Thermo-diffusional influence on Cu₃Zn₂-Co nanomaterial with porous medium using intelligent Levenberg-Marquardt method.

Abstract

The suggested model has important applicability in advanced heat and mass transfer systems using Cu3Zn2-Co / Distilled water nanomaterial in porous media. It is applicable to thermal energy storage, heat exchangers, catalytic reactors, filtration systems, geothermal engineering, and cooling technologies in which thermo-diffusion has a substantial impact on transport processes. This study paper presents an assessment of the effect of LTNE effects on Cu3Zn2-Co / Distilled water nanomaterial flow with Dufour and Soret impacts. The Bvp4c solver creates a reference dataset for training the algorithm of Levenberg-Marquardt optimizer with back propagation neural network (ALMO-BNN) model. The model's low mean squared error (MSE) suggests significant convergence and precision. A high degree of agreement among predicted and numerical results is indicated by the regression coefficient is near to unity. The thermal and concentration profiles rise as the values of Dufour and Soret numbers increase.

Abstract as supplied by the indexing source. © the authors / publisher.