Polynomial regression analysis on electromagnetohydrodynamic hybrid nanofluid flow over a rotating disk: Applications in next-generation thermal systems

• The heat transfer analysis on electromagnetohydrodynamic engine oil/copper-copper oxide hybrid nanofluid flow across a rotating porous disk is quantitatively investigated. • The effects of a linear, non-linear and quadratic thermal radiations, viscosity and porous medium, and Darcy-Forchheimer flow (DFF) are investigated in the present study. • The computational solutions of a mathematical problems deriving from the engine oil Cu-CuO model are made more accurate with its enhanced framework. • The governing highly non-linear PDEs are converted into ODEs by using self-similarity variables, and computational results are obtained using the Bvp4c scheme in MATLAB software. • The primary goal is to develop and validate an efficient engine oil/Cu-CuO hybrid framework that integrates traditional numerical approaches with polynomial regression analysis to accurately predict the flow and thermal features of complex nanofluid systems. Thermal systems need effective cooling and heating processes, therefore thermal transfer innovation is critical in modern times, playing an important role in the manufacturing, aerospace, and electronic equipment sectors, in addition to automobiles and other modes of transportation. This study investigates the heat transfer properties of an electromagnetohydrodynamic mixed nanofluid across a porous spinning disk. The primary goal is to develop and validate an efficient engine oil/Cu-CuO hybrid framework that integrates traditional numerical approaches with polynomial regression analysis to accurately predict the flow and thermal features of complex nanofluid systems. The conversion of PDEs into ODEs is achieved by utilizing similarity variables. Thereafter, for graphical purposes, the study employed the bvp4c numerical method. The resultant structure is solved with the bvp4c scheme, and polynomial regression analysis is created to train the solution data for accurate estimation across different parameter (Q, Ec, Rd) settings. The suggested polynomial regression analysis has regression coefficients are 0.98, 0.87, 0.97, indicating high predicted accuracy and effectiveness. The influence of different parameters participating in the mathematical modeling is shown in various graphs and tables. In the result section the investigation noticed the velocity outlines increased, and on the other hand, there was a decreasing tendency in the energy outline for enlarging electric field parameter values. The heat generation and eckert number parameter values are increased, resulting in an improved energy profile. The results of this simulation have the potential to contribute significantly to more advanced study and research in the fields of bioengineering and bio-nanofluid dynamics. Thermal systems require efficient cooling and heating techniques, so heat transfer technology is crucial in today’s world and plays a crucial part in the industrial, aerospace, and electronic equipment industries, as well as in automobiles and other forms of transportation. A hybrid nanofluid flowing on a spinning disk under electromagnetohydrodynamic, heat source/sink effects are investigated using computational simulation. Heat transfer is improved when (Eg/Cu-CuO) hybrid nanoparticles and Linear, Non-Linear and Quadratic Thermal Radiations work together.