Bio-inspired computational heuristics for Sisko fluid flow and heat transfer models

Abstract

A novel approach of integrated meta-heuristic computing is designed for Sisko fluid dynamics (SFD) system using artificial neural networks (ANN) based differential equation models being optimized with exploration capabilities of Genetic Algorithms (GAs) and efficient convergence of active-set algorithm (ASA) for different scenarios based on variation of magnetic interaction parameter, stretching parameter, generalized Prandtl and Biot numbers. Governing partial differential equations (PDEs) of SFD system are converted into coupled nonlinear ordinary differential equations (ODEs) through similarity variables and then ANN based models are constructed for the transformed problem. These networks are trained globally using GAs with fine tuning by ASA for speedy optimization. Accurate and consistent convergence of this scheme is established by comparison of results with standard Adams numerical approach and further validated through statistical performance indices.