ANN-Assisted Numerical Study on Buoyant Heat Transfer of Hybrid Nanofluid in an Annular Chamber with Magnetic Field Inclination and Thermal Source–Sink Effects

Mani Sankar, Maimouna S. Al Manthari, Praveen Kumar Poonia () and Suresh Rasappan ()
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Mani Sankar: College of Computing and Information Sciences, University of Technology and Applied Sciences, Ibri 516, Oman
Maimouna S. Al Manthari: College of Computing and Information Sciences, University of Technology and Applied Sciences, Ibri 516, Oman
Praveen Kumar Poonia: College of Computing and Information Sciences, University of Technology and Applied Sciences, Ibri 516, Oman
Suresh Rasappan: College of Computing and Information Sciences, University of Technology and Applied Sciences, Ibri 516, Oman

Energies, 2025, vol. 18, issue 17, 1-32

Abstract: A significant challenge in thermal device designs across diverse industries is optimizing heat dissipation rates to enhance system performance. Among different geometric configurations, a partially heated–cooled annular system containing magneto-nanofluids presents unique complexities due to the curvature ratio and strategic positioning of thermal sources–sinks, which substantially influences flow dynamics and thermal transfer mechanisms. The present investigation examines the buoyancy-driven heat transfer in an annular cavity containing a hybrid nanofluid under the influence of an inclined magnetic field and thermal source–sink pairs. Five different thermal source–sink arrangements and a wide range of magnetic field orientations are considered. The governing equations are solved using a finite difference approach that combines the Alternating Direction Implicit (ADI) method with relaxation techniques to capture the flow and thermal characteristics. An artificial neural network (ANN) is trained using simulation data to estimate the average Nusselt number for a range of physical conditions. Among different source–sink arrangements, the Case-1 arrangement is found to produce a stronger flow circulation and thermal dissipation rates. Also, an oblique magnetic field offers greater control compared with vertical or horizontal magnetic orientations. The network, structured with multiple hidden layers and optimized using a conjugate gradient algorithm, produces predictions that closely match the numerical results. Our analysis reveals that Case-1 demonstrates superior thermal performance, with approximately 19% greater heat dissipation compared with other chosen heating configurations. In addition, the Case-1 heating configuration combined with blade-shaped nanoparticles yields more than 27% superior thermal performance among the considered configurations. The outcomes suggest that at stronger magnetic fields ( H a = 50 ) , the orientation angle becomes critically important, with perpendicular magnetic fields ( γ = 90 ∘ ) significantly outperforming other orientations.

Keywords: hybrid nanofluid; magnetic field; annular geometry; source–sink; ANN model (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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