Multivariate regression-based entropy optimization in Cattaneo–Christov modeled Casson hybrid nanofluid flow for biothermal applications
摘要
This study presents a computational analysis of entropy optimization in MHD Casson hybrid fluid flow with non-linear thermal radiation and the Cattaneo–Christov heat flux model over a curved stretching sheet. It also incorporates multiple linear regression analysis and explores the behavior of Silver (Ag) and Gold (Au) nanoparticles within blood flow. The important self-similarity variables are used to change the non-linear partial differential equation system into a simpler ordinary differential equation, which is then solved using the fourth-order Runge–Kutta method along with the shooting method and the Homotopy Perturbation Method. The semi-analytical results have been compared to numerical outcomes from the shooting method and existing literature as a limiting instance. Graphical projections are provided with the impact of active parameters including velocity, temperature, Bejan number, entropy production, skin friction, and streamline. The increasing magnetic field increases the Lorentz force, which counteracts the flow and decreases the velocity profile in the presence of a curved surface. Temperature profiles decline with rising thermal relaxation in both radiation models, but the impact is more effective in the case of non-linear radiation. The proposed system uses unique method by using regression analysis and correlation techniques with the Nusselt number and skin friction. According to the estimated regression skin friction coefficient equations, the related parameters