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中国沙漠  2019, Vol. 39 Issue (3): 7-16    DOI: 10.7522/j.issn.1000-694X.2018.00110
    
高速影像中风沙颗粒灰度和表观粒径的变化特征及对多假设追踪算法的意义
梅凡民, 张梦莲
西安工程大学 环境与化学工程学院, 陕西 西安 710048
Analyzing Variation in Gray Level and Apparent Grain Size of Aeolian Saltating Particles from High-speed Images in Terms of Multiple Hypothesis Tracking
Mei Fanmin, Zhang Menglian
School of Environmental and Chemical Engineering, Xi'an Polytechnic University, Xi'an 710048, China
 全文: PDF(1417 KB)  
摘要: 沙粒轨迹形成过程是理解风沙两相流输运力学机制的关键问题。多假设追踪算法通过融合追踪目标的多个信息特征和建立后验概率控制的动态递归算法来实现对目标的准确追踪,其精度可能优于传统的基于位置信息的轨迹追踪算法。基于多假设追踪算法的需要,以沙粒灰度和表观粒径(沙粒在影像上显现的大小)为对象,通过分析沙粒在轨迹形成过程中灰度和表观粒径的辨识度及稳定性来揭示它们作为多假设轨迹算法的特征参数的可能性和可靠性。结果表明:沙粒灰阶25~255,具有多峰分布的特征,表明不同沙粒灰度具有很好的区分度;80%~85%的沙粒在入射、反弹过程及粒-床碰撞后,灰度差在1倍标准偏差之内,表明大多数沙粒的灰度具有相对的稳定性;沙粒的表观粒径分布于77~1 001 μm,表明表观粒径具有较好的区分度;97%~100%的沙粒在入射、反弹过程中、77%的沙粒在粒-床碰撞后,表观粒径差均是稳定的(77~154 μm);仅有15%~23%的沙粒发生了显著的侧向运动或自旋,表明多数沙粒的灰度和表观粒径的稳定性不受侧向运动或自旋的影响;灰度和表观粒径可以作为多假设追踪算法的重要辨识参数。
关键词: 高速影像风沙颗粒灰度表观粒径多假设追踪算法    
Abstract: As a key issue that many scientists have taken efforts to sovle since 1980's, the non-correspondence between dynamic paremeters of turblent wind and saltation flux at high time resolution is not been understood perfectly. A high-precision algorithm to be developed for tracking massive sand particles from 2D high-speed images is very fundamental to understand this issue in terms of individual saltating particle trajectory formation driven by turbluent wind. It can enhance precision of multiple hypothesis tracking by introduction of new parameters as gray level and apparent grain size of saltating particles if they are discriminative and stable well in the 2D images. However, discrimination and stability of gray level and apparent grain size have not been discussed as so far. As a result, this paper aims to discuss this topic based on 1 000 frames of high-speed images. Gray level of saltating particles varies around 25-255, showing a nice discrimination. Gray level difference of about 80%-85% saltating particles is below one times standard deviation as they impact and rebound, which indicates the parameter sound stable. Range around 77-1 001 μm shows apparent grain size can discriminate one well from anothor particle, while apparent grain size difference of 97%-100% incidence or rebound particles and that of 77% incidence and rebound particles is stable. Only about estimated 15%-23% of saltating particles moves transversely or rotationally based on variations in gray level and apparent grain size, not affecting stability of gray level and apparent grain size of majority of saltating particles. Thus, gray level and apparent grain size can be used as characteristic parameters besides position for the multiple hypothesis tracking.
Key words: high-speed images    aeolian saltation    gray level    apparent grain size    multiple hypothesis tracking
收稿日期: 2018-05-19 出版日期: 2019-06-10
:  X169  
基金资助: 国家自然科学基金项目(41340043);陕西省科技厅项目(2014JM5207);陕西省教育厅项目(14JK1291)
作者简介: 梅凡民(1968-),男,陕西高陵人,博士,教授,主要从事风沙两相流力学与大气环境研究。E-mail:meifanmin@xpu.edu.cn
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引用本文:

梅凡民, 张梦莲. 高速影像中风沙颗粒灰度和表观粒径的变化特征及对多假设追踪算法的意义[J]. 中国沙漠, 2019, 39(3): 7-16.

