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

2026-02-25

AI Summary

This study addresses the high computational cost of high-fidelity simulations for urban pollutant dispersion under complex geometries and multiparametric conditions, which hinders real-time emergency response. For the first time, it systematically compares intrusive and non-intrusive model order reduction (MOR) methods for parameterized incompressible Navier–Stokes and convection–diffusion coupled problems, and develops a non-intrusive reduced-order model that accounts for variations in both wind speed and direction. Built on a two-dimensional domain derived from real building footprints, the model enables rapid spatiotemporal predictions, Monte Carlo-based uncertainty quantification, and interactive visualization. Experimental results demonstrate that the approach achieves faster-than-real-time prediction of pollutant dispersion, substantially enhancing decision-making efficiency and reliability in emergency scenarios.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Sampling/Simulation-based Search
• Reasoning under Uncertainty: Stochastic Optimization

Application Category

• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Systems and Infrastructure for Web, Mobile and WoT: Web applications in cross-disciplinary domains and verticals such as mixed reality, smart cities, and digital health
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

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.

Problem

• model order reduction
• contaminant transport
• computational cost
• uncertainty quantification
• Navier-Stokes equations

Innovation

• Model Order Reduction
• Non-Intrusive ROM
• Uncertainty Quantification
• Contaminant Transport
• Real-Time Prediction