深度学习在边界层流动稳定性分析中的应用

樊佳坤; 姚方舟; 黄江涛; 徐家宽; 乔磊; 白俊强

doi:10.7638/kqdlxxb-2023.0073

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深度学习在边界层流动稳定性分析中的应用

研究论文 | 更新时间：2026-05-28

- 深度学习在边界层流动稳定性分析中的应用
- Application of deep learning in boundary layer flow instability analysis
- 空气动力学学报 2024年42卷第3期页码：30-46
- 作者机构：
  
  1. 西北工业大学航空学院,西安,710072
  2. 西安航天动力技术研究所,西安,710025
  3. 中国空气动力研究与发展中心空天技术研究所,绵阳,621000
  4. 西北工业大学无人系统技术研究院,西安,710072
- 作者简介：
  
  樊佳坤（2000—），男，陕西咸阳人，硕士研究生，研究方向：流动稳定性. E-mail：fjk2486@163.com
  徐家宽*，男，博士，副教授，研究方向：转捩-湍流模式，流动稳定性，计算流体力学. E-mail：jk.xu@nwpu.edu.cn
- 基金信息：
  
  国家自然科学基金（12102361）;中央高校基本科研业务费（G2021KY05101）
- DOI：10.7638/kqdlxxb-2023.0073
  中图分类号： V211;V411;TP183
- 纸质出版：2024
- 稿件说明：
移动端阅览
樊佳坤, 姚方舟, 黄江涛, 等. 深度学习在边界层流动稳定性分析中的应用[J]. 空气动力学学报, 2024, 42(3): 30−46.

FAN Jiakun, YAO Fangzhou, HUANG Jiangtao, et al. Application of deep learning in boundary layer flow instability analysis[J]. 2024, 42(3): 30-46.
樊佳坤, 姚方舟, 黄江涛, 等. 深度学习在边界层流动稳定性分析中的应用[J]. 空气动力学学报, 2024, 42(3): 30−46. DOI： 10.7638/kqdlxxb-2023.0073

FAN Jiakun, YAO Fangzhou, HUANG Jiangtao, et al. Application of deep learning in boundary layer flow instability analysis[J]. 2024, 42(3): 30-46. DOI： 10.7638/kqdlxxb-2023.0073.

摘要

基于线性稳定性理论（linear stability theory

LST）的e

方法是边界层转捩预测中比较可靠的方法之一。为了将传统LST特征值问题的求解过程大幅度简化和自动化，使用卷积神经网络（convolutional neural network

CNN）在边界层相似性解的LST分析样本集上进行训练，针对流向和横流不稳定性，分别在自然层流翼型和无限展长后掠翼上预测扰动的当地增长率、

因子和转捩位置，结果与标准LST一致性良好；验证了CNN可以将边界层剖面速度型导数信息编码为满足伽利略不变性的标量特征，在翼型边界层中起到了表征压力梯度的作用，在后掠翼边界层中起到了表征横流强度的作用；在CNN对LST特征值预测的基础上，以LST控制方程、边界条件和平凡解惩罚项构造总损失函数来训练内嵌物理信息神经网络（physics-informed neural network

PINN），实现了在不依赖样本的情况下对LST特征函数的准确预测，结果表明PINN可以为LST的特征函数问题提供有效的建模方法。

Abstract

The e

method based on linear stability theory (LST) is one of the more reliable methods in the prediction of boundary layer transition. In order to greatly simplify and automate the solution process of the traditional LST eigenvalue problem

the convolutional neural network (CNN) is trained on the LST analysis sample set of the boundary layer similarity solution. For the streamwise and crossflow instabilities

the local growth rate

factor and transition location are p

redicted by CNN on a naturally laminar airfoil and an infinite swept-back wing respectively

which are in good agreement with the results of standard LST. It is verified that CNN can encode the velocity derivative information of the boundary layer profile into a scalar feature that satisfies the Galilean invariance

and plays a role in characterizing the pressure gradient in the boundary layer of an airfoil or the crossflow intensity in the boundary layer of a swept-back wing. Based on the prediction of LST eigenvalues by CNN

the total loss function is constructed by the governing equations of LST

the boundary conditions and the trivial solution penalty term to train the physics-informed neural network (PINN)

which realizes an accurate prediction of LST eigenfunctions without relying on samples. The results show that the PINN model can provide an effective modeling method for the eigenfunction problem of LST.

