Intelligent Bayesian regularization‐based solution predictive procedure for hybrid nanoparticles of AA7072‐AA7075 oxide movement across a porous medium

Abstract The research community has shown great interest for investigation in the nanofluids models involving Aluminum Alloys AA7072 and AA7072 +AA7075 due to their advantageous impact on heat transfer, physical and mechanical characteristic exploiting in broad engineering applications such as manufacturing of spacecraft, aircraft parts and building testing. The hybrid nanomaterial AA7072‐AA7075 based fluidic system is investigated in this paper using an artificial neural network with Bayesian regularization scheme (ANNs‐BRS). The derived partial differential equations (PDEs) are transformed into ordinary differential equations system ODEs and obtained the reference datasets for the estimated solution dynamics of hybrid nanofluidic system. For prominent parameters, the influence of flow on the temperature distribution and velocity plot are investigated. The performance on 80% training samples, 5% testing and 15% validation dataset for ANNs‐BRS is well‐established in terms of error histogram plots, regression analysis, and MSE based statistics. The results for entropy generation, Eckert number Ec , magnetic interaction parameter M , suction parameter S , and heat generation parameter Q are also discussed. The results show that the Eckert number Ec has the effect of slowing down the rate of heat transfer while increasing the temperature and increases in suction parameter causes decrease in temperature while increase in temperature profile due to enhancement of suction parameter.