Machine learning investigation of marangoni convection in hybrid nanofluids with Darcy-Forchheimer

This research utilizes machine learning to investigate Marangoni convection in a hybrid nanofluid [Formula: see text] within a Darcy-Forchheimer porous framework. We conduct both qualitative and quantitative assessments of heat transfer, mass transfer, and viscous dissipation irreversibility during the flow. Numerical results are obtained using a Python finite difference algorithm, after which MATLAB is employed for AI-based analysis. Additionally, the Levenberg-Marquardt neural network algorithm is trained and utilized. Our findings show that fluid velocity diminishes as the inverse Darcy parameter, Marangoni ratio, and Forchheimer parameter increase. Moreover, the temperature rises with the Eckert number and Prandtl ratio. As concentration increases, activation energy and Schmidt parameter also grow. Mean Square Error for the results reaches up to 10^-11 across various impacts. The findings indicate that the LMNN model fits well with low error in training, testing and validation dataset. Notably, the results indicate that this hybrid AI-based method could be used as a credible surrogate of the intricate simulations in porous media heat transfer tasks providing a computationally effective device of real-time analysis in engineering.