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学者姓名：郑祥豪
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Abstract ：
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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Abstract ：
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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