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Cool By Design
SOLFD: Extending SOLWEIG for Urban Design Decision Making on Outdoor Thermal Comfort
As urbanization and climate change intensify, managing the urban microclimate becomes increasingly challenging, affecting outdoor thermal comfort. The practical integration of urban microclimate research into urban design remains limited, in part due to the complexity and inaccessibility of existing tools. To support early-stage, climate-sensitive urban design at the neighbourhood scale, I present SOLWEIG For Design (SOLFD): a computation framework that builds on the existing SOLWEIG tool. SOLFD enables urban designers to visualize current microclimatic conditions and assess the impact of design interventions on outdoor thermal comfort. In particular, it focuses specifically on (in)direct solar radiation and its effect as quantified by the mean radiant temperature. Key contributions include: (1) extending SOLWEIG’s 2.5D model to a layered 3D representation for improved accuracy in complex urban geometries; (2) automating the data pipeline using open Dutch geospatial datasets; (3) enabling the modification of the existing urban scene; (4) enhancing output usability through temporally grouped mean radiant temperature maps, derived physiological equivalent temperature maps, and comparison statistics; and (5) significantly reducing simulation time with GPU acceleration. The accuracy of SOLFD was validated using sensor data, achieving an RMSE of 5.39°C. Underneath structures, the RMSE increases to 5.83 °C. The potential of SOLFD is further demonstrated with a case study across various Dutch urban typologies. By laying the foundation for an accessible decision-support tool for outdoor thermal comfort, SOLFD takes a step toward integrating climate-responsive strategies into the urban design process. ...
As urbanization and climate change intensify, managing the urban microclimate becomes increasingly challenging, affecting outdoor thermal comfort. The practical integration of urban microclimate research into urban design remains limited, in part due to the complexity and inaccessibility of existing tools. To support early-stage, climate-sensitive urban design at the neighbourhood scale, I present SOLWEIG For Design (SOLFD): a computation framework that builds on the existing SOLWEIG tool. SOLFD enables urban designers to visualize current microclimatic conditions and assess the impact of design interventions on outdoor thermal comfort. In particular, it focuses specifically on (in)direct solar radiation and its effect as quantified by the mean radiant temperature. Key contributions include: (1) extending SOLWEIG’s 2.5D model to a layered 3D representation for improved accuracy in complex urban geometries; (2) automating the data pipeline using open Dutch geospatial datasets; (3) enabling the modification of the existing urban scene; (4) enhancing output usability through temporally grouped mean radiant temperature maps, derived physiological equivalent temperature maps, and comparison statistics; and (5) significantly reducing simulation time with GPU acceleration. The accuracy of SOLFD was validated using sensor data, achieving an RMSE of 5.39°C. Underneath structures, the RMSE increases to 5.83 °C. The potential of SOLFD is further demonstrated with a case study across various Dutch urban typologies. By laying the foundation for an accessible decision-support tool for outdoor thermal comfort, SOLFD takes a step toward integrating climate-responsive strategies into the urban design process.
To develop a full BIM Legal registration, it is necessary to also look at buildings without a BIM model. In such cases, BIM Legal models could be derived from existing legal documents, specifically division drawings. This research investigates the (semi-)automatic reconstruction of 3D legal apartment models from 2D vectorized division drawings. The proposed pipeline starts with already vectorized division drawings and applies shape-based georeferencing techniques, estimating storey heights, vertical alignment, and ends with generating 3D BIM Legal models.
The georeferencing methods tested achieved sufficient alignment across the 10 sample buildings. When outside spaces were included in the vectorized division drawing, it resulted in an average containment of 81.5%. When they were removed or non existing it resulted in 95.2% containment. Storey alignment relies on shape similarity and floor-to-floor matching, which performed well in typical cases but struggled with floors with low similarity to the floor below it. The accuracy of height estimation improves when cross sections are included in the division drawing. Otherwise averaged based on values retrieved from the 3DBAG.
The resulting models conform to the BIM Legal standard, written in CityJSON format at a LoD1+. While the schematic nature of division drawings limits the achievable level of detail, and the geometric and positional accuracy, the models offer a valuable 3D visualization of private and shared ownership spaces. Improvements to the prior vectorization would also improve the accuracy and computation time of the 3D reconstruction. Large scale testing is necessary to research the potential incorporation of BIM legal models from division drawings with a complete BIM Legal registration.
This research not only advances the automation of BIM Legal models from division drawings, but also provides methods which can be applied in other 3D reconstructions, such as georeferencing polygons with a reference to a cadastral dataset.
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To develop a full BIM Legal registration, it is necessary to also look at buildings without a BIM model. In such cases, BIM Legal models could be derived from existing legal documents, specifically division drawings. This research investigates the (semi-)automatic reconstruction of 3D legal apartment models from 2D vectorized division drawings. The proposed pipeline starts with already vectorized division drawings and applies shape-based georeferencing techniques, estimating storey heights, vertical alignment, and ends with generating 3D BIM Legal models.
The georeferencing methods tested achieved sufficient alignment across the 10 sample buildings. When outside spaces were included in the vectorized division drawing, it resulted in an average containment of 81.5%. When they were removed or non existing it resulted in 95.2% containment. Storey alignment relies on shape similarity and floor-to-floor matching, which performed well in typical cases but struggled with floors with low similarity to the floor below it. The accuracy of height estimation improves when cross sections are included in the division drawing. Otherwise averaged based on values retrieved from the 3DBAG.
The resulting models conform to the BIM Legal standard, written in CityJSON format at a LoD1+. While the schematic nature of division drawings limits the achievable level of detail, and the geometric and positional accuracy, the models offer a valuable 3D visualization of private and shared ownership spaces. Improvements to the prior vectorization would also improve the accuracy and computation time of the 3D reconstruction. Large scale testing is necessary to research the potential incorporation of BIM legal models from division drawings with a complete BIM Legal registration.
