神经网络增强SED-SL建模应用于翼型绕流湍流计算

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中文摘要：本文采用SED-SL-RBF的新型建模方法，利用有限NACA机翼的空气动力学数据增强了 SED-SL(壁湍流的结构系综动力学-应力长)模型，构建了机翼上湍流边界层的多层结构(MLS)，并利用机器学习从实验数据中重建模型参数。该方法应用于九种不同类型的NACA机翼上的湍流，具有广泛的雷诺数和攻角范围。研究采用RBF(径向基函数)神经网络重建模型参数(l∞0和y∞buf)，并将其应用于SED-SL的CFD数值计算。相较Menter SST湍流模型，SED-SL-RBF模型提升了在同样几何形状和流动条件下升力和阻力系数的预测精度。预测升力系数CL的精确度超过了95％，而预测阻力系数CD的误差则小于6 count。神经网络增强的SED-SL模型对压力场的预测精度也非常高。NACA 2421的MLS参数表现出不随攻角变化的相似性，并可视其为由雷诺数刻画的函数。该结果表明，NACA 2421的MLS参数与失速前的攻角大小无关。该相似行为为模拟各种物理条件下的机翼流动提供了一种可行的方案。未来期望整合数据以揭示模型参数方面的模型内在差异，从而将SED-SL-RBF模型的适用性扩展到更广泛的流动场景。

外文标题：Neural network-augmented SED-SL modeling of turbulent flows over airfoils

外文摘要：A novel modeling paradigm,named as SED-SL-RBF,enhances the structural ensemble dynamics-stress length(SED-SL)model of wall-bounded turbulence using limited aerodynamic data from NACA airfoils.It constructs a multi-layer structure(MLS)of the turbulent boundary layer(BL)over the airfoils and uses machine learning to reconstruct model parameters from experimental data.This approach has been applied to turbulent flows over nine distinct NACA airfoil types,with a broad spectrum of Reynolds numbers and angles of attack.The model parameters,l∞0 and y∞buf,are reconstructed using a radial basis function(RBF)neural network and applied to an SED-SL computational fluid dynamics(CFD)solver.This results in improved predictions of lift and drag coefficients for geometries and flow conditions previously calculated using the Menter shear stress transport(SST)turbu-lence model.The accuracy of the predictive lift coefficient CL exceeded 95％,while the error in the predictive drag coefficient CD was less than 6 counts.The neural network-augmented SED-SL model also demonstrated exceptional predictive accuracy for the pressure field.The MLS parameters for NACA 2421 exhibit similarities with angle of attack(AOA),which can be treated as functions of the Reynolds number.These findings suggested that the MLS parameters for NACA 2421 are independent of the AOA prior to stall.This similarity behavior provides a promising approach to model airfoil flows under various physical conditions.The broader vision is to integrate data to reveal innate model discrepancies in terms of model parameters,thereby extending the applicability of the SED-SL-RBF model to a wider range of flow scenarios.

外文关键词：

Structural ensemble dynamicsRANS modelTurbulent boundary layerMachine learningNeural network

作者：

黄文霄、刘溢浪、毕卫涛、高毅卓、陈军

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作者单位：

State Key Laboratory for Turbulence and Complex Systems,College of Engineering,Peking University,Beijing 100871,China

School of Aeronautics,Northwestern Polytechnical University,Xi'an 710072,China

School of Aeronautic Science and Engineering,Beihang University Beijing 100191,China

关键词：

Structural ensemble dynamics RANS model Turbulent boundary layer Machine learning Neural network

基金：

National Natural Science Foundation of China

项目编号：

91952201

出版年：

2024

DOI：

10.1007/s10409-023-23517-x

力学学报(英文版)

CSTPCD

影响因子：0.363

ISSN：0567-7718

年,卷(期)：2024.40(3)