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Towards Real-Time Urban Physics Simulations with Digital Twins

Jacopo Bonari, Lisa Kühn, Max von Danwitz, Alexander Popp

TL;DR

The paper tackles real-time predictive analysis of dangerous gas dispersion in urban built environments, a crucial need for evacuation planning. It presents an automated digital twin workflow that builds a computational domain from geo-referenced data, meshes with FEM via GMSH, and solves two coupled PDEs: steady incompressible Navier–Stokes for wind flow and an advection–diffusion model for contaminant transport, using $\mathbf{u}$, $p$, and $c$ as velocity, pressure, and concentration. To achieve real-time capability, the authors apply POD-based MOR with DEIM to accelerate the wind-field solve while preserving accuracy. Validations on two real urban geometries (Munich and Düsseldorf) demonstrate substantial speedups (up to ~$110\times$) and acceptable error, supporting the prospect of a functional DT for crisis management. The work lays groundwork for data–driven hybrid DTs that integrate physics with sensor data to provide informed, real-time evacuation support.

Abstract

Urban populations continue to grow, highlighting the critical need to safeguard civilians against potential disruptions, such as dangerous gas contaminant dispersion. The digital twin (DT) framework offers promise in analyzing and predicting such events. This study presents a computational framework for modelling airborne contaminant dispersion in built environments. Leveraging automatic generation of computational domains and solution processes, the proposed framework solves the underlying physical model equations with the finite element method (FEM) for numerical solutions. Model order reduction (MOR) methods are investigated to enhance computational efficiency without compromising accuracy. The study outlines the automatic model generation process, the details of the employed model, and the future perspectives for the realization of a DT. Throughout this research, the aim is to develop a reliable predictive model combining physics and data in a hybrid DT to provide informed real-time support within evacuation scenarios.

Towards Real-Time Urban Physics Simulations with Digital Twins

TL;DR

The paper tackles real-time predictive analysis of dangerous gas dispersion in urban built environments, a crucial need for evacuation planning. It presents an automated digital twin workflow that builds a computational domain from geo-referenced data, meshes with FEM via GMSH, and solves two coupled PDEs: steady incompressible Navier–Stokes for wind flow and an advection–diffusion model for contaminant transport, using , , and as velocity, pressure, and concentration. To achieve real-time capability, the authors apply POD-based MOR with DEIM to accelerate the wind-field solve while preserving accuracy. Validations on two real urban geometries (Munich and Düsseldorf) demonstrate substantial speedups (up to ~) and acceptable error, supporting the prospect of a functional DT for crisis management. The work lays groundwork for data–driven hybrid DTs that integrate physics with sensor data to provide informed, real-time evacuation support.

Abstract

Urban populations continue to grow, highlighting the critical need to safeguard civilians against potential disruptions, such as dangerous gas contaminant dispersion. The digital twin (DT) framework offers promise in analyzing and predicting such events. This study presents a computational framework for modelling airborne contaminant dispersion in built environments. Leveraging automatic generation of computational domains and solution processes, the proposed framework solves the underlying physical model equations with the finite element method (FEM) for numerical solutions. Model order reduction (MOR) methods are investigated to enhance computational efficiency without compromising accuracy. The study outlines the automatic model generation process, the details of the employed model, and the future perspectives for the realization of a DT. Throughout this research, the aim is to develop a reliable predictive model combining physics and data in a hybrid DT to provide informed real-time support within evacuation scenarios.
Paper Structure (16 sections, 5 equations, 5 figures, 1 table)

This paper contains 16 sections, 5 equations, 5 figures, 1 table.

Table of Contents

  1. Introduction
  2. Workflow Overview
  3. Domain definition and data Collection
  4. Description of the simulation domain
  5. Geo-referenced data collection
  6. Virtual replica
  7. Mesh generation
  8. Boundary and initial conditions
  9. Physics-based Simulation
  10. Simulation of Wind Flow Field
  11. Full-Order Model
  12. Simulation of Contaminant Transport
  13. Application Examples: Munich and Düsseldorf
  14. Analysis of computed wind and concentration fields
  15. Quantitative performance analysis of the reduced-order model
  16. ...and 1 more sections

Figures (5)

  • Figure 1: Urban physics simulation workflow. Starting from geo-referenced building data in a database, here OpenStreetMap (OSM), the highly automatized workflow generates a FEM-mesh using GMSH remacle_gmsh_2009 and performs simulations based on the PDE solver FEniCS AlnaesEtal2015.
  • Figure 2: Computational domain with highlighted inflow and outflow boundaries and location of initial gas source.
  • Figure 3: Analysis-ready FEM mesh with automatic local refinement in the narrow crevices between the buildings.
  • Figure 4: Qualitative results for the two use-cases in terms of wind vector field and concentration distribution in [ppm].
  • Figure 5: Performance analysis of the reduced-order model (ROM) for both geometries.