Neuro-computing-based Levenberg Marquardt algorithm for entropy optimized Darcy-Forchheimer nanofluid with variable viscosity

Abstract

AI-based machine learning and intelligent computing solvers are famous among scientists and researchers for their resilience, robustness, stability, and fast convergence. Artificial neural network (ANN) is one of the important and burgeoning fields in artificial intelligence. The work in hand exploits the strength of artificial back propagated neural networks with the Levenberg Marquardt algorithm (ABNN-LMA) to examine the entropy generation of the magnetohydrodynamic Darcy-Forchheimer nanofluid flow model (MHD-DFNM) over a stretched surface. Two types of nanoparticles, i.e Silicon dioxide (SiO2) and Molybdenum disulfide (MoS2), and continuous phase liquid, i.e propylene glycol, are considered. The PDEs of MHD-DFNM are transformed into coupled ODEs by appropriate (similarity) transformations. The reference dataset is obtained by the variation of influential parameters of MHD-DFNM from the Homotopy Analysis Method (HAM). The reference HAM data are executed in training/testing/validation sets to find and analyze the approximated solution of the designed ABNN-LMA and its comparison with the reference data solution. The better performance is consistently certified with mean squared error (MSE) curves, regression index, and error histogram study. The results reveal that an increase in values of porosity parameter declines the velocity profile for both SiO2 and MoS2 nanoparticles suspended in propylene glycol nanofluids. The thermal profile improves for both SiO2 − propylene glycol and MoS2 − propylene glycol nanofluids when the heat generation parameter increases.