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.

A mass conservative Kalman filter algorithm for computational thermo-fluid dynamics

Introini, Carolina;Lorenzi, Stefano;Cammi, Antonio;
2018-01-01

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.
Computational fluid-dynamics; Data assimilation; Kalman filter; Lid-driven cavity; Mass conservation; OpenFOAM; Materials Science (all)
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1069114
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