基于数据驱动转捩模型的翼型动态失速气动力计算

李金瑛; 戴玉婷; 杨超

doi:10.19527/j.cnki.2096-1642.1069

基于数据驱动转捩模型的翼型动态失速气动力计算

doi: 10.19527/j.cnki.2096-1642.1069

李金瑛¹,
戴玉婷^1,2, ,,
杨超¹

1. 北京航空航天大学航空科学与工程学院, 北京 100083;
2. 天目山实验室, 浙江杭州 310023

详细信息

作者简介:
李金瑛(2000-)女,博士,主要研究基于机器学习的流固耦合分析。E-mail:cdwlljy@126.com

通讯作者:
戴玉婷(1985-)女,教授,主要研究飞行器设计、气动弹性、流固耦合等。E-mail:yutingdai@buaa.edu.cn

中图分类号: V221.3
计量
- 文章访问数: 26
- HTML全文浏览量: 2
- PDF下载量: 3
- 被引次数: 0
出版历程
- 收稿日期: 2023-06-28
- 修回日期: 2023-08-21
- 网络出版日期: 2023-11-06

Aerodynamic Calculation of Airfoil Dynamic Stall Based on Data-Driven Transition Model

1. School of Aeronautic Science and Engineering, Beihang University, Beijing 100083, China;
2. Tianmushan Laboratory, Hangzhou 310023, China

摘要

摘要: 低Reynolds数下层流分离和分离诱导转捩现象复杂,数值仿真难度大。基于全连接反向传播神经网络,建立了低Reynolds数转捩间歇因子的数据驱动模型,通过优化设计选择了能够反映转捩过程的数据驱动模型的流场输入参数,辨识了转捩间歇因子,据此修正了k-ω SST二方程湍流模型,求解二维翼型动态失速下的流场演化和非定常气动力特性。结果表明,数据驱动的转捩方程耦合二方程湍流模型具有一定的迎角泛化能力,能够反映动态失速下前缘涡增长与脱落、流动再附着等典型流动状态。基于数据驱动转捩模型的动态失速下非定常气动升力预测结果与基于SST-γ三方程模型的CFD计算结果相比,相对误差小于12%。
- 转捩模型 /
- 流动转捩 /
- 数据驱动 /
- 神经网络 /
- 动态失速
Abstract: The laminar flow separation and separation-induced transition at low Reynolds number are complex, and have great difficulty in numerical simulation. Based on fully-connected back-propagation neural network, a data-driven model of intermittency at low Reynolds number was established. The input parameters of the data-driven model to reflect transition process and predict intermittency were selected through optimization design. By modifying the k-ω SST two equation turbulence model with a data-driven transition equation, the flow field evolution and unsteady aerodynamic characteristics of a two-dimensional airfoil under dynamic stall were solved. Results show that the data-driven transition equation combined with two equation turbulence model has the generalization ability for the angle of attack, and clearly reflects the typical flow conditions such as the growth and shedding of the leading-edge vortex and the reattachment of the flow under dynamic stall. The relative error of unsteady aerodynamic lift in dynamic stall between the data-driven transition model and the SST-γ three equation model is lower than 12%.
- turbulence model /
- flow transition /
- data-driven /
- neural network /
- dynamic stall

HTML全文

参考文献(32)

