Supporting data and code for: Investigating the risk and operational feasibility for drones using wind simulation data: A case study for the city of Rotterdam.

DOI:10.4121/908dd8a2-821e-485b-b659-21995f273e4e.v1
The DOI displayed above is for this specific version of this dataset, which is currently the latest. Newer versions may be published in the future. For a link that will always point to the latest version, please use
DOI: 10.4121/908dd8a2-821e-485b-b659-21995f273e4e

Datacite citation style

Morfin Veytia, Andres; Patil, Akshay; Pađen, Ivan; Ellerbroek, Joost; Garcia Sanchez, Clara et. al. (2025): Supporting data and code for: Investigating the risk and operational feasibility for drones using wind simulation data: A case study for the city of Rotterdam. Version 1. 4TU.ResearchData. dataset. https://doi.org/10.4121/908dd8a2-821e-485b-b659-21995f273e4e.v1
Other citation styles (APA, Harvard, MLA, Vancouver, Chicago, IEEE) available at Datacite

Dataset

Supporting data and code for: Investigating the risk and operational feasibility for drones using 3D wind simulation data: A case study for the city of Rotterdam


This research investigates drone safety and operational feasibility in urban environments using computational fluid dynamics (CFD) simulations of Rotterdam, Netherlands. The study employs Reynolds-Averaged Navier-Stokes (RANS) equations to model 3D wind fields across the city, using uncertainty quantification through polynomial chaos expansion to select optimal inflow angles based on 2022 weather data.


The research demonstrates that comprehensive city-wide risk analysis can be achieved with relatively few well-selected inflow conditions, and shows that drones capable of withstanding wind speeds of 12 m/s and medium turbulence levels could operate safely approximately 90% of the time.


The methodology involves preprocessing geometric data from Dutch national datasets, running CFD simulations across four mesh regions, and performing both city-wide risk assessment and yearly operational feasibility analysis. The code used for this work is included in this repository and summarised below.



Pre-processing geometry and running City4CFD

city4cfd_process.zip - Preprocesses geometric data from Dutch national datasets (3DBAG, PDOK, AHN4) by splitting combined geometries into four manageable areas for City4CFD processing. Outputs processed geometries ready for OpenFOAM.


Requirements: City4CFD, Python 3


Dakota Uncertainty Quantification

dakota_run.zip - Performs uncertainty quantification using polynomial chaos expansion to determine optimal inflow angles for CFD simulations based on 2022 KNMI weather data from Rotterdam. Generates histogram data and extracts wind angles for OpenFOAM simulations.


Requirements: Dakota 6.19, Python 3


OpenFOAM Sampling Preparation

prep_openfoam_sampling.zip - Prepares OpenFOAM sampling by aligning sampling points across the 4 different regions in OpenFOAM coordinates, enabling precise averaging of results in overlapping areas. Outputs aligned sampling surfaces and probes.


Requirements: Python 3, OpenFOAM-7, and rusterizer (https://github.com/ipadjen/rusterizer)


Running OpenFOAM

openfoam_cases.zip - Contains OpenFOAM simulation setup to solve Reynolds-Averaged Navier-Stokes (RANS) equations for computing 3D wind fields over Rotterdam's urban terrain for the 4 different regions. Simulations performed for multiple inflow angles determined through uncertainty quantification.


Requirements: OpenFOAM-7 with custom turbulence models from https://github.com/gsclara/UrbanFoam


Post-Processing Pipeline

post_processing.zip - Transforms raw OpenFOAM results into comprehensive drone safety analysis through statistical processing, city-wide risk assessment, and operational feasibility evaluation. The pipeline processes simulation data using weather-weighted statistics, generating risk maps, contour plots, and publication-ready visualizations.


Requirements: Python 3


Mesh Overlap Check

check_mesh_overlap.zip - Checks CFD simulation consistency across different mesh regions by analysing overlapping areas. Processes VTK files, transforms coordinates between reference frames, and quantifies field differences at overlapping mesh boundaries.


Requirements: Python3

History

  • 2025-07-04 first online, published, posted

Publisher

4TU.ResearchData

Format

script/.py, script/.sh, data/.vtk, data/.csv, data/.parquet, image/.png, spatial/.gpkg, data/.pickle

Funding

  • Investigating wind for safe U-space operations (grant code EINF-10954) SURF Cooperative, supplemented by the Dutch Research Council (NWO)

Organizations

TU Delft, Faculty of Aerospace Engineering, Department of Control & Operations;
TU Delft, Faculty of Architecture and the Built Environment, 3D Geoinformation group

DATA

Files (8)