Application of Machine Learning for Forced Plume in Linearly Stratified Medium

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 modeling the Reynolds stress tensor, do not perform well for complex flows (Shih et al. in Comput Methods Appl Mech Eng 125:287–302, 1995). In this work, we intend to study the performance of nonlinear 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-ϵ 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.