On stable wrapper-based parameter selection method for efficient ANN-based data-driven modeling of turbulent flows

TL;DR

This paper develops a stable, gradient-based wrapper method for ANN parameter selection to improve model efficiency and consistency in turbulent flow modeling, outperforming existing methods in parameter reduction and prediction accuracy.

Contribution

A novel gradient-based wrapper method for ANN parameter selection that enhances stability, reduces parameters effectively, and improves prediction in turbulent flow models.

Findings

Gradient-based subset selection improves consistency-over-trials.

Parameter reduction enhances model prediction accuracy.

Reduced models train faster with minimal loss of accuracy.

Abstract

To model complex turbulent flow and heat transfer phenomena, this study aims to analyze and develop a reduced modeling approach based on artificial neural network (ANN) and wrapper methods. This approach has an advantage over other methods such as the correlation-based filter method in terms of removing redundant or irrelevant parameters even under non-linearity among them. As a downside, the overfitting and randomness of ANN training may produce inconsistent subsets over selection trials especially in a higher physical dimension. This study analyzes a few existing ANN-based wrapper methods and develops a revised one based on the gradient-based subset selection indices to minimize the loss in the total derivative or the directional consistency at each elimination step. To examine parameter reduction performance and consistency-over-trials, we apply these methods to a manufactured subset selection problem, modeling of the bubble size in a turbulent bubbly flow, and modeling of the spatially varying turbulent Prandtl number in a duct flow. It is found that the gradient-based subset selection to minimize the total derivative loss results in improved consistency-over-trials compared to the other ANN-based wrapper methods, while removing unnecessary parameters successfully. For the reduced turbulent Prandtl number model, the gradient-based subset selection improves the prediction in the validation case over the other methods. Also, the reduced parameter subsets show a slight increase in the training speed compared to the others.