航空学报2025，Vol.46Issue(10)：1-39,39.DOI:10.7527/S1000-6893.2025.31679
飞行器生成式模型气动设计研究进展与展望
Research progress and prospects of aircraft aerodynamic design based on generative models
摘要
Abstract
As one of the fastest-growing directions in deep learning,the generative model has achieved remarkable success in realms such as computer vision and has also introduced novel paradigms and methodologies for research endeavors within the scientific fields like aerodynamics.This paper focuses on the latest research advancements of generative models in the field of aircraft aerodynamic configuration design,and systematically summarizes the relevant research achievements in recent years.Firstly,a representation-generation-evaluation framework for generative aero-dynamic configuration design of aircraft is established.Subsequently,the key technologies and current development progress involved in aerodynamic configuration design are examined and discussed from the perspectives of aerody-namic configuration representation,the development of generative aerodynamic configuration design models,and methods for evaluating design quality.Additionally,a brief overview of aerodynamic data construction methods and typical datasets is provided,serving as a data foundation for generative aerodynamic design.Lastly,the future key development directions in the field of generative aerodynamic configuration design are discussed,including exploration of hybrid generative model architectures,construction of large models and domain-specific agents for aerodynamic de-sign,establishment of a comprehensive evaluation system for generative aerodynamic design quality,and integration of domain knowledge into generative aerodynamic design models.关键词
气动布局设计/生成式模型/气动布局表征/气动性能评估/深度学习Key words
aerodynamic configuration design/generative model/aerodynamic configuration representation/aero-dynamics performance evaluation/deep learning分类
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林杰,唐志共,钱炜祺,王岳青,张鹏,徐炜遐,刘杰..飞行器生成式模型气动设计研究进展与展望[J].航空学报,2025,46(10):1-39,39.基金项目
国家重点研发计划(2023YFA1011704,2021YFBO300101,2022YFB4501702) （2023YFA1011704,2021YFBO300101,2022YFB4501702）
国家自然科学基金(12472300) National Key Research and Development Program of China(2023YFA1011704,2021YFBO300101,2022YFB4501702) （12472300）
National Natural Science Foundation of China(12472300) （12472300）
