ANN prediction through sensitivity analysis under MHD Williamson micropolar fluid flow for metallurgical processing of aluminum
摘要
Potential key benefits of magnetohydrodynamics (MHD) Williamson micropolar fluid over a non-Darcy porous surface include enhanced oil recovery, advancements in environmental engineering, applications in the biomedical field, energy generation, and improved heat transfer systems, particularly relevant for understanding the heat transfer enhancement parameter for the industrial aluminum metallurgy process. These outcomes are consistent with earlier studies and provide substantial information for future researchers to investigate their findings. This study intends to investigate the micro-rotation of MHD Williamson fluid flow on a non-Darcy porous surface with different thermal conductivity. The fundamental equations for momentum, energy, and microrotation are associated with an arrangement of partial differential equations with boundary conditions and utilized similarity transformation to convert ordinary differential equations (ODEs). The MATLAB module containing the BVP4C solver is employed to solve these ODEs and discover the physical quantities. The suggested machine learning under Multiple Linear Regression (MLR) and Artificial Neural Network (ANN) for handling complicated non-linear issues, increasing efficiency, and producing precise forecasts, optimizations, and error reductions to validate the findings. MLR produced an accuracy level of