论文标题
基于人工神经网络的湍流建模的特征选择和处理
Feature selection and processing of turbulence modeling based on an artificial neural network
论文作者
论文摘要
数据驱动的湍流建模已被认为是提高雷诺平均Navier-Stokes方程的预测准确性的有效方法。相关研究旨在通过通过机器学习方法（例如人工神经网络）从高保真数据中获取从高保真数据的特定模式来解决传统湍流建模的差异。本研究的重点是从特征选择和处理方面的不平滑性和预测错误问题。总结了输入特征的选择标准，并构建了有效的输入集。研究了计算网格对光滑度的影响。提出了一种用于雷诺应力的空间取向特征的修改特征分解方法。然后将改进的机器学习框架应用于具有尤为不同的几何形状的周期性山丘数据库。修改方法的结果显示出预测准确性和光滑度的显着增强，包括分离区域的形状和大小以及壁上的摩擦和压力分布，这证实了该方法的有效性。
Data-driven turbulence modeling has been considered an effective method for improving the prediction accuracy of Reynolds-averaged Navier-Stokes equations. Related studies aimed to solve the discrepancy of traditional turbulence modeling by acquiring specific patterns from high-fidelity data through machine learning methods, such as artificial neural networks. The present study focuses on the unsmoothness and prediction error problems from the aspect of feature selection and processing. The selection criteria for the input features are summarized, and an effective input set is constructed. The effect of the computation grid on the smoothness is studied. A modified feature decomposition method for the spatial orientation feature of the Reynolds stress is proposed. The improved machine learning framework is then applied to the periodic hill database with notably varying geometries. The results of the modified method show significant enhancement in the prediction accuracy and smoothness, including the shape and size of separation areas and the friction and pressure distributions on the wall, which confirms the validity of the approach.
