Numerical Investigation of Homogeneous–Heterogeneous Reaction Induced Thermal Analysis for Efficient Heat Transfer in Tri‐Hybrid Nanofluid Flow

ABSTRACT Efficient thermal transmission is a necessary requirement of diversified engineering units to attain optimized output. For this purpose, the induction of tri‐hybrid nanoparticles to achieve advanced heat transfer is considered as an innovative strategy because of an increase in the specific heat capacity of a system in different ways. The dispersion of silver (Ag), copper (Cu), and alumina (Al 2 O 3 ) in water with different physical factors controlled the flow dynamics. A practical approach to enhance heat transfer effectiveness is to improve the thermal properties of the working fluid. Nanofluids, which are suspensions of nanoparticles in a base fluid, are recommended. In addition, the synergistic effects of homogeneous and heterogeneous reactions among nanoparticle interactions are also accounted for to improve overall thermal efficiency. The findings demonstrated that tri‐hybrid nanofluids provide superior thermal conductivity and absorption rates compared with those of conventional fluids. A machine learning framework is employed to predict complex heat transfer behaviors and optimize system performance. Therefore, a machine learning paradigm based on an artificial neural network is used to analyze the particle concentration impacts for the estimation of the friction factor and heat transfer analysis of a water‐based tri‐hybrid nanofluid. The ANN model training for larger needle sizes exhibits higher accuracy and stability than that for smaller needle sizes, owing to the higher sensitivity and non‐linearity, which are also indicated by the larger MSE. Validation failures remain zero in most cases, which represents fitness along with good generalization to the data.