球形颗粒垂直管水力提升压力波动特性检测
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摘要
为了揭示水力提升过程中颗粒运动诱发的压力波动,开展以水为循环工质、圆形玻璃珠为颗粒的垂直管水力提升实验,利用多路压力变送器测量实验段不同位置的压力信号,利用高速摄影仪对透明实验段内颗粒运动进行直接观测,结合差压信号互相关系数及颗粒运动图像确定代表性压力信号,运用统计分析和快速傅里叶变换进行信号处理,提取差压均值pv、差压平均幅值Av、主峰频率fp、频率主峰幅值Afp和时频熵S等时频域特征参数。研究结果表明:距基准面向上0.5倍管道内径处的取压孔能够更加准确、灵敏地检测到颗粒流的特性;差压均值等时频域特征参数与管道内压力波动之间未呈现出明显的规律性;以AvS和Afp/S这2个组合参数为基础建立的实验关联式的相关系数分别高达0.982和0.952,拟合值与实验值的相对误差分别为0.36%～1.68%和1.05%～5.02%。
In order to reveal the pressure fluctuation induced by particle movement in hydraulic lifting processes,experiments were carried out in a vertical pipe with water as circulating working medium and circular glass beads as particles. The pressure signals at different positions in the experimental section were measured by several pressure transmitters, and the particle motion in the transparent experimental section was observed directly with high speed photographer. The representative pressure signal was determined by analyzing the correlation coefficients of the differential pressure signals and the particle motion images. Using statistical analysis and fast Fourier transform, the average differential pressure pv, average amplitude of differential pressure Av, peak frequency fp, peak frequency amplitude Afp and time-frequency entropy S were extracted. The results show that the characteristics of the particle flow can be detected more accurately and sensitively by the pressure port at 0.5 times of the inner diameter of the pipe. There is no obvious regularity between the characteristic parameters extracted and the pressure fluctuation in the pipe. The experimental correlations based on AvS and Afp/S have the correlation coefficients up to 0.982 and 0.952, and the relative errors between the fitting value and the experimental value are 0.36%-1.68% and 1.05%-5.02%, respectively.
In order to reveal the pressure fluctuation induced by particle movement in hydraulic lifting processes,experiments were carried out in a vertical pipe with water as circulating working medium and circular glass beads as particles. The pressure signals at different positions in the experimental section were measured by several pressure transmitters, and the particle motion in the transparent experimental section was observed directly with high speed photographer. The representative pressure signal was determined by analyzing the correlation coefficients of the differential pressure signals and the particle motion images. Using statistical analysis and fast Fourier transform, the average differential pressure pv, average amplitude of differential pressure Av, peak frequency fp, peak frequency amplitude Afp and time-frequency entropy S were extracted. The results show that the characteristics of the particle flow can be detected more accurately and sensitively by the pressure port at 0.5 times of the inner diameter of the pipe. There is no obvious regularity between the characteristic parameters extracted and the pressure fluctuation in the pipe. The experimental correlations based on AvS and Afp/S have the correlation coefficients up to 0.982 and 0.952, and the relative errors between the fitting value and the experimental value are 0.36%-1.68% and 1.05%-5.02%, respectively.
引文
[1]MATHIESEN V,SOLBERG T.Laser-based flow measurements of dilute vertical pneumatic transport[J].Chemical Engineering Communications,2004,191(3):414-433.
[2]陈鸿伟,姜华伟,高建强,等.鼓泡流化床风帽压力波动特性分析[J].中国电机工程学报,2011,31(23):1-7.CHEN Hongwei,JIANG Huawei,GAO Jianqiang,et al.Analysis of characteristic pressure fluctuation in wind cap of bubbling fluidized bed[J].Proceedings of the CSEE,2011,31(23):1-7.
[3]JOHNSSON F,ZIJERVELD R C,SCHOUTEN J C,et al.Characterization of fluidization regimes by time-series analysis of pressure fluctuations[J].International Journal of Multiphase Flow,2000,26(4):663-715.
