Skip to main navigation Skip to search Skip to main content

FEM-ANN-based thermosolutal and entropy analysis of MHD hybrid nanofluid flow in complex wavy geometry

  • Air University, Islamabad
  • Prince Sattam Bin Abdulaziz University
  • Coll Sci & Humanities Alkharj

Research output: Contribution to journal › Review article › peer-review

Abstract

This study numerically investigates thermosolutal magnetoconvection and entropy generation in a sinusoidal plus-shaped enclosure containing a centrally embedded wavy cold cylinder filled with a water-based hybrid nanofluid and subjected to a uniform horizontal magnetic field. The finite element method (FEM) implemented in COMSOL Multiphysics is employed to solve the steady, laminar, incompressible governing equations under the Boussinesq approximation, and the numerical model is validated against benchmark solutions. The effects of key dimensionless parameters, including the Rayleigh number, Hartmann number, hybrid nanoparticle volume fraction, Lewis number, buoyancy ratio, and cylinder position, on flow structure, heat and mass transfer, and thermodynamic irreversibility are systematically examined. The analysis of results indicates that there is a notable improvement in convection with the increment of the Rayleigh number, which is manifested in a 78.8% boost of the average Nusselt number and a 213% increase in the Sherwood number. Conversely, using greater magnetic fields significantly increases entropy formation while reducing fluid velocity and heat transmission by 29.6%. Stronger gradients in the presence of hybrid nanoparticles have the dual impact of enhancing thermal performance and raising entropy generation. When Rayleigh and Hartmann numbers rise, entropy analysis shows that thermal irreversibility gives way to viscous irreversibility. Apart from the numerical simulations, an artificial neural network (ANN) model was developed to estimate the average Nusselt number using the Rayleigh number, Hartmann number, and nanoparticle concentration as inputs. As a result, there is almost perfect correlation and an extremely low mean squared error (3.82 & times; 10(-)1). The findings not only provide important physical insights but also make it possible to more precisely predict the design of energy-efficient thermal systems, such as electronics cooling devices, energy storage units, and chemical reactors where the effects of heat, solute, and magnetism are coupled.
Original languageEnglish
Pages (from-to)5991-6010
Number of pages20
JournalJournal of Thermal Analysis and Calorimetry
Volume151
Issue number7
Early online dateMar 2026
DOIs
StatePublished - Apr 2026

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 7 - Affordable and Clean Energy
    SDG 7 Affordable and Clean Energy

Keywords

  • Artificial neural network (ANN)
  • Entropy production
  • Finite element method (FEM)
  • Heat and mass transport
  • Hybrid nanofluid
  • Magneto-thermosolutal convection
  • Wavy plus-shaped cavity

Fingerprint

Dive into the research topics of 'FEM-ANN-based thermosolutal and entropy analysis of MHD hybrid nanofluid flow in complex wavy geometry'. Together they form a unique fingerprint.

Cite this