基于物理知识约束的数据驱动式湍流模型修正及槽道湍流计算验证

doi:10.7527/S1000-6893.2019.23282

流体力学与飞行力学

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基于物理知识约束的数据驱动式湍流模型修正及槽道湍流计算验证

张亦知, 程诚, 范钇彤, 李高华, 李伟鹏

上海交通大学航空航天学院, 上海 200240

收稿日期:2019-07-12 修回日期:2019-11-28 出版日期:2020-03-15 发布日期:2019-11-28
通讯作者: 李伟鹏 E-mail:liweipeng@sjtu.edu.cn
基金资助:
国家自然科学基金（11772194，91952302）

Data-driven correction of turbulence model with physics knowledge constrains in channel flow

ZHANG Yizhi, CHENG Cheng, FAN Yitong, LI Gaohua, LI Weipeng

School of Aeronautics and Astronautics, Shanghai Jiao Tong University, Shanghai 200240, China

Received:2019-07-12 Revised:2019-11-28 Online:2020-03-15 Published:2019-11-28
Supported by:
National Natural Science Foundation of China(11772194, 91952302)

摘要/Abstract

摘要： 对湍流摩擦阻力的精准预测是学术界和工业界普遍关心的重要问题，而数据驱动式的湍流模型修正方法对此显示出较大的潜力和前景。提出了一种基于物理知识约束的数据驱动式湍流模型修正方法，根据湍流摩擦阻力分解获得先验物理知识，在S-A湍流模型的生成项中引入非均匀分布的修正因子，以修正因子为设计变量，设定包含物理知识约束的目标函数，利用离散伴随方法求解目标函数与设计变量之间的梯度关系，通过高效率的迭代求解获得修正因子的分布。以槽道湍流为例，验证了包含物理知识约束的数据驱动式建模方法的优势，并分析了物理知识约束对湍流摩擦阻力预测精度的影响，结果表明引入物理知识约束可进一步提高湍流摩擦阻力的预测精度。

关键词: 数据驱动式建模, 物理知识约束, 湍流模型修正, 伴随方法, 湍流摩擦阻力

Abstract: The accurate prediction of turbulence skin friction is one of the essential problems in engineering design and academic field, and the data-based design of turbulence model has shown great potential and prospects in this field. In this paper, we propose a data-driven method in turbulence model correction with constrain of physics knowledge. The prior physics knowledge is gained through skin friction decomposition in turbulence flow and a spatially-varying factor is introduced to the production term of S-A model as the design variable. The objective function contains the prior physics knowledge. The discrete adjoint method is used to solve the derivatives of the objective function with respect to the design variable, and the distribution of the factor is effectively obtained through iterations. The turbulence channel flow is taken as the case to verify the effectiveness of this data-based turbulence model correction and analyze the influence of the physics knowledge constrains on prediction accuracy. The results show that the introduction of physics knowledge constrains can further improve the accuracy of turbulence skin friction prediction.

Key words: data-driven modeling, physics knowledge constrain, turbulence model correction, adjoint method, turbulence skin friction

中图分类号:

V211.3

张亦知, 程诚, 范钇彤, 李高华, 李伟鹏. 基于物理知识约束的数据驱动式湍流模型修正及槽道湍流计算验证[J]. 航空学报, 2020, 41(3): 123282-123282.

ZHANG Yizhi, CHENG Cheng, FAN Yitong, LI Gaohua, LI Weipeng. Data-driven correction of turbulence model with physics knowledge constrains in channel flow[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2020, 41(3): 123282-123282.

