自适应变分模态分解算法在高温高压水空化特性分析中的应用

许博; 胡鸿飞; 王海军

doi:10.7652/xjtuxb202501006

您当前的位置：

首页 >

文章列表页 >

自适应变分模态分解算法在高温高压水空化特性分析中的应用

更新时间：2026-01-06

- 自适应变分模态分解算法在高温高压水空化特性分析中的应用
- Application of Adaptive Variational Mode Decomposition Algorithm in Cavitation Characteristics Analysis of High-Temperature and High-Pressure Water
- 西安交通大学学报 2025年59卷第1期页码：56-67
- 作者机构：
  
  西安交通大学能源与动力工程学院， 710049，西安
- 作者简介：
  
  许博（1995—），男，博士生；
  王海军（通信作者），男，教授。
- 基金信息：
  
  陕西省创新能力支撑计划资助项目(2023-CX-TD-18)
- DOI：10.7652/xjtuxb202501006
  中图分类号： TK121
- 收稿：2024-05-31，
  
  网络首发：2024-09-13，
  
  纸质出版：2025-01-10
- 稿件说明：
移动端阅览
许博, 胡鸿飞, 王海军. 自适应变分模态分解算法在高温高压水空化特性分析中的应用[J]. 西安交通大学学报, 2025,59(1):56-67.

XU Bo, HU Hongfei, WANG Haijun. Application of Adaptive Variational Mode Decomposition Algorithm in Cavitation Characteristics Analysis of High-Temperature and High-Pressure Water[J]. Journal of Xi’an Jiaotong University, 2025, 59(1): 56-67.
许博, 胡鸿飞, 王海军. 自适应变分模态分解算法在高温高压水空化特性分析中的应用[J]. 西安交通大学学报, 2025,59(1):56-67. DOI： 10.7652/xjtuxb202501006.

XU Bo, HU Hongfei, WANG Haijun. Application of Adaptive Variational Mode Decomposition Algorithm in Cavitation Characteristics Analysis of High-Temperature and High-Pressure Water[J]. Journal of Xi’an Jiaotong University, 2025, 59(1): 56-67. DOI： 10.7652/xjtuxb202501006.

摘要

针对高温高压流动工况下，空化状态判断困难、传统分析方法难以有效提取压力脉动信号中的有效信息的问题，以孔板为对象，开展了高温高压水的空化实验，并提出了一种基于遗传算法的自适应变分模态分解（AVMD）算法。该算法通过结合中心频率法、遗传算法、功率谱熵和相对能量等技术，自适应地确定变分模态分解算法中的超参数并有效去除信号中的噪声成分，提高了空化特征的提取精度。结果表明：AVMD算法能够精确捕捉到高温高压水流经孔板时空化现象的发生和发展，识别空化起始点、转捩点以及空化强度的变化；当高温高压水流经孔板后，压力脉动的无量纲频率在0.04~0.35、压力脉动的无量纲幅值在0.014~0.067时，空化现象开始出现；随着空化强度增加，管内压力脉动幅值和频率整体呈增大趋势；空化起始转捩点及空化严重转捩点与入口压力和工质入口过冷度密切相关。AVMD算法能够有效提高空化特性分析的精度，尤其是在复杂流动条件下的空化预测，为压水堆核电站冷却剂系统和高压蒸汽系统的稳定运行提供理论依据和参考。

Abstract

In high-temperature and high-pressure flow conditions

accurately determining cavitation states and extracting useful information from pressure fluctuation signals pose significant challenges. To address these issues

cavitation experiments are conducted with high-temperature and high-pressure water flowing through orifices

and an adaptive variational mode decomposition (AVMD) algorithm based on a genetic algorithm is proposed. This algorithm combines techniques such as the central frequency method

genetic algorithm

power spectral entropy

and relative energy to adaptively determine the hyperparameters of the variational mode decomposition and effectively remove noise from the signals

thus improving the precision of cavitation feature extraction. The results show that the AVMD algorithm can accurately capture the onset and development of cavitation phenomena in high-temperature

high-pressure water flowing through orifices

and can identify the initiation points

transition points

and variations in cavitation intensity. When high-temperature

high-pressure water passes through the orifice

cavitation occurs when the dimensionless frequency of pressure fluctuations falls within the range of 0.04 to 0.35

and the dimensionless amplitude is between 0.014 and 0.067. As cavitation intensity increases

the pressure fluctuation amplitude and frequency within the pipe generally increase. The initiation and severe transition points of cavitation are closely related to the inlet pressure and the subcooling degree of the working fluid at the entrance. The AVMD algorithm effectively improves the accuracy of cavitation characteristic analysis

particularly in cavitation prediction under complex flow conditions

providing theoretical support and references for the stable operation of pressurized water reactor (PWR) coolant systems and high-pressure steam systems.

关键词

Keywords

references

HATANO S , KANG D , KAGAWA S , et al . Study of cavitation instabilities in double-suction centrifugal pump [J ] . International Journal of Fluid Machinery and Systems , 2014 , 7 ( 3 ): 94 - 100 .

KIKUTA K , YOSHIDA Y , WATANABE M , et al . Thermodynamic effect on cavitation performances and cavitation instabilities in an inducer [J ] . Journal of Fluids Engineering , 2008 , 130 ( 11 ): 111302 .

TAMURA K , KONDO S , KAWASAKI S , et al . The similarity law of cavitation number on cavitation instability in liquid rocket inducer [J ] . Journal of Fluids Engineering , 2023 , 145 ( 11 ): 111201 .

