知识驱动的超临界翼型气动设计智能体研究-AI+空天科学

  • 李润泽 ,
  • 杨韫加 ,
  • 张宇飞 ,
  • 陈海昕
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  • 清华大学

收稿日期: 2026-01-23

  修回日期: 2026-04-17

  网络出版日期: 2026-04-20

基金资助

国家自然科学基金;国家自然科学基金;国家自然科学基金;国家自然科学基金;国家自然科学基金;中国科学技术协会青年人才托举工程

Knowledge-Driven Aerodynamic Design Agent for Supercritical Airfoils

  • LI Run-Ze ,
  • YANG Yun-Jia ,
  • ZHANG Yu-Fei ,
  • CHEN Hai-Xin
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Received date: 2026-01-23

  Revised date: 2026-04-17

  Online published: 2026-04-20

摘要

跨声速气动设计受非线性流动物理与复杂工程约束的共同影响,导致设计困难、长期依赖于设计人员的知识和经验。针对现有数据驱动建模和设计方法通用性差、可解释性不足、气动知识融入能力有限的问题,提出了一种面向流场结构特征的气动设计智能体方法,以视觉语言模型作为核心决策单元,以强化学习为训练框架,实现超临界翼型的知识驱动与可解释设计。该方法将翼型几何参数与壁面马赫数分布等关键流场特征统一表征为设计状态,并通过结构化指令模板向模型引入气动设计知识与设计历史,引导其生成满足工程约束的几何修改策略。通过与快速气动分析环境的交互,模型逐步建立“流场特征—性能指标—几何修型动作”之间的关联,实现物理知识驱动的气动设计。以超临界翼型的减阻问题为测试算例,结果表明该智能体在设计性能上可达到与贪婪搜索方法相当的水平,同时在设计过程表达与策略演化方面具有更好的可解释性与稳定性。进一步比较中英文形式的指令模板和不同参数规模的大模型,表明针对气动设计任务的微调过程对智能体性能的提升作用显著强于提示语言本身的影响。综上,提出的框架为面向工程应用的可信气动设计智能体研究提供了新的思路。

本文引用格式

李润泽 , 杨韫加 , 张宇飞 , 陈海昕 . 知识驱动的超临界翼型气动设计智能体研究-AI+空天科学[J]. 航空学报, 0 : 1 -0 . DOI: 10.7527/S1000-6893.2026.33411

Abstract

Transonic aerodynamic design is strongly influenced by nonlinear flow dynamics and complex engineering constraints, which makes the design process challenging and heavily reliant on designers’ experience and domain knowledge. The existing data-driven modeling and design methods have several challenges, i.e., poor generalization capability, insufficient interpretability, and limited ability to incorporate aerodynamic knowledge. To address these challenges, this paper proposes a flow-feature-oriented aerodynamic design agent, with a large language model serving as the core decision-making unit, to enable knowledge-driven and interpretable design of supercritical airfoils. In the proposed approach, airfoil geometric parameters and key flow features, such as wall Mach number distributions, are jointly represented as the state. Aerodynamic design knowledge and design history are introduced through structured prompts, guiding the model to generate geometry modification strategies that satisfy engineering constraints. Through interaction with a fast aerodynamic analysis environment, the model progressively establishes correlations among flow features, aerodynamic performance, and geometric modification actions, thereby realizing physics-guided aerodynamic design. A drag-reduction task for supercritical airfoils is used as the test case. The results demonstrate that the proposed agent achieves design performance comparable to greedy search methods, while exhibiting superior interpretability and stability in design process. Further comparisons between Chinese and English prompt, as well as large language models with different parameter scales, indicate that the task-specific fine-tuning process plays a significantly more important role in improving the performance of the aerodynamic design agent than the choice of prompt language itself. Overall, the proposed framework provides a new perspective for the development of trustworthy aerodynamic design agents for engineering applications.

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