Urbanization has led to more than half of the world’s population living in cities. The design of sustainable and resilient urban environments is becoming increasingly critical to optimize the well-being of their inhabitants and the planet. One of the major challenges in achieving this is the complex wind flow patterns in densely built-up areas, which require accurate prediction and analysis to effectively harness the potential benefits of wind flow, such as natural ventilation and wind power generation. However, the unique features of urban landscapes, such as high-rise buildings, narrow streets, and irregular building shapes, make the analysis of wind flow within urban canopies complicated.

In recent years, Computational Fluid Dynamics (CFD) has become a vital tool for studying wind flow in urban areas. Still, the complex geometries of buildings can lead to challenges, including recirculation, reattachment, intense turbulence, and dead zones. Moreover, vegetation plays a crucial role in controlling wind flow in dense areas by acting as a physical barrier, significantly reducing the wind speed and alter the wind flow direction. Additionally, generating geometry for computa-tional fluid dynamics (CFD) simulation in complex urban environments is a chal-lenging and time-consuming process.

Hence, for this thesis, the City4CFD software will be used to automatically recon-struct a 3D model of Stanford University at LoD1.2, which will significantly reduce the time and effort required to generate the complex geometry necessary for com-putational fluid dynamics (CFD) simulations in urban environments. The results will be compared to those obtained from an already manually reconstructed model at LoD2.1 and real-world measurements conducted within the area of interest. This will allow me to determine the differences introduced by different level of detail.

The research will address several sub-questions, such as the steps needed to auto-matically reconstruct a 3D city model, the potential improvements in simulation accuracy by increasing LoD, and the impact of complex geometries on wind flow. The results of the thesis indicated that a more complex geometry Level of Detail (LoD) can enhance the accuracy of simulations by providing a more precise de-piction of wind flow patterns. In other words, a higher LoD geometry, such as LoD2.1, can more accurately predict wind patterns in urban environments based on real-world measurements. The study observed that the LoD2.1 model, which in-corporates more complex features, generated simulation outcomes that were closer to the measurements compared to the less detailed LoD1.2 model.

The plastic pollution of aquatic environment is undoubtedly an emerging environmental risk, as it negatively affects ecosystems globally to a great extent. To prevent the plastic soup from growing even further, a Delft-based start-up Noria has developed plastic collectors, to remove plastic from rivers and canals before it reaches the ocean. In order for these devices to give maximum positive effect, they need to be installed in areas where plastic is more likely to accumulate - the plastic hotspots. Taking into consideration various natural attributes that affect the movement of the plastic waste in the water, such as wind direction, water flow, canal geometry, vegetation and man made structures in waterways; potential hotspots can be predicted in a model which would allow more efficient coordination of the cleaning process. Thus, this project aims to locate plastic accumulation zones in the city of Delft in a (semi-) automated manner using open spatial data analysed in GIS and a network simulation model.
The methodology developed in this project results in the visualisation of potential plastic hotspots where Noria’s collectors could be placed in order to remove and recycle the plastic. The potential hotspots suggested by the model were compared with ground truth data collected. The final result yielded only 20% accuracy and therefore did not meet the initial expectation. An evaluation of the shortcomings was made with suggestions for future research.