Thoughts and prospects on large model research in aerodynamics

Tang Zhigong; QIAN Weiqi; HE Lei; LIN Jie; HUANG Mingji; ZHAO Tun; WANG Yueqing; YUAN Xianxu

doi:10.7638/kqdlxxb-2024.0056

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Thoughts and prospects on large model research in aerodynamics

更新时间：2026-05-28

- Thoughts and prospects on large model research in aerodynamics
- Vol. 42, Issue 12, Pages: 1-11(2024)
- 作者机构：
  
  1. 中国空气动力研究与发展中心,绵阳,621000
  2. 空天飞行空气动力科学与技术全国重点实验室,绵阳,621000
  3. 国防科技大学计算机学院,长沙,410073
- 作者简介：
- 基金信息：
- DOI：10.7638/kqdlxxb-2024.0056
  CLC： V211
- Published：2024
- 稿件说明：
移动端阅览
唐志共, 钱炜祺, 何磊, 等. 空气动力学领域大模型研究思考与展望[J]. 空气动力学学报, 2024, 42(12): 1−11.

Tang Zhigong, QIAN Weiqi, HE Lei, et al. Thoughts and prospects on large model research in aerodynamics[J]. 2024, 42(12): 1-11.
唐志共, 钱炜祺, 何磊, 等. 空气动力学领域大模型研究思考与展望[J]. 空气动力学学报, 2024, 42(12): 1−11. DOI： 10.7638/kqdlxxb-2024.0056

Tang Zhigong, QIAN Weiqi, HE Lei, et al. Thoughts and prospects on large model research in aerodynamics[J]. 2024, 42(12): 1-11. DOI： 10.7638/kqdlxxb-2024.0056.

摘要

大模型技术作为人工智能领域发展最为迅速的方向，在自然语言处理和计算机视觉等领域取得巨大成功，也在朝着赋能科学研究领域蓬勃发展，已成为空气动力学领域研究的全新手段，在指导加速空气动力实验与计算、辅助空气动力理论和知识发现等方面存在巨大潜力。本文首先对大模型进行了概述，分析了大模型的4个主要特征，并将大模型分为大语言模型、视觉大模型和科学大模型。其次，初步给出了空气动力学领域科学计算大模型的概念内涵，从流场预测、湍流建模、气动性能预测、气动外形设计等方面介绍了研究现状。然后，从模型架构、反馈对齐、大规模气动数据的生成等角度对空气动力学领域大模型的关键技术进行了深入分析和探讨。最后，对空气动力学领域大模型未来的重点发展方向，包括构建统一的预训练基础模型、融入气动知识支撑科学发现、发展领域智能体等，进行了展望。

Abstract

As one of the fastest-growing directions in artificial intelligence

the large model technology has achieved remarkable success in realms such as natural language processing and computer vision and is vigorously expanding its influence in empowering scientific research. It has also become a powerful tool in aerodynamics

possessing significant potential to expedite aerodynamic experiments and computations and assist aerodynamic theory and knowledge discovery. This paper begins by presenting an overview of large models for language processing

computer vision

and scientific computing. Subsequently

the paper outlines the conceptual framework of large models for scientific computing in aerodynamics

reviewing the current research progress from various perspectives

including flow field prediction

turbulence modeling

aerodynamic performance prediction

and aerodynamic configuration design. Furthermore

key techniques of large models in aerodynamics are discussed in-depth from the perspectives of model architecture

feedback alignment

and the generation of big aerodynamic data. Lastly

developing directions of large models in aerodynamics are prospected

including the construction of a unified pre-trained foundational model

the integration of aerodynamic knowledge to support scientific discoveries

and the development of discipline-specific agents.

关键词

Keywords

references

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