IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v11y2023i9p2089-d1134998.html
   My bibliography  Save this article

An Implicit Finite Difference Scheme and Neural Network Approach for Non-Newtonian Nanofluid Flow Using Induced Magnetic Field

Author

Listed:
  • Hassan J. Al Salman

    (Department of Mathematics and Statistics, College of Science, King Faisal University, Hofuf, Al Ahsa 319832, Saudi Arabia)

  • Yasir Nawaz

    (Department of Mathematics, Air University, PAF Complex E-9, Islamabad 44000, Pakistan)

  • Ahmed A. Al Ghafli

    (Department of Mathematics and Statistics, College of Science, King Faisal University, Hofuf, Al Ahsa 319832, Saudi Arabia)

Abstract

The aim of this contribution is to propose a numerical scheme for solving linear and nonlinear boundary value problems. The scheme is implicit and it is constructed on three grid points. The stability of the proposed implicit scheme is provided. In addition to this, a mathematical model for heat and mass transfer using induced magnetic field (IMF) is modified. Furthermore, this model is transformed into boundary value problems by employing similarity transformations. The dimensionless model of boundary value problems is solved using the proposed numerical scheme. The scheme is applied with a combination of a shooting approach and an iterative method. From the obtained results, it can be seen that velocity profile declines with enhancing Weissenberg number. The results are also compared with those given in past research. In addition to this, a neural network approach is applied that is based on the input and outputs of the considered model with specified values of parameters.

Suggested Citation

  • Hassan J. Al Salman & Yasir Nawaz & Ahmed A. Al Ghafli, 2023. "An Implicit Finite Difference Scheme and Neural Network Approach for Non-Newtonian Nanofluid Flow Using Induced Magnetic Field," Mathematics, MDPI, vol. 11(9), pages 1-18, April.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:9:p:2089-:d:1134998
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/11/9/2089/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/11/9/2089/
    Download Restriction: no
    ---><---

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:11:y:2023:i:9:p:2089-:d:1134998. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.