Analysis of a bioconvection flow of magnetocross nanofluid containing gyrotactic microorganisms with activation energy using an artificial neural network scheme

The current study proposes the technique of intelligent numerical computing using back-propagated neural networks (BNNs) for the numerical treatment of a magnet cross-bioconvection flow analysis of nanofluid modal (MHDBC-NFM) including gyrotactic microorganisms with activation energy. Utilizing efficient information, the original system model MHDBC-NFM in partial differential equations (PDEs) is remade into non-linear ordinary differential equations (ODEs). Using the computational capability of the Lobatto IIIA technique, these generated non-linear ODEs are then bettered to provide a dataset of Levenberg Marquardt algorithm-based backpropagated neural networks (LMA-BNNs) for twelve scenarios of this proposed model. The validation, training, and testing processes are carried out simultaneously to acclimate the neural networks. Levenberg-Marquardt backpropagation is used to lower the mean square error (MSE) function, which is then used to derive the estimated solution for MHDBC-NFM for various scenarios. The comparative investigations and performance assessments using correlation, error histograms, MSE, and regression results illustrate the efficacy of the suggested BNNs methodology. The observed diminishing behavior of the nondimensional gyrotactic microorganism field is correlated with the bioconvection Lewis number. Additionally, the results show that temperature profiles become more intense for higher radiation parameter values.