航空学报2025，Vol.46Issue(10)：94-104,11.DOI:10.7527/S1000-6893.2025.31172
基于无监督学习的飞行器表面网格平滑方法
A surface mesh smoothing method for aircraft based on unsupervised learning
摘要
Abstract
In numerical simulations for aircraft design,mesh smoothing methods are crucial for enhancing mesh qual-ity 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 tech-niques 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 in-troduces local surface coordinate transformation to ensure the conformity of smoothed mesh nodes.Experimental re-sults 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.关键词
飞行器设计/网格平滑/局部曲面拟合/优化式平滑方法/无监督学习Key words
aircraft design/mesh smoothing/local surface fitting/optimization-based smoothing method/unsuper-vised learning分类
航空航天引用本文复制引用
王志超,陈新海,邓亮,刘杨,庞宇飞,刘杰..基于无监督学习的飞行器表面网格平滑方法[J].航空学报,2025,46(10):94-104,11.基金项目
国家自然科学基金(12402349) （12402349）
湖南省自然科学基金(2024JJ6468) （2024JJ6468）
国防科技大学青年基金(ZK2023-11) （ZK2023-11）
国家重点研发计划(2021YFB0300101) National Natural Science Foundation of China(12402349) （2021YFB0300101）
Natural Science Foundation of Hunan Province(2024JJ6468) （2024JJ6468）
Youth Foundation of the National University of Defense Technology(ZK2023-11) （ZK2023-11）
National Key Research and Development Program of China(2021YFB0300101) （2021YFB0300101）
