Rapid Identification of Moving Contaminant Sources Through Physics-Based Modelling
This addresses the challenge of timely detection and response to hazardous releases in emergency scenarios like sabotage or terrorism, which is critical for protecting individuals and infrastructure.
The paper tackles the problem of rapidly identifying moving and time-varying airborne contaminant sources using scarce sensor data, and demonstrates a novel algorithm that couples sensor data with an advection-diffusion model to detect, localize, and quantify such sources in a computational domain.
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.