Intrusive and Non-Intrusive Model Order Reduction for Airborne Contaminant Transport: Comparative Analysis and Uncertainty Quantification
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| Format: | Preprint |
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2026
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| author | Kühn, Lisa Bonari, Jacopo von Danwitz, Max Popp, Alexander |
| author_facet | Kühn, Lisa Bonari, Jacopo von Danwitz, Max Popp, Alexander |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2602_21996 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Intrusive and Non-Intrusive Model Order Reduction for Airborne Contaminant Transport: Comparative Analysis and Uncertainty Quantification Kühn, Lisa Bonari, Jacopo von Danwitz, Max Popp, Alexander Computational Engineering, Finance, and Science 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. |
| title | Intrusive and Non-Intrusive Model Order Reduction for Airborne Contaminant Transport: Comparative Analysis and Uncertainty Quantification |
| topic | Computational Engineering, Finance, and Science |
| url | https://arxiv.org/abs/2602.21996 |