This research not only advances the automation of BIM Legal models from division drawings, but also provides methods which can be applied in other 3D reconstructions, such as georeferencing polygons with a reference to a cadastral dataset.
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. ...
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.
In this thesis, a case study is done on the localisation of fisher boats in restricted areas. Several components are integrated to create a prototype. The pretrained YOLOv3 detection model is trained on acquired nadir boat images, which makes it able to predict the bounding boxes of boats on images captured with drones. A positioning algorithm is constructed, which calculates the geographical coordinates from the pixel coordinates for images taken both in a nadir and an oblique angle. A real time connection is constructed between the drone and the prototype. This is done by creating a connection with Google Drive with the drone controller and the prototype. The positioned polygon bounding boxes are localised using a real time dashboard, which visualises the bounding boxes in a map with other relevant layers.
The results indicate that the performance of the components and prototype as a whole are satisfactory for this use case. To deploy this prototype in other object localisation use cases, it is recommended to train the pretrained model further, use a drone with more accurate equipment and run the prototype on the drone controller. ...
In this thesis, a case study is done on the localisation of fisher boats in restricted areas. Several components are integrated to create a prototype. The pretrained YOLOv3 detection model is trained on acquired nadir boat images, which makes it able to predict the bounding boxes of boats on images captured with drones. A positioning algorithm is constructed, which calculates the geographical coordinates from the pixel coordinates for images taken both in a nadir and an oblique angle. A real time connection is constructed between the drone and the prototype. This is done by creating a connection with Google Drive with the drone controller and the prototype. The positioned polygon bounding boxes are localised using a real time dashboard, which visualises the bounding boxes in a map with other relevant layers.
The results indicate that the performance of the components and prototype as a whole are satisfactory for this use case. To deploy this prototype in other object localisation use cases, it is recommended to train the pretrained model further, use a drone with more accurate equipment and run the prototype on the drone controller.
Future inner city live-work typologies for Bandung
Development of climate resilient inner city live-work typologies
This leads to the following challenge: Given a limited number of beacons, where should they be placed in a specified indoor environment, such that the geometry contributes to optimal positioning results?
This challenge is approached by using theoretical design computations. The design computations require the definition of a chosen 3D space, the number of beacons, possible user tag locations and a performance threshold (e.g. required precision). For any given set of beacon and receiver locations, the precision, internal- and external reliability can be determined on forehand. The results of a given geometry have been validated by deploying an IPS of BlooLoc and comparing the observed precision with the modeled precision for a chosen set of user tag locations. The theoretical model showed a precision pattern with several equivalent precision patterns of the measured data, however, some significant differences could not be explained physically and the model has to be adapted further. Besides determining the precision based on a set of beacon and receiver locations, the model is able to select the optimal geometric configuration based on a performance threshold (e.g. required precision). Depending on the performance threshold, the optimal configurations can either be a single solution or consist of multiple solutions that satisfy the performance threshold of the customer. The performance threshold varies depending on the use case and the user requirements. Therefore, the amount of possible combinations in terms of 3D space, amount of available beacons, possible beacon locations, user tag locations and performance thresholds are limitless and the model can thus be used for all kind of applications.
All in all, the initial model (design computations) can be used by IPS customers for all kind of applications. The model is able to select the optimal geometric configuration in terms of precision based on a performance threshold specified by the user. Furthermore, the model requires user specified input parameters and the amount of possible combinations are therefore unlimited. Although the initial model has to be adapted further to account for the differences in modeled and measured data as a consequence of environmental factors, the initial model is a good initiative for the rising indoor positioning market and its customers. Therefore, the model can be adapted further such that it can explain significant differences between the modeled and the measured data by including factors that influence the system performance in real life, such as materialistic properties, signal attenuation, interference, multipath, NLOS etc. ...
This leads to the following challenge: Given a limited number of beacons, where should they be placed in a specified indoor environment, such that the geometry contributes to optimal positioning results?
This challenge is approached by using theoretical design computations. The design computations require the definition of a chosen 3D space, the number of beacons, possible user tag locations and a performance threshold (e.g. required precision). For any given set of beacon and receiver locations, the precision, internal- and external reliability can be determined on forehand. The results of a given geometry have been validated by deploying an IPS of BlooLoc and comparing the observed precision with the modeled precision for a chosen set of user tag locations. The theoretical model showed a precision pattern with several equivalent precision patterns of the measured data, however, some significant differences could not be explained physically and the model has to be adapted further. Besides determining the precision based on a set of beacon and receiver locations, the model is able to select the optimal geometric configuration based on a performance threshold (e.g. required precision). Depending on the performance threshold, the optimal configurations can either be a single solution or consist of multiple solutions that satisfy the performance threshold of the customer. The performance threshold varies depending on the use case and the user requirements. Therefore, the amount of possible combinations in terms of 3D space, amount of available beacons, possible beacon locations, user tag locations and performance thresholds are limitless and the model can thus be used for all kind of applications.
All in all, the initial model (design computations) can be used by IPS customers for all kind of applications. The model is able to select the optimal geometric configuration in terms of precision based on a performance threshold specified by the user. Furthermore, the model requires user specified input parameters and the amount of possible combinations are therefore unlimited. Although the initial model has to be adapted further to account for the differences in modeled and measured data as a consequence of environmental factors, the initial model is a good initiative for the rising indoor positioning market and its customers. Therefore, the model can be adapted further such that it can explain significant differences between the modeled and the measured data by including factors that influence the system performance in real life, such as materialistic properties, signal attenuation, interference, multipath, NLOS etc.