[1]	张健, 张德虎. 高空长航时太阳能无人机总体设计要点分析[J]. 航空学报, 2016, 37(S1):S1-S7. Zhang J, Zhang D H. Essentials of configuration design of HALE solar-powered UAVs[J]. Acta Aeronautica et Astronautica Sinica, 2016, 37(S1):S1-S7(in Chinese).
[2]	Barnes C J, Visbal M R. Stiffness effects on laminar se- paration flutter[J]. Journal of Fluids and Structures, 2019, 91:102767.
[3]	McCroskey W J, Carr L W, McAlister K W. Dynamic stall experiments on oscillating airfoils[J]. AIAA Journal, 1976, 14(1):57-63.
[4]	Sheng W, Galbraith R A, Coton F N. Prediction of dynamic stall onset for oscillatory low-speed airfoils[J]. Journal of Fluids Engineering, 2008, 130(10):101204.
[5]	Wu Y, Dai Y T, Yang C, et al. Effect of trailing-edge morphing on flow characteristics around a pitching airfoil[J]. AIAA Journal, 2023, 61(1):160-173.
[6]	Spentzos A, Barakos G, Badcock K, et al. Investigation of three-dimensional dynamic stall using computational fluid dynamics[J]. AIAA Journal, 2005, 43(5):1023-1033.
[7]	Wang S Y, Ingham D B, Ma L, et al. Turbulence modeling of deep dynamic stall at relatively low Reynolds number[J]. Journal of Fluids and Structures, 2012, 33:191-209.
[8]	Kim Y, Xie Z T. Modelling the effect of freestream turbulence on dynamic stall of wind turbine blades[J]. Computers & Fluids, 2016, 129:53-66.
[9]	Menter F R. Two-equation eddy-viscosity turbulence models for engineering applications[J]. AIAA Journal, 1994, 32(8):1598-1605.
[10]	Van Ingen J L. The eN method for transition prediction. Historical review of work at TU Delft[R]. AIAA 20083830, 2008:3830.
[11]	朱震, 宋文萍, 韩忠华. 基于双eN方法的翼身组合体流动转捩自动判断[J]. 航空学报, 2018, 39(2):123-134. Zhu Z, Song W P, Han Z H. Automatic transition prediction for wing-body configuration using dual eN method[J]. Acta Aeronautica et Astronautica Sinica, 2018, 39(2):123-134(in Chinese).
[12]	韩忠华, 王绍楠, 韩莉, 等. 一种基于动模态分解的翼型流动转捩预测新方法[J]. 航空学报, 2017, 38(1):30-46. Han Z H, Wang S N, Han L, et al. A novel method for automatic transition prediction for flows over airfoils based on dynamic mode decomposition[J]. Acta Aeronautica et Astronautica Sinica, 2017, 28(1):30-46(in Chinese).
[13]	Launder B E, Sharma B I. Application of the energy-dissipation model of turbulence to the calculation of flow near a spinning disc[J]. Letters in Heat and Mass Transfer, 1974, 1(2):131-137.
[14]	Menter F R, Langtry R, Völker S. Transition modelling for general purpose CFD codes[J]. Flow, Turbulence and Combustion, 2006, 77(1/4):277-303.
[15]	Lodefier K, Merci B, De Langhe C, et al. Transition modelling with the SST turbulence model and an intermittency transport equation[C]. ASME Turbo Expo 2003, Collocated with the 2003 International Joint Power Generation Conference. Atlanta, Georgia, USA:ASME, 2003:771-777.
[16]	Wang L, Fu S. Development of an intermittency equation for the modeling of the supersonic/hypersonic boundary layer flow transition[J]. Flow, Turbulence and Combustion, 2011, 87(1):165-187.
[17]	Fu S, Wang L. RANS modeling of high-speed aerodynamic flow transition with consideration of stability theory[J]. Progress in Aerospace Sciences, 2013, 58:36-59.
[18]	Xu J K, Bai J Q, Fu Z Y, et al. Parallel compatible transition closure model for high-speed transitional flow[J]. AIAA Journal, 2017, 55(9):3040-3050.
[19]	Dhawan S, Narasimha R. Some properties of boundary layer flow during the transition from laminar to turbulent motion[J]. Journal of Fluid Mechanics, 1958, 3(4):418-436.
[20]	Suzen Y B, Huang P G. Modeling of flow transition using an intermittency transport equation[J]. Journal of Fluids Engineering, 2000, 122(2):273-284.
[21]	Cho J R, Chung M K. A k-ε-γ equation turbulence model[J]. Journal of Fluid Mechanics, 1992, 237:301-322.
[22]	Steelant J, Dick E. Modelling of bypass transition with conditioned Navier-Stokes equations coupled to an intermittency transport equation[J]. International Journal for Numerical Methods in Fluids, 1996, 23(3):193-220.
[23]	Menter F R, Smirnov P E, Liu T, et al. A one-equation local correlation-based transition model[J]. Flow, Turbulence and Combustion, 2015, 95(4):583-619.
[24]	Singh A P, Duraisamy K. Using field inversion to quantify functional errors in turbulence closures[J]. Physics of Fluids, 2016, 28(4):045110.
[25]	Singh A P, Medida S, Duraisamy K. Machine-learning-augmented predictive modeling of turbulent separated flows over airfoils[J]. AIAA Journal, 2017, 55(7):2215-2227.
[26]	Yang M C, Xiao Z X. Improving the k-ω-γ-Ar transition model by the field inversion and machine learning framework[J]. Physics of Fluids, 2020, 32(6):064101.
[27]	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.
[28]	Ott J, Pritchard M, Best N, et al. A Fortran-Keras deep learning bridge for scientific computing[J]. Scientific Programming, 2020, 2020:8888811.
[29]	Maulik R, Sharma H, Patel S, et al. Deploying deep learning in OpenFOAM with TensorFlow[R]. AIAA 2021-1485, 2021:1485.
[30]	Maulik R, Sharma H, Patel S, et al. A turbulent eddy-viscosity surrogate modeling framework for Reynolds-averaged Navier-Stokes simulations[J]. Computers & Fluids, 2021, 227:104777.
[31]	Suluksna K, Juntasaro E. Assessment of intermittency transport equations for modeling transition in boundary layers subjected to freestream turbulence[J]. International Journal of Heat and Fluid Flow, 2008, 29(1):48-61.
[32]	Lee T, Gerontakos P. Investigation of flow over an oscillating airfoil[J]. Journal of Fluid Mechanics, 2004, 512:313-341.

