室外近地面流场特性分析及烟羽重现
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摘要
为适应生存环境,自然界中大多生物都有相当灵敏嗅觉系统。很多生物能够利用其敏锐的嗅觉通过气味信息进行界定领地、吸引同伴、跟踪猎物、抵御敌人等生命活动。上世纪九十年代初,一些学者通过模拟生物嗅觉功能,研究如何使用移动机器人实现气味源定位功能,即使用配备有风速/风向与气味/气体传感器的移动机器人确定气味源头的过程。
气味/气体传播的主要影响因素有风、温度层结、风速廓线以及地面粗糙度等。其中风是影响气体传播的最主要因子,而大气边界层下的流场处于湍流运动状态,决定了室外近地面流场的运动特征。
本文利用超声风速风向仪与气体传感器分别测量了室外近地面的风速/风向和气体浓度信息,分析了近地面流场特性并结合空间插值方法对流场中的风速/风向与气体浓度信息进行了重现。重点开展完成了如下工作:
第一、采集了20组室外近地面单点风速/风向信息。考虑到大气湍流的多尺度特征,首先应用了子波变换多分辨方法分析了分尺度风速/风向的统计特性,然后应用混沌多尺度熵方法对风速的复杂性进行了分析,在此过程中,还研究了分尺度风速/风向统计特性及其复杂性随风速变化的规律。
第二、分别在室外8 m、10 m和20 m三种空间尺度范围内使用风速仪与气体传感器采集了风场与浓度场的数据,并分析了风场中各空间-时间点的平均风速/风向特性及不同空间-时间点之间风速的关系。此外,根据大气湍流的多层次性,分析了分尺度风速相干结构的条件相位平均波形,从而得出了风场中不同时间尺度下风速的平均演化规律。
第三、分析了流场中气体浓度的统计特性,并研究了风速与浓度的相关关系。通过空间插值的方法,推算出流场中其它未测量点的风速以及浓度值。最终通过软件动态显示室外近地面流场的演变过程。
In natural world, most creatures have quite sensitive olfaction sensing in order to be adaptable to environmental change. Many animals can make use of odor information to define territories, attract partners, stalk prey and avoid predators. In the early 1990s, some scholars began to study how to locate odor source by imitating biological olfaction. Odor/gas source localization means using mobile robot equipped with odor/gas sensor and wind speed/direction sensor to confirm where the odor/gas source is.
The spread of odor/gas in atmospheric boundary layer is influenced by wind, temperature stratification, wind profile, ground surface roughness and so on, and the wind is the main factor affecting the odor/gas dispersion. The turbulent flow in atmospheric boundary layer determines the movement characteristics of flow field in outdoor near-surface environments.
In this thesis, gas concentration and wind speed/direction are sampled using gas and wind sensors in outdoor near-surface environments. The properties of flow field, including collected wind speed/direction and gas concentration, are analyzed. Combining with spatial interpolation method, the wind speed/direction and gas concentration information in flow field are reconstructed finally. The main contributions can be summarized as follow:
Firstly, twenty groups of wind speed/direction information are sampled at a single point in outdoor near-surface environments. Taking into consideration that the atmospheric turbulence is of multi-scale characteristics, the statistical characteristics of wind speed/direction under different time scales are studied with multi-resolution analysis in wavelet transform. The complexity properties of wind speed are analyzed using multi-scale entropy. The influence of wind-speed changes on the statistics of wind speed/direction and on multi-scale entropy of wind speed under different time scales are studied.
Secondly, the wind and concentration data are collected on three different spatial scales, which are 8 m, 10 m and 20 m, respectively. The mean wind speed/direction characteristics for all measured points and the wind speed relationship between different space-time points are analyzed. Besides, according to multilevel nature of atmospheric turbulence, conditional phase-averaged waveforms of multi-scale coherent eddy structures for wind speed are studied. The evolution rule of wind speed under different time scales is concluded.
