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

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 vibra...

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Bibliographic Details
Published inJournal of marine science and engineering Vol. 12; no. 5; p. 813
Main Authors Xu, Bin, Zhang, Liwen, Zhang, Weibin, Deng, Yilin, Wong, Teck Neng
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.05.2024
Subjects
Algorithms
Analysis
Cavitation
data-driven
Decomposition
Eigenvalues
Eigenvectors
Energy
Engineering
Fluid dynamics
Hydraulic equipment
Hydraulic machinery
Hydraulics
Hydrodynamics
Information processing
Machine learning
Machinery
Methods
modal decomposition
Proper Orthogonal Decomposition
Signal processing
Time series
Vibration
Vibration mode
United Kingdom
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Summary: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.
ISSN:2077-1312
2077-1312
DOI:10.3390/jmse12050813