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.
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Janelle Katherine Hammond, Rachida Chakir. A non-intrusive reduced order data assimilation method applied to the monitoring of urban flows. CSMA2019 - 14ème Colloque National en Calcul des Structures, May 2019, Presqu'île de Giens, France. 7p. ⟨hal-02186298⟩

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