Improving built environment aerodynamics with deep learning
Time: Fri 2025-09-26 13.00
Location: B3, Brinellvägen 23, Stockholm
Language: English
Subject area: Civil and Architectural Engineering, Fluid and Climate Theory
Doctoral student: Giovanni Calzolari , Hållbara byggnader
Opponent: Associate Professor Twan van Hooff, TU/e Eindhoven University of Technology, Netherlands
Supervisor: Professor Folke Björk, Hållbara byggnader; Professor Wei Liu, Tianjin University, China
QC 20250905
Abstract
This thesis explores the intersection of deep learning (DL) and computational fluid dynamics (CFD) to improve the modeling and analysis of built environmentaerodynamics. As urbanization accelerates and sustainability challenges intensify, accurate and efficient tools for airflow prediction in cities and buildings are increasingly vital. Traditional CFD methods, while powerful, are computationally demanding and limited by model assumptions, especially in turbulence modeling. This work investigates whether deep learning techniques can enhance both the speed and generalizability of aerodynamic simulations, and whether they can support experimental measurements such as those obtained from wind tunnels. The thesis presents a comprehensive framework that integrates convolutional neural networks (CNNs), graph neural networks (GNNs), and generative adversarial networks (GANs) to accelerate large eddy simulations (LES), reconstruct flow fields, and improve experimental data processing. Notably, GNN-based models are used to operate directly on unstructured CFD meshes, preserving geometric and topological information critical for urban flow predictions. Hybrid approaches that combine physics-based knowledge with data-driven models are also introduced. Applications span both simulated and experimental domains, including a case study on wind tunnel shape optimization using reinforcement learning. While deep learning models showed strong potential for improving both simulation accuracy and speed, the work also highlights important challenges, including the need for better generalization, model interpretability, and the lack of publicly available CFD datasets.The findings suggest that combining deep learning with traditional fluid dynamics offers a promising path forward, especially when supported by open data, physical constraints, and collaborative research efforts. The thesis concludes by outlining directions for future research in physics-informed learning, dataset curation, and real-time integration of predictive models into sustainable urban design.