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Optimization-Based Atmospheric Plume Source Identification

机译:基于优化的大气羽源识别

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摘要

This work applies optimization and an Eulerian inversion approach presented by Bagtzoglou and Baun in 2005 in order to reconstruct contaminant plume time histories and to identify the likely source of atmospheric contamination using data from a real test site for the first time. Present-day distribution of an atmospheric contaminant plume as well as data points reflecting the plume history allow the reconstruction and provide the plume velocity, distribution, and probable source. The method was tested to a hypothetical case and with data from the Forest Atmosphere Transfer and Storage (FACTS) experiment in the Duke experimental forest site. In the scenarios presented herein, as well as in numerous cases tested for verification purposes, the model conserved mass, successfully located the peak of the plume, and managed to capture the motion of the plume well but underestimated the contaminant peak.
机译:这项工作应用了优化和Bagtzoglou和Baun在2005年提出的欧拉反演方法,目的是重建污染物羽流的时间历史,并首次使用来自真实测试地点的数据来识别可能的大气污染源。当前大气污染物羽流的分布以及反映羽流历史的数据点可以进行重构,并提供羽流的速度,分布和可能的来源。在假设的情况下对该方法进行了测试,并使用了杜克实验林场中的森林大气转移和存储(FACTS)实验数据。在本文介绍的场景中,以及在为验证目的而进行的众多测试中,该模型均节省了质量,成功定位了羽流的峰值,并成功捕获了羽流的运动,但低估了污染物的峰值。

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