Aerodynamic Modeling And Optimization Methods Based On Generative Adversarial Network

The vigorous development and progress of the national aerospace industry are closely related to the research of aerodynamics.How to effectively perform aerodynamic modeling has always been a key issue for experts in this field.For traditional methods based on physical models,there are some complex and Partial differential equations that are difficult to solve make their calculation efficiency unable to meet actual needs.And it needs to consume a lot of resources.It is difficult to meet actual needs.Therefore,some people have carried out research on data-driven model-free methods,but there is also the problem of low accuracy of generated data.Based on the good performance of the GAN model in many fields,this thesis establishes a research on aerodynamic data modeling and optimization methods based on generative adversarial network(GAN),and optimizes the various problems that appear in the training process of the GAN model.The main work of this thesis is as follows:(1)Before developing aerodynamic data modeling based on GAN,considering that the original Gan model is too free to generate the specified data according to the will,conditional generative adversarial network(CGAN)will be used instead of GAN Carry out aerodynamic data modeling,and then design the network structure of each model,and conduct experimental verification.(2)Combining the results,we analyzed the fitting performance of the GAN model on the aerodynamic data set.Considering the characteristics of the aerodynamic data set,we analyzed the problem of aerodynamic data modeling based on GAN,and based on this,we put forward a theorem,which is When non-linear sparse data from continuous function,RBFNN is the best discriminator for GAN.And gave a theoretical proof.(3)Based on the above theorem,we proposed a radial basis function-based GAN(RBF-GAN),and designed the network structure model of RBF-GAN,and conducted experimental verification.Then combined with the idea of cluster neural network,a radial basis function cluster-based GAN(RBFC-GAN)is proposed,and the network model structure of RBFC-GAN is also designed,and we have carried out experimental verification.After analyzing the experimental results,it is obtained that using RBFNN as a discriminator can improve the accuracy of nonlinear aerodynamic data generation,Compared with traditional GAN,the accuracy of aerodynamic data generated by RBF-GAN and RBFC-GAN is improved by an order of magnitude;and Compared with fully connected neural networks,RBFNN is more suitable as a discriminator of GAN for nonlinear aerodynamic data.;Regarding model stability,from the overall training process,RBFC-GAN has the best stability.