关键词

Keywords

references

XU J K, DUAN Y, YANG J S, et al . Progress on prediction models for crossflow instabilities dominated transition based on local variables[J ] . Physics of Gases, 2023 , 8 ( 3 ): 19 − 34 (in Chinese).

SONG W P, WU M M, ZHU Z, et al . Transition prediction methods towards significant drag reduction via laminar flow technology[J ] . Acta Aerodynamica Sinica, 2018 , 36 ( 2 ): 213 − 228 (in Chinese).

NIE H, SONG W P, HAN Z H, et al . Automatic transition prediction for natural-laminar-flow wing design of supersonic transports[J ] . Acta Aeronautica et Astronautica Sinica, 2022 , 43 ( 11 ): 163 − 181 (in Chinese).

LI X L . Direct numerical simulation techniques for hypersonic turbulent flows[J ] . Acta Aeronautica et Astronautica Sinica, 2015 , 36 ( 1 ): 147 − 158 (in Chinese).

ZHAO X H, DENG X B, MAO M L, et al . Large eddy simulation for forced transition flow at hypersonic inlet[J ] . Acta Aeronautica et Astronautica Sinica, 2016 , 37 ( 8 ): 2445 − 2453 (in Chinese).

YAN C, QU F, ZHAO Y T, et al . Review of development and challenges for physical modeling and numerical scheme of CFD in aeronautics and astronautics[J ] . Acta Aerodynamica Sinica, 2020 , 38 ( 5 ): 829 − 857 (in Chinese).

ZHANG D M, LUO J S, ZHOU H . Dynamic model of coherent structure in the wall region of a turbulent boundary layer[J ] . Science in China Series G: Physics, Mechanics and Astronomy, 2003 , 46 ( 3 ): 291 − 299 .

HUANG Z F, XIAO L C, LUO J S . Transition prediction e N method and its software development for supersonic boundary layers[J ] . Acta Aerodynamica Sinica, 2018 , 36 ( 2 ): 279 − 285 (in Chinese).

ARNAL D, CASALIS G, HOUDEVILLE R. Practica l transition prediction methods: subsonic and transonic flows[C ] // RTO-EN-AVT-151—Advances in Laminar-Turbulent Transition Modelling——Proc of AVT-151 RTO AVT/VKI Lecture Series, Rhode St. Genèse, Belgium, 2008. http://ftp.rta.nato.int/public/PubFullText/RTO/EN/RTO-EN-AVT-151/EN-AVT-151-07.doc

ARNAL D, HOUDEVILLE R, SERAUDIE A, et al. Overview of laminar-turbulent transition investigations at ONERA Toulouse[C]// 41st AIAA Fluid Dynamics Conference and Exhibit, Honolulu, Hawaii. Reston, Virigina: AIAA, 2011.

CLIQUET J. Calcul de la transition laminaire-turbulent dans les codes Navier–Stokes: application aux géométries complexes[D]. Toulouse: Institut Supérieur de I'Aéronautique et de I'Espace, 2007.

CLIQUET J, HOUDEVILLE R, ARNAL D . Application of laminar-turbulent transition criteria in Navier-Stokes computations[J ] . AIAA Journal, 2008 , 46 ( 5 ): 1182 − 1190 .

PERRAUD J, DURANT A . Stability-based Mach zero to four longitudinal transition prediction criterion[J ] . Journal of Spacecraft and Rockets, 2016 , 53 ( 4 ): 730 − 742 .

PASCAL L, DELATTRE G, DENIAU H, et al . Stability-based transition model using transport equations[J ] . AIAA Journal, 2020 , 58 ( 7 ): 2933 − 2942 .

KRUMBEIN A . Automatic transition prediction and application to three-dimensional wing configurations[J ] . Journal of Aircraft, 2007 , 44 ( 1 ): 119 − 133 .

PERRAUD J, ARNAL D, CASALIS G, et al . Automatic transition predictions using simplified methods[J ] . AIAA Journal, 2009 , 47 ( 11 ): 2676 − 2684 .

FISCHER J S, SOEMARWOTO B I, VAN DER WEIDE E T A . Automatic transition prediction in a Navier-Stokes solver using linear stability theory[J ] . AIAA Journal, 2021 , 59 ( 7 ): 2409 − 2426 .

