A review of advances towards efficient reduced-order models (ROM) for predicting urban airflow and pollutant dispersion

Computational fluid dynamics (CFD) models have been used for the simulation of urban airflow and pollutant dispersion, due to their capability to capture different length scales and turbulence nature of the flow field. However, their high computational costs prevent them from being used for (near) real-time simulations, long-term predictions, and simulations with dynamic boundary conditions. Reduced-order models (ROMs) are proposed as reliable alternatives to CFD approaches to solve the mentioned issues. This article aims to comprehensively review the state-of-the-art application of different methodologies to develop a non-intrusive ROM (NIROM) for predicting urban airflow and pollutant dispersion. Developing such models comprises two steps: dimensionality reduction and computing the feature dynamics of the reduced space. Various methodologies, with the focus on machine learning algorithms, are proposed for the mentioned stages, while their capabilities and limitations are discussed. Furthermore, different approaches are introduced to overcome the issue of the physical interpretation of these models. Also, several methods are proposed to make the models suitable for being used in long-term predictions and multi-query problems (i.e., considering changes in boundary conditions).