Lastly, the statistical properties of gas concentrations and the relationship between wind and concentration in flow field are studied. The wind speed/direction and concentration which are not measured in flow field are acquired by spatial interpolation. The evolution process of wind speed/direction and concentration in flow field are showed dynamically by software.
气味/气体传播的主要影响因素有风、温度层结、风速廓线以及地面粗糙度等。其中风是影响气体传播的最主要因子,而大气边界层下的流场处于湍流运动状态,决定了室外近地面流场的运动特征。
本文利用超声风速风向仪与气体传感器分别测量了室外近地面的风速/风向和气体浓度信息,分析了近地面流场特性并结合空间插值方法对流场中的风速/风向与气体浓度信息进行了重现。重点开展完成了如下工作:
第一、采集了20组室外近地面单点风速/风向信息。考虑到大气湍流的多尺度特征,首先应用了子波变换多分辨方法分析了分尺度风速/风向的统计特性,然后应用混沌多尺度熵方法对风速的复杂性进行了分析,在此过程中,还研究了分尺度风速/风向统计特性及其复杂性随风速变化的规律。
第二、分别在室外8 m、10 m和20 m三种空间尺度范围内使用风速仪与气体传感器采集了风场与浓度场的数据,并分析了风场中各空间-时间点的平均风速/风向特性及不同空间-时间点之间风速的关系。此外,根据大气湍流的多层次性,分析了分尺度风速相干结构的条件相位平均波形,从而得出了风场中不同时间尺度下风速的平均演化规律。
第三、分析了流场中气体浓度的统计特性,并研究了风速与浓度的相关关系。通过空间插值的方法,推算出流场中其它未测量点的风速以及浓度值。最终通过软件动态显示室外近地面流场的演变过程。
In natural world, most creatures have quite sensitive olfaction sensing in order to be adaptable to environmental change. Many animals can make use of odor information to define territories, attract partners, stalk prey and avoid predators. In the early 1990s, some scholars began to study how to locate odor source by imitating biological olfaction. Odor/gas source localization means using mobile robot equipped with odor/gas sensor and wind speed/direction sensor to confirm where the odor/gas source is.
The spread of odor/gas in atmospheric boundary layer is influenced by wind, temperature stratification, wind profile, ground surface roughness and so on, and the wind is the main factor affecting the odor/gas dispersion. The turbulent flow in atmospheric boundary layer determines the movement characteristics of flow field in outdoor near-surface environments.
In this thesis, gas concentration and wind speed/direction are sampled using gas and wind sensors in outdoor near-surface environments. The properties of flow field, including collected wind speed/direction and gas concentration, are analyzed. Combining with spatial interpolation method, the wind speed/direction and gas concentration information in flow field are reconstructed finally. The main contributions can be summarized as follow:
Firstly, twenty groups of wind speed/direction information are sampled at a single point in outdoor near-surface environments. Taking into consideration that the atmospheric turbulence is of multi-scale characteristics, the statistical characteristics of wind speed/direction under different time scales are studied with multi-resolution analysis in wavelet transform. The complexity properties of wind speed are analyzed using multi-scale entropy. The influence of wind-speed changes on the statistics of wind speed/direction and on multi-scale entropy of wind speed under different time scales are studied.
Secondly, the wind and concentration data are collected on three different spatial scales, which are 8 m, 10 m and 20 m, respectively. The mean wind speed/direction characteristics for all measured points and the wind speed relationship between different space-time points are analyzed. Besides, according to multilevel nature of atmospheric turbulence, conditional phase-averaged waveforms of multi-scale coherent eddy structures for wind speed are studied. The evolution rule of wind speed under different time scales is concluded.
Lastly, the statistical properties of gas concentrations and the relationship between wind and concentration in flow field are studied. The wind speed/direction and concentration which are not measured in flow field are acquired by spatial interpolation. The evolution process of wind speed/direction and concentration in flow field are showed dynamically by software.
