Non‐linear electromagnetic fields on mixed convection thermal flux in complex hybrid nanofluid with microorganism dynamics: Novel supervised machine learning exogenous neural networks

Abstract Gyrotactic microorganisms, including unicellular algae and bacteria, exhibit directed movements in fluid flow. They form three‐dimensional coherent structures in complex flows from mixing by commercial mixers. This study uses gyrotactic microorganisms as a new approach to chemical reaction systems, flow visualization and fluid mechanical measurements. The resulting planktonic fluid motion promotes mixing, and gyrotactic organism trajectories are used to visualize perpendicular flow components produced by different mixer configurations. The motions of sunset‐colored bodies indicate complex fluid motion. Bioconvection is a phenomenon where microorganisms swim in response to gravity or light, affecting the boundary layer flow of stretching surfaces. In this work, a computational machine learning method is used to investigate the thermal performance of an EMHD non‐Newtonian fluid on a porous stretching surface with Dufour–Soret effect of a hybrid nanofluid. MATLAB's built‐in function bvp4c is used to create and transform the governing flow equations into appropriate ODEs. The Levenberg‐Marquardt scheme with a Backpropagation Neural Network is the basis for supervised machine learning, which is used to calculate over a porous stretching surface of an approximated solution of a hybrid nanofluid of a non‐Newtonian fluid. This study uses the MSE, correlation index, error histogram, and linear regression to validate the supervised machine learning approach. An optimal solution is obtained at epochs 344, 1000, 92, 195, 51, 56, and 126, with MSEs is 1.62 , 1.97 , 1.26 , 8.28 , 7.84 , 7.84 , and 7.97 , respectively. This investigation shows an increment in thermal, concentration, and microorganism boundary layer thickness against the raising value of pertained parameters, that is, thermal radiation, Lewis number and bio‐convection.