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学者姓名：郑祥豪
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Abstract ：
Swirling vortex rope in draft tube (DT) is a typical hydraulic instability of a pump turbine (PT) in the pumped storage plant (PSP). In view of the potential hazards of the vortex rope, accurate recognition of its intensity is of great significance to maintain the stable operation of the PT. Due to the limitations of shallow learning algorithms during intelligent recognition, an adaptive deep learning framework is innovatively proposed in this study. Firstly, the measured high-precision pressure fluctuation signals based on the prototype PT in a Chinese PSP that can reflect different intensities of vortex ropes in the DT are utilized as the input data. Secondly, a preliminary deep learning framework that integrates convolutional neural network (CNN), bidirectional long short-term memory (BiLSTM) and multi-head self-attention mechanism (MHSAM) is constructed. Then, the Bayesian optimization algorithm (BOA) is utilized to adaptively determine several hyperparameters of the framework. And an adaptive BOA-CNN-BiLSTM-MHSAM framework is established to recognize different intensities of vortex ropes in the DT. Finally, the recognition performance of the proposed framework is demonstrated through comparing with other deep learning frameworks. And the recognition results illustrate that the proposed BOA-CNN-BiLSTM-MHSAM framework can be utilized to effectively recognize different intensities of vortex ropes in the DT. It will be a good technical reserve to improve the intelligent level of the monitoring system of the PSP.
Keyword ：
Draft tube Draft tube Pressure fluctuation Pressure fluctuation Pumped hydro energy storage Pumped hydro energy storage Pump turbine Pump turbine Unsteady flow Unsteady flow Vortex rope Vortex rope
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| GB/T 7714 | Zheng, Xianghao , Yang, Chenxin , Zeng, Lan et al. Intensity recognition of vortex ropes in draft tube of a prototype pump turbine using an optimized CNN-BiLSTM framework with multi-head self-attention mechanism [J]. | JOURNAL OF ENERGY STORAGE , 2025 , 106 . |
| MLA | Zheng, Xianghao et al. "Intensity recognition of vortex ropes in draft tube of a prototype pump turbine using an optimized CNN-BiLSTM framework with multi-head self-attention mechanism" . | JOURNAL OF ENERGY STORAGE 106 (2025) . |
| APA | Zheng, Xianghao , Yang, Chenxin , Zeng, Lan , He, Yuanshuai , Tian, Yulong , Zhang, Yuning et al. Intensity recognition of vortex ropes in draft tube of a prototype pump turbine using an optimized CNN-BiLSTM framework with multi-head self-attention mechanism . | JOURNAL OF ENERGY STORAGE , 2025 , 106 . |
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Abstract ：
水泵水轮机信号分析技术对于保障抽水蓄能电站的安全、稳定及高效运行具有重要的科学价值和工程意义.该文首先汇总了我国抽水蓄能产业相关文件和部分在建抽水蓄能项目.而后,围绕无叶区流态识别、尾水管压力脉动信号特征提取、主轴轴心轨迹提取、顶盖振动信号识别以及空化引起的压力脉动信号降噪这5个方面,综述了信号分解方法在水泵水轮机中的应用,并重点介绍了信号分解方法在原型水泵水轮机的典型工程案例.最后,归纳总结了信号分解方法在水泵水轮机信号分析应用中的作用,为进一步推广信号分解方法在水泵水轮机中的实际应用提供了参考.
Keyword ：
信号分解方法 信号分解方法 信号降噪 信号降噪 原型水泵水轮机 原型水泵水轮机 机器学习 机器学习 特征提取 特征提取
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| GB/T 7714 | 杨辰昕 , 郑祥豪 , 张宇宁 . 原型水泵水轮机信号分解方法及工程应用综述 [J]. | 水动力学研究与进展A辑 , 2024 , 39 (6) : 934-943 . |
| MLA | 杨辰昕 et al. "原型水泵水轮机信号分解方法及工程应用综述" . | 水动力学研究与进展A辑 39 . 6 (2024) : 934-943 . |
| APA | 杨辰昕 , 郑祥豪 , 张宇宁 . 原型水泵水轮机信号分解方法及工程应用综述 . | 水动力学研究与进展A辑 , 2024 , 39 (6) , 934-943 . |
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The present paper investigates the dynamic behaviors of a bubble restricted by two parallel plates near an elliptical wall. The typical experimental phenomena of the bubble are recorded employing the high-speed photography and a theoretical Kelvin impulse model is established. The impacts of the spatial position and the curvature of the wall on the bubble collapse behaviors are quantitatively investigated through the theoretical model and verified against the experimental results. The Kelvin impulse intensity and the direction during the bubble collapse process are compared and discussed for different elliptical-shaped walls. The main conclusions include: (1) During the bubble collapse process, the phenomenon of the bubble uneven splitting is discovered. (2) At different spatial positions and wall curvatures, the bubble collapse jet angle, movement distance, and velocity are in good agreement with the theoretical Kelvin impulse predictions. (3) As the short-to-long axis ratio increases, the differences in the distributions of the Kelvin impulse intensity and the direction near the elliptical wall gradually become larger, and the range of the influence of the impulse intensity expands.
