Dynamic mode structure analysis of the near-wake region of a Savonius-type hydrokinetic turbine

Wu, Yanzhao; Wang, Guang; Tao, Ran; Liu, Jie; Xiao, Ruofu

doi:10.1016/j.oceaneng.2023.114965

Cited by 6 publications

(3 citation statements)

References 50 publications

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“…Zhang et al [75] Turbine FFT and DMD methods are used to analyze the dynamics of the near wake region. Wu et al [76] Pump turbine…”

Section: Song Et Al [73]mentioning

confidence: 99%

Data-Driven Modal Decomposition Methods as Feature Detection Techniques for Flow Fields in Hydraulic Machinery: A Mini Review

Xu,

Zhang,

Zhang

et al. 2024

JMSE

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Cavitation is a quasi-periodic process, and its non-stationarity leads to increasingly complex flow field structures. On the other hand, characterizing the flow field with greater precision has become increasingly feasible. However, accurately and effectively extracting the most representative vibration modes and spatial structures from these vast amounts of data has become a significant challenge. Researchers have proposed data-driven modal decomposition techniques to extract flow field information, which have been widely applied in various fields such as signal processing and fluid dynamics. This paper addresses the application of modal decomposition methods, such as dynamic mode decomposition (DMD), Proper Orthogonal Decomposition (POD), and Spectral Proper Orthogonal Decomposition (SPOD), in cavitation feature detection in hydraulic machinery. It reviews the mathematical principles of these three algorithms and a series of improvements made by researchers since their inception. It also provides examples of the applications of these three algorithms in different hydraulic machinery. Based on this, the future development trends and possible directions for the improvement of modal decomposition methods are discussed.

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“…Zhang et al [75] Turbine FFT and DMD methods are used to analyze the dynamics of the near wake region. Wu et al [76] Pump turbine…”

Section: Song Et Al [73]mentioning

confidence: 99%

Data-Driven Modal Decomposition Methods as Feature Detection Techniques for Flow Fields in Hydraulic Machinery: A Mini Review

Xu,

Zhang,

Zhang

et al. 2024

JMSE

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“…Khani et al [19] first used different software calculation methods such as Catboost, artificial neural networks, and random forests to predict the power coefficients of the Savonius hydrodynamic turbine, and the results showed that the Catboost method was more accurate. Wu et al [20] combined the fast Fourier transform and dynamic modal decomposition to provide an in-depth analysis of the frequency distribution and dynamic patterns of velocity fluctuations in the near wake region, comparing the advantages and disadvantages of the two methods and providing a new approach to flow analysis.…”

Section: E Pmentioning

confidence: 99%

Optimization of Blade Geometry of Savonius Hydrokinetic Turbine Based on Genetic Algorithm

Lu,

Zhang,

Guang

et al. 2023

ENERGY

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Savonius hydrokinetic turbine is a kind of turbine set which is suitable for low-velocity conditions. Unlike conventional turbines, Savonius turbines employ S-shaped blades and have simple internal structures. Therefore, there is a large space for optimizing the blade geometry. In this study, computational fluid dynamics (CFD) numerical simulation and genetic algorithm (GA) were used for the optimal design. The optimization strategies and methods were determined by comparing the results calculated by CFD with the experimental results. The weighted objective function was constructed with the maximum power coefficient C p and the high-power coefficient range R under multiple working conditions. GA helps to find the optimal individual of the objective function. Compared the optimal scheme with the initial scheme, the overlap ratio β increased from 0.2 to 0.202, and the clearance ratio ε increased from 0 to 0.179, the blade circumferential angle γ increased from 0°to 27°, the blade shape extended more towards the spindle. The overall power of Savonius turbines was maintained at a high level over 22%, R also increased from 0.73 to 1.02. In comparison with the initial scheme, the energy loss of the optimal scheme at high blade tip speed is greatly reduced, and this reduction is closely related to the optimization of blade geometry. As R becomes larger, Savonius turbines can adapt to the overall working conditions and meet the needs of its work in low flow rate conditions. The results of this paper can be used as a reference for the hydrodynamic optimization of Savonius turbine runners.

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“…Jin et al [23] conducted a fast Fourier transform analysis of the leakage flow in the Kaplan turbine, focusing on the impact of the flow of the tip gap on turbine performance and hydraulic efficiency. Wu et al [24] compared the DMD method with the FFT method and retrograde method for the dynamic pattern structure analysis of the Savonius turbine near the wake region, providing a new idea for flow state analysis. Tian et al [25] improved the traditional FFT and proposed that ZOOM-FFT can effectively diagnose and analyze the fault of submersible sewage pumps, aiming at the operation of mixed-flow pumps as turbines.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Pressure Pulsation Characteristics of Francis Turbine with Different Draft Tube Arrangement Direction

Zhang,

Hu,

Liu

et al. 2023

Water

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Hydroelectric power generation is an important clean energy source, and the stability of water turbine operation determines the quality of hydro energy utilization. For hydro turbines, the layout direction of the draft tube is often only considered from a structural perspective, ignoring the hydrodynamic characteristics. This study adopts the computational fluid dynamics method and verifies the effectiveness of numerical simulation with experimental results, analyzing the influence of asymmetric draft tube layout direction on pressure pulsation of mixed flow turbine. The results show that under different working conditions, there is basically no difference in efficiency corresponding to different inclined directions of the draft tube, and the relative difference in performance values is less than 1%. From the perspective of internal flow, the working condition has a greater impact on the flow, and the draft tube tilt has a smaller impact. Under strong swirling flow conditions in the draft tube, the variational mode decomposition of pressure fluctuation is carried out. Research has found 7 characteristic frequency bands including 140 Hz, 80–90 Hz, 40–46 Hz, 5.5–6.5 Hz, 2.5–3 Hz, 1.67 Hz, and <1 Hz. The frequency of the dominant mode corresponding to the left tilt is higher, while the amplitude of the runner frequency is slightly lower. In general, left tilt is not only more suitable for unit layout but also has a better effect on draft tube pressure fluctuation, which is worthy of application in engineering.

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Dynamic mode structure analysis of the near-wake region of a Savonius-type hydrokinetic turbine

Cited by 6 publications

References 50 publications

Data-Driven Modal Decomposition Methods as Feature Detection Techniques for Flow Fields in Hydraulic Machinery: A Mini Review

Data-Driven Modal Decomposition Methods as Feature Detection Techniques for Flow Fields in Hydraulic Machinery: A Mini Review

Optimization of Blade Geometry of Savonius Hydrokinetic Turbine Based on Genetic Algorithm

Comparison of Pressure Pulsation Characteristics of Francis Turbine with Different Draft Tube Arrangement Direction

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