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基于热红外成像的坡面薄层水流流速测量方法
引用本文:张艳, 史海静, 郭明航, 赵军, 展小云, 丁成琴. 基于热红外成像的坡面薄层水流流速测量方法[J]. 农业工程学报, 2021, 37(21): 108-115. DOI: 10.11975/j.issn.1002-6819.2021.21.013
作者姓名:张艳  史海静  郭明航  赵军  展小云  丁成琴
作者单位:1.中国科学院水利部水土保持研究所黄土高原土壤侵蚀与旱地农业国家重点实验室,杨凌 712100;2.中国科学院大学,北京 100049;3.西北农林科技大学水土保持研究所,杨凌 712100
基金项目:中国科学院战略性先导科技专项(XDA20040202);黄土丘陵区地形微生境分类评价与潜在植物群落分布模拟(XAB2020YN04);国家科技基础条件平台建设项目(2005DKA32300)
摘    要:流速是表征水流水力学特性的重要物理量.为了准确获取薄层水流流速,基于热红外成像技术、计算机视觉识别技术,设计了一种薄层水流流速测量系统.该系统通过对热示踪剂的自动控制以及对其热成像图的瞬时采集、影像校正、噪点去除、质心确定等手段,获取薄层水流的流速等参数,从而实现对坡面薄层水流流速的动态观测.该系统精确度和准确度高,可...

关 键 词:流速  坡面  热红外成像观测系统  薄层水流  热红外标靶  精准度评价
收稿时间:2021-05-12
修稿时间:2021-10-10

Thermal infrared imaging measurement method for shallow flow velocity
Zhang Yan, Shi Haijing, Guo Minghang, Zhao Jun, Zhan Xiaoyun, Ding Chengqin. Thermal infrared imaging measurement method for shallow flow velocity[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2021, 37(21): 108-115. DOI: 10.11975/j.issn.1002-6819.2021.21.013
Authors:Zhang Yan  Shi Haijing  Guo Minghang  Zhao Jun  Zhan Xiaoyun  Ding Chengqin
Affiliation:1.State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau, Institute of Soil and Water Conversation, Chinese Academy of Sciences and Ministry of Water Resources, Yangling 712100, China;2.University of Chinese Academy of Sciences, Beijing 100049, China;3.Institute of Soil and Water Conservation, Northwest A&F University, Yangling 712100, China
Abstract:Abstract: A flow velocity is one of the most important physical parameters to quantify the hydraulic characteristics of water flow. The accurate measurement of shallow flow velocities can greatly contribute to understanding and simulating the sediment transport and soil erosion. In this study, a novel observation system of thermal infrared imaging and computer vision was established to measure the velocities of shallow flow. The observation system consisted of three subsystems, such as the thermal tracer control, image acquisition, and transmission, as well as image calculation. Specifically, the control subsystem of the thermal tracer was mainly responsible for the constant temperature of hot water, including the electric heater, temperature sensor, and hot water pump. The subsystem of image acquisition and transmission consisted of a FLIR ONE 3.1.0 thermal infrared camera, 4 thermal infrared targets, and a wireless router, particularly for the thermal infrared images of thermal tracer migration. The subsystem of image calculation was used to extract the high frame rate from the thermal infrared images, including the data storage and computing matching. As such, the velocity of shallow flow was dynamically monitored using the automatic control of thermal tracer, instantaneous image acquisition, image correction, noise removal, and centroid determination. Furthermore, a series of experiments were conducted to verify the system in the Simulated Runoff Hall of the State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau. The experimental glass tank was in the size of 4.6 × 0.196 × 0.1 m3 at a gradient of 15° to the horizontal, together with the different flows (0.2-1.5 L/s). The results showed that the observation system presented a higher accuracy than before, suitable for the dynamic transport of thermal tracer in the different temporal and spatial scales. Specifically, an excellent performance was achieved, where the measurement standard deviation of the system was 0.0201 m/s, the observation accuracy reached 98.33%, the observation time resolution was 1/9 s, and the spatial resolution was up to 2 mm. Moreover, the accuracy of the observation system with the thermal infrared imaging was much higher than that with the traditional tracer techniques (dye and salt tracer). The maximum and minimum relative errors of the observation system were –9.61% and 0.16%, respectively, and the range of the relative errors was within ?10%. The maximum relative error of the dye tracer was 75.92%, and the minimum relative error was 12.03%. The maximum and minimum relative errors of salt tracer were –26.67% and 0.11%, respectively, where 52% of the samples presented the relative errors within ?10%. Correspondingly, the observation system with thermal infrared imaging was provided a reliable way to measure the shallow flow velocity. By contrast, either the dye tracer or salt tracer cannot calculate the overall situation of water flow, due to the manually recording one-time value during measurement. Fortunately, the observation system with thermal infrared imaging can be widely expected to accurately record the overall situation of the water flow. More importantly, the leading edge points and centroid points of the thermal tracer area can be identified at different time intervals, thereby accurately calculating the leading edge velocity and centroid velocity of the water flow. The images can also be used to trace the development of water flow in various tracer sections at different times. As such, it can be possible to measure the tracer migration velocity along the direction of water flow and the diffusion velocity perpendicular to the direction of water flow in the future. This technique can also be applied to monitor the rainfall erosion and runoff scour in the process of soil erosion.
Keywords:flow velocity   slope   thermal infrared imaging observation system   shallow flow   thermal infrared target   accuracy evaluation
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