Inverse multiquadric kernel-based neuro heuristic approach to analyze the unsteady MHD nanofluid flow via permeable elongating surface. (English) Zbl 07840211
Summary: In this study, a novel neuro heuristic approach is designed to investigate the flow properties of magnetohydrodynamic (MHD) nanofluid along an exponentially extending sheet with a permeable medium with the impact of radiation as well as fluctuating heat source/sink. The designed scheme to handle the suggested problem is established through the well-known biologically inspired neural networks (BINNs) by exploiting the inverse multiquadric kernel (IMQK), that is, BINNs-IMQK which is quite a new approach. The partial differential equations (PDEs) which govern the fluidic flow are reformed into a nonlinear system of ordinary differential equations (ODEs) using the most fitted similarity transformations rules and numerically solved by varying the parametric values including unsteady parameter, Brownian motion parameter, suction/injection parameter, radiation parameter, Schmidt number together with Prandtl number to visualize the velocity, thermal gradient, and mass transfer in the suggested fluid problem. It is noticed that nanofluid temperature hikes by uplifting the value of the Brownian motion parameter but this effect is reversed in case of unsteady parameter. The obtained numerical results are verified through reference solution using the well-known Adams method and the efficacy of the suggested solver is endorsed using a variety of statistical operators.
© 2023 The Authors. ZAMM - Journal of Applied Mathematics and Mechanics
© 2023 The Authors. ZAMM - Journal of Applied Mathematics and Mechanics
MSC:
| 76W05 | Magnetohydrodynamics and electrohydrodynamics |
| 76T20 | Suspensions |
| 76M99 | Basic methods in fluid mechanics |
| 80A19 | Diffusive and convective heat and mass transfer, heat flow |
| 80A21 | Radiative heat transfer |
| 68T07 | Artificial neural networks and deep learning |
Keywords:
exponentially extending sheet; radiation; fluctuating heat source/sink; biologically-inspired neural network; similarity transform; numerical solution; parametric studyReferences:
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