ELIASSON P, HANIFI A, PENG S H. Influence of transition on high-lift prediction with the NASA trap wing model[C]// 29th AIAA Applied Aerodynamics Conference, Honolulu, Hawaii. Reston, Virigina: AIAA, 2011.

CODER J G, MAUGHMER M D . Computational fluid dynamics compatible transition modeling using an amplification factor transport equation[J ] . AIAA Journal, 2014 , 52 ( 11 ): 2506 − 2512 .

XU J K, BAI J Q, ZHANG Y, et al . Transition study of 3D aerodynamic configures using improved transport equations modeling[J ] . Chinese Journal of Aeronautics, 2016 , 29 ( 4 ): 874 − 881 .

XU J K, HAN X, QIAO L, et al . Fully local amplification factor transport equation for stationary crossflow instabilities[J ] . AIAA Journal, 2019 , 57 ( 7 ): 2682 − 2693 .

XU J K, QIAO L, BAI J Q . A CFD-compatible amplification factor transport equation for oblique tollmien-schlichting waves in supersonic boundary layers[J ] . International Journal of Aerospace Engineering, 2020 , 2020 : 1 − 12 .

XU J K, QIAO L, BAI J Q . Improved local amplification factor transport equation for stationary crossflow instabilit y in subsonic and transonic flows[J ] . Chinese Journal of Aeronautics, 2020 , 33 ( 12 ): 3073 − 3081 .

XU J K . Linear amplification factor transport equation for stationary crossflow instabilities in supersonic boundary layers[J ] . Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering, 2021 , 235 ( 6 ): 703 − 717 .

RAJNARAYAN D, STURDZA P. Extensible rapid transition prediction for aircraft conceptual design using modal decomposition[C]// 51st AIAA Aerospace Sciences Meeting including the New Horizons Forum and Aerospace Exposition, Grapevine (Dallas/Ft. Worth Region), Texas. Reston, Virigina: AIAA, 2013.

PINNA F, ZANUS L, DEMANGE S, et al. Reduced model for transition prediction in hypersonic flows[C]// 2018 Fluid Dynamics Conference, Atlanta, Georgia. Reston, Virginia: AIAA, 2018. doi: 10.2514/6.2018-3697

NIE H, SONG W P, HAN Z H, et al . A surrogate-based e N method for compressible boundary-layer transition prediction[J ] . Journal of Aircraft, 2022 , 59 ( 1 ): 89 − 102 .

NIE H, SONG W P, HAN Z H, et al . Application of surrogate models to stability analysis and transition prediction in hypersonic flows[J ] . Advances in Aerodynamics, 2022 , 4 ( 1 ): 1 − 22 .

KUTZ J N . Deep learning in fluid dynamics[J ] . Journal of Fluid Mechanics, 2017 , 814 : 1 − 4 .

ZHANG W W, ZHU L Y, LIU Y L, et al . Progresses in the application of machine learning in turbulence modeling[J ] . Acta Aerodynamica Sinica, 2019 , 37 ( 3 ): 444 − 454 (in Chinese).

ZHANG W W, KOU J Q, LIU Y L . Prospect of artificial intelligence empowered fluid mechanics[J ] . Acta Aeronautica et Astronautica Sinica, 2021 , 42 ( 4 ): 524689 − 524689 (in Chinese).

FULLER R, SAUNDERS W, VANDSBURGER U, et al. Neural network estimation of disturbance growth using a linear stability numerical model[C]. 35th Aerospace Sciences Meeting and Exhibit, Reno, NV, USA. Reston, Virigina: AIAA, 1997.

CROUCH J D, CROUCH I W M, NG L L . Transition prediction for three-dimensional boundary layers in computational fluid dynamics applications[J ] . AIAA Journal, 2002 , 40 ( 8 ): 1536 − 1541 .

DANVIN F, OLAZABAL-LOUME M, PINNA F. Laminar to turbulent transition prediction in hypersonic flows with metamodels[C]// 2018 Fluid Dynamics Conference, Atlanta, Georgia. Reston, Virginia: AIAA, 2018.

DANVIN F, OLAZABAL M, PINNA F. Laminar to turbulent transition prediction in hypersonic flows with neural networks committee[C]// AIAA Aviation 2019 Forum, Dallas, Texas. Reston, Virginia: AIAA, 2019.

