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

作　　者：许博 胡鸿飞 王海军[1] XU Bo;HU Hongfei;WANG Haijun(School of Energy and Power Engineering,Xi’an Jiaotong University,Xi’an 710049,China)

机构地区：[1]西安交通大学能源与动力工程学院,西安710049

出　　处：《西安交通大学学报》2025年第1期56-67,共12页Journal of Xi'an Jiaotong University

基　　金：陕西省创新能力支撑计划资助项目(2023-CX-TD-18)。

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。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：高温高压水 空化特性 自适应变分模态分解 孔板 

分 类 号：TK121[动力工程及工程热物理—工程热物理]