Artificial neural network based computational evaluation of bioconvective gyrotactic microorganisms wedge flow of MHD Garreau fluid with thermal radiations

Purpose An innovative model is proposed to analyze the boundary layer flow of a Carreau fluid, including bioconvection effects from gyrotactic microorganisms, and thermal radiation, specifically focusing on a wedge geometry. This paper aims to understand the complex interplay of these factors on fluid dynamics and heat transfer. Design/methodology/approach Artificial neural networks (ANNs)-based technique, specifically backpropagation neural networks, are being used to analyze the bioconvective wedge flow. These techniques are used to understand the intricate thermal and momentum interactions within the flow. To train the ANN, the MATLAB built-in function bvp4c solver is used to generate a reference solution. Findings Artificial Intelligence-based neural networks are indeed powerful tools for enhancing simulation accuracy, particularly in complex flow scenarios, by learning intricate patterns from data and offering more efficient solutions than traditional methods. The training of these networks involves iterating through epochs, where each epoch represents a full pass of the training data, allowing the model to adjust its parameters (weights and biases). The optimal number of epochs is determined by monitoring performance metrics, such as accuracy and error, as the network trains. In the specific context of nanoparticle movement due to thermophoresis, neural networks can be trained to predict this behavior, and their performance is evaluated through metrics like linear regression and fitness. Validation is achieved by comparing the results with existing literature, treating the special cases as a validation method. Originality/value The simultaneous effects of bioconvection, gyrotactic microorganisms, magnetohydrodynamics, Carreau fluid, wedge flow, thermal radiation and the application of ANN with regression analysis in a single study appear to be a novel research area, as it is not explicitly documented in the existing literature.