Python-based machine learning procedure for radiative Sutterby multiple-hybrid nanofluid flow comprising features of chemical processes

The present investigation refers to a novel comparison approach to evaluate the thermal conduction attributes and other thermal properties of Sutterby multi-hybrid nanofluids. These nanofluids include the colloidal suspension with composition of Copper, Zinc, Silver, and Iron oxide ( C u , Z n , A g , F e 3 O 4 ) nanoparticles traditional base fluid Ethanol. The Python based collocation method is utilized, that uses piecewise polynomial of higher order spline. Modelled problems incorporate momentum, energy and concentration. The method has an adaptive mesh refinement algorithm that enhances the number of collocation points at high resolution complex regions. The prime goal is to explore the viability of multi-HNF with increasing rate of thermal conductivity for various systems in engineering fields. Maxwell-Garnett and specific empirical correlations are used for making an approximation of the thermal-conduction coefficient and dynamic-viscosity changes of these nanofluids. Densities and specific thermal abilities of individual constituents are combined through homogenization analysis. The study gives insight into their use of different technological disciplines. It is found that Tetra-HNF have a noteworthy augmentation in thermal characteristics; therefore, it can be a potential contender for the application in sustainable energy systems, heat transfer devices and thermal management systems. Magnetic Parameter, Debora, Prandtl, intermolecular vibration, Schmidt, chemical reaction and porosity are analyzed against flow rate, temperature and engineering parameters. The results showed a significant reduction in viscosity and enhancement in thermal conductivity of tetra-HNF compared to other species, with an approximate increase of 25% in thermal efficiency and 15% decrease in drag force, making it an advanced candidate for thermal management.