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Computational Analysis on Magnetized and Non-Magnetized Boundary Layer Flow of Casson Fluid Past a Cylindrical Surface by Using Artificial Neural Networking

Author

Listed:
  • Khalil Ur Rehman

    (Department of Mathematics and Sciences, College of Humanities and Sciences, Prince Sultan University, Riyadh 11586, Saudi Arabia
    Department of Mathematics, Air University, PAF Complex E-9, Islamabad 44000, Pakistan)

  • Wasfi Shatanawi

    (Department of Mathematics and Sciences, College of Humanities and Sciences, Prince Sultan University, Riyadh 11586, Saudi Arabia
    Department of Mathematics, Faculty of Science, The Hashemite University, P.O. Box 330127, Zarqa 13133, Jordan)

  • Andaç Batur Çolak

    (Information Technologies Application and Research Center, Istanbul Commerce University, Istanbul 34445, Turkey)

Abstract

In this article, we constructed an artificial neural networking model for the stagnation point flow of Casson fluid towards an inclined stretching cylindrical surface. The Levenberg–Marquardt training technique is used in multilayer perceptron network models. Tan–Sig and purelin transfer functions are carried in the layers. For better novelty, heat and mass transfer aspects are taken into account. The viscous dissipation, thermal radiations, variable thermal conductivity, and heat generation effects are considered by way of an energy equation while the chemical reaction effect is calculated by use of the concentration equation. The flow is mathematically modelled for magnetic and non-magnetic flow fields. The flow equations are solved by the shooting method and the outcomes are concluded by means of line graphs and tables. The skin friction coefficient is evaluated at the cylindrical surface for two different flow regimes and the corresponding artificial neural networking estimations are presented. The coefficient of determination values’ proximity to one and the low mean squared error values demonstrate that each artificial neural networking model predicts the skin friction coefficient with high accuracy.

Suggested Citation

  • Khalil Ur Rehman & Wasfi Shatanawi & Andaç Batur Çolak, 2023. "Computational Analysis on Magnetized and Non-Magnetized Boundary Layer Flow of Casson Fluid Past a Cylindrical Surface by Using Artificial Neural Networking," Mathematics, MDPI, vol. 11(2), pages 1-25, January.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:2:p:326-:d:1028755
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    References listed on IDEAS

    as
    1. Khalil Ur Rehman & Andaç Batur Çolak & Wasfi Shatanawi, 2022. "Artificial Neural Networking (ANN) Model for Convective Heat Transfer in Thermally Magnetized Multiple Flow Regimes with Temperature Stratification Effects," Mathematics, MDPI, vol. 10(14), pages 1-19, July.
    2. Khalil Ur Rehman & Andaç Batur Çolak & Wasfi Shatanawi, 2022. "Artificial Neural Networking (ANN) Model for Drag Coefficient Optimization for Various Obstacles," Mathematics, MDPI, vol. 10(14), pages 1-20, July.
    3. Megahed, Ahmed M. & Reddy, M. Gnaneswara & Abbas, W., 2021. "Modeling of MHD fluid flow over an unsteady stretching sheet with thermal radiation, variable fluid properties and heat flux," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 185(C), pages 583-593.
    Full references (including those not matched with items on IDEAS)

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