A machine learning approach to the prediction of transport and thermodynamic processes in multiphysics systems - heat transfer in a hybrid nanofluid flow in porous media

Alizadeh, R., Abad, J. M. N., Ameri, A., Mohebbi, M. R., Mehdizadeh, A., Zhao, D. and Karimi, N. (2021) A machine learning approach to the prediction of transport and thermodynamic processes in multiphysics systems - heat transfer in a hybrid nanofluid flow in porous media. Journal of the Taiwan Institute of Chemical Engineers, 124, pp. 290-306. (doi: 10.1016/j.jtice.2021.03.043)

Abstract

Comprehensive analyses of transport phenomena and thermodynamics of complex multiphysics systems are laborious and computationally intensive. Yet, such analyses are often required during the design of thermal and process equipment. As a remedy, this paper puts forward a novel approach to the prediction of transport behaviours of multiphysics systems, offering significant reductions in the computational time and cost. This is based on machine learning techniques that utilize the data generated by computational fluid dynamics for training purposes. The physical system under investigation includes a stagnation-point flow of a hybrid nanofluid (Cu−Al2O3/Water) over a blunt object embedded in porous media. The problem further involves mixed convection, entropy generation, local thermal non-equilibrium and non-linear thermal radiation within the porous medium. The SVR (Support Machine Vector) model is employed to approximate velocity, temperature, Nusselt number and shear-stress as well as entropy generation and Bejan number functions. Further, PSO meta-heuristic algorithm is applied to propose correlations for Nusselt number and shear stress. The effects of Nusselt number, temperature fields and shear stress on the surface of the blunt-body as well as thermal and frictional entropy generation are analysed over a wide range of parameters. Further, it is shown that the generated correlations allow a quantitative evaluation of the contribution of a large number of variables to Nusselt number and shear stress. This makes the combined computational and artificial intelligence (AI) approach most suitable for design purposes.

• Item Type: Articles
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Karimi, Dr Nader
• Authors: Alizadeh, R., Abad, J. M. N., Ameri, A., Mohebbi, M. R., Mehdizadeh, A., Zhao, D., and Karimi, N.
• College/School: College of Science and Engineering > School of Engineering
• Journal Name: Journal of the Taiwan Institute of Chemical Engineers
• Publisher: Elsevier
• ISSN: 1876-1070
• ISSN (Online): 1876-1089
• Published Online: 21 April 2021
• Copyright Holders: Copyright © 2021 Taiwan Institute of Chemical Engineers
• First Published: First published in Journal of the Taiwan Institute of Chemical Engineers 124: 290-306
• Publisher Policy: Reproduced in accordance with the publisher copyright policy