Using Flow 3D Simulation, Multiple Nonlinear Regression Approach, and Artificial Neural Network Approach Approaches to Study the Behavior of Vertical Drop Structures
The “Vertical Drop” is a hydraulic structure widely utilized in irrigation and wastewater collection systems to equalize height differences between channel slopes and the natural terrain. Previous studies of vertical drops primarily focused on experimental investigations of their hydraulic propertie...
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| Format: | Article |
| Language: | English |
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2024-12-01
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| Series: | Civil and Environmental Engineering |
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| Online Access: | https://doi.org/10.2478/cee-2024-0050 |
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| author | Shaikhli Hasan Al Mohammed Sarah Hashim Al-Khafaji Zainab |
| author_facet | Shaikhli Hasan Al Mohammed Sarah Hashim Al-Khafaji Zainab |
| author_sort | Shaikhli Hasan Al |
| collection | DOAJ |
| description | The “Vertical Drop” is a hydraulic structure widely utilized in irrigation and wastewater collection systems to equalize height differences between channel slopes and the natural terrain. Previous studies of vertical drops primarily focused on experimental investigations of their hydraulic properties. This study numerically analyzes the hydraulic features of vertical drops with inverse aprons using FLOW3D software and the finite volume method. The volume of fluid (VOF) technique was employed to simulate the free surface. Key flow parameters, such as downstream depth, pool depth, and energy loss, were calculated and validated against experimental data. Various turbulence models and grid configurations were assessed. The numerical results, achieved with a grid size of 20,000 nodes, a downstream channel length of 2 meters, the standard k-ε turbulence model, and a standard wall function, exhibited excellent agreement with theoretical equations. Downstream depth, pool depth, and energy loss closely matched experimental findings. Additionally, numerical impact velocities were compared with empirical equations across different scenarios, demonstrating minimal deviation. These findings confirm that the velocity characteristics of the falling jet can be reliably estimated numerically. |
| format | Article |
| id | doaj-art-13311849a4214932b6a48734a88d2517 |
| institution | Kabale University |
| issn | 2199-6512 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Sciendo |
| record_format | Article |
| series | Civil and Environmental Engineering |
| spelling | doaj-art-13311849a4214932b6a48734a88d25172025-02-02T15:47:53ZengSciendoCivil and Environmental Engineering2199-65122024-12-0120265468310.2478/cee-2024-0050Using Flow 3D Simulation, Multiple Nonlinear Regression Approach, and Artificial Neural Network Approach Approaches to Study the Behavior of Vertical Drop StructuresShaikhli Hasan Al0Mohammed Sarah Hashim1Al-Khafaji Zainab2Civil Engineering Department, College of Engineering, University of Warith Al-Anbiyaa, Karbala, IraqMinistry of Environment, Environmental Protection and Improvement Directorate, Karbala, IraqNew Era and Development in Civil Engineering Research Group, Scientific Research Center, Al-Ayen University, Thi-Qar, 64001, IraqThe “Vertical Drop” is a hydraulic structure widely utilized in irrigation and wastewater collection systems to equalize height differences between channel slopes and the natural terrain. Previous studies of vertical drops primarily focused on experimental investigations of their hydraulic properties. This study numerically analyzes the hydraulic features of vertical drops with inverse aprons using FLOW3D software and the finite volume method. The volume of fluid (VOF) technique was employed to simulate the free surface. Key flow parameters, such as downstream depth, pool depth, and energy loss, were calculated and validated against experimental data. Various turbulence models and grid configurations were assessed. The numerical results, achieved with a grid size of 20,000 nodes, a downstream channel length of 2 meters, the standard k-ε turbulence model, and a standard wall function, exhibited excellent agreement with theoretical equations. Downstream depth, pool depth, and energy loss closely matched experimental findings. Additionally, numerical impact velocities were compared with empirical equations across different scenarios, demonstrating minimal deviation. These findings confirm that the velocity characteristics of the falling jet can be reliably estimated numerically.https://doi.org/10.2478/cee-2024-0050vertical dropnumerical modelscfdflow 3dmnlrann. |
| spellingShingle | Shaikhli Hasan Al Mohammed Sarah Hashim Al-Khafaji Zainab Using Flow 3D Simulation, Multiple Nonlinear Regression Approach, and Artificial Neural Network Approach Approaches to Study the Behavior of Vertical Drop Structures Civil and Environmental Engineering vertical drop numerical models cfd flow 3d mnlr ann. |
| title | Using Flow 3D Simulation, Multiple Nonlinear Regression Approach, and Artificial Neural Network Approach Approaches to Study the Behavior of Vertical Drop Structures |
| title_full | Using Flow 3D Simulation, Multiple Nonlinear Regression Approach, and Artificial Neural Network Approach Approaches to Study the Behavior of Vertical Drop Structures |
| title_fullStr | Using Flow 3D Simulation, Multiple Nonlinear Regression Approach, and Artificial Neural Network Approach Approaches to Study the Behavior of Vertical Drop Structures |
| title_full_unstemmed | Using Flow 3D Simulation, Multiple Nonlinear Regression Approach, and Artificial Neural Network Approach Approaches to Study the Behavior of Vertical Drop Structures |
| title_short | Using Flow 3D Simulation, Multiple Nonlinear Regression Approach, and Artificial Neural Network Approach Approaches to Study the Behavior of Vertical Drop Structures |
| title_sort | using flow 3d simulation multiple nonlinear regression approach and artificial neural network approach approaches to study the behavior of vertical drop structures |
| topic | vertical drop numerical models cfd flow 3d mnlr ann. |
| url | https://doi.org/10.2478/cee-2024-0050 |
| work_keys_str_mv | AT shaikhlihasanal usingflow3dsimulationmultiplenonlinearregressionapproachandartificialneuralnetworkapproachapproachestostudythebehaviorofverticaldropstructures AT mohammedsarahhashim usingflow3dsimulationmultiplenonlinearregressionapproachandartificialneuralnetworkapproachapproachestostudythebehaviorofverticaldropstructures AT alkhafajizainab usingflow3dsimulationmultiplenonlinearregressionapproachandartificialneuralnetworkapproachapproachestostudythebehaviorofverticaldropstructures |