Heat Transfer in Chemically Reactive Dual Diffusive Casson Nanofluid Flow: An Intelligent Computing Paradigm

In this research-oriented study, dual diffusive Casson nanofluid stretching flow embedded in a Darcy-Forchheimer-type porous medium is scrutinized. A quite new computerized neuro-heuristic optimization technique based on Ricker wavelet neural networks fabricated through global and local solvers namely genetic algorithms and sequential quadratic programming respectively. The flow of the suggested fluid model is characterized by thermal radiation, chemical reaction and Robin conditions for the analysis of heat and mass transfer effects. The similarity approach simplifies the governing partial differential equations of the currently discussed flow model into a dimensionless nonlinear system of ordinary differential equations. This system of equations is then solved using the crafted solver and the obtained numerical outcomes are successfully compared with the reference solution compiled through Adam’s numerical technique. It is observed that an increase in the value of the Casson parameter diminishes the nanofluid velocity however this effect is reversed on the thermal profile. A detailed form convergence analysis of the novel design created solver is accomplished using various statistical performance operators.