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<p>In this work, the flow of magnetohydrodynamic (MHD) tri-hybrid nanofluid (THNF) over rotating disk and cone assembly is studied under the effect of thermal radiation, viscous dissipation and convective boundaries. The leading equations are simplified by using the von Kármán similarity variables and then evaluated by bvp4c approach. The system sensitivity is investigated by the use of a dual-layer optimization framework, which consists of response surface methodology (RSM) and an artificial neural network (ANN). Advanced three-dimensional colored streamlines, interaction surfaces and 2D iso-sensitivity contours show the local behavior of the Nusselt number, skin friction, entropy generation and Bejan number. The results show that the thermal conductivity of THNF is enhanced and their heat transfer enhancement is higher than that of mono- and binary nanofluids. The iso-sensitivity contours indicate that the best thermal efficiency is obtained at high values of Reynolds number and radiation factor and at a minimum value of magnetic intensities. Moreover, the trained artificial neural network (Levenberg–Marquardt) exhibits highly accurate predictive capability compared with the response surface models, with Mean Squared Errors as low as 10<sup>−10</sup> and a correlation coefficient of 1. The skin friction enhances up to 30.5451% at the disk surface, whereas 31.8698% at the cone surface by varying the THNF nanoparticles. The energy transfer rate remarkably enhances up to 53.35 and 52.883% with rising effect of rotation parameter from 0.1 to 0.3 at both disk and cone. The findings support that the THNF offers a substantial enhancement in optimizing the heat transfer performance and can be promising in thermal management applications.</p>

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Artificial neural network simulation and RSM-based sensitivity analysis of trihybrid nanofluid flow over a rotating disk and cone: modeling and prediction of MHD and thermal radiation effects

  • 期刊论文

  • 发表日期：

    2026年7月28日

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  • Amjid Rashid,
  • Ebrahem A. Algehyne,
  • Fahad Maqbul Alamrani,
  • Laila A. AL-Essa,
  • Anwar Saeed,
  • Gabriella Bognár

摘要

In this work, the flow of magnetohydrodynamic (MHD) tri-hybrid nanofluid (THNF) over rotating disk and cone assembly is studied under the effect of thermal radiation, viscous dissipation and convective boundaries. The leading equations are simplified by using the von Kármán similarity variables and then evaluated by bvp4c approach. The system sensitivity is investigated by the use of a dual-layer optimization framework, which consists of response surface methodology (RSM) and an artificial neural network (ANN). Advanced three-dimensional colored streamlines, interaction surfaces and 2D iso-sensitivity contours show the local behavior of the Nusselt number, skin friction, entropy generation and Bejan number. The results show that the thermal conductivity of THNF is enhanced and their heat transfer enhancement is higher than that of mono- and binary nanofluids. The iso-sensitivity contours indicate that the best thermal efficiency is obtained at high values of Reynolds number and radiation factor and at a minimum value of magnetic intensities. Moreover, the trained artificial neural network (Levenberg–Marquardt) exhibits highly accurate predictive capability compared with the response surface models, with Mean Squared Errors as low as 10−10 and a correlation coefficient of 1. The skin friction enhances up to 30.5451% at the disk surface, whereas 31.8698% at the cone surface by varying the THNF nanoparticles. The energy transfer rate remarkably enhances up to 53.35 and 52.883% with rising effect of rotation parameter from 0.1 to 0.3 at both disk and cone. The findings support that the THNF offers a substantial enhancement in optimizing the heat transfer performance and can be promising in thermal management applications.