Dynamics of bioconvection radiative MHD flow with gyrotactic microorganisms: an intelligent computation approach

Abstract

Bioconvection flows relate significantly to real-life problems, such as designing bio-conjugates, bio-microsystems, and bio-cells. This study investigates a mathematical characterization of the electrically time-dependent conducting fluid flow containing gyrotactic microorganisms with activation energy, mass, and heat transfer. It is assumed that the flow is towards the extended surface and affects thermal radiations. In model formulation, the Navier–Stokes equations are reduced to a system of ordinary differential equations (ODEs) using a similarity transformation approach. For reference solutions, the RK4 technique is used with Dorman Prince coefficients. We have designed a new backpropagation neural network architecture to formulate new solution models with Bayesian regularization. The effects of variations in two parameters, Schmidt number (sc) and Prandtl number (pr), are investigated. Our experimental outcomes are linked to the reference outputs. It was observed that there is a strong agreement between our solutions and the reference solutions. Our approach can be used for systems of fractional differential equations.