Subgrid-scale Flux Modeling of a Passive Scalar in Turbulent Channel Flow using Artificial Neural Network

Document Type : Research Paper

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Faculty of Mechanical and Energy Engineering, Shahid Beheshti University, Tehran, Iran

Abstract

A deep neural network (DNN) has been developed to model the subgrid-scale (SGS) flux associated with a passive scalar in incompressible turbulent channel flow. To construct the training dataset for the DNN, a direct numerical simulation (DNS) was performed for a channel flow at the friction Reynolds number Re_τ=179 encompassing a passive scalar transport with Prandtl number Pr=0.71 using a pseudo-spectral in-house code. The DNS data of velocity and scalar fields was filtered to obtain the SGS scalar flux vector, q_i, filtered scalar gradient, and filtered strain-rate tensor, which were subsequently used to train the DNN, enabling it to predict q_i for large-eddy simulation. A priori evaluation of the DNN’s performance in predicting q_i revealed a close match with filtered DNS data, demonstrating correlations of up to 98%, 79% and 85% for the three components of q_i. Additionally, analysis of the mean SGS dissipation and its probability density function indicated promising predictions by the DNN. Notably, this study extends the applications of DNNs for predicting q_i to the case of turbulent channel flow.

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Iranian Journal of Mechanical Engineering Transactions of the ISME
Volume 24, Issue 2 - Serial Number 41
September 2023
Pages 157-165

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