[4]KIM S M,KIM J,MUDAWAR I.Flow condensation in parallel micro-channels.Part 1:experimental results and assessment of pressure drop correlations[J].International Journal of Heat and Mass Transfer,2012,55(4):971-983.
[5]王彭,周峰,黄震宇,等.基于时域准同步的谐波和间谐波检测算法[J].仪器仪表学报,2013,34(2):275-280.WANG Peng,ZHOU Feng,HUANG Zhenyu,et al.Harmonic and interharmonic detection algorithm based on time-domain quasi-synchronous technique[J].Chinese Journal of Scientific Instrument,2013,34(2):275-280.
[6]SONG Yuewen,ZHU Xiaojun,SUN Zhiqiang,et al.Experimental investigation of particle-induced pressure loss in solid-liquid lifting pipe[J].Journal of Central South University,2017,24(9):2114-2120.
[7]齐国清,贾欣乐.插值FFT估计正弦信号频率的精度分析[J].电子学报,2004,32(4):625-629.QI Guoqing,JIA Xinle.Accuracy analysis of frequency estimation of sinusoid based on interpolated FFT[J].Acta Electronica Sinica,2004,32(4):625-629.
[8]张鸿博,蔡晓锋,卢改风.基于双窗全相位FFT双谱线校正的电力谐波分[J].仪器仪表学报,2015,36(12):2835-2841.ZHANG Hongbo,CAI Xiaofeng,LU Gaifeng.Doublespectrum-line correction method based on double-window all-phase FFT for power harmonic analysis[J].Chinese Journal of Scientific Instrument,2015,36(12):2835-2841.
[9]桑林琼,邱明国,王莉,等.基于统计阈值的脑肿瘤MRI图像的分割方法[J].生物医学工程研究,2010,29(4):237-239.SANG Linqiong,QIU Mingguo,WANG Li,et al.Brain tumor MRI image segmentation based on statistical thresholding method[J].Journal of Biomedical Engineering Research,2010,29(4):237-239.
[10]张超,陈建军,郭迅.基于EMD能量熵和支持向量机的齿轮故障诊断方法[J].振动与冲击,2010,29(10):216-220.ZHANG Chao,CHEN Jianjun,GUO Xun.A gear fault diagnosis method based on EMD energy entropy and SVM[J].Journal of Vibration and Shock,2010,29(10):216-220.
[11]SUN Zhiqiang,SHAO Shuai,GONG Hui.Gas-liquid flow regime recognition based on wavelet packet energy entropy of vortex-induced pressure fluctuation[J].Measurement Science Review,2013,13(2):83-88.
[12]陈露阳,尹佳雯,孙志强,等.基于EEMD-Hilbert谱的气液两相流钝体绕流流型识别[J].仪器仪表学报,2017,38(10):2536-2546.CHEN Luyang,YIN Jiawen,SUN Zhiqiang,et al.Flow regime identification of gas-liquid two-phase flow with flow around bluff-body based on EEMD-Hilbert spectrum[J].Chinese Journal of Scientific Instrument,2017,38(10):2536-2546.
[13]谢胜,陈航,于平.基于FFT并二次修正的Rife频率估计算法[J].探测与控制学报,2010,32(4):48-53.XIE Sheng,CHEN Hang,YU Ping.Rife frequency estimation algorithm based on FFT and twice modifying[J].Journal of Detection and Control,2010,32(4):48-53.
[14]QU Liangsheng,XIE Ailin,LI Xiao.Study and performance evaluation of some nonlinear diagnostic methods for large rotating machinery[J].Mechanism and Machine Theory,1993,28(5):699-713.
