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

Deng Kewei, Sun Jianhong
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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v245-kewei24a, title = {Research on Random Forest Regression Algorithms for Predicting Reynolds Stress Anisotropy in Separation Flow around Near-Wall Cylinder}, author = {Kewei, Deng and Jianhong, Sun}, booktitle = {Proceedings of 2024 International Conference on Machine Learning and Intelligent Computing}, pages = {215--224}, year = {2024}, editor = {Nianyin, Zeng and Pachori, Ram Bilas}, volume = {245}, series = {Proceedings of Machine Learning Research}, month = {26--28 Apr}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v245/main/assets/kewei24a/kewei24a.pdf}, url = {https://proceedings.mlr.press/v245/kewei24a.html}, 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.} }
Endnote
%0 Conference Paper %T Research on Random Forest Regression Algorithms for Predicting Reynolds Stress Anisotropy in Separation Flow around Near-Wall Cylinder %A Deng Kewei %A Sun Jianhong %B Proceedings of 2024 International Conference on Machine Learning and Intelligent Computing %C Proceedings of Machine Learning Research %D 2024 %E Zeng Nianyin %E Ram Bilas Pachori %F pmlr-v245-kewei24a %I PMLR %P 215--224 %U https://proceedings.mlr.press/v245/kewei24a.html %V 245 %X 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.
APA
Kewei, D. & Jianhong, S.. (2024). 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, in Proceedings of Machine Learning Research 245:215-224 Available from https://proceedings.mlr.press/v245/kewei24a.html.

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