A Mass Conservative Kalman Filter Algorithm for Computational Thermo-Fluid Dynamics
This paper studies Kalman filtering applied to Reynolds-Averaged Navier⁻Stokes (RANS) equations for turbulent flow. The integration of the Kalman estimator is extended to an implicit segregated method and to the thermodynamic analysis of turbulent flow, adding a sub-stepping procedure that...
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MDPI AG
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| Series: | Materials |
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| Online Access: | https://www.mdpi.com/1996-1944/11/11/2222 |
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doaj-2c641ea4ecb24555bcce6ee62ebd817e2020-11-25T00:32:58ZengMDPI AGMaterials1996-19442018-11-011111222210.3390/ma11112222ma11112222A Mass Conservative Kalman Filter Algorithm for Computational Thermo-Fluid DynamicsCarolina Introini0Stefano Lorenzi1Antonio Cammi2Davide Baroli3Bernhard Peters4Stéphane Bordas5Politecnico di Milano, Department of Energy, via La Masa 34, I-20156 Milano, ItalyPolitecnico di Milano, Department of Energy, via La Masa 34, I-20156 Milano, ItalyPolitecnico di Milano, Department of Energy, via La Masa 34, I-20156 Milano, ItalyDepartment of Computational Engineering Sciences, Faculty of Science, Engineering and Communication, University of Luxembourg, 6 Avenue de la Fonte, 4364 Esch-sur-Alzette, LuxembourgDepartment of Computational Engineering Sciences, Faculty of Science, Engineering and Communication, University of Luxembourg, 6 Avenue de la Fonte, 4364 Esch-sur-Alzette, LuxembourgDepartment of Computational Engineering Sciences, Faculty of Science, Engineering and Communication, University of Luxembourg, 6 Avenue de la Fonte, 4364 Esch-sur-Alzette, LuxembourgThis paper studies Kalman filtering applied to Reynolds-Averaged Navier⁻Stokes (RANS) equations for turbulent flow. The integration of the Kalman estimator is extended to an implicit segregated method and to the thermodynamic analysis of turbulent flow, adding a sub-stepping procedure that ensures mass conservation at each time step and the compatibility among the unknowns involved. The accuracy of the algorithm is verified with respect to the heated lid-driven cavity benchmark, incorporating also temperature observations, comparing the augmented prediction of the Kalman filter with the Computational Fluid-Dynamic solution found on a fine grid.https://www.mdpi.com/1996-1944/11/11/2222computational fluid-dynamicsOpenFOAMKalman filtermass conservationdata assimilationlid-driven cavity |
| collection |
DOAJ |
| language |
English |
| format |
Article |
| sources |
DOAJ |
| author |
Carolina Introini Stefano Lorenzi Antonio Cammi Davide Baroli Bernhard Peters Stéphane Bordas |
| spellingShingle |
Carolina Introini Stefano Lorenzi Antonio Cammi Davide Baroli Bernhard Peters Stéphane Bordas A Mass Conservative Kalman Filter Algorithm for Computational Thermo-Fluid Dynamics Materials computational fluid-dynamics OpenFOAM Kalman filter mass conservation data assimilation lid-driven cavity |
| author_facet |
Carolina Introini Stefano Lorenzi Antonio Cammi Davide Baroli Bernhard Peters Stéphane Bordas |
| author_sort |
Carolina Introini |
| title |
A Mass Conservative Kalman Filter Algorithm for Computational Thermo-Fluid Dynamics |
| title_short |
A Mass Conservative Kalman Filter Algorithm for Computational Thermo-Fluid Dynamics |
| title_full |
A Mass Conservative Kalman Filter Algorithm for Computational Thermo-Fluid Dynamics |
| title_fullStr |
A Mass Conservative Kalman Filter Algorithm for Computational Thermo-Fluid Dynamics |
| title_full_unstemmed |
A Mass Conservative Kalman Filter Algorithm for Computational Thermo-Fluid Dynamics |
| title_sort |
mass conservative kalman filter algorithm for computational thermo-fluid dynamics |
| publisher |
MDPI AG |
| series |
Materials |
| issn |
1996-1944 |
| publishDate |
2018-11-01 |
| description |
This paper studies Kalman filtering applied to Reynolds-Averaged Navier⁻Stokes (RANS) equations for turbulent flow. The integration of the Kalman estimator is extended to an implicit segregated method and to the thermodynamic analysis of turbulent flow, adding a sub-stepping procedure that ensures mass conservation at each time step and the compatibility among the unknowns involved. The accuracy of the algorithm is verified with respect to the heated lid-driven cavity benchmark, incorporating also temperature observations, comparing the augmented prediction of the Kalman filter with the Computational Fluid-Dynamic solution found on a fine grid. |
| topic |
computational fluid-dynamics OpenFOAM Kalman filter mass conservation data assimilation lid-driven cavity |
| url |
https://www.mdpi.com/1996-1944/11/11/2222 |
| work_keys_str_mv |
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