通过物理建模快速识别移动污染源
Rapid Identification of Moving Contaminant Sources Through Physics-Based Modelling
AI总结：
本文提出一种结合advection-diffusion模型和稀疏性先验信息的算法，用于快速定位和量化时间变化的污染源，并通过风洞实验校准三维流动模型，以提升数字孪生环境中的实时决策支持能力。
摘要：
在恐怖袭击或破坏事件中，危险物质可能被故意释放到大气中，威胁个体，例如关键基础设施的操作人员。此类场景中的危险物质包括有毒工业化学品（TICs），这些物质通常肉眼不可见，使得及时检测和响应变得困难。本文考虑了需要快速且可靠评估的空中危险释放场景，其中化学、生物、放射性及核（CBRN）传感器系统提供稀少的局部测量数据。我们提出了一种新颖的算法，将这些数据与对流-扩散模型结合，以检测、定位和量化移动且时间变化的污染源。与许多现有方法不同，该方法通过结合空间稀疏性作为先验信息来识别发生时间和轨迹未知的污染源。该方法的可行性在二维计算域中得到验证。为进一步提高技术成熟度，我们还提出了基于风洞实验的三维流动模型校准方法。最后，概述了一种将框架与数字孪生环境中的实时传感器数据耦合的策略，以实现紧急情况下的预测决策支持。
英文摘要：
In an act of sabotage or terrorism, hazardous material might be released deliberately into the atmosphere to threaten individuals, e.g., those operating critical infrastructure. Hazardous materials in such a scenario include toxic industrial chemicals (TICs), which are often invisible to the human eye, making it difficult to detect and respond to releases in a timely manner. This contribution considers the scenario of an airborne hazardous release requiring rapid and reliable assessment, with a chemical, biological, radiological, and nuclear (CBRN) sensor system providing scarce and local measurements. We present a novel algorithm that couples these data with an advection-diffusion model to detect, localize, and quantify a moving and time-varying contaminant source. Unlike many existing methods, the approach identifies sources with unknown occurrence time and trajectory by incorporating spatial sparsity as prior information. The feasibility of the approach is demonstrated in a two-dimensional computational domain. To further increase the technology readiness level, we additionally propose a calibration methodology for the required three-dimensional flow models based on wind tunnel experiments. Finally, a strategy for coupling the framework with real-time sensor data within a digital twin environment is outlined to enable predictive decision support in emergency scenarios.
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