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Levenberg–Marquardt Training Technique Analysis of Thermally Radiative and Chemically Reactive Stagnation Point Flow of Non-Newtonian Fluid with Temperature Dependent Thermal Conductivity

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, 34445 Istanbul, Turkey)

Abstract

We have examined the magnetized stagnation point flow of non-Newtonian fluid towards an inclined cylindrical surface. The mixed convection, thermal radiation, viscous dissipation, heat generation, first-order chemical reaction, and temperature-dependent thermal conductivity are the physical effects being carried for better novelty. Mathematical equations are constructed for four different flow regimes. The shooting method is used to evaluate the heat transfer coefficient at the cylindrical surface with and without heat generation/thermal radiation effects. For better examination, we have constructed artificial neural networking models with the aid of the Levenberg–Marquardt training technique and Purelin and Tan-Sig transfer functions. The Nusselt number strength is greater for fluctuations in the Casson fluid parameter, Prandtl number, heat generation, curvature, and Eckert number when thermal radiations are present.

Suggested Citation

  • Khalil Ur Rehman & Wasfi Shatanawi & Andaç Batur Çolak, 2023. "Levenberg–Marquardt Training Technique Analysis of Thermally Radiative and Chemically Reactive Stagnation Point Flow of Non-Newtonian Fluid with Temperature Dependent Thermal Conductivity," Mathematics, MDPI, vol. 11(3), pages 1-27, February.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:3:p:753-:d:1055309
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    References listed on IDEAS

    as
    1. Khan, Arif Ullah & Al-Zubaidi, A. & Munir, Shahid & Saleem, S. & Duraihem, Faisal Z., 2021. "Closed form solutions of cross flows of Casson fluid over a stretching surface," Chaos, Solitons & Fractals, Elsevier, vol. 149(C).
    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.
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