参考文献 29

[1]	SLOTNICK J, KHODADOUST A, ALONSO J, et al. CFD vision 2030 study:A path to revolutionary computational aerosciences:NASA/CR-2014-218178[R]. Washington,D.C.:NASA, 2014.
[2]	BALDWIN B S. Thin layer approximation and algebraic model for separated turbulent flows[C]//16th Aerospace Sciences Meeting, 1978:78-257.
[3]	POPE S B. Turbulent flows[J]. Measurement Science and Technology, 2000, 12(11):806.
[4]	HOLLAND J R, BAEDER J D, DURAISAMY K. Towards integrated field inversion and machine learning with embedded neural networks for RANS modeling[C]//AIAA Scitech 2019 Forum. Reston, VA:AIAA, 2019.
[5]	赵辉, 胡星志, 张健, 等. 湍流模型系数不确定度对翼型绕流模拟的影响[J]. 航空学报, 2019, 40(6):122581. ZHAO H, HU X Z, ZHANG J, et al. Effects of uncertainty in turbulence model coefficients on flow over airfoil simulation[J] Acta Aeronautica et Astronautica Sinica, 2019, 40(6):122581 (in Chinese).
[6]	DURAISAMY K, SPALART P R, RUMSEY C L. Status, emerging ideas and future directions of turbulence modeling research in aeronautics:NASA/TM-2017-219682[R]. Washington, D.C.:NASA Langley Research Center, 2017.
[7]	陈海昕, 邓凯文, 李润泽. 机器学习技术在气动优化中的应用[J]. 航空学报, 2019, 40(1):522480. CHEN H X, DENG K W, LI R Z. Utilization of machine learning technology in aerodynamic optimization[J].Acta Aeronautica et Astronautica Sinica, 2019, 40(1):522480 (in Chinese).
[8]	PARISH E J, DURAISAMY K. A paradigm for data-driven predictive modeling using field inversion and machine learning[J]. Journal of Computational Physics, 2015, 305:758-774.
[9]	SINGH A P, DURAISAMY K. Using field inversion to quantify functional errors in turbulence closures[J]. Physics of Fluids, 2016, 28(4):045110.
[10]	LING J, TEMPLETON J. Evaluation of machine learning algorithms for prediction of regions of high Reynolds averaged Navier-Stokes uncertainty[J]. Physics of Fluids, 2015, 27(8):085103.
[11]	XIAO H, WU J, WANG J, et al. Quantifying model-form uncertainties in Reynolds averaged Navier-Stokes equations:An open-box, physics-based, bayesian approach[J]. Journal of Computational Physics, 2016, 324:115-136.
[12]	WANG J X, WU J L, XIAO H. Physics-informed machine learning approach for reconstructing Reynolds stress modeling discrepancies based on DNS data[J]. Physical Review Fluids, 2017, 2(3):034603.
[13]	WU J L, WANG J X, XIAO H, et al. A priori assessment of prediction confidence for data-driven turbulence modeling[J]. Flow, Turbulence and Combustion, 2017, 99(1):25-46.
[14]	ZHANG W, ZHU L, LIU Y, et al. Machine learning methods for turbulence modeling in subsonic flows over airfoils[EB/OL]. arXiv preprint arXiv:1806. 05904, 2018.
[15]	DURAISAMY K, IACCARINO G, XIAO H. Turbulence modeling in the age of data[J]. Annual Review of Fluid Mechanics, 2019, 51:357-377.
[16]	RAISSI M, WANG Z, TRIANTAFYLLOU M S, et al. Deep learning of vortex-induced vibrations[J]. Journal of Fluid Mechanics, 2019, 861:119-137.
[17]	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, 378:686-707.
[18]	FAN Y, CHENG C, LI W. Effects of the Reynolds number on the mean skin friction decomposition in turbulent channel flows[J]. Applied Mathematics and Mechanics, 2019(6):1-12.
[19]	LI W, FAN Y, MODESTI D, et al. Decomposition of the mean skin-friction drag in compressible channel flows[J]. Journal of Fluid Mechanics, 2019, 875:101-123.
[20]	FAN Y, LI W, SERGIO P. Decomposition of the mean friction drag in zero-pressure-gradient turbulent boundary layers[J]. Physics of Fluids, 2019[in press].
[21]	SPALART P R, ALLMARAS S R. A one-equation turbulence model for aerodynamic flows[J]. Recherche Aerospatiale, 1994, 1:5-21.
[22]	LI X L, FU D X, MA Y W, et al. Direct numerical simulation of shock/turbulent boundary layer interaction in a supersonic compression ramp[J]. Science in China Series G (Physics, Mechanics and Astronomy), 2010, 53(9):1651-1658.
[23]	RENARD N, DECK S. A theoretical decomposition of mean skin friction generation into physical phenomena across the boundary layer[J]. Journal of Fluid Mechanics, 2016, 790:339-367.
[24]	LEE M, Moser R D. Direct numerical simulation of turbulent channel flow up to Re=5 200[J]. Journal of Fluid Mechanics, 2015, 774:395-415.
[25]	SCHAEFER J A. Uncertainty quantification of turbulence model closure coefficients for transonic wall-bounded flows[J]. AIAA Journal, 2015, 55(1):195-213.
[26]	JAMESON A. Aerodynamic design via control theory[J]. Journal of Scientific Computing, 1988, 3(3):233-260.
[27]	NIELSEN E J, ANDERSON W K. Aerodynamic design optimization on unstructured meshes using the Navier-Stokes equations[M]. Washington, D.C.:NASA, 1998.
[28]	SAAD Y, SCHULTZ M H. GMRES:A generalized minimal residual algorithm for solving nonsymmetric linear systems[J]. SIAM Journal on Scientific and Statistical Computing, 1986, 7(3):856-869.
[29]	赵锐利. 双曲型偏微分方程数值解及反问题的研究[D]. 西安:西安理工大学, 2014. ZHAO R L. The research of hyperbolic partial differential equation of numerical method and the inverse problem[D]. Xi'an:Xi'an University of Technology, 2014 (in Chinese).

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基于物理知识约束的数据驱动式湍流模型修正及槽道湍流计算验证

Data-driven correction of turbulence model with physics knowledge constrains in channel flow

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