Application of time–frequency entropy from wake oscillation to gas–liquid flow pattern identification

Abstract

Gas–liquid two-phase flow abounds in industrial processes and facilities. Identification of its flow pattern plays an essential role in the field of multiphase flow measurement. A bluff body was introduced in this study to recognize gas–liquid flow patterns by inducing fluid oscillation that enlarged differences between each flow pattern. Experiments with air–water mixtures were carried out in horizontal pipelines at ambient temperature and atmospheric pressure. Differential pressure signals from the bluff-body wake were obtained in bubble, bubble/plug transitional, plug, slug, and annular flows. Utilizing the adaptive ensemble empirical mode decomposition method and the Hilbert transform, the time–frequency entropy S of the differential pressure signals was obtained. By combining S and other flow parameters, such as the volumetric void fraction β, the dryness x, the ratio of density φ and the modified fluid coefficient ψ, a new flow pattern map was constructed which adopted S(1–x)φ and (1–β)ψ as the vertical and horizontal coordinates, respectively. The overall rate of classification of the map was verified to be 92.9% by the experimental data. It provides an effective and simple solution to the gas–liquid flow pattern identification problems.

Keywords

gas–liquid two-phase flow / wake oscillation / flow pattern map / time–frequency entropy / ensemble empirical mode decomposition / Hilbert transform

References

[1] JiH, LongJ, FuY, HuangZ, WangB, LiHai. Flow pattern identification based on EMD and LS-SVM for gas–liquid two-phase flow in a minichannel [J]. IEEE Transactions on Instrumentation and Measurement, 2011, 60(5): 1917-1924

[2] ArunkumarS, AdhavanJ, VenkatesanM, DasS K, BalakrishnanA R. Two phase flow regime identification using infrared sensor and volume of fluids method [J]. Flow Measurement and Instrumentation, 2016, 51: 49-54

[3] WangL, HuangZ, WangB, JiH, LiHai. Flow pattern identification of gas–liquid two-phase flow based on capacitively coupled contactless conductivity detection [J]. IEEE Transactions on Instrumentation and Measurement, 2012, 61(5): 1466-1475

[4] PouryoussefiS M, ZhangY. Identification of two-phase water–air flow patterns in a vertical pipe using fuzzy logic and genetic algorithm [J]. Applied Thermal Engineering, 2015, 85: 195-206

[5] SunZ, ChenY, GongHui. Classification of gas–liquid flow patterns by the norm entropy of wavelet decomposed pressure fluctuations across a bluff body [J]. Measurement Science and Technology, 2012, 23(12): 125301

[6] LiX, LiZ, WangE, FengJ, KongX, LiangC, LiB, LiNan. Analysis of natural mineral earthquake and blast based on Hilbert–Huang transform (HHT) [J]. Journal of Applied Geophysics, 2016, 128: 79-86

[7] WangD, TsuiK L. Dynamic Bayesian wavelet transform: New methodology for extraction of repetitive transients [J]. Mechanical Systems and Signal Processing, 2017, 88137-144

[8] DingH, HuangZ, SongZ, YanYong. Hilbert–Huang transform based signal analysis for the characterization of gas–liquid two-phase flow [J]. Flow Measurement and Instrumentation, 2007, 18(1): 37-46

[9] ChenZ, LianX, HeLiang. Using acoustic technique to detect leakage in city gas pipelines [J]. Journal of Central South University, 2012, 19(8): 2373-2379

[10] LiS, SunZhi. Harvesting vortex energy in the cylinder wake with a pivoting vane [J]. Energy, 2015, 88: 783-792

[11] SunZ, ZhangHong. Neural networks approach for prediction of gas–liquid two-phase flow pattern based on frequency domain analysis of vortex flowmeter signals [J]. Measurement Science and Technology, 2008, 19(1): 015401

[12] LiS, ZhouT, SunZ, DongZhen. External forced convection trom circular Cylinders with surface protrusions [J]. International Journal of Heat and Mass Transfer, 2016, 99: 20-30

[13] HuangS, YinJ, SunZ, LiS, ZhouTian. Characterization of gas-liquid two-phase flow by correlation dimension of vortex-induced pressure fluctuation [J]. IEEE Access, 2017, 510307-10314

[14] SunZ, GongHui. Energy of intrinsic mode function for gas–liquid flow pattern identification [J]. Metrology and Measurement Systems, 2012, 19(4): 759-766

[15] SunZ, ZhangH, ZhouJie. Investigation of the pressure probe properties as the sensor in the vortex flowmeter [J]. Sensors and Actuators A: Physical, 2007, 136(2): 646-655

[16] JiangH, LiC, LiHua. An improved EEMD with multiwavelet packet for rotating machinery multi-fault diagnosis [J]. Mechanical Systems and Signal Processing, 2013, 36(2): 225-239

[17] HuangN E, ShenZ, LongS R, WuM C, ShihH H, ZhengQ, YenN C, TungC C, LiuH. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis [J]. Proceedings of Royal Society London Series A, 1998, 454: 903-95

[18] DingJ, LinJ, HeL, ZhaoJie. Dynamic unbalance detection of cardan shaft in high-speed train based on EMD-SVD-NHT [J]. Journal of Central South University, 2015, 22: 2149-2157

[19] LeiY, HeZ, ZiYan. Application of the EEMD method to rotor fault diagnosis of rotating machinery [J]. Mechanical Systems and Signal Processing, 2009, 23(4): 1327-1338

[20] ZhangJ, OuJ, ZhanRong. Automatic target recognition of moving target based on empirical mode decomposition and genetic algorithm support vector machine [J]. Journal of Central South University, 2015, 22: 1389-1396

[21] SunZ, ZhouJ, ZhouPing. Application of Hilbert-Huang transform to denoising in vortex flowmeter [J]. Journal of Central South University of Technology, 2006, 13(5): 501-505

[22] YuD, YuY, ChengJun. Application of time–frequency entropy method based on Hilbert–Huang transform to gear fault diagnosis [J]. Measurement, 2007, 40(9): 823-830