Computational Fluid Dynamics (CFD) is widely used to analyse wind flow around buildings; however, creating detailed input geometries and corresponding meshes can be a time-consuming process. This thesis investigates voxelization as a means to simplify building models for their use in CFD and analyses the impact of voxel resolution on simulation accuracy.

Three building geometries with varying roof shapes and footprints were converted from detailed continuous models into voxel models with increasingly finer voxel resolutions. The voxelized models were compared to a non-voxelized LoD 3.2 model to assess accuracy under four key wind directions (90°, 45°, 22.5°, and 0°).

The CFD simulations were performed using OpenFOAM’s RANS solver with a $k–\epsilon$ turbulence model. Due to its higher computational efficiency compared to other turbulence-resolving frameworks, the RANS approach enabled a large number of simulations while maintaining sufficient accuracy for urban CFD applications. A grid-independence test was conducted using the Grid Convergence Index (GCI) method for one model. The resulting grid-independent mesh was then scaled for the other models, ensuring that all simulations remained grid-independent.

The results show that coarse voxel resolutions (1 m and 0.5 m) significantly increase the size of the building geometry and leads to large velocity differences compared to the non-voxelized model. Sloped roofs were most affected by voxelization, as these models showed greater velocity differences than those with rounded roofs.

Wind direction also plays a significant role in voxelization accuracy. While the 90°, 22.5°, and 0° wind directions showed similar results across voxel resolutions, the 45° direction produced notable velocity differences. An exception was observed for the model with a rounded roof, which showed more consistent results across all wind directions.

Overall, the velocity difference between non-voxelized and voxelized models decreases as voxel size decreases. However, below a voxel size of 0.1 m, the reduction in velocity difference stagnates, indicating that smaller voxel sizes offer limited additional benefit to CFD accuracy.

This study explores the effectiveness of Virtual Reality (VR) compared to the use of 2D interfaces in interpreting point cloud data, focusing on user perception, interaction and relative measurement accuracy. Visualizing point clouds is often challenging due to the limitations in translating three-dimensional data into two-dimensional screens. VR offers a potential solution to enhance depth perception and deepen user understanding. The research utilizes Omnibase, a platform developed by Geodelta, that integrates various spatial data types, including point clouds, for applications such as municipal boundary measurements.

The study involved participants that are either familiar or unfamiliar with point clouds, to evaluate VR versus Omnibase. Quantitative measurements and qualitative feedback were collected on either platform. Results indicate that while VR provides better depth perception and a more immersive experience, it presents a steeper learning curve, especially for inexperienced users, additionally, it comes with physical side effects. The measurements in Omnibase showed higher consistency, though not necessarily greater accuracy, due to depth misinterpretations.

In addition to the study, the VR testing environment was developed using Potree.