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article · International Journal of Numerical Methods for Heat &amp Fluid Flow

Effect of both cavity and magnetic field inclinations on natural convection flow through a porous medium under variable thermal conditions

Abstract

Purpose The present paper aims to analyze the problem of natural convection in an inclined porous chamber filled by a porous media saturated by a Newtonian fluid under the influence of an oblique magnetic field and with a sinusoidal temperature distribution on a side wall. Design/methodology/approach The problem is presented for different cases of the inclinations of the magnetic field and of the cavity from the horizontal. Dimensionless equations, together with the boundary conditions, are obtained for the given model and the problem is solved by finite difference method of second order accuracy. Findings The numerical results of streamlines, isotherms and entropy generation are investigated considering the main parameters like inclination angle of the magnetic field, inclination of the chamber, Hartman number, Rayleigh number, Darcy number. The combined effects of the two angles, namely, that of the cavity and that of the magnetic field are studied for the first time in special literature and a diagram showing the average Nusselt number as a function of the two angles, is analyzed. The results obtained for the case of vertical cavity without magnetic field are compared and successfully validated with previous reported results of the literature. Originality/value The mathematical model which includes both, the inclined magnetic field and the inclined chamber with considered geometrical cases, is original. The novelty is also given by the results focused on entropy generation minimization for the newly considered conditions. The energy performance coefficient was measured and the torque-angle combination that achieved the best thermal performance of the system was determined.

Research topics

  • Nanofluid Flow and Heat Transfer
  • Heat and Mass Transfer in Porous Media
  • Heat Transfer and Optimization

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DOI: 10.1108/hff-07-2025-0518

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