Mei Fanmin, Zhang Menglian. Analyzing Variation in Gray Level and Apparent Grain Size of Aeolian Saltating Particles from High-speed Images in Terms of Multiple Hypothesis Tracking. Journal of Desert Research, 2019, 39(3): 7-16.

链接本文:

http://www.desert.ac.cn/CN/10.7522/j.issn.1000-694X.2018.00110        http://www.desert.ac.cn/CN/Y2019/V39/I3/7

[1] Bagnold R A.The Physics of Blown Sand and Desert Dunes[M].London,UK:Methuen,1954:265-270.
[2] Alfaro S C,Gomes L.Modeling mineral aerosol production by wind erosion:emission intensities and aerosol size distribution in source areas[J].Journal of Geophysical Research,2001,106(D16):18075-18084.
[3] Yang Y,Russell L M,Lou S J,et al.Dust-wind interactions can intensify aerosol pollution over eastern China[J].Nature Communnications,2017,8:15333.
[4] Dumka U C,Kaskaoutis D G,Srivastava M K,et al.Scattering and absorbing properties of near-surface aerosols over Gangetic-Himalayan region:the role of boundary layer dynamics and long-range transport[J].Atmospheric Chemistry and Physics,2015,15:1555-1572.
[5] Mahowald N M,Baker A R,Bergametti G,et al.Atmospheric global dust cycle and iron input to ocean[J].Global Biogeochemical Cycles,2005,19:GB4025.
[6] Dong Z B,Liu X P,Wang H T,et al.Aeolian sand transport:a wind tunnel model[J].Sedimentary Geology,2003,161:71-83.
[7] Sørensen M.On the rate of aeolian sand transport[J].Geomorphology,2004,59:53-62.
[8] Mei F M,Rajot J,Alfaro S,et al.Validating a dust production model by field experiment in Mu Us Desert,China[J].Chinese Science Bulletin,2006,51(7):878-884.
[9] Sherman D J,Li B L.Predicting aeolian sand transport rates:a reevaluation of models[J].Aeolian Research,2012,3:371-378.
[10] Lee J A.A field experiment on the role of small scale wind gustiness in Aeolian sand transport[J].Earth Surface Processes and Landforms,1987,12:331-335.
[11] Butterfield G R.Grain transport rates in steady and unsteady turbulent airflows[J].Acta Mechanics,1991,1(Sup):97-122.
[12] Jackson D W,McCloskey J.Preliminary results from a field investigation of aeolian sand transport using high resolution wind and transport measurements[J].Geophysical Research Letters,1997,24(2):163-166.
[13] Leenders J K,van Boxel J H,Sterk G.Wind forces and related saltation transport[J].Geomorphology,2005,71:357-372.
[14] Baas A C W,Sherman D.J.Formation and behaviour of aeolian streamers[J].Journal of Geophysical Research,2005,110:F03011.
[15] Pfeifer S,Schönfeldt H J.The response of saltation to wind speed fluctuations[J].Earth Surface Processes and Landforms,2012,37:1056-1064.
[16] Shi F,Huang N.Measurement and simulation of sand saltation movement under fluctuating wind in a natural field environment[J].Physica A Statistical Mechanics & Its Applications,2012,391:474-484.
[17] Li B L,McKenna N C.A wind tunnel study of aeolian sediment transport response to unsteady winds[J].Geomorphology,2014,214:261-269.
[18] Bauer B O,Davidson-Arnott R G D.Aeolian particle flux profiles and transport unsteadiness[J].Journal of Geophysical Research (Earth Surface),2014,119:JF003128.
[19] Martin R L,Kok J F,Hugenholtz C H,et al.High-frequency measurements of aeolian saltation flux:field-based methodology and applications[J].Aeolian Research,2018,30:97-114.
[20] Baas A C W.Complex systems in aeolian geomorphology[J].Geomorphology,2007,91:311-331.
[21] Anderson R S,Haff P K.Simulation of eolian saltation[J].Science,1988,241:820-823.
[22] Zou X Y,Cheng H,Zhang C L,et al.Effects of the Magnus and Saffman forces on the saltation trajectories of sand grain[J].Geomorphology,2007,90:11-22.
