Optimising a Machine Learning Model for Reynolds Averaged Turbulence Modelling of Internal Flows

Anthony Man, Mohammad Jadidi, Amir Keshmiri, Hujun Yin, Yasser Mahmoudi Larimi

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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Abstract

Various machine learning models in recent years have demonstrated capability in improving the accuracy of Reynoldsaveraged Navier-Stokes (RANS) simulations. One such exampleis the tensor-basis neural network (TBNN), which has been shown to introduce improvements that would be challenging or impossible for RANS approaches to model, such as anisotropic normal Reynolds stresses. This paper reports on a hyperparameter tuning exercise, to find optimal parameter settings for TBNNs predicting Reynolds stress anisotropy in channel flow, and these are also used in TBNNs deployed on internal flow over a forward-backward facing step. The accuracy of ensemble TBNN predictions is investigated, and it is shown that further improvements to Reynolds stress anisotropy predictions can be made when an ensemble size of 25 or more is used.
Original languageEnglish
Title of host publication16TH INTERNATIONAL CONFERENCE ON HEAT TRANSFER, FLUID MECHANICS AND THERMODYNAMICS AND EDITORIAL BOARD OF APPLIED THERMAL ENGINEERING
Number of pages6
Publication statusAccepted/In press - 13 May 2022

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