Generative machine learning provides an end-to-end framework for aerodynamic design optimization by generating diverse aerodynamic configurations conditioned on target performance. However, the generated designs may violate geometric constraints or deviate from the prescribed aerodynamic performance.
Therefore, we propose a Condition-Enhanced Generative Adversarial Network (CEGAN) that embeds geometric and aerodynamic information into the generative process.
方法细节
A condition-informed loss function is developed by combining a geometry-guided feasibility penalty with an aerodynamic performance penalty evaluated by surrogate models trained on the same dataset. Additionally, an output filtering mechanism discards infeasible designs using these surrogate models, ensuring compliance with prescribed conditions.
实验与验证
The method is demonstrated on aircraft configurations similar to the Cessna Citation X. Each configuration is represented by 28 shape parameters, whose aerodynamic performance is evaluated using OpenVSP. Interpolation and extrapolation tests are conducted for different network architectures and conditioning strategies.
结果对比
- 与基线模型相比，CEGAN在插值条件下将平均生成误差降低了至少36%，在极端外推条件下降低了至少46%。
- 同时，CEGAN成功生成了多样且物理可行的设计，这些设计可以扩展到训练分布之外。
结论与意义
因此，所提出的CEGAN框架为概念设计阶段的快速气动配置探索提供了一种鲁棒且准确的工具。