Gradient Descent-Based Prediction of Heat-Transmission Rate of Engine Oil-Based Hybrid Nanofluid over Trapezoidal and Rectangular Fins for Sustainable Energy Systems

Abstract

Fluid dynamic research on rectangular and trapezoidal fins is aimed at increasing heat transfer by means of large surfaces. The trapezoidal cavity form is compared with its thermal and flow performance, and it is revealed that trapezoidal fins tend to be more efficient, particularly when material optimization is critical. Motivated by the increasing need for sustainable energy management, this work analyses the thermal performance of inclined trapezoidal and rectangular porous fins utilising a unique hybrid nanofluid. The effectiveness of nanoparticles in a working fluid is primarily determined by their thermophysical properties; hence, optimising these properties can significantly improve overall performance. This study considers the dispersion of Graphene Oxide (GO) and Molybdenum Disulphide in the base fluid, engine oil. Temperature profiles are analysed by altering the radiative, porosity, wet porous, and angle of inclination parameters. Surface and contour plots are constructed by using the Lobatto IIIa Collocation Method with BVP5C solver in MATLAB and Gradient Descent Optimisation to predict the combined heat transfer rate. According to the study, fluid temperature consistently decreases when the angle of inclination, wet porous parameter, porosity parameter, and radiative parameter increase, suggesting significantly improved heat dissipation. The trapezoidal fin consistently exhibits a superior heat transfer mechanism than a rectangular fin. It is found that the trapezoidal fin transmits heat at a rate that is 0.05% higher than that of the rectangular fin. Validation of the present study is done through the comparison of previous studies. This research provides useful design insights for sophisticated engineering uses, including electrical cooling devices, heat exchangers, radiators, and solar heaters.