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Keywords = LBB-stable finite element pair

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20 pages, 3422 KB  
Article
Artificial Neural Networking (ANN) Model for Drag Coefficient Optimization for Various Obstacles
by Khalil Ur Rehman, Andaç Batur Çolak and Wasfi Shatanawi
Mathematics 2022, 10(14), 2450; https://doi.org/10.3390/math10142450 - 14 Jul 2022
Cited by 20 | Viewed by 3353
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Artificial Neural Networks: Design and Applications)
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14 pages, 4986 KB  
Article
Finite Element Analysis on Bingham–Papanastasiou Viscoplastic Flow in a Channel with Circular/Square Obstacles: A Comparative Benchmarking
by Asif Mehmood, Waqar A. Khan, Rashid Mahmood and Khalil Ur Rehman
Processes 2020, 8(7), 779; https://doi.org/10.3390/pr8070779 - 3 Jul 2020
Cited by 7 | Viewed by 3902
Abstract
A CFD (computational fluid dynamics) analysis was carried out for the Bingham viscoplastic fluid flow simulations around cylinders of circular and square shapes. The governing equations in space were discretized with the finite element approach via a weak formulation and utilizing Ladyzhenskaya–Babuška–Brezzi-stable pair [...] Read more.
A CFD (computational fluid dynamics) analysis was carried out for the Bingham viscoplastic fluid flow simulations around cylinders of circular and square shapes. The governing equations in space were discretized with the finite element approach via a weak formulation and utilizing Ladyzhenskaya–Babuška–Brezzi-stable pair Q 2 / P 1 disc for approximation of the velocity and pressure profiles. The discrete non-linear system was linearized through Newton’s method, and a direct linear solver was iterated as an inner core solver. The study predicts the functional dependence and impact of Bingham number, B n , on the drag coefficient and lift coefficient. The effect of the shape of an obstacle is also provided by providing comparative data for the hydrodynamic forces with the published results. Full article
(This article belongs to the Special Issue Fluid Flow and Heat Transfer of Nanofluids)
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