A neuro‐computational study of viscous dissipation and nonlinear Arrhenius chemical kinetics during the hypodicarbonous acid‐based hybrid nanofluid flow past a Riga plate

Abstract

We examine the flow of Casson hybrid nanofluid (Cu+/) through a Riga plate sensor with perforations that act as an electromagnetic actuator. The hypodicarbonous acid is considered a base fluid. The impact of Arrhenius chemical kinetics and viscous dissipation are taken into account during the dynamics. The problem is formulated by considering the heat and mass transfer. An appropriate scaling is used to reduce the complexity of the problem, and further transform it into a system of ordinary differential equations (ODEs). The reduced system is further set for the first‐order system of equations that are analyzed with the Artificial Neural Network (ANN) which is trained with the Levenberg–Marquardt algorithm. The results for the state variables are displayed through graphs and tables by performing 1000 independent iterations with tolerance and . The Hartman, Casson, and Richardson numbers with their increasing values enhance the velocity profile. The chemical reaction parameter and the Prandtl number decline the thermal and concentration profiles, respectively. The Statistical analysis in the form of regression and histograms is also carried out in each case. The absolute error (AE) ranges up to and validations that range up to are presented for the varying values of each parameter. A comparative analysis of the nanofluid (NF) and hybrid nanofluid (HNF) is performed in each case study. The results for skin friction and Nusselt number are displayed numerically in the form of tables and are compared with the available literature, where the accuracy and performance of ANN are proved.