Bioconvection and chemical reaction impacts on Casson nanofluid flow on a thin needle: A stochastic approach

Carbon nanotubes possess high yield strength, better thermal and electrical conductivity, larger surface area, and functional versatility, allowing them to be incorporated into a variety of industrial applications such as electronics, nanomedicine, and energy storage. Hence, in the presence of carbon nanotubes, this study focuses on heat and mass transfer analysis of gyrostatic MHD nanofluid flow over a thin moving needle under the influence of a non-uniform heat source/sink and chemical reaction. Incorporating Artificial Neural Networks (ANNs) in this study introduces a novel approach to modelling complex nonlinear interactions between several parameters and engineering factors. This ANN-driven framework enhances accuracy and offers predictions in capturing multifactor dependencies, offering a transformative tool for optimizing nanofluid behaviour. With suitable similarities, non-linear PDEs are converted into non-linear ODEs and further solved numerically via RKF-45th method. The impact of several dimensionless parameters on momentum, temperature, concentration, and motile density profiles is analysed and portrayed graphically. From the results, it is seen that an upsurge in casson fluid parameter decreases velocity profile. The rise in non-uniform heat source/sink parameter boosts temperature profile. The increase in the chemical reaction parameter reduces concentration profile, and the boost in Lewis number decreases motile density profile. Skin friction reduces for increasing magnetic field parameter. Elevated values of temperature-dependent heat source/sink parameter enhances Nusselt number. Both Sherwood number and density number for microbes reduce for chemical reaction and Peclet number. Furthermore, the present study's numerical results matched the previous study.