Optimization of Tetra Hybrid Nanofluid Flow Over a Convectively Heated Moving Plate with Heat Generation

Abstract

This study introduces a novel integrated computational and statistical approach for analyzing steady boundary layer flow and heat transfer in a tetra hybrid nanofluid containing alumina, copper, silica and titania nanoparticles dispersed in water past a convectively heated moving plate with internal heat generation. The governing nonlinear equations are reduced via similarity transformations and solved using Matlab’s bvp4c solver to obtain highly accurate velocity, temperature, skin friction, and Nusselt number distributions. Response Surface Methodology (RSM) and sensitivity analysis are employed to quantify and rank the influence of heat generation, Biot number, and suction parameter. The numerical results reveal that increasing the Biot number may enhance the heat transfer rate by approximately 42.6 for opposing flow case and 53.6% for assisting flow case, whereas stronger wall suction improves heat transfer by about 0.5-6.2%. However, higher heat generation slightly weakens the heat transfer rate up to 3.6% and 0.2%, for opposing and assisting flow cases, respectively. The response surface methodology and sensitivity analysis also reveal that the Biot number exerts the dominant influence on heat transfer, followed by suction strength and heat generation rate. The principal novelty lies in the exclusive integration of numerical simulation with statistical optimization for a tetra hybrid nanofluid under convective heating, an area rarely addressed in the literature. The proposed framework not only identifies the most influential parameters but also determines optimal ranges for maximizing thermal performance. These findings establish a benchmark for designing advanced thermal management systems in high-temperature industrial and energy applications.