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