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<p>The present paper examines the dynamics of heat transfer in a ferromagnetic hybrid nanofluid (ferromagnetic nanoparticle mixtures MnZnFe<sub>2</sub>O<sub>4</sub> and Fe<sub>3</sub>O<sub>4</sub> in the motor oil) that has been subjected to the forces of magnetic fields and thermal radiation, which is of great interest to study the literature lacked on such hybrid nanofluids. The partial differential equations (PDEs) that controlled the mathematical model were converted to ordinary ones (ODEs) and then its solution was found by applying the Spectral Relaxation Method (SRM) and Artificial neural Network (ANN) methods. The findings indicate that a rise in the magnetic parameter (M = 0.2) has the important avenue of slowing down the velocity profiles as a result of Lorentz forces and increase permeability (K = 0.2) has the significant advantage of increasing fluid motion. Thermal radiation (R = 2) and heat production (Q<sub>0</sub> = 0.2) raise temperature and the thickness of boundary layer greatly, and a large Prandtl number (Pr = 30) reduces thermal conductivity, which causes the fluid temperature to decrease greatly. The robustness of the results obtained using the ANN validation was confirmed by the minimal level of error at 9.89e−08. Its results suggest that it is possible to increase the thermal efficiency of such parameters, which are of concern to thermal engineering and car transportation industries.</p>

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SRM-ANN analyse on heat transfer exploration of a ferromagnetic hybrid nanofluid under the impact of magnetic forces and thermal emissions

  • 期刊论文

  • 发表日期：

    2025年9月10日

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  • P. Bindu,
  • G. Venkata Ramana Reddy,
  • Mohamed Kezzar,
  • Farhan Lafta Rashid,
  • K. Thanesh Kumar,
  • M. Nagapavani,
  • Mohamed Rafik Sari,
  • Abdellatif M. Sadeq,
  • Atef Chibani,
  • Ibrahim Mahariq

摘要

The present paper examines the dynamics of heat transfer in a ferromagnetic hybrid nanofluid (ferromagnetic nanoparticle mixtures MnZnFe2O4 and Fe3O4 in the motor oil) that has been subjected to the forces of magnetic fields and thermal radiation, which is of great interest to study the literature lacked on such hybrid nanofluids. The partial differential equations (PDEs) that controlled the mathematical model were converted to ordinary ones (ODEs) and then its solution was found by applying the Spectral Relaxation Method (SRM) and Artificial neural Network (ANN) methods. The findings indicate that a rise in the magnetic parameter (M = 0.2) has the important avenue of slowing down the velocity profiles as a result of Lorentz forces and increase permeability (K = 0.2) has the significant advantage of increasing fluid motion. Thermal radiation (R = 2) and heat production (Q0 = 0.2) raise temperature and the thickness of boundary layer greatly, and a large Prandtl number (Pr = 30) reduces thermal conductivity, which causes the fluid temperature to decrease greatly. The robustness of the results obtained using the ANN validation was confirmed by the minimal level of error at 9.89e−08. Its results suggest that it is possible to increase the thermal efficiency of such parameters, which are of concern to thermal engineering and car transportation industries.