Estimating RANS model uncertainty using machine learning
Estimating RANS model uncertainty using machine learning
复制标题
使用机器学习估计 RANS 模型不确定性
DOI：
10.33737/jgpps/134643
复制
发表时间：
2021
影响因子：
0.9
通讯作者：
G. Iaccarino
中科院分区：
文献类型：
--
作者：
J. Heyse;A. Mishra;G. Iaccarino
在这项工作中，我们提出了一种机器学习策略，用于估计用于预测湍流分离流的湍流模型引入的不确定性。该方法是基于引入雷诺应力各向异性的特征值摄动；扰动的数量是由一个随机森林算法训练的高保真模拟波浪壁的流动预测。该方法应用于非对称扩散器中的流动，并演示了该方法如何正确识别建模误差发生的区域，并与实验观察结果相比较，准确地量化误差的数量。
In this work we present a machine-learning strategy developed to estimate the uncertainty introduced by a turbulence model for the prediction of a turbulent separated flows. The approach is based on the introduction of eigenvalue perturbations of the Reynolds stress anisotropy; the amount of perturbation is predicted by a random forest algorithm trained on high-fidelity simulations of the flow over a wavy wall. The proposed method is applied to the flow in an asymmetric diffuser and demonstrates how the approach correctly identifies the regions in which modeling errors occur and accurately quantifies the amount of errors when compared to experimental observations.