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Materials 2018, 11(11), 2222; https://doi.org/10.3390/ma11112222

A Mass Conservative Kalman Filter Algorithm for Computational Thermo-Fluid Dynamics

1
Politecnico di Milano, Department of Energy, via La Masa 34, I-20156 Milano, Italy
2
Department of Computational Engineering Sciences, Faculty of Science, Engineering and Communication, University of Luxembourg, 6 Avenue de la Fonte, 4364 Esch-sur-Alzette, Luxembourg
3
School of Engineering, Cardiff University, Queen’s Building, The Parade, Cardiff CF24 3AA, Wales, UK
4
Department of Medical Research, China Medical University Hospital, China Medical University, Taichung 40402, Taiwan
*
Author to whom correspondence should be addressed.
Received: 11 October 2018 / Revised: 26 October 2018 / Accepted: 30 October 2018 / Published: 8 November 2018
(This article belongs to the Special Issue Randomness and Uncertainty)
Full-Text   |   PDF [855 KB, uploaded 8 November 2018]   |  

Abstract

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. View Full-Text
Keywords: computational fluid-dynamics; OpenFOAM; Kalman filter; mass conservation; data assimilation; lid-driven cavity computational fluid-dynamics; OpenFOAM; Kalman filter; mass conservation; data assimilation; lid-driven cavity
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Introini, C.; Lorenzi, S.; Cammi, A.; Baroli, D.; Peters, B.; Bordas, S. A Mass Conservative Kalman Filter Algorithm for Computational Thermo-Fluid Dynamics. Materials 2018, 11, 2222.

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