Micro-urban environment experimental dataset to validate performance of different Computational Fluid Dynamics methodologies.
Authors/Creators
- 1. Bettair Cities SL
-
2.
Barcelona Supercomputing Center
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3.
Universitat Politècnica de Catalunya
Description
This dataset enclosed wind 3D geolocated wind flow and air concentrations 5-minutal data collected in El Prat del Llobregat (Spain) between January and August 2022 in the context of the experiment 1012-ibam of the FF4EuroHPC European project. The intention of this dataset is to provide a resource to do performance benchmark of micro-urban chemical – dispersion models to assess their performance. To do so, we enclose experimental data collected by Bettair Mk2 Series Air quality monitors, 2 Air Quality Monitoring stations equipped with reference instruments from “La Xarxa de Vigilància i Previsió de la Contaminació Atmosfèrica (XVPCA)”, and different data from the repository of the ECMWF Era-5 land and CAMS. We also provide the 3D watertight geometry model of the el Prat de Llobregat (Spain) in step file format (layout from 2020).
Notes
The Bettair team generated the .step file based on data from OpenStreetMaps corresponding to the geometry of 2020. The Large Scale Computational Fluid Dynamics team at the Barcelona Supercomputing Center, led by Oriol Lehmkuhl, corrected and verified that the resulting .step file is watertight. Notice that not all the building heights from .step file are in buildings.geojson file.
The quality and accuracy of pollutant and wind data provided by Bettair are not guaranteed. Third-party assessment verified that, for a different set of devices, the performance of the NO2 and O3 sensors was class 1 according CEN/TS 17660-1 in a field test done throughout 2022 in Barcelona, which indicates that the data quality objective is indicative according to 2008/50/EC. PM2.5 sensor data quality objective was “indicative measurement” according MCERTS certification scheme (field test done in 2023). Note that, for the specific devices deployed in the pilot site, error margins cannot not be specified. For more information, please contact Bettair Cities.
Files
buildings.geojson
Files
(42.8 MB)
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Additional details
References
- Calafell, J.; Bustillo, J.; Gómez, S.; Ramírez, F.; Radhakrishnan, S.; Lehmkuhl , O. Fast urban flow predictions through Convolutional Neural Networks. A: Spanish Fluid Mechanics Conference. "SFMC 2023: 2nd Spanish Fluid Mechanics Conference: Barcelona, Spain, 2-5 July 2023: book of abstracts". 2023, p. 223-224. ISBN 978-84-123222-4-8.
- Calafell, J.; Bustillo, J.; Gómez, S.; Ramírez, F.; Lehmkuhl , O.Data-Driven fast urban flow predictions featuring extreme events. ECCOMAS 2024 - 9th European Congress on Computational Methods in Applied Sciences and Engineering: Lisbon, Portugal, 3-7 June 2024.