Keyword ：
Bubble dynamics Bubble dynamics Collapsing jet Collapsing jet Elliptical wall Elliptical wall High-speed photographic technique High-speed photographic technique Kelvin impulse Kelvin impulse
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| GB/T 7714 | Shen, Junwei , Li, Shaowei , Wang, Congtao et al. Investigation on the effects of an elliptical wall on the dynamic behaviors of a bubble restricted by two parallel plates [J]. | ULTRASONICS SONOCHEMISTRY , 2024 , 107 . |
| MLA | Shen, Junwei et al. "Investigation on the effects of an elliptical wall on the dynamic behaviors of a bubble restricted by two parallel plates" . | ULTRASONICS SONOCHEMISTRY 107 (2024) . |
| APA | Shen, Junwei , Li, Shaowei , Wang, Congtao , Zhang, Shurui , Wang, Xiaoyu , Zhang, Yuning et al. Investigation on the effects of an elliptical wall on the dynamic behaviors of a bubble restricted by two parallel plates . | ULTRASONICS SONOCHEMISTRY , 2024 , 107 . |
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In fluid machinery, the concurrent presence of cavitation bubbles and particle clusters leads to considerably damage to material surfaces. This study investigates the dynamics of a bubble situated among triple particles based on the Kelvin impulse model and high-frame-rate photography, focusing on the impact of the dimensionless distance of particles and the bubble size. Specifically, the jet, bubble motion, and bubble interface evolution characteristics are quantitatively evaluated. The following conclusions are obtained: (1) The collapse shapes of the bubble can be divided into three typical cases: equilateral triangle shape, isosceles triangle shape, and arcuate shape. (2) Among the triple particles, four zero-Kelvin-impulse locations are present, around which the jet direction is extremely sensitive to the bubble initial position. As the bubble initial position moves along the central line, the bubble motion direction dramatically changes during its collapse. (3) The relative position of bubble and particles is the key parameter that affects the bubble dynamics. As the bubble-particle distance decreases, the non-uniformity of bubble collapse morphology and the bubble motion distance will become more significant.
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| GB/T 7714 | Zhang, Yuning , Ding, Zhiling , Hu, Shuzheng et al. Investigation on laser-induced bubble collapse among triple particles based on high-frame-rate photography and the Kelvin impulse model [J]. | PHYSICS OF FLUIDS , 2024 , 36 (5) . |
| MLA | Zhang, Yuning et al. "Investigation on laser-induced bubble collapse among triple particles based on high-frame-rate photography and the Kelvin impulse model" . | PHYSICS OF FLUIDS 36 . 5 (2024) . |
| APA | Zhang, Yuning , Ding, Zhiling , Hu, Shuzheng , Hu, Jingrong , Wang, Xiaoyu , Zheng, Xianghao . Investigation on laser-induced bubble collapse among triple particles based on high-frame-rate photography and the Kelvin impulse model . | PHYSICS OF FLUIDS , 2024 , 36 (5) . |
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Feature extraction and intelligent recognition of the vibration signals of pump turbines are significant to reliable and safe operation of a pumped storage power station. Due to its complicated operational conditions, a pump turbine in operation can create a large number of physical sources that excite its vibrations, and the frequency components of the vibration signals are quite complicated. The traditional methods suffer a poor accuracy of feature extraction from a complicated vibration signal. To improve the accuracy, this paper describes a new model of feature extraction and intelligent recognition of the vibration signals, based on the variational mode decomposition (VMD), bubble entropy (BE), and long short-term memory (LSTM) neural network. First, this method analyzes the vibration signal using VMD and obtains several modes. Then for each mode, its BE value is calculated and a BE eigenvector is constructed. Finally, the eigenvectors of the vibration signal are trained and recognized using a LSTM neural network. We have verified the method against the complicated vibration signals measured at the top cover of a pump turbine at the Pushihe pumped storage station, and achieved a signal recognition accuracy of 97.87%, indicating its important engineering application value. © 2023 Tsinghua University Press. All rights reserved.
Keyword ：
bubble entropy bubble entropy long short-term memory long short-term memory pump turbine pump turbine variational mode decomposition variational mode decomposition vibration signal vibration signal
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| GB/T 7714 | Zhang, S. , Li, H. , Zhang, Y. et al. Feature extraction and intelligent recognition of complicated vibration signals of pump turbine; [水泵水轮机复杂振动信号特征提取与智能识别] [J]. | Journal of Hydroelectric Engineering , 2023 , 42 (12) : 70-78 . |
| MLA | Zhang, S. et al. "Feature extraction and intelligent recognition of complicated vibration signals of pump turbine; [水泵水轮机复杂振动信号特征提取与智能识别]" . | Journal of Hydroelectric Engineering 42 . 12 (2023) : 70-78 . |
| APA | Zhang, S. , Li, H. , Zhang, Y. , Zheng, X. , Ding, H. , Li, J. . Feature extraction and intelligent recognition of complicated vibration signals of pump turbine; [水泵水轮机复杂振动信号特征提取与智能识别] . | Journal of Hydroelectric Engineering , 2023 , 42 (12) , 70-78 . |
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