Predictions of Reynolds and Nusselt numbers in turbulent convection using machine-learning models

TL;DR

This paper develops machine learning models to predict Reynolds and Nusselt numbers in turbulent convection, demonstrating superior accuracy over traditional models by closely matching experimental and numerical data.

Contribution

The paper introduces neural network and multivariate regression models for turbulent convection prediction, outperforming existing convection models in accuracy.

Findings

Machine learning models match experimental data better.

ML models outperform traditional convection models.

Predictions are close to numerical simulations.

Abstract

In this paper, we develop a multivariate regression model and a neural network model to predict the Reynolds number (Re) and Nusselt number in turbulent thermal convection. We compare their predictions with those of earlier models of convection: Grossmann-Lohse~[Phys. Rev. Lett. \textbf{86}, 3316 (2001)], revised Grossmann-Lohse~[Phys. Fluids \textbf{33}, 015113 (2021)], and Pandey-Verma [Phys. Rev. E \textbf{94}, 053106 (2016)] models. We observe that although the predictions of all the models are quite close to each other, the machine learning models developed in this work provide the best match with the experimental and numerical results.