Performance Comparison of Prediction of Hydraulic Jump Length Under Multiple Neural Network Models
Hydraulic jump is a common physical phenomenon in the field of hydraulic engineering. The essence of hydraulic jump is the conversion and dissipation of a large amount of energy due to the interaction between vortex structures, mainly released in the form of turbulence and water waves. This process...
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2024-01-01
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| author | Ziyuan Xu Zirui Liu Yingzi Peng |
| author_facet | Ziyuan Xu Zirui Liu Yingzi Peng |
| author_sort | Ziyuan Xu |
| collection | DOAJ |
| description | Hydraulic jump is a common physical phenomenon in the field of hydraulic engineering. The essence of hydraulic jump is the conversion and dissipation of a large amount of energy due to the interaction between vortex structures, mainly released in the form of turbulence and water waves. This process significantly reduces the kinetic energy of water flow, thereby mitigating downstream erosion and protecting hydraulic structures, which in turn extends their service life. As a crucial factor in the energy dissipation design of discharge structures, the length of the hydraulic jump is influenced by various factors, including flow velocity, upstream and downstream water depths, riverbed roughness height, and Froude number. In this study, we applied dimensional analysis to identify the key parameters influencing hydraulic jumps on the dataset provided by literature. We utilized a multi-task learning strategy, incorporating a shared feature extraction layer for characteristic modeling of hydraulic jumps within Physics-Informed Neural Networks (PINNs). Furthermore, we compared the performance of PINNs with other data-driven models such as Deep Neural Networks (DNNs), Convolutional Neural Networks (CNNs), and Transformers. The results demonstrated that these models are effective in estimating the length of hydraulic transitions and distinguishing between steady and unsteady hydraulic jump processes. Notably, the PINNs model exhibited better performance than other models, achieving an <inline-formula> <tex-math notation="LaTeX">$\mathbf {R^{2}}$ </tex-math></inline-formula> score of <inline-formula> <tex-math notation="LaTeX">$\mathbf {0.8818}$ </tex-math></inline-formula>, RMSE of 4.4627(cm), MAE of 3.3784(cm), precision of <inline-formula> <tex-math notation="LaTeX">$\mathbf {0.9677}$ </tex-math></inline-formula> and recall of 0.9677 on the test set. These findings are significant for elucidating the characteristics and effects of hydraulic jumps in hydraulic structures, providing a scientific basis for the safe operation and design of practical hydraulic engineering projects. |
| format | Article |
| id | doaj-art-b9c4345f969e4503be3cb24046f7fa41 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-b9c4345f969e4503be3cb24046f7fa412025-08-20T03:53:17ZengIEEEIEEE Access2169-35362024-01-011212288812290110.1109/ACCESS.2024.343007510600680Performance Comparison of Prediction of Hydraulic Jump Length Under Multiple Neural Network ModelsZiyuan Xu0https://orcid.org/0009-0006-5824-3007Zirui Liu1Yingzi Peng2Civil and Hydraulic Engineering, Qinghai University, Xining, ChinaHunan Diantou Education Technology Company Ltd., Changsha, Hunan, ChinaHunan Diantou Education Technology Company Ltd., Changsha, Hunan, ChinaHydraulic jump is a common physical phenomenon in the field of hydraulic engineering. The essence of hydraulic jump is the conversion and dissipation of a large amount of energy due to the interaction between vortex structures, mainly released in the form of turbulence and water waves. This process significantly reduces the kinetic energy of water flow, thereby mitigating downstream erosion and protecting hydraulic structures, which in turn extends their service life. As a crucial factor in the energy dissipation design of discharge structures, the length of the hydraulic jump is influenced by various factors, including flow velocity, upstream and downstream water depths, riverbed roughness height, and Froude number. In this study, we applied dimensional analysis to identify the key parameters influencing hydraulic jumps on the dataset provided by literature. We utilized a multi-task learning strategy, incorporating a shared feature extraction layer for characteristic modeling of hydraulic jumps within Physics-Informed Neural Networks (PINNs). Furthermore, we compared the performance of PINNs with other data-driven models such as Deep Neural Networks (DNNs), Convolutional Neural Networks (CNNs), and Transformers. The results demonstrated that these models are effective in estimating the length of hydraulic transitions and distinguishing between steady and unsteady hydraulic jump processes. Notably, the PINNs model exhibited better performance than other models, achieving an <inline-formula> <tex-math notation="LaTeX">$\mathbf {R^{2}}$ </tex-math></inline-formula> score of <inline-formula> <tex-math notation="LaTeX">$\mathbf {0.8818}$ </tex-math></inline-formula>, RMSE of 4.4627(cm), MAE of 3.3784(cm), precision of <inline-formula> <tex-math notation="LaTeX">$\mathbf {0.9677}$ </tex-math></inline-formula> and recall of 0.9677 on the test set. These findings are significant for elucidating the characteristics and effects of hydraulic jumps in hydraulic structures, providing a scientific basis for the safe operation and design of practical hydraulic engineering projects.https://ieeexplore.ieee.org/document/10600680/Hydraulic jumpfeature predictionPINNsneural networks |
| spellingShingle | Ziyuan Xu Zirui Liu Yingzi Peng Performance Comparison of Prediction of Hydraulic Jump Length Under Multiple Neural Network Models IEEE Access Hydraulic jump feature prediction PINNs neural networks |
| title | Performance Comparison of Prediction of Hydraulic Jump Length Under Multiple Neural Network Models |
| title_full | Performance Comparison of Prediction of Hydraulic Jump Length Under Multiple Neural Network Models |
| title_fullStr | Performance Comparison of Prediction of Hydraulic Jump Length Under Multiple Neural Network Models |
| title_full_unstemmed | Performance Comparison of Prediction of Hydraulic Jump Length Under Multiple Neural Network Models |
| title_short | Performance Comparison of Prediction of Hydraulic Jump Length Under Multiple Neural Network Models |
| title_sort | performance comparison of prediction of hydraulic jump length under multiple neural network models |
| topic | Hydraulic jump feature prediction PINNs neural networks |
| url | https://ieeexplore.ieee.org/document/10600680/ |
| work_keys_str_mv | AT ziyuanxu performancecomparisonofpredictionofhydraulicjumplengthundermultipleneuralnetworkmodels AT ziruiliu performancecomparisonofpredictionofhydraulicjumplengthundermultipleneuralnetworkmodels AT yingzipeng performancecomparisonofpredictionofhydraulicjumplengthundermultipleneuralnetworkmodels |