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清华大学学报(自然科学版)  2021, Vol. 61 Issue (2): 135-143    DOI: 10.16511/j.cnki.qhdxxb.2020.25.032
  专题:安全监测 本期目录 | 过刊浏览 | 高级检索 |
周成龙, 陈涛
清华大学 工程物理系, 公共安全研究院, 北京 100084
Instability of flow field in chemical industry park based on wavelet entropy
ZHOU Chenglong, CHEN Tao
Institute of Public Safety Research, Department of Engineering Physics, Tsinghua University, Beijing 100084, China
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摘要 石化园区的挥发性有机化合物(volatile organic compounds,VOCs)无组织排放的监测溯源技术是研究热点,而监测点处的局部流场特征是溯源的关键。针对大庆炼化芳烃抽提设备,该文设计了一套分布式监测溯源系统。采用数值仿真对自然风下的设备区瞬态流动进行了研究,基于小波熵理论对流场的时变信号进行了处理,并研究了各种参数与流场稳定性的相关性。结果表明:小波熵可以表征流场的稳定性,风向小波熵越大,流动的不稳定性越强。关联性分析表明风速小波熵以及风向方差与风向小波熵具有强相关性,可以共同作为风场不稳定性的评价指标。风速变化量与风向小波熵呈现中等程度负相关,而风向平均偏转量和风速方差与不稳定性无关。
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关键词 挥发性有机化合物(VOCs)无组织排放监测溯源流场不稳定性小波熵    
Abstract:Monitoring and tracing of unorganized volatile organic compounds (VOCs) emissions in petrochemical parks is important to maintaining public safety with the local flow field characteristics at the monitoring points being the key to accurate tracing. A distributed monitoring system was designed to identify unorganized emissions of volatile organic compounds in petrochemical parks with numerical simulations to study the transient flows in equipment areas. The time-varying signal of the measured flow field was processed using wavelet entropy theory to relate the flow field stability to various flow parameters. The results show that the wavelet entropy can characterize the flow instabilities. The correlation analysis shows that the wind speed wavelet entropy and the wind direction variance strongly correlate with the wind direction wavelet entropy and the flow field instabilities. The results also show that the wind speed changes correlate negatively with the wind direction wavelet entropy, while the mean wind deflection and the wind speed variance are not related to the flow instabilities.
Key wordsvolatile organic compounds (VOCs)    unorganized emissions    monitoring traceability    flow field instabilities    wavelet entropy
收稿日期: 2020-03-23      出版日期: 2020-12-29
周成龙, 陈涛. 基于小波熵的化工园区流场不稳定性[J]. 清华大学学报(自然科学版), 2021, 61(2): 135-143.
ZHOU Chenglong, CHEN Tao. Instability of flow field in chemical industry park based on wavelet entropy. Journal of Tsinghua University(Science and Technology), 2021, 61(2): 135-143.
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[1] WANG H L, NIE J, LI J, et al. Characterization and assessment of volatile organic compounds (VOCs) emissions from typical industries[J]. Chinese Science Bulletin, 2013, 58(7):724-730.
[2] WEI W, WANG S X, CHATANI S, et al. Emission and speciation of non-methane volatile organic compounds from anthropogenic sources in China[J]. Atmospheric Environment, 2008, 42(20):4976-4988.
[3] DUMANOGLU Y, KARA M, ALTIOK H, et al. Spatial and seasonal variation and source apportionment of volatile organic compounds (VOCs) in a heavily industrialized region[J]. Atmospheric Environment, 2014, 98:168-178.
[4] HUTCHINSON M, OH H, CHEN W H. A review of source term estimation methods for atmospheric dispersion events using static or mobile sensors[J]. Information Fusion, 2017, 36:130-148.
[5] JAIN S, SHARMA S K, CHOUDHARY N, et al. Chemical characteristics and source apportionment of PM2.5 using PCA/APCS, UNMIX, and PMF at an urban site of Delhi, India[J]. Environmental Science and Pollution Research, 2017, 24(17):14637-14656.
[6] BROWN S G, FRANKEL A, HAFNER H R. Source apportionment of VOCs in the Los Angeles area using positive matrix factorization[J]. Atmospheric Environment, 2007, 41(2):227-237.
[7] WANG X L. Analysis of ambient VOCs levels and potential sources in Windsor[D]. Windsor, Caneda:University of Windsor, 2014.
[8] SHARAN M, SINGH S K, ISSARTEL J P. Least square data assimilation for identification of the point source emissions[J]. Pure and Applied Geophysics, 2012, 169(3):483-497.
[9] ALDEN C B, GHOSH S, COBURN S, et al. Bootstrap inversion technique for atmospheric trace gas source detection and quantification using long open-path laser measurements[J]. Atmospheric Measurement Techniques, 2018, 11(3):1565-1582.
[10] 周莉, 席光. 高层建筑群风场的数值分析[J]. 西安交通大学学报, 2001, 35(5):471-474.ZHOU L, XI G. Numerical analysis of the wind field on high buildings[J]. Journal of Xi'an Jiaotong University, 2001, 35(5):471-474. (in Chinese)
[11] ZHOU X H, MENG F K, JIANG Y L. Three-dimensional numerical simulation and analysis on wind environment of group buildings[J]. Science Technology and Engineering, 2007, 7(14):3604-3606.
[12] 中华人民共和国住房和城乡建设部. 建筑结构荷载规范:GB50009-2012[S]. 北京:中国建筑工业出版社, 2012.Ministry of Housing and Urban-Rural Development of the People's Republic of China. Building Structure Load Specification:GB50009-2012[S]. Beijing:China Architecture & Building Press, 2012. (in Chinese)
[13] UNSER M, ALDROUBI A. A review of wavelets in biomedical applications[J]. Proceedings of the IEEE, 1996, 84(4):626-638.
[14] MALLAT S G. A theory for multiresolution signal decomposition:The wavelet representation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1989, 11(7):674-693.
[15] CLAUSIUS R. On a mechanical theorem applicable to heat[J]. Philosophical Magazine, Series 4, 1870, 40(265):122-127.
[16] SHANNON C E. A mathematical theory of communication[J]. Bell System Technical Journal, 1948, 27(3):379-423.
[17] BLANCO S, FIGLIOSA A, QUIAN Q R, et al. Time-frequency analysis of electroencephalogram series(Ⅲ):Information transfer function and wavelets packets[J]. Physical Review E, 1998, 57(1):932-940.
[18] ROSSO O A, BLANCO S, YORDANOVA J, et al. Wavelet entropy:A new tool for analysis of short duration brain electrical signals[J]. Journal of Neuroscience Methods, 2001, 105(1):65-75.
[19] QUIROGA R Q, ROSSO O A, BAŞAR E, et al. Wavelet entropy in event-related potentials:A new method shows ordering of EEG oscillations[J]. Biological Cybernetics, 2001, 84(4):291-299.
[20] ROSSO O A, MAIRAL M L. Characterization of time dynamical evolution of electroencephalographic epileptic records[J]. Physica A:Statistical Mechanics and Its Applications, 2002, 312(3-4):469-504.
[21] ROSSO O A, MARTIN M T, PLASTINO A. Brain electrical activity analysis using wavelet-based information tools[J]. Physica A:Statistical Mechanics and Its Applications, 2002, 313(3-4):587-608.
[22] EGGERS J J, BAUML R, TZSCHOPPE R, et al. Scalar costa scheme for information embedding[J]. IEEE Transactions on Signal Processing, 2003, 51(4):1003-1019.
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