Nanotechnology‐Driven Analysis of MHD Maxwell‐Casson Nanofluid Flow With Activation Energy Using Artificial Neural Networks

ABSTRACT The Maxwell‐Casson nanofluid (NF) flow past a porous inclined plate has attracted much attention, since it has extensive potential applications in thermal energy systems, chemical processes, biomedical transport, and advanced cooling technology. This paper aims to simulate the flow and heat and mass transfer (HMT) characteristics of Maxwell‐Casson NF past an inclined porous plate under the influence of a magnetic field, Darcy‐Forchheimer porous resistance, thermal radiation, and a chemical reaction with activation energy. A Casson parameter is included in the mathematical modeling for the yield stress nature of the fluid, and Maxwell relaxation time to describe the viscoelasticity of the fluid. The momentum equation contains Darcy‐Forchheimer drag, Lorentz forces, and thermal and solutal buoyancy factors; an energy equation involving viscous dissipation, thermal radiation, and internal heat generation is utilized; a temperature‐dependent chemical reaction with Arrhenius activation energy is incorporated in the mass transfer equation. The problem is described by nonlinear boundary layer governing equations, which are solved numerically through the help of the MATLAB bvp4c method, and a feedforward artificial neural network (ANN) is developed to predict the velocity, temperature, and concentration profiles. Our results demonstrate that the magnetic field strength and porous resistance suppress the motion of fluid, while buoyancy effects tend to enhance velocity distribution. Thermal radiation, viscous dissipation, and activation energy have a significant effect on the thermal and concentration distributions. The predicted results using ANN are found to be in excellent agreement with numerical outcomes; this confirms the validity of the proposed data‐driven strategy. From this, an understanding of the interaction of Casson yield‐stress, Maxwell relaxation time, nanoparticles, magnetic field, porous resistance, and activation energy has been assessed for complex non‐Newtonian NF flow.

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Journal
Engineering Reports
Published
2026-09-30
DOI
https://doi.org/10.1002/eng2.71064
Primary Topic
Nanofluid Flow and Heat Transfer
Type
article
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Nanotechnology‐Driven Analysis of MHD Maxwell‐Casson Nanofluid Flow With Activation Energy Using Artificial Neural Networks

Shajar Abbas, Emad Ahmad Az-Zo’bi, D. Prabu, Ruzimurod Abiyev et al.
Engineering Reports
Nanofluid Flow and Heat Transfer
article

Nanotechnology‐Driven Analysis of MHD Maxwell‐Casson Nanofluid Flow With Activation Energy Using Artificial Neural Networks

Shajar Abbas, Emad Ahmad Az-Zo’bi, D. Prabu, Ruzimurod Abiyev, Abdul Raqib Muslimyar, Razaullah Hafiz Ullah
article en

Abstract

ABSTRACT The Maxwell‐Casson nanofluid (NF) flow past a porous inclined plate has attracted much attention, since it has extensive potential applications in thermal energy systems, chemical processes, biomedical transport, and advanced cooling technology. This paper aims to simulate the flow and heat and mass transfer (HMT) characteristics of Maxwell‐Casson NF past an inclined porous plate under the influence of a magnetic field, Darcy‐Forchheimer porous resistance, thermal radiation, and a chemical reaction with activation energy. A Casson parameter is included in the mathematical modeling for the yield stress nature of the fluid, and Maxwell relaxation time to describe the viscoelasticity of the fluid. The momentum equation contains Darcy‐Forchheimer drag, Lorentz forces, and thermal and solutal buoyancy factors; an energy equation involving viscous dissipation, thermal radiation, and internal heat generation is utilized; a temperature‐dependent chemical reaction with Arrhenius activation energy is incorporated in the mass transfer equation. The problem is described by nonlinear boundary layer governing equations, which are solved numerically through the help of the MATLAB bvp4c method, and a feedforward artificial neural network (ANN) is developed to predict the velocity, temperature, and concentration profiles. Our results demonstrate that the magnetic field strength and porous resistance suppress the motion of fluid, while buoyancy effects tend to enhance velocity distribution. Thermal radiation, viscous dissipation, and activation energy have a significant effect on the thermal and concentration distributions. The predicted results using ANN are found to be in excellent agreement with numerical outcomes; this confirms the validity of the proposed data‐driven strategy. From this, an understanding of the interaction of Casson yield‐stress, Maxwell relaxation time, nanoparticles, magnetic field, porous resistance, and activation energy has been assessed for complex non‐Newtonian NF flow.

Engineering ReportsVol. 8(10)
Kabul University (AF), Mutah University (JO), Kabul Education University (AF), Biruni University (TR), National Pedagogical University of Uzbekistan (UZ), University of Business and Technology (SA)
Affordable and clean energy
Openalex Percentile: Top 22%
Nanofluid Flow and Heat Transfer
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