南京航空航天大学学报（英文版）2025，Vol.42Issue(6)：789-800,12.DOI:10.16356/j.1005-1120.2025.06.006
翼型盒式机翼布局的气动优化及其机器学习集成方法
Aerodynamic Optimization of Box-Wing Planform Through Machine Learning Integration
摘要
Abstract
This study discusses a machine learning-driven methodology for optimizing the aerodynamic performance of both conventional,like common research model(CRM),and non-conventional,like Bionica box-wing,aircraft configurations.The approach leverages advanced parameterization techniques,such as class and shape transformation(CST)and Bezier curves,to reduce design complexity while preserving flexibility.Computational fluid dynamics(CFD)simulations are performed to generate a comprehensive dataset,which is used to train an extreme gradient boosting(XGBoost)model for predicting aerodynamic performance.The optimization process,using the non-dominated sorting genetic algorithm(NSGA-Ⅱ),results in a 12.3%reduction in drag for the CRM wing and an 18%improvement in the lift-to-drag ratio for the Bionica box-wing.These findings validate the efficacy of machine learning based method in aerodynamic optimization,demonstrating significant efficiency gains across both configurations.关键词
气动优化/盒式机翼/机器学习/计算流体力学Key words
aerodynamic optimization/box-wing/machine learning/computational fluid dynamics(CFD)分类
信息技术与安全科学引用本文复制引用
哈桑·梅赫迪,邓中敏,雷东内·斯特凡,萨努西·B.穆罕默德..翼型盒式机翼布局的气动优化及其机器学习集成方法[J].南京航空航天大学学报（英文版）,2025,42(6):789-800,12.基金项目
We would like to extend our sincere gratitude to Azad Khandoker from JKU Linz for his continu-ous support and guidance in research and innovation,which served as a major inspiration for this study.Our heartfelt thanks also go to the Bionica-aircraft team for their valuable resources,insightful feedback,and constructive criticism,which significantly improved the quality of this manuscript. （）
