Computational assessment of flow and heat transfer characteristics through Cu + Al2O3 + TiO2 ternary hybrid nanomaterial host-water with shape effect

Abstract

ABSTRACT Solar energy stands out as a vital source of thermal power derived from the sun. Its capacity finds applications across various technological domains, including photovoltaic panels, solar vehicles, solar lighting infrastructure, and solar water pumps. The contemporary landscape emphasizes the integration of solar energy in industries, notably exemplified by the prevalence of solar-powered charging stations. This article proposes a new perspective on heat transport analysis, incorporating the shape effect on the ternary hybrid nanofluid model along with considerations of thermal radiation, Cattaneo–Christov heat flux factor, heat source, and sink. For the evaluation, three differently shaped ternary hybrid nanoparticles are taken into account including platelet-shaped Cu , cylindrical-shaped A l 2 O 3 , and disc-shaped Ti O 2 . Through boundary layer approximations, the contributions of radiation, heat source, and sink variables are implemented into the regulating equations. The redesigned ordinary differential equations are numerically solved employing the NDSolve method after treating similarity variables. In the case of physical quantities, multiple linear regression is introduced to discover solutions for the flow elements. Augmentation in the momentum of dissimilarly shaped Cu , A l 2 O 3 and Ti O 2 nanoparticles enhance the velocity of ternary hybrid nanofluid flow more than that of spherical-shaped nanoparticles. The significant heat transfer is observed in mono, hybrid and ternary nanofluids with the existence of radiation and heat source phenomena. The multiple linear regression precisely forecasted the physical quantities with the error 10 − 3 . An application of a ternary hybrid nanofluid in a solar powered charging station can efficiently perform and successfully address an energy crisis by exhibiting an acceptable amount of power. This capability contributes to the reliable charging of electronic devices. Also, the machine learning technique can reliably and accurately perform the heat transfer analysis.