Abstract

The growing concern of the effects of potential releases of chemical, biological or radiological materials in populated areas has led to an increase in urban dispersion modelling over the past several decades. More recently, there has been a surge of research in the area of source term estimation (STE), in which inverse computational methods are used to predict a source (release) location and strength based on sensor readings. Many studies to date have focused on idealized, free-field scenarios estimating continuous or instantaneous gaseous releases. There have been limited efforts including geometry (e.g. terrain or urban structures) effects using computational fluid dynamics (CFD) and no efforts towards estimating highly complex, transient sources. The first contribution of this work is the development of a proposed methodology to approximate the strength and location of transient source terms, whether mobile or changing in strength. The transient prediction tool is demonstrated to accurately predict the location and strength of sources exhibiting low to moderate transient behaviour. For fast moving or rapidly changing sources, the model becomes heavily reliant on adequate sensor positioning. The second contribution of this work is to quantify the uncertainty of the STE tool given uncertain measurements in atmospheric conditions (e.g. wind speed, wind angle and surface roughness) which are often sparse and prone to variations. The uncertainty quantification study is performed on steady, instantaneous, mobile and variable-strength sources in an idealized free-field setting. The wind angle was found to have the most effect on the prediction of the release position. The true release location was within 10-90th percentiles, with standard deviations on the order of one CFD cell size, for all cases assessed indicating a robustness of the algorithm to handle uncertain inputs. The free-field analysis is used as a baseline for applying the uncertainty quantification to predictions in a full-scale urban environment using the Joint Urban 2003 experimentation. Despite uncertain atmospheric conditions in the urban setting, the predicted source location was generally in the correct vicinity, although sometimes in the adjacent upwind street. It is recommended that the uncertainty quantification be applied to a probabilistic prediction tool to quantify the uncertainty of a statistical source term representation. Further, the analysis could be applied for more complex, highly transient and multi-source scenarios to fully assess the robustness of the algorithm.

Article highlights

• A source term estimation (STE) methodology is proposed for approximating the strength and location of transient atmospheric releases based on sensor concentrations.

• An error analysis is performed on the methodology to bound the predictive errors based on the level of transiency of the release.

• An uncertainty quantification study is performed to characterize the uncertainty in the prediction given uncertain measurements in atmospheric conditions.

中文翻译：

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城市环境中稳态和瞬态源项估计的不确定性量化

摘要

在人口稠密地区对化学，生物或放射性物质潜在释放的影响的日益增长的关注已导致过去几十年中城市扩散模型的增加。最近，在源项估计（STE）领域中出现了大量研究，其中使用逆向计算方法基于传感器读数来预测源（释放）的位置和强度。迄今为止，许多研究都集中在估计连续或瞬时气体释放的理想自由场方案上。使用计算流体动力学（CFD）所做的努力有限，包括几何形状（例如地形或城市结构）效果，并且没有努力估算高度复杂的瞬态源。这项工作的第一个贡献是开发了一种拟议的方法，以估算瞬态源项的强度和位置，无论是移动的还是强度变化的。演示了瞬态预测工具，可以准确地预测表现出低到中等瞬态行为的信号源的位置和强度。对于快速移动或快速变化的源，模型将严重依赖于适当的传感器位置。这项工作的第二个贡献是，鉴于大气条件下的测量不确定性（例如风速，风角和表面粗糙度）通常是稀疏的并且容易变化的，因此可以量化STE工具的不确定性。不确定性量化研究是在理想的自由场环境中对稳定，瞬时，移动和可变强度的源进行的。发现风角对释放位置的预测影响最大。真正的释放位置在第10-90个百分位之内，标准偏差约为一个CFD像元大小，在所有评估的情况下，都表明该算法可处理不确定的输入。自由场分析用作基线，用于使用“联合城市2003年”实验将不确定性量化应用于完整城市环境中的预测。尽管城市环境中的大气条件不确定，但预测的气源位置通常在正确的附近，尽管有时在相邻的上风街。建议将不确定性量化应用于概率预测工具，以量化统计源项表示形式的不确定性。更多，

文章重点

• 提出了一种源项估算（STE）方法，用于基于传感器浓度来估算瞬时大气释放的强度和位置。

• 对方法进行了错误分析，以根据发布的透明性级别限制预测错误。

• 在给定大气条件下的不确定性的情况下，进行了不确定性量化研究以表征预测中的不确定性。