Artificial neural network framework for MHD micropolar nanofluid flow over stretching surfaces with thermal source

R. Manojkumar, S. Sridhar (), D. Prabu and S. Karthikeyan
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R. Manojkumar: Erode Arts and Science College
S. Sridhar: Chennai Institute of Technology
D. Prabu: Chennai Institute of Technology
S. Karthikeyan: Erode Arts and Science College

The European Physical Journal B: Condensed Matter and Complex Systems, 2025, vol. 98, issue 11, 1-26

Abstract: Abstract This study conducts a thorough examination of two-dimensional, incompressible magnetohydrodynamic (MHD) micropolar nanofluid flow over a stretching sheet, with particular attention to internal heat generation and convective boundary conditions. The primary objective is to establish an effective hybrid computational framework that integrates numerical methods with Artificial Neural Networks (ANN) to accurately analyze velocity, temperature, microrotation, and nanoparticle concentration fields within such intricate flow systems. The specific aims include investigating the influence of Brownian motion, thermophoresis, magnetic field strength, and viscoelastic parameters on fluid flow and heat/mass transfer characteristics, as well as assessing the predictive capability of ANN models. The study analyzes two-dimensional MHD micropolar nanofluid flow over a stretching sheet with heat generation and convective boundary conditions, incorporating Brownian motion and thermophoresis effects. Numerical (bvp4c) and ANN approaches reveal reliable predictions for velocity, temperature, and concentration, with applications in biomedical engineering, thermal management, and material processing. Graphical abstract

Date: 2025
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DOI: 10.1140/epjb/s10051-025-01087-x

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