Significance of bioconvective flow of MHD thixotropic nanofluid passing through a vertical surface by machine learning algorithm

Scientists have made significant contributions in the current decade due to the importance of bioconvection in biotechnology and a variety of biological systems. In this study, a theoretical bioconvective model is constructed in the current framework to investigate the thermally developed thixotropic nanoparticles flow by incorporating narrative flow features such as thermophoresis, Brownian motion, convective condition, and radiation features, along with artificial neural network models. The non-linear complex equations were numerically calculated using the Runge–Kutta fourth-order shooting procedure. The Sherwood number, motile density of micro-organism, skin friction coefficient and Nusselt number were calculated utilizing different parameters, and four different artificial neural networks were established depending on the outcomes. R values for the developed neural network models were obtained as higher than 0.99. The results showed that artificial neural networks can give high accuracy results in the analysis of thermally developed thixotropic nanoparticles flow. Theoretical findings gained reveal industrial applications, engineering, and thermal procedures involving heat transfer. The claimed outcomes can be used to enhance cooling and heating procedures, thermal devices, energy generation, solar systems, and manufacturing procedures, among other things.