ZAFAR M I, XIAO H, CHOUDHARI M M, et al . Convolutional neural network for transition modeling based on linear stability theory[J ] . Physical Review Fluids, 2020 , 5 ( 11 ): 113903 .

PAREDES P, VENKATACHARI B, CHOUDHARI M M, et al . Toward a practical method for hypersonic transition prediction based on stability correlations[J ] . AIAA Journal, 2020 , 58 ( 10 ): 4475 − 4484 .

ZAFAR M I, CHOUDHARI M M, PAREDES P, et al . Recurrent neural network for end-to-end modeling of laminar-turbulent transition[J ] . Data-Centric Engineering, 2021 , 2 : e17 .

RAISSI M, PERDIKARIS P, KARNIADAKIS G E . Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential Equations[J ] . Journal of Computational Physics, 2019 , 378 ( 1 ): 686 − 707 .

JIN X W, CAI S Z, LI H, et al . NSFnets (Navier-Stokes flow nets): physics-informed neural networks for the incompressible Navier-Stokes equations[J ] . Journal of Computational Physics, 2021 , 426 : 109951 .

CAI S Z, WANG Z C, WANG S F, et al . Physics-informed neural networks for heat transfer problems[J ] . Journal of Heat Transfer, 2021 , 143 ( 6 ): 060801 .

SUN L N, GAO H, PAN S W, et al . Surrogate modeling for fluid flows based on physics-constrained deep learning without simulation dat a[J ] . Computer Methods in Applied Mechanics and Engineering, 2020 , 361 : 112732 .

JIVANI U N, VAIDYA O B, BHATTACHARYA A, et al. A swarm variant for the Schrödinger solver[C]// 2021 International Joint Conference on Neural Networks (IJCNN), Shenzhen, China. IEEE, 2021: 1-8.

YANG Y, GONG H L, ZHANG S Q, et al . A data-enabled physics-informed neural network with comprehensive numerical study on solving neutron diffusion eigenvalue problems[J ] . Annals of Nuclear Energy, 2023 , 183 : 109656 .

MALIK M, ORSZAG S. Efficient computation of the stability of three-dimensional compressible boundary layers[C]// 14th Fluid and Plasma Dynamics Conference, Palo Alto, CA, USA. Reston, Virigina: AIAA, 1981.

CHEN H, QIAN W Q, HE L . Aerodynamic coefficient prediction of airfoils based on deep learning[J ] . Acta Aerodynamica Sinica, 2018 , 36 ( 2 ): 294 − 299 (in Chinese).

HUI X Y, YUAN Z L, BAI J Q, et al . A method of unsteady periodic flow field prediction based on the deep learning[J ] . Acta Aerodynamica Sinica, 2019 , 37 ( 3 ): 462 − 469 (in Chinese).

HAN S Y, SHAO H D, JIANG H K, et al . Intelligent fault diagnosis of aero-engine high-speed bearings using enhanced CNN[J ] . Acta Aeronautica et Astronautica Sinica, 2022 , 43 ( 9 ): 625479 − 625479 (in Chinese).

LIANG R K, ZHANG W, YANG S F, et al . Applications of deep learning-based super-resolution for reconstruction of flow around the CAARC benchmark model[J ] . Acta Aerodynamica Sinica, 2023 , 41 ( 11 ): 116 − 126 (in Chinese).

YE S R, ZHANG Z, WANG Y W, et al . Progress in deep convolutional neural network based flow field recognition and its applications[J ] . Acta Aeronautica et Astronautica Sinica, 2021 , 42 ( 4 ): 524736 − 524736 (in Chinese).

FU J Q, ZHONG B W, ZHONG Y Q, et al . A physics informed neural network based method for aircraft aerodynamic parameter identification[J ] . Acta Aerodynamica Sinica, 2023 , 41 ( 9 ): 30 − 37 (in Chinese).

REIBERT M, SARIC W, CARRILLO R Jr, et al. Experiments in nonlinear saturation of stationary crossflow vortices in a swept-wing boundary layer[C]// 34th Aerospace Sciences Meeting and Exhibit, Reno, NV, USA. Reston, Virigina: AIAA, 1996.

LANGTRY R. Extending the gamma-rethetat correlation based transition model for crossflow effects (invited)[C]// 45th AIAA Fluid Dynamics Conference, Dallas, TX. Reston, Virginia: AIAA, 2015.

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