论文标题
自适应退化空间方法，用于使用向后拉格朗加随机模型的源期限估计
Adaptive degenerate space method for source term estimation using a backward Lagrangian stochastic model
论文作者
论文摘要
基于浓度测量值表征气源参数的总体问题是一项艰巨的任务。由于许多反问题，准确估计的主要障碍之一是缺乏足够信息引起的解决方案的非唯一性。随着探测器的数量降低，这不仅仅是许多实际情况的合理情况，可以表征来源的可能解决方案的数量会大大增加，从而导致严重的错误。在本文中，制定和分析了一种基于拉格朗日随机的方法，用于识别这些可疑点，这些方法将称为“退化空间”。然后，用于定量预测在空间中部署新检测器的效果的新程序用于设计一种自适应方案以供源期限估计。该方案已针对几种情况进行了测试，与初始检测器的位置不同，并显示出可大大减少信息不足所形成的退化。退化空间与新的自适应方案的组合配方显示可提高准确性，尤其是相对较少的检测器。
The general problem of characterizing gas source parameters based on concentration measurements is known to be a difficult task. As many inverse problems, one of the main obstacles for accurate estimation is the non-uniqueness of solution, induced by the lack of sufficient information. As the number of detectors is lowered, which is more than a plausible scenario for many practical situations, the number of possible solutions that can characterize the source increases dramatically, leading to severe errors. In this paper, a Lagrangian stochastic based method for identifying these suspected points, which will be referred to as 'degenerate space', is formulated and analysed. Then, a new procedure for quantitative prediction of the effect of deploying a new detector in space is used to design an adaptive scheme for source term estimation. This scheme has been tested for several scenarios, differing by the location of the initial detectors, and is shown to reduce dramatically the degeneracy formed by insufficient information. The combined formulation of degenerate space with the new adaptive scheme is shown to give improved accuracy, and in particular for a relatively small number of detectors.
