Predictions of Reynolds and Nusselt numbers in turbulent convection using machine-learning models
This work addresses the prediction of key parameters in turbulent convection for researchers in fluid dynamics, but it is incremental as it builds on existing models with minor improvements.
The paper tackled the problem of predicting Reynolds and Nusselt numbers in turbulent convection by developing multivariate regression and neural network models, finding that these machine learning models provided the best match with experimental and numerical results compared to earlier models.
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.