Neuro-computing analysis of model-based Casson hybrid nanofluid flow via three-dimensional radiative Riga plate with irregular heat source/sink

In this study, an analysis is conducted on a hybrid nanofluid using the Yamada-Ota and Hamilton-Crosser model in combination with the Casson fluid model. The hybrid nanofluid is analyzed in the context of a three-dimensional radiative Riga plate, which incorporates non-uniform heat source/sink effects. The thermal features and their behavior of the hybridized nanofluid is explored in the current investigation considering the flow over a radiative Riga plate with the impact of space and temperature dependent heat source/sink. The flow and thermal characteristics of the hybridized nanomaterial are presented for the consideration of Yamada-Ota & Hamilton Crosser models. Additionally, the analysis is enhanced by the implementation of Artificial neural network. Further the machine learning approach, a neuro-computing intelligent network is adopted for the computational techniques. The neural networks, which effectively uses the data and make predictions based on their acquired knowledge. The outcomes of the proposed analysis provide the flow behavior of the hybrid nanofluid. The effect of non-uniform heat source/sink of the Casson fluid model contributes to a greater hike in the fluid temperature. Moreover, the neuro-computing intelligent network shows efficient predictions of the involved thermal parameters.