TY  - 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 . .