智能赋能气动计算与外形设计：途径、前沿与挑战-“AI+空天科学”专刊

doi:10.7527/S1000-6893.2026.33587

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智能赋能气动计算与外形设计：途径、前沿与挑战-“AI+空天科学”专刊

唐志共¹,李鸿宇¹,何磊²,叶晰萌¹,钱炜祺³,张家铭⁴

1. 中国空气动力研究与发展中心
2. 中国空气动力研究与发展中心计算空气动力研究所；中国空气动力研究与发展中心空气动力学国家重点实验室
3. 中国空气动力研究所与发展中心计算所
4. 西安交通大学

收稿日期:2026-03-18 修回日期:2026-06-07 出版日期:2026-06-16 发布日期:2026-06-16
通讯作者: 李鸿宇

Intelligence-empowered aerodynamic computation and shape design: Pathways, frontiers and challenges

Received:2026-03-18 Revised:2026-06-07 Online:2026-06-16 Published:2026-06-16
Contact: Hong-Yu LI

摘要/Abstract

摘要： 以深度神经网络为代表的智能技术正推动气动科学与工程设计向数据驱动的新阶段演进，形成新的交叉学科——智能空气动力学。沿着气动计算正问题与外形设计反问题两大主线，文章为这一新学科方向整理出一条清晰脉络，分层次、分阶段梳理了智能方法在经验预测、物理建模与计算、生成式设计及启发优化等方面的赋能途径，并揭示出智能方法在可解释性、泛化能力及工程闭环融合等方面面临的深层挑战。此基础上，文章进一步指明了该领域智能学习理论深化、大规模工程验证、多途径协同融合以及新赋能途径开拓等未来发展方向，展现出智能空气动力学正从局部突破走向系统融合、从方法探索迈向工程可信的新发展阶段。

关键词: 智能空气动力学, 气动计算, 气动外形设计, 神经算子, 生成式模型, 强化学习, 大语言模型

Abstract: Intelligent technologies, represented by deep neural networks, are driving aerodynamic science and engineering design into a new data-driven paradigm, thereby giving rise to an interdisciplinary field—Intelligent Aerodynamics (IA). Along two main lines, forward problems in aerodynamic computation and inverse problems in shape design, this paper presents a clear stem for this emerging discipline. It systematically reviews intelligence-empowering approaches across empirical prediction, physical modeling and computation, generative design, and heuristic optimization, while revealing their underlying challenges in interpretability, generalization, and engineering integration. On this basis, the paper further outlines future directions, including the deepening of learning theories, large-scale engineering verification, multi-approach collaboration, and new pathway exploration. All these demonstrate that intelligent aerodynamics is evolving from scattered breakthroughs to systematic integration, and from methodological exploration to becoming credible engineering tools.

Key words: intelligent aerodynamics, aerodynamic computation, aerodynamic shape design, neural operator, deep generative model, reinforcement learning, large language models

中图分类号:

唐志共李鸿宇何磊叶晰萌钱炜祺张家铭. 智能赋能气动计算与外形设计：途径、前沿与挑战-“AI+空天科学”专刊[J]. 航空学报, doi: 10.7527/S1000-6893.2026.33587.

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Intelligence-empowered aerodynamic computation and shape design: Pathways, frontiers and challenges

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