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

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.

Keywords

• computational fluid-dynamics
• OpenFOAM
• Kalman filter
• mass conservation
• data assimilation
• lid-driven cavity