Rapid Identification of Moving Contaminant Sources Through Physics-Based Modelling

TL;DR

This paper introduces a novel physics-based algorithm for rapid detection, localization, and quantification of moving hazardous contaminant sources using limited sensor data and advection-diffusion modeling, with applications in emergency response.

Contribution

It presents a new method that combines sparse sensor data with a coupled advection-diffusion model to identify unknown, moving contaminant sources in real-time scenarios.

Findings

Demonstrated feasibility in a 2D computational domain.

Proposed calibration methodology using wind tunnel experiments.

Outlined integration with digital twins for predictive decision support.

Abstract

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.