Intrusive and Non-Intrusive Model Order Reduction for Airborne Contaminant Transport: Comparative Analysis and Uncertainty Quantification

TL;DR

This paper compares intrusive and non-intrusive model order reduction methods for simulating airborne contaminant transport, developing a non-intrusive reduced-order model that enables real-time predictions and uncertainty quantification in complex urban scenarios.

Contribution

It provides a comprehensive comparison of MOR techniques and introduces a non-intrusive ROM for efficient, real-time contaminant dispersion prediction considering wind variability.

Findings

Non-intrusive ROM achieves faster than real-time predictions.

The model effectively accounts for wind velocity and direction.

Uncertainty quantification via Monte Carlo is feasible with the ROM.

Abstract

Numerical simulations of contaminant dispersion, as after a gas leakage incident on a chemical plant, can provide valuable insights for both emergency response and preparedness. Simulation approaches combine incompressible Navier-Stokes (INS) equations with advection-diffusion (AD) processes to model wind and concentration field. However, the computational cost of such high-fidelity simulations increases rapidly for complex geometries like urban environments, making them unfeasible in time-critical or multi-query "what-if" scenarios. Therefore, this study focuses on the application of model order reduction (MOR) techniques enabling fast yet accurate predictions. To this end, a thorough comparison of intrusive and non-intrusive MOR methods is performed for the computationally more demanding parametric INS problem with varying wind velocities. Based on these insights, a non-intrusive reduced-order model (ROM) is constructed accounting for both wind velocity and direction. The study is conducted on a two-dimensional domain derived from real-world building footprints, preserving key features for analyzing the dispersion of, for instance, denser contaminants. The resulting ROM enables faster than real-time predictions of spatio-temporal contaminant dispersion from an instantaneous source under varying wind conditions. This capability allows assessing wind measurement uncertainties through a Monte Carlo analysis. To demonstrate the practical applicability, an interactive dashboard provides intuitive access to simulation results.