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Artificial neural network simulation and sensitivity analysis for optimal thermal transport of magnetic viscous fluid over shrinking wedge via RSM

A. Zeeshan (Department of Mathematics and Statistics, International Islamic University, Islamabad, Pakistan)
Muhammad Imran Khan (Department of Mathematics and Statistics, International Islamic University, Islamabad, Pakistan)
R. Ellahi (Department of Mathematics and Statistics, International Islamic University, Islamabad, Pakistan and Department of Mechanical Engineering, University of California Riverside, Riverside, California, USA)
Zaheer Asghar (Centre for Mathematical Sciences, Pakistan Institute of Engineering and Applied Sciences, Islamabad, Pakistan and Centre for Physics and Applied Mathematics, Pakistan Institute of Engineering and Applied Sciences, Islamabad, Pakistan)

International Journal of Numerical Methods for Heat & Fluid Flow

ISSN: 0961-5539

Article publication date: 7 July 2023

Issue publication date: 17 August 2023

118

Abstract

Purpose

This study aims to model the important flow response quantities over a shrinking wedge with the help of response surface methodology (RSM) and an artificial neural network (ANN). An ANN simulation for optimal thermal transport of incompressible viscous fluid under the impact of the magnetic effect (MHD) over a shrinking wedge with sensitivity analysis and optimization with RSM has yet not been investigated. This effort is devoted to filling the gap in existing literature.

Design/methodology/approach

A statistical experimental design is a setup with RSM using a central composite design (CCD). This setup involves the combination of values of input parameters such as porosity, shrinking and magnetic effect. The responses of skin friction coefficient and Nusselt number are required against each parameter combination of the experimental design, which is computed by solving the simplified form of the governing equations using bvp4c (a built-in technique in MATLAB). An empirical model for Cfx and Nux using RSM and ANN adopting the Levenberg–Marquardt algorithm based on trained neural networks (LMA-TNN) is attained. The empirical model for skin friction coefficient and Nusselt number using RSM has 99.96% and 99.99% coefficients of determination, respectively.

Findings

The values of these matrices show the goodness of fit for these quantities. The authors compared the results obtained from bvp4c, RSM and ANN and found them all to be in good agreement. A sensitivity analysis is performed, which shows that Cfx as well as Nux are most affected by porosity. However, they are least affected by magnetic parameters.

Originality/value

This study aims to simulate ANN and sensitivity analysis for optimal thermal transport of magnetic viscous fluid over shrinking wedge.

Keywords

Acknowledgements

Declaration of competing interest: None.

Citation

Zeeshan, A., Khan, M.I., Ellahi, R. and Asghar, Z. (2023), "Artificial neural network simulation and sensitivity analysis for optimal thermal transport of magnetic viscous fluid over shrinking wedge via RSM", International Journal of Numerical Methods for Heat & Fluid Flow, Vol. 33 No. 10, pp. 3492-3518. https://doi.org/10.1108/HFF-03-2023-0135

Publisher

:

Emerald Publishing Limited

Copyright © 2023, Emerald Publishing Limited

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