TY - GEN
T1 - Nonperiodic cycle detection and application in gas liquid two-phase flow
AU - Du, Yuncheng
AU - Wang, Huaxiang
AU - Du, Dongping
PY - 2010
Y1 - 2010
N2 - Nonperiodic cycle detection methods for gas/liquid two phase flow system were discussed. Cycle detection methods, such as Hurst analysis, the V statistic and the P statistic were briefly reviewed; in addition, a modified P statistic method was introduced. Two types of time series, i.e. mathematically sine wave time series and experimental electrical capacitance tomography time series of plug flow and slug flow under different flow conditions were investigated to verify the effectiveness of different cycle detection methods. The sine wave time series and sine wave time series under gauss noise were used to validate different cycle detection methods. For the electrical capacitance tomography time series of different flow regime, discrete wavelet transform (DWT) was adopted to decompose the original signal into approximate signal and detail signals, and then different cycle detection methods were mainly applied to realize cyclic characterization analysis of detail signals for plug flow and slug flow. The results show that the nonperiodic cycle characteristic analysis can reflect the property of different flow regimes.
AB - Nonperiodic cycle detection methods for gas/liquid two phase flow system were discussed. Cycle detection methods, such as Hurst analysis, the V statistic and the P statistic were briefly reviewed; in addition, a modified P statistic method was introduced. Two types of time series, i.e. mathematically sine wave time series and experimental electrical capacitance tomography time series of plug flow and slug flow under different flow conditions were investigated to verify the effectiveness of different cycle detection methods. The sine wave time series and sine wave time series under gauss noise were used to validate different cycle detection methods. For the electrical capacitance tomography time series of different flow regime, discrete wavelet transform (DWT) was adopted to decompose the original signal into approximate signal and detail signals, and then different cycle detection methods were mainly applied to realize cyclic characterization analysis of detail signals for plug flow and slug flow. The results show that the nonperiodic cycle characteristic analysis can reflect the property of different flow regimes.
KW - Cycle detection
KW - Electrical capacitance tomography
KW - Hurst exponent
KW - Modified P statistic
KW - Wavelet
UR - http://www.scopus.com/inward/record.url?scp=77957845612&partnerID=8YFLogxK
U2 - 10.1109/IMTC.2010.5488171
DO - 10.1109/IMTC.2010.5488171
M3 - Conference contribution
AN - SCOPUS:77957845612
SN - 9781424428335
T3 - 2010 IEEE International Instrumentation and Measurement Technology Conference, I2MTC 2010 - Proceedings
SP - 255
EP - 258
BT - 2010 IEEE International Instrumentation and Measurement Technology Conference, I2MTC 2010 - Proceedings
Y2 - 3 May 2010 through 6 May 2010
ER -