[23] Kok J F,Renno N O.A comprehensive numerical model of steady state saltation (COMSALT)[J].Journal of Geophysics Research,2009,114:D17204.
[24] Huang N,Wang C,Pan X Y.Simulation of aeolian sand saltation with rotational motion[J].Journal of Geophysical Research:Atmospheres,2010,115:D22211.
[25] Ho D T,Valance A,Dupont P,et al.Aeolian sand transport:Length and height distributions of saltation trajectories[J].Aeolian Research,2014,12:65-74.
[26] Nalpanis P,Hunt J C R,Barrett C.Saltating particles over flat beds[J].Journal of Fluid Mechanics,1993,251:661-685.
[27] Zhang W,Kang J,Lee S.Tracking of saltating sand trajectories over a flat surface embedded in an atmospheric boundary layer[J].Geomorphology,2007,86:320-331.
[28] Yang B,Wang Y,Zhang Y.The 3-D spread of saltation sand over a flat bed surface in aeolian sand transport[J].Advanced Powder Technology,2009,20(4):303-309.
[29] Wang Z T,Zhang Q H,Dong Z B.A Wind tunnel investigation on the transverse motion of aeolian sand[J].Sciences in Cold and Arid Regions,2011,3(1):13-16.
[30] O'Brien P,McKenna N C.PTV measurement of the spanwise component of aeolian transport in steady state[J].Aeolian Research,2016,20:126-138.
[31] Zhang Y,Wang Y,Jia P.Improving the Delaunay-tessellation particle tracking algorithm in the 3D field[J].Measurement,2014,49:1-14.
[32] Zhang Y,Wang Y,Yang B,et al.A particle tracking velocimetry algorithm based on the Voronoi diagram[J]. Measurement Science and Technology,2015,26(7):75302.
[33] Reid D B.An algorithm for tracking multiple targets[J].IEEE Transactions on Automatic Control,1979,24(6):843-854.
[34] 韩崇昭,朱洪艳,段战胜,等.多源信息融合[M].北京:清华大学出版社,2010.
[35] 刘江,王元,杨斌.高频测量输沙浓度对湍流脉动的频率响应[J].西安交通大学学报,2010,44(11):113-118.
[36] 梅凡民,蒋缠文.风沙颗粒运动的数字高速摄影图像的分割算法[J].力学学报,2012,44(1):83-85.
[37] 梅凡民,雒遂,陈金广.一种改进的高浓度风沙图像的动态灰度阈值分割算法[J].力学学报,2018,50(3):1-9.
[38] White B R,Schulz J C.Magnus effect in saltation[J].Journal of Fluid Mechanics,1977,81:497-512.
[39] White B R.Two-phase movement of saltating turbulent boundary layer flow[J].International Journal of Multiphase Flow,1982,8(5):459-473.
[40] 吴正.风沙地貌学[M].北京:科学出版社,1987:59-68.
[41] 刘贤万.实验风沙物理学及风沙控制工程[M].北京:科学出版社,1995.
[42] 郑晓静,谢莉.沙粒起跳自旋的分析[J].中国沙漠,2003,23(6):632-636.
[43] 王等明,周又和.沙粒斜碰的动力学分析[J].兰州大学学报(自然科学版),2003,39(4):19-23.
[44] Cheng H,Zou X Y,Zhang C L.Probability distribution functions for the initial liftoff velocities of saltating sand grains in air[J].Journal of Geophysical Research,2006,111:D22205.
[45] Xie L,Ling Y Q,Zheng X J.Laboratory measurement of saltating sand particles' angular velocities and simulation of its effect on saltation trajectory[J]. Journal of Geophysical Research,2007,112:D12116.
[46] Zheng X J,Xie L,Zou X Y.Theoretic predication of lift off angular velocity distribution of sand particles in blown sand flux[J].Journal of Geophysical Research,2006,111:D11109.
[47] Zou X Y,Cheng H,Zhang C L,et al.Effects of the Magnus and Saffman forces on the saltation trajectories of sand grain[J].Geomorphology,2007,90:11-22.
[48] Zheng X J,Cheng N,Xie L.A three-dimensional analysis on lift-off velocities of sand grains in wind-blown sand flux[J].Earth Surface Processes and Landforms,2008,33:1824-1838.
[49] Kang L Q,Zou X Y.Vertical distribution of wind-sand interaction forces in aeolian sand transport[J].Geomorphology,2011,125:361-373.
[50] Pokorny M L,Horender S.Measurement of particle rotation in a saltation layer[J].Earth Surface Processes and Landforms,2014,39:1803-1811.
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