CHEN Guanghao , WANG Guoyu , HU Changli , et al . Combined experimental and computational investigation of cavitation evolution and excited pressure fluctuation in a convergent-divergent channel [J ] . International Journal of Multiphase Flow , 2015 , 72 : 133 - 140 .

李贝贝 , 孙深圳 , 刘秀梅 , 等 . 基于改进SVM算法的调节阀空化状态识别 [J ] . 机械工程学报 , 2023 , 59 ( 24 ): 312 - 322 .

LI Beibei , SUN Shenzhen , LIU Xiumei , et al . Cavitation state recognition of regulating valve based on improved SVM algorithm [J ] . Journal of Mechanical Engineering , 2023 , 59 ( 24 ): 312 - 322 .

ESPOSITO C , MENDEZ M A , STEELANT J , et al . Spectral and modal analysis of a cavitating flow through an orifice [J ] . Experimental Thermal and Fluid Science , 2021 , 121 : 110251 .

CHEN Tairan , CHEN Hui , LIU Wenchuan , et al . Unsteady characteristics of liquid nitrogen cavitating flows in different thermal cavitation mode [J ] . Applied Thermal Engineering , 2019 , 156 : 63 - 76 .

BAGHERI A , OZBULUT O E , HARRIS D K . Structural system identification based on variational mode decomposition [J ] . Journal of Sound and Vibration , 2018 , 417 : 182 - 197 .

HUANG Nantian , WU Yinyin , CAI Guowei , et al . Short-term wind speed forecast with low loss of information based on feature generation of OSVD [J ] . IEEE Access , 2019 , 7 : 81027 - 81046 .

CARVALHO V R , MORAES M F D , BRAGA A P , et al . Evaluating five different adaptive decomposition methods for EEG signal seizure detection and classification [J ] . Biomedical Signal Processing and Control , 2020 , 62 : 102073 .

DRAGOMIRETSKIY K , ZOSSO D . Variational mode decomposition [J ] . IEEE Transactions on Signal Processing , 2014 , 62 ( 3 ): 531 - 544 .

CHEN Mingyuan , CHENG Qiaorui , FENG Xie , et al . Optimized variational mode decomposition algorithm based on adaptive thresholding method and improved whale optimization algorithm for denoising magnetocardiography signal [J ] . Biomedical Signal Processing and Control , 2024 , 88 : 105681 .

WANG Yanxue , LIU Fuyun , JIANG Zhansi , et al . Complex variational mode decomposition for signal processing applications [J ] . Mechanical Systems and Signal Processing , 2017 , 86 ( Part A ): 75 - 85 .

LIU Wei , CAO Siyuan , CHEN Yangkang . Applications of variational mode decomposition in seismic time-frequency analysis [J ] . Geophysics , 2016 , 81 ( 5 ): V365 - V378 .

XUE Yajuan , CAO Junxing , WANG Daxing , et al . Application of the variational-mode decomposition for seismic time-frequency analysis [J ] . IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , 2016 , 9 ( 8 ): 3821 - 3831 .

LIU Yingbin , ZHANG Yang , YU Shuai , et al . An efficient new adaptive variational mode decomposition algorithm for extracting adventitious lung sounds [J ] . Biomedical Signal Processing and Control , 2024 , 89 : 105700 .

JIANG Xingxing , WANG Jun , SHEN Changqing , et al . An adaptive and efficient variational mode decomposition and its application for bearing fault diagnosis [J ] . Structural Health Monitoring , 2021 , 20 ( 5 ): 2708 - 2725 .

RUDOLF P , KUBINA D , HUDEC M , et al . Experimental investigation of hydrodynamic cavitation through orifices of different geometries [C ] // EFM16-Experimental Fluid Mechanics 2016 . Les Ulis, France : EDP Sciences , 2017 : 02098 .

ANGELE K . Prediction of cavitation in orifice plates: a novel and simple rule-of-thumb [J ] . Experimental and Computational Multiphase Flow , 2021 , 3 ( 1 ): 68 - 76 .

张孝石 . 水下航行体空化流动与压力脉动特性研究 [D ] . 哈尔滨 : 哈尔滨工业大学 , 2017 .

季斌 , 程怀玉 , 黄彪 , 等 . 空化水动力学非定常特性研究进展及展望 [J ] . 力学进展 , 2019 , 49 ( 1 ): 428 - 479 .

JI Bin , CHENG Huaiyu , HUANG Biao , et al . Research progresses and prospects of unsteady hydrodynamics characteristics for cavitation [J ] . Advances in Mechanics , 2019 , 49 ( 1 ): 428 - 479 .

FENG Jianjun , ZHAO Nannan , ZHU Guojun , et al . Cavitation identification in a hydraulic bulb turbine based on vibration and pressure fluctuation measurements [J ] . Mechanical Systems and Signal Processing , 2024 , 208 : 111042 .

ARNDT R E A . Cavitation in fluid machinery and hydraulic structures [J ] . Annual Review of Fluid Mechanics , 1981 , 13 : 273 - 326 .

BRENNEN C E . Cavitation and bubble dynamics [M ] . Oxford, UK : Oxford University Press , 1995 .

FRANC J P , MICHEL J M . Fundamentals of cavitation [M ] . Dordrecht, Netherlands : Springer Netherlands , 2005 .

浏览量

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

张角凹坑气膜孔对双层壁气膜冷却特性的影响

超临界二氧化碳向心透平轮背间隙内风阻损失及流动特性研究

电子级超纯氮深冷空分流程节能优化研究

多工况下离心压气机叶轮-机匣一体联合设计优化

语义区分和子图整合驱动的知识图谱表示学习方法