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Intelligent neuro-computing for entropy generated Darcy–Forchheimer​ mixed convective fluid flow

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  • Raja, M. Asif Zahoor
  • Shoaib, M.
  • Zubair, Ghania
  • Khan, M. Ijaz
  • Punith Gowda, R.J.
  • Prasannakumara, B.C.
  • Guedri, Kamel

Abstract

In the present article, the Darcy–Forchheimer mixed convective flow model (DFMC-FM) is examined by utilizing the algorithm of Levenberg Marquardt with backpropagated artificial neural networks (ALM-BANN). Partial differential equations representing the proposed DFMC-FM are converted to non-linear ordinary differential equations (ODEs) by similarity transformation. These ODEs are solved by Adam numerical method to interpret the reference dataset of ALM-BANN for various scenarios of DFMC-FM by varying curvature parameter, Forchheimer number, chemical reaction parameter, slip parameter, Schmidt number and activation energy parameter. By testing, validation and training process, the solutions for designed DFMC-FM are interpreted. The performance analysis of DFMC-FM is validated through regression analysis, error histogram studies and MSE results. Graphs are given in figures for velocity profile, temperature profile and concentration profile. The velocity and temperature distributions show an increasing behavior with the upsurge in the curvature parameter, whereas the velocity profile decreases with the growth in Forchheimer number and slip parameter. An increase in the chemical reaction parameter and Schmidt number values leads to a decline in concentration profile, but the increase in activation energy parameter increases the concentration profile.

Suggested Citation

  • Raja, M. Asif Zahoor & Shoaib, M. & Zubair, Ghania & Khan, M. Ijaz & Punith Gowda, R.J. & Prasannakumara, B.C. & Guedri, Kamel, 2022. "Intelligent neuro-computing for entropy generated Darcy–Forchheimer​ mixed convective fluid flow," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 201(C), pages 193-214.
    • Handle: RePEc:eee:matcom:v:201:y:2022:i:c:p:193-214
    DOI: 10.1016/j.matcom.2022.05.004
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    References listed on IDEAS

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    1. Jadoon, Ihtesham & Raja, Muhammad Asif Zahoor & Junaid, Muhammad & Ahmed, Ashfaq & Rehman, Ata ur & Shoaib, Muhammad, 2021. "Design of evolutionary optimized finite difference based numerical computing for dust density model of nonlinear Van-der Pol Mathieu’s oscillatory systems," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 181(C), pages 444-470.
    2. Punith Gowda, R.J. & Al-Mubaddel, Fahad S. & Naveen Kumar, R. & Prasannakumara, B.C. & Issakhov, Alibek & Rahimi-Gorji, Mohammad & Al-Turki, Yusuf A., 2021. "Computational modelling of nanofluid flow over a curved stretching sheet using Koo–Kleinstreuer and Li (KKL) correlation and modified Fourier heat flux model," Chaos, Solitons & Fractals, Elsevier, vol. 145(C).
    3. Nazeer, Mubbashar & Hussain, Farooq & Khan, M. Ijaz & Asad-ur-Rehman, & El-Zahar, Essam Roshdy & Chu, Yu-Ming & Malik, M.Y., 2022. "Theoretical study of MHD electro-osmotically flow of third-grade fluid in micro channel," Applied Mathematics and Computation, Elsevier, vol. 420(C).
    4. Chu, Yu-Ming & Shankaralingappa, B.M. & Gireesha, B.J. & Alzahrani, Faris & Khan, M. Ijaz & Khan, Sami Ullah, 2022. "Combined impact of Cattaneo-Christov double diffusion and radiative heat flux on bio-convective flow of Maxwell liquid configured by a stretched nano-material surface," Applied Mathematics and Computation, Elsevier, vol. 419(C).
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    Cited by:

    1. Ali Hassan & Qusain Haider & Najah Alsubaie & Fahad M. Alharbi & Abdullah Alhushaybari & Ahmed M. Galal, 2022. "Investigation of Mixed Convection in Spinning Nanofluid over Rotating Cone Using Artificial Neural Networks and BVP-4C Technique," Mathematics, MDPI, vol. 10(24), pages 1-20, December.

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