Application of Adaptive Variational Mode Decomposition Algorithm in Cavitation Characteristics Analysis of High-Temperature and High-Pressure Water

XU Bo; HU Hongfei; WANG Haijun

doi:10.7652/xjtuxb202501006

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Application of Adaptive Variational Mode Decomposition Algorithm in Cavitation Characteristics Analysis of High-Temperature and High-Pressure Water

更新时间：2026-01-06

- Application of Adaptive Variational Mode Decomposition Algorithm in Cavitation Characteristics Analysis of High-Temperature and High-Pressure Water
- Journal of Xi’an Jiaotong University Vol. 59, Issue 1, Pages: 56-67(2025)
- 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：
- 基金信息：
- DOI：10.7652/xjtuxb202501006
  CLC： TK121
- Received：31 May 2024，
  
  Online First：13 September 2024，
  
  Published：10 January 2025
- 稿件说明：
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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：

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.

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references

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