Thermophoresis effects on Sn-W/ C₃H₈O₂ nanomaterial using intelligent neuro-computing paradigm: Yamada-Ota model

The Yamada-Ota thermal conductivity model, along with an intelligent neuro-computing paradigm, is commonly used to study the thermophysical behavior of Sn-W/C₃H₈O₂ nanomaterials during thermophoresis and Soret-Dufour effects. The concept has numerous applications, including electronic device cooling, solar energy systems, thermal energy storage, chemical reactors, biomedical heat transfer, and nanofluid-based heat exchangers. It accurately predicts effective thermal conductivity, heat and mass transfer, and nanoparticle transport, while the intelligent predictive-computing approach improves prediction accuracy, lowers computational costs, and helps to optimize advanced thermal management systems and industrial processes. This report uses Intelligent Bayesian optimization trained by back propagating neural networks to examine the mass and heat transport parameters of a nanofluid made of Sn-W/C₃H₈O₂ nanoparticles dispersed in water. This study inspects the impacts of Soret and Dufour effects on Sn-W/C₃H₈O₂ hybrid nanomaterial under thermophoresis effects using Yamada-Ota model. The resulting higher-order nonlinear ODEs are numerically solved using MATLAB's bvp4c technique. The thermal field rises as increase the values of nanoparticles volume fraction.