空气动力学学报2026，Vol.44Issue(5)：160-176,17.DOI:10.7638/kqdlxxb-2025.0076
基于图卷积的三维气动外形伴随优化设计
GCN-based 3D aerodynamic optimization design based on discrete adjoint method
摘要
Abstract
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.关键词
梯度优化算法/离散伴随方法/图卷积神经网络/气动外形优化/计算效率Key words
gradient-based optimization/discrete adjoint method/graph convolutional neural network/aerodynamic shape optimization/computational efficiency分类
航空航天引用本文复制引用
赵典,向锦鹏,宋述芳..基于图卷积的三维气动外形伴随优化设计[J].空气动力学学报,2026,44(5):160-176,17.基金项目
国家自然科学基金(12272316) （12272316）
国家重点研发计划(2023YFB3002800) （2023YFB3002800）
