A surface mesh smoothing method for aircraft based on unsupervised learning

In numerical simulations for aircraft design, mesh smoothing methods are crucial for enhancing mesh quality in the preprocessing stage and reducing simulation errors. Traditional optimization-based smoothing methods are limited by complex iterative solving processes, leading to high memory consumption and low computational efficiency. To address these issues, existing intelligent smoothing methods use neural networks to learn the smoothing process, achieving a balance between smoothing efficiency and quality. However, when applied to three-dimensional surface meshes, these methods often rely on projection operations or supervised learning to ensure mesh node conformity, which introduces additional computation or data generation overhead. This study develops an intelligent smoothing surrogate model, GMSNet3D, specifically designed for aircraft surface meshes, based on unsupervised learning techniques and local surface fitting. The model uses an unsupervised loss function tailored for surface mesh smoothing, enabling intelligent training without the need for high-quality supervised data. Furthermore, the model innovatively introduces local surface coordinate transformation to ensure the conformity of smoothed mesh nodes. Experimental results demonstrate that the local surface coordinate transformation method used in the GMSNet3D model achieves a speedup of 13.82 times compared to projection operations in existing methods. Additionally, while maintaining mesh smoothing quality, GMSNet3D achieves a 29.81-fold improvement in optimization efficiency compared to traditional optimization-based smoothing methods.