Augmented state estimation of urban settings using on-the-fly sequential Data Assimilation
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A data-driven investigation of the flow around a high-rise building is per- formed by combining heterogeneous experimental samples and numerical models based on the Reynolds-Averaged Navier–Stokes (RANS) equations. The experimental data, which include velocity and pressure measurements obtained by local and sparse sensors, replicate realistic conditions of future automated urban settings. The coupling between experiments and the nu- merical model is performed using techniques based on the Ensemble Kalman Filter (EnKF), including advanced manipulations such as localization and inflation. The augmented state estimation obtained via EnKF has also been employed to improve the predictive features of the RANS model via opti- mization of the free global model constants of two turbulence models used to close the equations, namely the K − ε and the K − ω SST turbulence models. The optimized inferred values are far from the classical values prescribed as general recommendations and implemented i...n codes, but also different from other data-driven analyses reported in the literature. The results obtained with this new optimized parametric description show a global improvement for both the velocity and pressure fields. In addition, some topological im- provements for the flow organization are observed downstream, far from the location of the sensors.
Keywords:
Urban settings / Data Assimilation / EnKF / CONESSource:
Computers and Fluids, 2023Publisher:
- Elsevier
Funding / projects:
- French Agence Nationale de la Recherche (ANR) through projects PRC 2020 ALEKCIA and JCJC 2021 IWP-IBM-DA and “Fonds National de la Recherche, Luxembourg” (FNR) through CORE Junior project DATA4WIND - “Data- Driven Approach for Urban Wind Energy Harvesting”, C19/SR/13639741
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GraFarTY - JOUR AU - Villanueva, Lucas AU - Valero, Miguel Martinez AU - Šarkić Glumac, Anina AU - Meldi, Marcello PY - 2023 UR - https://grafar.grf.bg.ac.rs/handle/123456789/3290 AB - A data-driven investigation of the flow around a high-rise building is per- formed by combining heterogeneous experimental samples and numerical models based on the Reynolds-Averaged Navier–Stokes (RANS) equations. The experimental data, which include velocity and pressure measurements obtained by local and sparse sensors, replicate realistic conditions of future automated urban settings. The coupling between experiments and the nu- merical model is performed using techniques based on the Ensemble Kalman Filter (EnKF), including advanced manipulations such as localization and inflation. The augmented state estimation obtained via EnKF has also been employed to improve the predictive features of the RANS model via opti- mization of the free global model constants of two turbulence models used to close the equations, namely the K − ε and the K − ω SST turbulence models. The optimized inferred values are far from the classical values prescribed as general recommendations and implemented in codes, but also different from other data-driven analyses reported in the literature. The results obtained with this new optimized parametric description show a global improvement for both the velocity and pressure fields. In addition, some topological im- provements for the flow organization are observed downstream, far from the location of the sensors. PB - Elsevier T2 - Computers and Fluids T1 - Augmented state estimation of urban settings using on-the-fly sequential Data Assimilation DO - 10.1016/j.compfluid.2023.106118 ER -
@article{ author = "Villanueva, Lucas and Valero, Miguel Martinez and Šarkić Glumac, Anina and Meldi, Marcello", year = "2023", abstract = "A data-driven investigation of the flow around a high-rise building is per- formed by combining heterogeneous experimental samples and numerical models based on the Reynolds-Averaged Navier–Stokes (RANS) equations. The experimental data, which include velocity and pressure measurements obtained by local and sparse sensors, replicate realistic conditions of future automated urban settings. The coupling between experiments and the nu- merical model is performed using techniques based on the Ensemble Kalman Filter (EnKF), including advanced manipulations such as localization and inflation. The augmented state estimation obtained via EnKF has also been employed to improve the predictive features of the RANS model via opti- mization of the free global model constants of two turbulence models used to close the equations, namely the K − ε and the K − ω SST turbulence models. The optimized inferred values are far from the classical values prescribed as general recommendations and implemented in codes, but also different from other data-driven analyses reported in the literature. The results obtained with this new optimized parametric description show a global improvement for both the velocity and pressure fields. In addition, some topological im- provements for the flow organization are observed downstream, far from the location of the sensors.", publisher = "Elsevier", journal = "Computers and Fluids", title = "Augmented state estimation of urban settings using on-the-fly sequential Data Assimilation", doi = "10.1016/j.compfluid.2023.106118" }
Villanueva, L., Valero, M. M., Šarkić Glumac, A.,& Meldi, M.. (2023). Augmented state estimation of urban settings using on-the-fly sequential Data Assimilation. in Computers and Fluids Elsevier.. https://doi.org/10.1016/j.compfluid.2023.106118
Villanueva L, Valero MM, Šarkić Glumac A, Meldi M. Augmented state estimation of urban settings using on-the-fly sequential Data Assimilation. in Computers and Fluids. 2023;. doi:10.1016/j.compfluid.2023.106118 .
Villanueva, Lucas, Valero, Miguel Martinez, Šarkić Glumac, Anina, Meldi, Marcello, "Augmented state estimation of urban settings using on-the-fly sequential Data Assimilation" in Computers and Fluids (2023), https://doi.org/10.1016/j.compfluid.2023.106118 . .