摘要
雷诺平均Navier-Stokes(RANS)方程由于计算成本较低,当前仍然在工程设计领域广泛应用。为了进一步提升计算精度和减少时间,应用深度神经网络(deep neural networks,DNN)方法自适应辨识稳态湍流涡黏性系数。以隔离段的激波串前缘位置检测流场生成为例,使用Wilcox-2006k-ω湍流模型进行模拟。在不同的背压情况下,产生稳定状态的湍流涡黏性流场作为模型学习的训练数据集。最后在不同背压条件下开展了测试。结果表明:提出的DNN方法能够快速预测湍流涡黏性系数的值,预测值与计算流体力学数值模拟计算的参数值相比,方均根误差较小,可决系数大于99%,预测的流场结果与真实流场基本一致,进一步验证了深度学习技术在湍流模型参数辨识的可行性。
The Reynolds-averaged Navier-Stokes(RANS)equation is still widely used in engineering design due to its low computational cost.In order to further improve the calculation accuracy and reduce the time,a deep neural network(deep neural networks,DNN)method was applied to adaptively identify the steady-state turbulent eddy viscosity coefficient.Taking the detection flow field generated at the front edge of the shock train in the isolation section as an example,the Wilcox-2006 k-ωturbulence model was used for simulation.A steady-state turbulent eddy-viscous flow field was generated as a training dataset for model learning under different back pressure conditions.Finally,tests were carried out under different back pressure conditions.The results showed that the proposed DNN method can quickly predict the value of the turbulent eddy viscosity coefficient.The coefficient of determination was greater than 99%,and the predicted flow field results were basically consistent with the real flow field,which further verified the feasibility of deep learning technology in turbulence model parameter identification.
作者
杨茂桃
梁爽
易淼荣
田野
郭明明
乐嘉陵
张华
YANG Maotao;LIANG Shuang;YI Miaorong;TIAN Ye;GUO Mingming;LE Jialing;ZHANG Hua(School of Information Engineering,Southwest University of Science and Technology,Mianyang Sichuan 621000,China;Institute of Aerospace Technology,China Aerodynamic Research and Development Center,Mianyang Sichuan 621000,China)
出处
《航空动力学报》
EI
CAS
CSCD
北大核心
2023年第2期312-324,共13页
Journal of Aerospace Power
基金
国家自然科学基金(12002362)。
关键词
湍流模型
深度神经网络
涡黏性系数
参数辨识
流场预测
turbulence model
deep neural network
eddy viscosity coefficient
parameter identification
flow field prediction