施引文献

资源附件(0)

访问统计

点击查看大图

计量

文章访问数: 26
HTML全文浏览量: 2
PDF下载量: 3
被引次数: 0

主管部门：	中国航天科技集团有限公司
主办单位：	中国航天空气动力技术研究院中国宇航学会中国宇航出版有限责任公司

姓名
邮箱
手机号码
标题
留言内容
验证码

留言板

基于数据驱动转捩模型的翼型动态失速气动力计算

doi: 10.19527/j.cnki.2096-1642.1069

作者简介:
李金瑛(2000-)女,博士,主要研究基于机器学习的流固耦合分析。E-mail:cdwlljy@126.com

通讯作者:
戴玉婷(1985-)女,教授,主要研究飞行器设计、气动弹性、流固耦合等。E-mail:yutingdai@buaa.edu.cn

计量

Aerodynamic Calculation of Airfoil Dynamic Stall Based on Data-Driven Transition Model

计量

目录

联系我们

友情链接

留言板

基于数据驱动转捩模型的翼型动态失速气动力计算

doi: 10.19527/j.cnki.2096-1642.1069

作者简介: 李金瑛(2000-)女,博士,主要研究基于机器学习的流固耦合分析。E-mail:cdwlljy@126.com

通讯作者: 戴玉婷(1985-)女,教授,主要研究飞行器设计、气动弹性、流固耦合等。E-mail:yutingdai@buaa.edu.cn

计量

出版历程

Aerodynamic Calculation of Airfoil Dynamic Stall Based on Data-Driven Transition Model

计量

出版历程

目录

联系我们

友情链接

作者简介:
李金瑛(2000-)女,博士,主要研究基于机器学习的流固耦合分析。E-mail:cdwlljy@126.com

通讯作者:
戴玉婷(1985-)女,教授,主要研究飞行器设计、气动弹性、流固耦合等。E-mail:yutingdai@buaa.edu.cn