机器学习在湍流模型构建中的应用进展

张伟伟; 朱林阳; 刘溢浪; 寇家庆

doi:10.7638/kqdlxxb-2019.0036

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机器学习在湍流模型构建中的应用进展

专栏——智能空气动力学研究探索 | 更新时间：2026-05-28

- 机器学习在湍流模型构建中的应用进展
- Progresses in the application of machine learning in turbulence modeling
- 空气动力学学报 2019年37卷第3期页码：444-454
- 作者机构：
  
  西北工业大学航空学院,西安,710072
- 作者简介：
  
  张伟伟(1979-), 男, 博士, 教授, 博士生导师, 主要研究方向:气动弹性力学, 非定常空气动力学, 流动控制, 叶轮机流固耦合力学.E-mail:aeroelastic@nwpu.edu.cn
- 基金信息：
  
  国家自然科学基金（11572252）
- DOI：10.7638/kqdlxxb-2019.0036
  中图分类号： TP181;O357.5
- 纸质出版：2019
- 稿件说明：
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张伟伟, 朱林阳, 刘溢浪, 寇家庆. 机器学习在湍流模型构建中的应用进展[J]. 空气动力学学报, 2019, 37(3): 444-454.

ZHANG Weiwei, ZHU Linyang, LIU Yilang, et al. Progresses in the application of machine learning in turbulence modeling[J]. 2019, 37(3): 444-454.
张伟伟, 朱林阳, 刘溢浪, 寇家庆. 机器学习在湍流模型构建中的应用进展[J]. 空气动力学学报, 2019, 37(3): 444-454. DOI： 10.7638/kqdlxxb-2019.0036.

ZHANG Weiwei, ZHU Linyang, LIU Yilang, et al. Progresses in the application of machine learning in turbulence modeling[J]. 2019, 37(3): 444-454. DOI： 10.7638/kqdlxxb-2019.0036.

摘要

借助于高性能计算机和数据共享平台的发展，研究者可以获取大量的高分辨率湍流计算数据。近年来，随着深度神经网络等人工智能技术的发展，数据驱动的机器学习方法也开始应用于湍流模型中不确定度的量化以及模型的改进和构建中。湍流大数据与人工智能相结合是湍流研究的一个新领域。研究者在取得一定成果的同时也面临着诸多困难和挑战，例如模型的泛化能力和鲁棒性等。模型构建过程包含了数据处理、特征选择以及模型框架的选取与优化等诸多方面，这些方面在不同程度上影响模型的性能。本文从机器学习在湍流建模过程中的实现方法和模型的不同作用两方面分析总结了目前主要的研究工作进展，并对这类问题面临的挑战和未来的研究展望进行了阐述。

Abstract

With the development of high performance computer and data sharing platform

a large number of high fidelity turbulence data can be obtained. Recently

due to the evolution of artificial intelligence

like deep neural network

data-driven machine learning methods have been adopted to quantify the model uncertainty and improve and construct turbulence models. The combination of big turbulence data and artificial intelligence becomes a new area of turbulence research. Although some encouraging results have been achieved

there are still many difficulties and challenges

such as the generalization ability and robustness of the models

etc. The modeling process involves various aspects including data process

feature selection and selection and optimization of the model framework

etc. This paper analyzes and summarizes the main research progress from two aspects:the implementation methods of machine learning in turbulence modeling and the different model targets. Besides

the challenges and future works in this area are also discussed.

关键词

Keywords

references

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