile based on the EFFN model relatively decreases by 18.0%.The loss performance of the opti-mized blade profile based on the EFFN model was highly improved.