Efficiency analysis of solar radiation on chemical radioactive nanofluid flow over a porous surface with magnetic field

Abstract

Artificial neural networks have revolutionized machine learning by providing exceptional capabilities for modeling complicated mechanisms and solving various challenges. Backpropagation is an important training technique in the field of artificial neural networks. However, this technique must be optimized when working with complicated fluid dynamics. This study analyzes the three-dimensional radiative flow of a tangent hyperbolic fluid driven by the Cattaneo-Christov flux system across a porous stretching sheet using ANN backpropagation enhanced by Bayesian Regularization approach. Heat and mass transfer analysis includes thermal radiation, chemical reactions and Cattaneo-Christov flux model. Porosity, radiation, chemical reaction rate, and ion slip effect are among the important physical characteristics that are modified to see how they affect fluid dynamics. Using MATLAB's BVP4C solver, the velocity, temperature, and concentration profiles that result from these model equations provide the training dataset for ANNs. The dataset is divided into 80 % for training, 10 % for testing, and 10 % for validation. Performance plots, regression graphs, and error histograms are used to analyze the performance of the LMT-based ANN and demonstrate its high accuracy and efficiency. With an R2 value of 1, the ANN produced a mean squared error of around 10⁻11. Fluid mobility drops as the magnetic parameter grows, while the thermal profile exhibits an increasing trend. Similarly, decreasing fluid velocity is the outcome of raising the porosity parameter. The study's conclusions have great potential for use in sectors that need sophisticated cooling and heating equipment.

Keywords

• Thermal radiation
• Chemical reaction
• Backpropagation
• Cattaneo–christov heat flux
• Bayesian regularization