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UAV-based measurements of spatio-temporal concentration distributions of fluorescent tracers in open channel flows

机译:基于无人飞行器的明渠流中荧光示踪剂的时空浓度分布测量

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摘要

A new method of unmanned aerial vehicle (UAV)-based tracer tests using RGB (red, green, blue) images was developed in order to acquire the spatio-temporal concentration distribution of tracer clouds in open channel flows. Tracer tests using Rhodamine WT were conducted to collect the RGB images using a commercial digital camera mounted on a UAV, and the concentration of Rhodamine WT using in-situ fluorometric probes. The correlation analysis showed that the in-situ measured concentrations of Rhodamine WT were strongly correlated with the digital number (DN) of the RGB images, even though the response of DN to the concentration was spatially heterogeneous. The empirical relationship between the DN values and the Rhodamine WT concentration data was estimated using artificial neural network (ANN) models. The trained ANN models, which consider the effect of water depth and river bed, accurately retrieved the detailed spatio-temporal concentration distributions of all study areas that had an R-2 higher than 0.9. The acquired spatio-temporal concentration distributions by the proposed method based on the UAV images gave general as well as detailed views of the tracer cloud moving dynamically in open channel flows that cannot be easily observed using conventional in-situ measurements.
机译:为了获取明渠流中示踪剂云的时空浓度分布,开发了一种使用RGB(红色,绿色,蓝色)图像的基于无人机的示踪剂测试的新方法。使用安装在无人机上的商业数码相机,进行了使用罗丹明WT的示踪剂测试,以收集RGB图像,并使用原位荧光探针对罗丹明WT进行了浓度测试。相关分析表明,即使DN对浓度的响应在空间上是异质的,原位测量的若丹明WT浓度也与RGB图像的数字(DN)密切相关。使用人工神经网络(ANN)模型估计DN值与若丹明WT浓度数据之间的经验关系。经过训练的ANN模型,考虑了水深和河床的影响,可以准确地检索R-2高于0.9的所有研究区域的详细时空浓度分布。通过所提出的基于UAV图像的方法所获得的时空浓度分布给出了示踪剂云在明渠流中动态运动的一般以及详细视图,而使用常规的原位测量很难轻易观察到该示踪剂云。

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