[15]张晗博,殷奕,殷奎喜.基于小波变换的非平稳信号分析与处理[J].南京师范大学学报(工程技术版),2014,14(1):63-69.ZHANG Hanbo,YIN Yi,YIN Kuixi.Non-stationary signals analysis and processing based on wavelet transform[J].Journal of Nanjing Normal University(Engineering and Technology Edition),2014,14(1):63-69.
[16]于德介,张嵬,程军圣,等.基于EMD的时频熵在齿轮故障诊断中的应用[J].振动与冲击,2002,24(5):26-27,29.YU Dejie,ZHANG Wei,CHENG Junsheng.Application of time-frequency entropy to gear fault diagnosis based on EMD[J].Journal of Vibration and Shock,2002,24(5):26-27,29.
[17]王小玲,陈进,从飞云.基于时频的频带熵方法在滚动轴承故障识别中的应用[J].振动与冲击,2012,31(18):29-33.WANG Xiaoling,CHEN Jin,CONG Feiyun.Application of spectral band entropy(SBE)method in rolling bearing fault diagnosis based on time-frequency analysis[J].Journal of Vibration and Shock,2012,31(18):29-33.
[18]SHANNON C E.A mathematical theory of communications[J].The Bell System Technical Journal,1948,27(3):379-423.
[19]ANTONI J.The spectral kurtosis:a useful tool for characterizing non-stationary signals[J].Mechanical Systems and Signal Processing,2006,20(2):282-307.
[20]JAIN V K,COLLINS W L,DAVIS D C.High-accuracy analog measurements via interpolated FFT[J].IEEE Transactions on Instrumentation and Measurement,1979,28(2):113-122.
[21]WU Jiangwei,WANG Xue,SUN Xinyao,et al.Pure harmonics extracting from time-varying power signal based on improved empirical mode decomposition[J].Measurement,2014,49(3):216-225.
[2]陈鸿伟,姜华伟,高建强,等.鼓泡流化床风帽压力波动特性分析[J].中国电机工程学报,2011,31(23):1-7.CHEN Hongwei,JIANG Huawei,GAO Jianqiang,et al.Analysis of characteristic pressure fluctuation in wind cap of bubbling fluidized bed[J].Proceedings of the CSEE,2011,31(23):1-7.
[3]JOHNSSON F,ZIJERVELD R C,SCHOUTEN J C,et al.Characterization of fluidization regimes by time-series analysis of pressure fluctuations[J].International Journal of Multiphase Flow,2000,26(4):663-715.
[4]KIM S M,KIM J,MUDAWAR I.Flow condensation in parallel micro-channels.Part 1:experimental results and assessment of pressure drop correlations[J].International Journal of Heat and Mass Transfer,2012,55(4):971-983.
[5]王彭,周峰,黄震宇,等.基于时域准同步的谐波和间谐波检测算法[J].仪器仪表学报,2013,34(2):275-280.WANG Peng,ZHOU Feng,HUANG Zhenyu,et al.Harmonic and interharmonic detection algorithm based on time-domain quasi-synchronous technique[J].Chinese Journal of Scientific Instrument,2013,34(2):275-280.
[6]SONG Yuewen,ZHU Xiaojun,SUN Zhiqiang,et al.Experimental investigation of particle-induced pressure loss in solid-liquid lifting pipe[J].Journal of Central South University,2017,24(9):2114-2120.
[7]齐国清,贾欣乐.插值FFT估计正弦信号频率的精度分析[J].电子学报,2004,32(4):625-629.QI Guoqing,JIA Xinle.Accuracy analysis of frequency estimation of sinusoid based on interpolated FFT[J].Acta Electronica Sinica,2004,32(4):625-629.
[8]张鸿博,蔡晓锋,卢改风.基于双窗全相位FFT双谱线校正的电力谐波分[J].仪器仪表学报,2015,36(12):2835-2841.ZHANG Hongbo,CAI Xiaofeng,LU Gaifeng.Doublespectrum-line correction method based on double-window all-phase FFT for power harmonic analysis[J].Chinese Journal of Scientific Instrument,2015,36(12):2835-2841.
