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Artificial Neural Networking (ANN) Model for Drag Coefficient Optimization for Various Obstacles

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)

  • Andaç Batur Çolak

    (Department of Mechanical Engineering, Engineering Faculty, Niğde Ömer Halisdemir University, Niğde 51240, Turkey)

  • Wasfi Shatanawi

    (Department of Mathematics and Sciences, College of Humanities and Sciences, Prince Sultan University, Riyadh 11586, Saudi Arabia
    Department of Medical Research, China Medical University Hospital, China Medical University, Taichung 40402, Taiwan
    Department of Mathematics, Faculty of Science, The Hashemite University, P.O. Box 330127, Zarqa 13133, Jordan)

Abstract

For various obstacles in the path of a flowing liquid stream, an artificial neural networking (ANN) model is constructed to study the hydrodynamic force depending on the object. The multilayer perceptron (MLP), back propagation (BP), and feed-forward (FF) network models were employed to create the ANN model, which has a high prediction accuracy and a strong structure. To be more specific, circular-, octagon-, hexagon-, square-, and triangular-shaped cylinders are installed in a rectangular channel. The fluid is flowing from the left wall of the channel by following two velocity profiles explicitly linear velocity and parabolic velocity. The no-slip condition is maintained on the channel upper and bottom walls. The Neumann condition is applied to the outlet. The entire physical design is mathematically regulated using flow equations. The result is presented using the finite element approach, with the LBB-stable finite element pair and a hybrid meshing scheme. The drag coefficient values are calculated by doing line integration around installed obstructions for both linear and parabolic profiles. The values of the drag coefficient are predicted with high accuracy by developing an ANN model toward various obstacles.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:14:p:2450-:d:862484
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    References listed on IDEAS

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    1. Varela, J. & Araújo, M. & Bove, I. & Cabeza, C. & Usera, G. & Martí, Arturo C. & Montagne, R. & Sarasúa, L.G., 2007. "Instabilities developed in stratified flows over pronounced obstacles," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 386(2), pages 681-685.
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    Cited by:

    1. 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.
    2. 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.

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