基于图卷积的三维气动外形伴随优化设计OA

Gradient-based optimization algorithms,particularly the discrete adjoint method,are widely used in aerodynamic shape optimization due to their independence from design variable dimensionality.However,solving the adjoint equations incurs a computational cost comparable to that of flow field solutions,making it crucial to reduce this burden.In this paper,a three-dimensional aerodynamic shape optimization framework integrating the discrete adjoint method with a graph convolutional neural network(GCN)was proposed.A database of flow fields and optimization gradients for various wings was constructed,and a GCN-based gradient prediction model was developed to replace the traditional discrete adjoint solution.Benefiting from the model's strong capability in topological aggregation and spatial feature extraction,its prediction accuracy reaches approximately 10-5.The proposed framework was applied to the aerodynamic shape optimization of the ONERA M6 wing under aerodynamic and geometric constraints.Compared with the traditional discrete adjoint method,the GCN-based framework reduces computational time by about 76.2%,significantly improving optimization efficiency.This method provides a new technical pathway for efficient and accurate aerodynamic shape optimization.