[9]桑林琼,邱明国,王莉,等.基于统计阈值的脑肿瘤MRI图像的分割方法[J].生物医学工程研究,2010,29(4):237-239.SANG Linqiong,QIU Mingguo,WANG Li,et al.Brain tumor MRI image segmentation based on statistical thresholding method[J].Journal of Biomedical Engineering Research,2010,29(4):237-239.
[10]张超,陈建军,郭迅.基于EMD能量熵和支持向量机的齿轮故障诊断方法[J].振动与冲击,2010,29(10):216-220.ZHANG Chao,CHEN Jianjun,GUO Xun.A gear fault diagnosis method based on EMD energy entropy and SVM[J].Journal of Vibration and Shock,2010,29(10):216-220.
[11]SUN Zhiqiang,SHAO Shuai,GONG Hui.Gas-liquid flow regime recognition based on wavelet packet energy entropy of vortex-induced pressure fluctuation[J].Measurement Science Review,2013,13(2):83-88.
[12]陈露阳,尹佳雯,孙志强,等.基于EEMD-Hilbert谱的气液两相流钝体绕流流型识别[J].仪器仪表学报,2017,38(10):2536-2546.CHEN Luyang,YIN Jiawen,SUN Zhiqiang,et al.Flow regime identification of gas-liquid two-phase flow with flow around bluff-body based on EEMD-Hilbert spectrum[J].Chinese Journal of Scientific Instrument,2017,38(10):2536-2546.
[13]谢胜,陈航,于平.基于FFT并二次修正的Rife频率估计算法[J].探测与控制学报,2010,32(4):48-53.XIE Sheng,CHEN Hang,YU Ping.Rife frequency estimation algorithm based on FFT and twice modifying[J].Journal of Detection and Control,2010,32(4):48-53.
[14]QU Liangsheng,XIE Ailin,LI Xiao.Study and performance evaluation of some nonlinear diagnostic methods for large rotating machinery[J].Mechanism and Machine Theory,1993,28(5):699-713.
[15]张晗博,殷奕,殷奎喜.基于小波变换的非平稳信号分析与处理[J].南京师范大学学报(工程技术版),2014,14(1):63-69.ZHANG Hanbo,YIN Yi,YIN Kuixi.Non-stationary signals analysis and processing based on wavelet transform[J].Journal of Nanjing Normal University(Engineering and Technology Edition),2014,14(1):63-69.
[16]于德介,张嵬,程军圣,等.基于EMD的时频熵在齿轮故障诊断中的应用[J].振动与冲击,2002,24(5):26-27,29.YU Dejie,ZHANG Wei,CHENG Junsheng.Application of time-frequency entropy to gear fault diagnosis based on EMD[J].Journal of Vibration and Shock,2002,24(5):26-27,29.
[17]王小玲,陈进,从飞云.基于时频的频带熵方法在滚动轴承故障识别中的应用[J].振动与冲击,2012,31(18):29-33.WANG Xiaoling,CHEN Jin,CONG Feiyun.Application of spectral band entropy(SBE)method in rolling bearing fault diagnosis based on time-frequency analysis[J].Journal of Vibration and Shock,2012,31(18):29-33.
[18]SHANNON C E.A mathematical theory of communications[J].The Bell System Technical Journal,1948,27(3):379-423.
[19]ANTONI J.The spectral kurtosis:a useful tool for characterizing non-stationary signals[J].Mechanical Systems and Signal Processing,2006,20(2):282-307.
[20]JAIN V K,COLLINS W L,DAVIS D C.High-accuracy analog measurements via interpolated FFT[J].IEEE Transactions on Instrumentation and Measurement,1979,28(2):113-122.
[21]WU Jiangwei,WANG Xue,SUN Xinyao,et al.Pure harmonics extracting from time-varying power signal based on improved empirical mode decomposition[J].Measurement,2014,49(3):216-225.
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