A non-intrusive reduced order data assimilation method applied to the monitoring of urban flows
Résumé
In this work we investigate a variational data assimilation method to rapidly estimate urban pollutant concentration around an area of interest using measurement data and CFD based models in a non-intrusive and computationally efficient manner. In case studies presented here, we used a sample of solutions from a dispersion model with varying meteorological conditions and pollution emissions to build a Reduced Basis approximation space and combine it with concentration observations. The method allows to correct for unmodeled physics, while significantly reducing online computational time.
Domaines
Autre
Origine : Fichiers produits par l'(les) auteur(s)
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