MHD-hybrid nanofluidic model on rotating plate with impact of thermal slip and heat absorption: a solution predictive neuro-computing approach

Abstract

The main objective of this research is to design a numerical computational solver-based two-layers structure of Levenberg–Marquardt backpropagation neural networks, i.e. LMB-NNs for the analyses of the heat transfer phenomenon and velocities structure in the MHD incompressible hybrid nanofluidic flow (MHD-IHNF) model with thermal slip and heat absorption/generation over the rotating plate through varying involved parameters, including velocity slip parameter, thermal slip parameter, concentration of Al₂O₃ nanoparticles, heat generation parameter, Prandtl number, Hartman number, and thermophoresis for sundry scenarios. The MHD-IHNF model is mathematically formulated as a system of PDEs that are converted in the desired system of ODEs by means of suitable transformation. The data-sets are constructed by explicit Runge–Kutta numerical technique that is exploited as a target data-set for the learning of LMP-NNs based on the process of validation, training, and testing to determine the solution of MHD-IHNF model for sundry physical scenarios. The validation, convergence, stability, and verification of LMP-NNs for solution predictive strength of the MHD-IHNF problem are certified in terms of achieved accuracy, regression index measurements, and analysis of error histogram illustrations.

KEYWORDS:

• Hybrid nanofluidic model
• Levenberg–Marquardt backpropagation
• MHD
• numerical computing
• neural networks

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

No data are associated with this manuscript.