哈尔滨工程大学学报2026，Vol.47Issue(7)：1399-1407,9.DOI:10.11990/jheu.202505004
基于压力信号统计特征的管道式离心分离器内流型失稳识别
Flow pattern instability identification in a tubular centrifugal separator based on statistical characteristics of pressure signals
摘要
Abstract
To address the shortcomings of existing methods in noise sensitivity and parameter dependency for judging flow pattern instability in vertical gas-liquid swirling flows,a flow pattern identification method is proposed that integrates kernel density estimation(KDE)and dimensionless parameters.First,the dynamic evolution of flow patterns was observed through a visualization experiment system.Based on an improved KDE method,time-domain statistical features of pressure signals were extracted.Dimensionless parameters such as standard deviation(σ),skewness coefficient(Sₖ),coefficient of variation(CV),and kurtosis(K)were introduced to establish a joint criterion for quantifying flow pattern differences.Finally,the criterion and transition boundary for unstable swirl-ing flow patterns were determined.Experiments show that the transition boundary follows an exponential relation-ship:jl=1.177 4 j0.63g(jg is the gas-phase superficial velocity;jl is the liquid-phase superficial velocity).This result provides a quantitative criterion for flow pattern instability warning.The proposed method can offer reliable technical support for online flow pattern monitoring,instability warning,and operational optimization of centrifugal separation devices.关键词
气液螺旋流/不稳定流型/核密度估计/时域特征/流型转变/压力波动/统计特征/无量纲参数Key words
gas-liquid swirl flow/unstable flow pattern/Kernel density estimation(KDE)/time-domain charac-teristics/flow pattern transition/pressure fluctuations/statistical characteristics/dimensionless parameter分类
能源科技引用本文复制引用
李佳明,欧奕城,曾晓波,范广铭..基于压力信号统计特征的管道式离心分离器内流型失稳识别[J].哈尔滨工程大学学报,2026,47(7):1399-1407,9.基金项目
国家自然科学基金面上项目(11875117) （11875117）
黑龙江省核动力装置性能与设备重点实验室重点项目(HLJS202301). （HLJS202301）
