Designing novel soft computing paradigm to compute the impact of thermal radiation for efficient thermal transportation of Maxwell hybrid nanofluid model: Renewable solar energy in solar collector application

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Soft computing and machine learning methods are currently attracting the attention of researchers for their robustness, accurate modeling, simulation, and effective evaluation in artificial intelligence applications. Using a Bayesian Regularization backpropagation neural network (BRB-NN), a stochastic network paradigm is developed in the investigation to clarify the dynamics of the magnetized Maxwell hybrid nanofluid flow model over stretching sheet with thermal radiation and entropy generation. The critical external resources in parabolic trough solar collector (PTSC), such as thermal radiative, viscous dissipative flow, and Joule heating characteristics, are taken into consideration. In this research the single wall and multi wall carbon nanotubes are dispersed in water with Molebdynim disuplate ( M o S 2 ). The governing flow equations are designed in partial differential equations (PDEs) system of magnetized Maxwell hybrid nanofluid. The partial differential equations (PDEs) system is transmuted to ordinary differential equations by adopting similarity transformations. The bvp4c solver is adopted to compute the reference dataset for the Bayesian Regularization backpropagation neural network (BRB-NN) paradigm with variation in magnetic parameter, velocity slip, Brinkmann number, thermal radiation and thermal Biot number . Results show that the fluid velocity is reduced in the presence of larger magnetic parameter. The outcomes witnessed that thermal field is increased with increment in thermal radiation parameter. From the low MSE accuracy of best performance is analyzed. The regression closed to unity, indicate the accuracy of model. The model precision is examined utilizing the absolute error, which is given for each study at around 10 − 8 to 10 − 4 and 10 − 9 to 10 − 4 .

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