Contaminant Dispersion Simulation in a Digital Twin Framework for Critical Infrastructure Protection

TL;DR

This paper presents a digital twin framework that combines fast simulation, inverse problem solving, and crowd modeling to improve emergency response for atmospheric contaminant dispersion in critical infrastructure.

Contribution

It introduces an integrated digital twin approach with reduced-order modeling and Bayesian inverse methods for real-time contaminant dispersion prediction and management.

Findings

Fast contaminant dispersion simulations using reduced-order models.

Effective source localization from sparse measurements.

Coupled dispersion and crowd models for evacuation planning.

Abstract

A digital twin framework for rapid predictions of atmospheric contaminant dispersion is developed to support informed decision making in emergency situations. In an offline preparation phase, the geometry of a built environment is discretized with a finite element (FEM) mesh and a reduced-order model (ROM) of the steady-state incompressible Navier-Stokes equations is constructed for various wind conditions. Subsequently, the ROM provides a fast wind field estimate based on the current wind speed during the online phase. To support crisis management, several methodological building blocks are combined. Automatic FEM meshing of built environments and numerical flow solver capabilities enable fast forward-simulations of contaminant dispersion using the advection-diffusion equation as transport model. Further methods are integrated in the framework to address inverse problems such as contaminant source localization based on sparse concentration measurements. Additionally, the contaminant dispersion model is coupled with a continuum-based pedestrian crowd model to derive fast and safe evacuation routes for people seeking protection during contaminant dispersion emergencies. The interplay of these methods is demonstrated in two critical infrastructure protection (CIP) test cases. Based on simulated real world interaction (measurements, communication), this article demonstrates a full Measurement-Inversion-Prediction-Steering (MIPS) cycle including a Bayesian formulation of the inverse problem.