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

Shashwat Bhattacharya, Mahendra K. Verma, Arnab Bhattacharya

Research output: Contribution to journalArticlepeer-review

12 Scopus citations


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. 86, 3316 (2001)], revised Grossmann-Lohse [Phys. Fluids 33, 015113 (2021)], and Pandey-Verma [Phys. Rev. E 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.
Original languageEnglish (US)
Pages (from-to)025102
JournalPhysics of Fluids
Issue number2
StatePublished - Feb 2022
Externally publishedYes

Bibliographical note

KAUST Repository Item: Exported on 2022-05-18
Acknowledgements: We thank J. Schumacher, K. R. Sreenivasan, A. Pandey, M. Sharma, R. Samuel, and S. Alam for useful discussions. The present work was mostly conducted at the Indian Institute of Technology Kanpur, India, and was funded by the Research Grant No. SPO/STC/PHY/2018037 from the Indian Space Research Organization, India. The simulations of convection were performed on Shaheen II of the King Abdullah University of Science and Technology, Saudi Arabia, under the Project No. k1416. Shashwat Bhattacharya is currently funded by a postdoctoral fellowship of Alexander von Humboldt Foundation (Germany).
This publication acknowledges KAUST support, but has no KAUST affiliated authors.

ASJC Scopus subject areas

  • Condensed Matter Physics


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