Towards Real-Time Urban Physics Simulations with Digital Twins

TL;DR

This paper develops a computational framework using finite element methods and model order reduction to enable real-time urban physics simulations within digital twins for emergency response.

Contribution

It introduces an automated process for generating models and solutions, integrating physics-based and data-driven approaches for real-time urban contaminant dispersion prediction.

Findings

Efficient model generation and solution process demonstrated.

Model order reduction improves computational speed without losing accuracy.

Framework supports real-time decision-making in evacuation scenarios.

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.