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# Research on Random Forest Regression Algorithms for Predicting Reynolds Stress Anisotropy in Separation Flow around Near-Wall Cylinder

*Proceedings of 2024 International Conference on Machine Learning and Intelligent Computing*, PMLR 245:215-224, 2024.

#### Abstract

Standard RANS model is widely used in turbulence modelling and numerical simulation, due to its linear assumption of eddy viscosity, it is not suitable to accurately predict the Reynolds stress, especially in separated ﬂows with anisotropy of Reynolds stress. In this study, a machine learning model is proposed by applying the random forest regression algorithm to learn the deviation between eddyviscosity and high-ﬁdelity models for separation ﬂows around nearwall cylinder. The output features which represent magnitude, shape and directions of Reynolds stress are extracted by decomposing Reynolds stress tensor, while 8 types of input features are extracted from raw local ﬂow data sets to represent the main physical characteristics of the ﬂow ﬁeld. Both input and output features satisfy Galilean invariance, which contributes to improving prediction accuracy and generalization performance of the random forest regression model. The results show that the random forest regression model has a great potential to effectively predict the Reynolds stress anisotropy distribution of different Reynolds numbers.