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.
high-temperature and high-pressure watercavitation characteristicsadaptive variational mode decompositionorifice plates
NETL
NSTL
万方数据
