Application of machine learning for forced plume in linearly stratified medium

TL;DR

This paper explores the use of machine learning to enhance RANS turbulence models for simulating forced plumes in stratified media, showing improved accuracy over traditional models.

Contribution

It introduces a TBNN-based K-epsilon model that improves turbulence prediction in stratified flows by modifying standard RANS coefficients with machine learning.

Findings

Significant improvement in maximum plume height prediction.

Enhanced accuracy of mean velocity profiles.

Better agreement with experimental data.

Abstract

Direct numerical simulation (DNS) is very accurate however, the computational cost increases significantly with the increase in Reynolds number. On the other hand, we have the Reynolds Averaged Navier Stokes (RANS) method for simulating turbulent flows, which needs less computational power. Turbulence models based on linear eddy viscosity models (LEVM) in the RANS method, which use a linear stress-strain rate relationship for modelling the Reynolds stress tensor do not perform well for complex flows \cite{shih1995new} . In this work, we intend to study the performance of non linear eddy viscosity model (NLEVM) hypothesis for turbulent forced plumes in a linearly stratified environment and modify the standard RANS model coefficients obtained from machine learning. The general eddy viscosity hypothesis supported by the closure coefficients generated from the tensor basis neural network (TBNN) is used to develop TBNN based K-$\epsilon$ model. The aforementioned model is used to evaluate the plume's mean velocity profile, and maximum height reached. The comparison between standard LEVM, NLEVM and the experimental results indicates a significant improvement in the maximum height achieved, and a good improvement in the mean velocity profile.