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<p>In recent scenarios, potential applications of hybrid nanofluid have presented their wide range of utilization in enhancing heat transfer efficiency significantly. Particularly, in cooling technologies, cooling of electronic devices, thermal management, and energy systems, the utility of hybrid nanofluid is vital. The current investigation aims to present a neuro-computing simulation to predict the heat transfer rate considering hybrid nanofluid flow across a shrinking surface. The hybrid nanofluid is composed of molybdenum disulfide and silicon oxide in ethylene glycol base fluid. Further, the topic blended with a novel approach for incorporating the combined effects of nanofluid viscosity assuming the Gharesim model and thermal conductivity assumed by the Hamilton–Crosser model. The suitable choice of similarity variables transforms the flow governing equations into a non-dimensional form. Further, the Runge—Kutta numerical scheme is utilized to solve the transformed flow equations. Moreover, artificial neural network, a machine learning approach, is employed to simulate and predict the heat transfer characteristics and the results are used to train and validate it. The impact of key factors, i.e., thermal radiation, Eckert number, and particle volume fraction, on the heat transfer rate is elaborated briefly with a significant validation with earlier studies. Further, the important findings are deployed as: the radiating heat flux gives rise to the impact of thermal radiation and the Eckert number combined with particle concentration favors in enhancing the heat transport phenomenon. Moreover, the regression model and the results of mean squared error (MSE) portray that the designed heat transfer model is significant.</p>

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Neuro-computing simulation for heat transfer rate on radiative hybrid nanofluid (MoS2 + SiO2–EG) through a shrinking surface using Gharesim viscosity model

  • 期刊论文

  • 发表日期：

    2025年3月4日

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  • Thirupathi Thumma,
  • R. Archana Reddy,
  • S. R. Mishra,
  • Devarsu Radha Pyari

摘要

In recent scenarios, potential applications of hybrid nanofluid have presented their wide range of utilization in enhancing heat transfer efficiency significantly. Particularly, in cooling technologies, cooling of electronic devices, thermal management, and energy systems, the utility of hybrid nanofluid is vital. The current investigation aims to present a neuro-computing simulation to predict the heat transfer rate considering hybrid nanofluid flow across a shrinking surface. The hybrid nanofluid is composed of molybdenum disulfide and silicon oxide in ethylene glycol base fluid. Further, the topic blended with a novel approach for incorporating the combined effects of nanofluid viscosity assuming the Gharesim model and thermal conductivity assumed by the Hamilton–Crosser model. The suitable choice of similarity variables transforms the flow governing equations into a non-dimensional form. Further, the Runge—Kutta numerical scheme is utilized to solve the transformed flow equations. Moreover, artificial neural network, a machine learning approach, is employed to simulate and predict the heat transfer characteristics and the results are used to train and validate it. The impact of key factors, i.e., thermal radiation, Eckert number, and particle volume fraction, on the heat transfer rate is elaborated briefly with a significant validation with earlier studies. Further, the important findings are deployed as: the radiating heat flux gives rise to the impact of thermal radiation and the Eckert number combined with particle concentration favors in enhancing the heat transport phenomenon. Moreover, the regression model and the results of mean squared error (MSE) portray that the designed heat transfer model is significant.