引文
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[3] Petit C, Hossaert-Mckey M, Perret P, et al. Blue tits use selected plants and olfaction to maintain an aromatic environment for nestlings. Ecology Letters, 2002, 5(4): 585~589
[4] Weissburg M J, Zimmer-Faust R K. Life and death in moving fluids: hydrodynamic effects on chemosensory-mediated predation. Ecology, 1993, 74(5): 1428~1443
[5] Grasso F W, Basil J A. How lobsters, crayfishes, and crabs locate sources of odor: current perspectives and future directions. Current opinion in neurobiology, 2002, 12(6): 721~727
[6]匡兴红,邵惠鹤.无线传感器网络在气体源预估定位中的应用.华东理工大学学报(自然科学版), 2006, 780~783
[7] Michaelides M P, Panayiotou C G. Plume source position estimation using sensor networks. Proceedings of the IEEE International Symposium on Intelligent Control, Mediterrean Conference on Control and Automation. USA: IEEE, 2005, 731~736
[8] Langer K. A guide to sensor design for land mine detection. Proceedings of the EUREL International Conference on the Detection of Abandoned Landmines: A Humanitarian Imperative Seeking a Technical Solution. Edinburgh, UK: IET, 1996, 30~32
[9] Rozas R, Morales J, Vega D. Artificial smell detection for robotic navigation. Proceedings of the 5th International Conference on Advanced Robotics. Pisa, Italy: IEEE, 1991, 1730~1733
[10] Ishida H, Suetsugu K, Nakamoto T, et al. Study of autonomous mobile sensing system for localization of odor source using gas sensors and anemometric sensors. Sensors and Actuators A: Physical, 1994, 45(2): 153~157
[11] Payne T L, Birch M C. Mechanisms in insect olfaction. USA: Oxford University Press, 1986
[12] Ishida H, Nakamoto T, Moriizumi T. Gas/odor plume tracing robot. Sensors update, 1999, 6(1): 397~418
[13] Farrell J A, Murlis J, Long X, et al. Filament-based atmospheric dispersion model to achieve short time-scale structure of odor plumes. Environmental Fluid Mechanics, 2002, 2(1): 143~169
[14] Genovese V, Dario P, Magni R, et al. Self organizing behavior and swarm intelligence in a pack of mobile miniature robots in search of pollutants. Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems. USA: IEEE, 1992, 1575~1582
[15] Moore P, Crimaldi J. Odor landscapes and animal behavior: tracking odor plumes in different physical worlds. Journal of marine systems, 2004, 49(1-4): 55~64
[16] Korczyk P, Malinowski S P, Kowalewski T A. Mixing of cloud and clear air in centimeter scales observed in laboratory by means of particle image velocimetry. Atmospheric research, 2006, 82(1-2): 173~182
[17] Malinowski S P, Andrejczuk M, Grabowski W W, et al. Laboratory and modeling studies of cloud-clear air interfacial mixing: anisotropy of small-scale turbulence due to evaporative cooling. New Journal of Physics, 2008, 10: 1~15
[18] Kowadlo G, Russell R A. Robot odor localization: a taxonomy and survey. The International Journal of Robotics Research, 2008, 27(8): 869~894
[19]李吉功.室外时变气流环境下机器人气味源定位: [博士学位论文],天津;天津大学, 2010
[20] Russell R A. Locating underground chemical sources by tracking chemical gradients in 3 dimensions. Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems. Sendal, Japan: IEEE, 2004, 325~330
[21] Smyth W D, Moum J N. 3D turbulence. Encyclopedia of ocean sciences. New York: Academic Press, 2001
[22]胡非.湍流、间歇性与大气边界层.北京:科学出版社, 1995
[23]朗道,栗弗席茨著.孔祥言等校译.流体力学.北京:高等教育出版社, 1984
[24]张兆顺,崔桂香,许春晓.走近湍流.力学与实践, 2002, 24(1): 1~8
[25] Hinze J O著.黄永念,颜大椿译.湍流(上册).北京:科学出版社, 1987
[26] Taylor G I. Diffusion by continuous movements. Proceedings of the London Mathematical Society, 1921, 20(2): 196~211
[27] Richardson L F. Weather prediction by numerical process. Cambridge University Press, 2007
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