Machine Learning-Enhanced Simulation of Maxwell Nanofluid Flow: A Bayesian Neural Network Approach with Thermal Relaxation and Variable Viscosity Effects

This study examines the Maxwell nanofluid's movement along an inclined plate, incorporating couple stress and variable viscosity effects via a machine learning approach. Key innovations include: (۱) a hybrid numerical-ML framework using MATLAB's bvp۴c and Bayesian Neural Networks (BNN) for uncertainty quantification; (۲) the novel integration of Cattaneo-Christov heat flux with Brownian motion and thermophoresis; and (۳) rigorous validation through grid tests and ANN correlations (R ≈ ۱). Results show the Maxwell parameter increases the friction factor by ۵.۸۲%, while thermophoresis parameter reduces the Nusselt number by ۹.۵۹% and the Brownian motion parameter enhances mass transfer by ۱.۷۴%. The BNN model achieves high accuracy (error < ۰.۵%), bridging non-Fourier heat transfer with data-driven methods to optimize thermal and microfluidic systems.

کلیدواژه ها:

Non-Newtonian fluid ، Variable viscosity ، Thermophoresis ، Brownian motion ، Activation energy ، bvp۴c