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N. Liu
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Urban wind simulations are essential for assessing pedestrian comfort, pollutant dispersion, and microclimate design, yet high-fidelity CFD remains computationally expensive. This thesis investigates the potential of a Swin-Transformer-based surrogate model to approximate steady-state 2D results for 100 m × 100 m tiles under a single inflow direction. A dataset of 690 urban tiles was extracted from the 3DBAG and simulated in Ansys Fluent at 0.5 m resolution across five inflow speeds (5–15 m/s) and five types of building layouts. Velocity fields were rasterized to 1 m grids. The surrogate architecture preserves the original Swin backbone, evaluated with three loss variants: original RMSE, buffer weighting, and a divergence penalty. Encoding wind as xy-component consistently outperformed magnitude-only training. Errors scaled linearly with inflow speed and were highest in Mixed and Attached urban forms. Architectural resolution was the dominant factor influencing accuracy since it introduced visible artifacts, while the buffer and divergence losses offered only marginal improvements. Limitations remain near building facades, where sharp gradients are smoothed by the patch-based architecture. Nonetheless, the surrogate offers potential for rapid wind flow estimation suitable for early-stage design or preliminary analysis.
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Urban wind simulations are essential for assessing pedestrian comfort, pollutant dispersion, and microclimate design, yet high-fidelity CFD remains computationally expensive. This thesis investigates the potential of a Swin-Transformer-based surrogate model to approximate steady-state 2D results for 100 m × 100 m tiles under a single inflow direction. A dataset of 690 urban tiles was extracted from the 3DBAG and simulated in Ansys Fluent at 0.5 m resolution across five inflow speeds (5–15 m/s) and five types of building layouts. Velocity fields were rasterized to 1 m grids. The surrogate architecture preserves the original Swin backbone, evaluated with three loss variants: original RMSE, buffer weighting, and a divergence penalty. Encoding wind as xy-component consistently outperformed magnitude-only training. Errors scaled linearly with inflow speed and were highest in Mixed and Attached urban forms. Architectural resolution was the dominant factor influencing accuracy since it introduced visible artifacts, while the buffer and divergence losses offered only marginal improvements. Limitations remain near building facades, where sharp gradients are smoothed by the patch-based architecture. Nonetheless, the surrogate offers potential for rapid wind flow estimation suitable for early-stage design or preliminary analysis.
Student report
(2023)
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D. MOUZAKIDIS, Puti Puti Nabila Riyadi, G.K. Nestoras, L. Xu, N. Liu, S. Köbben, G.A.K. Arroyo Ohori, S. Vitalis, J.E. Stoter
Modern navigation heavily relies on Global Navigation Satellite Systems (GNSS) and digitized road network databases, but faces limitations in GNSS-denied areas and complex 2D road netowrks. This project addresses these challenges by developing a methodology to create and store a comprehensive 3D road and terrain dataset for enhanced navigation. In collaboration with TomTom, a company that aims to fulfill software requirements, making significant advancements in geolocation technology and societal contributions. The main research question of the project is: ”How can we create a 3D map of roads using information about the center of the road and elevation data?”. The approach to answer this question involves extracting 2D road polygons from centerline data based on width of the roads, the direction and the amount of lanes of them. These 2D polygons undergo enrichment with elevation data, with techniques like filtering, segmentation, and primitive extraction ensuring alignment with the digital terrain model. The methodology encompasses data acquistion, creation of polygons using the centerlines dataset, 2D-to-3D polygon conversion, elevation integration and data storage in CityJSON format.
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Modern navigation heavily relies on Global Navigation Satellite Systems (GNSS) and digitized road network databases, but faces limitations in GNSS-denied areas and complex 2D road netowrks. This project addresses these challenges by developing a methodology to create and store a comprehensive 3D road and terrain dataset for enhanced navigation. In collaboration with TomTom, a company that aims to fulfill software requirements, making significant advancements in geolocation technology and societal contributions. The main research question of the project is: ”How can we create a 3D map of roads using information about the center of the road and elevation data?”. The approach to answer this question involves extracting 2D road polygons from centerline data based on width of the roads, the direction and the amount of lanes of them. These 2D polygons undergo enrichment with elevation data, with techniques like filtering, segmentation, and primitive extraction ensuring alignment with the digital terrain model. The methodology encompasses data acquistion, creation of polygons using the centerlines dataset, 2D-to-3D polygon conversion, elevation integration and data storage in CityJSON format.