A machine learning approach to predicting the heat convection and thermodynamics of an external flow of hybrid nanofluid

Alizadeh, R., Abad, J. M. N., Fattahi, A., Mohebbi, M. R., Doranehgard, M. H., Li, L. K.B., Alhajri, E. and Karimi, N. (2021) A machine learning approach to predicting the heat convection and thermodynamics of an external flow of hybrid nanofluid. Journal of Energy Resources Technology, 143(7), 070902. (doi: 10.1115/1.4049454)

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Abstract

This study numerically investigates heat convection and entropy generation in a hybrid nanofluid (Al2O3-Cu-water) flowing around a cylinder embedded in porous media. An artificial-neural-network is used for predictive analysis, in which numerical data are generated to train an intelligence algorithm and to optimize the prediction errors. Results show that the heat transfer of the system increases when the Reynolds number, permeability parameter, or volume fraction of nanoparticles increases. However, the functional forms of these dependencies are complex. In particular, increasing the nanoparticle concentration is found to have a non-monotonic effect on entropy generation. The simulated and predicted data are subjected to particle swarm optimization to produce correlations for the shear stress and Nusselt number. This work demonstrates the capability of artificial intelligence algorithms in predicting the thermohydraulics and thermodynamics of thermal and solutal systems.

Item Type:Articles
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Karimi, Dr Nader
Authors: Alizadeh, R., Abad, J. M. N., Fattahi, A., Mohebbi, M. R., Doranehgard, M. H., Li, L. K.B., Alhajri, E., and Karimi, N.
College/School:College of Science and Engineering > School of Engineering
Journal Name:Journal of Energy Resources Technology
Publisher:American Society of Mechanical Engineers
ISSN:0195-0738
ISSN (Online):1528-8994
Published Online:23 December 2020
Copyright Holders:Copyright © 2020 by ASME
First Published:First published in Journal of Energy Resources Technology 143(7): 070902
Publisher Policy:Reproduced in accordance with the publisher copyright policy

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