Support vector machine learning classification of heat transfer rate in tri-hybrid nanofluid over a 3D stretching surface with suction effects for water at 10°C and 50°C

Significance of the present study: Using machine learning and nanofluids for accurate heat transfer analysis, this study helps optimise thermal management systems in sectors such as advanced heat exchangers, biomedical devices, and electronics cooling. Several researchers have expressed interest in the potentially lucrative field of research that uses nanoparticles to improve heat transfer. Many fields, including cooling, heat exchangers, hyperthermia, energy generation, etc., use the dynamic features of nanofluids. Engineers and industrialists face significant challenges with heat transmission and energy storage. Aim and Objective of the present study: So far, we have presented the idea of novel heat transfer liquids, such as nanofluids (NFs) and hybrid nanofluids (HNFs); ternary nanomaterials have been used to create a new era of heat transfer. Given the applications of nanofluids, this paper used a tri-hybrid AA7072 +SWCNT+MWCNT model to examine the impacts of a magnetohydrodynamic flow of 100 C and 500 C at H2O as base fluid in two unique instances. The novelty of the manuscript is that it will analyse a ternary AA7072 +SWCNT+MWCNT nanofluid model regarding tri-hybrid nanoparticles. Methodology: To convert the controlling PDEs into ODEs, the similarity transformations are used. The MATLAB function BVP4C was used to solve these translated equations. Conclusion: The results of this investigation broadly agreed with those that had been previously reported in the literature. Examining these phenomena might guide the development of potential real-world engineering applications, such as those involving biosensors, medicine, and extreme heat. Plots and numerical interpretations are utilised to investigate how the flow phenomena are affected by physical factors; the composition of Case II has a greater Local Nusselt number than Case I because of its thermophysical character.