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.
Highlights
Data assimilation is a process that allows handling experimental or real-time measurements inside the modeling framework
Verification is performed by comparing the results obtained by the application of the developed algorithm with the ones obtained by application of the standard segregated method on a very fine numerical grid
Where qCFD represents the solution as computed by the standard algorithm on a fine grid and qKalman is the solution as evaluated by the developed algorithm
Summary
Data assimilation is a process that allows handling experimental or real-time measurements inside the modeling framework. The dynamic data-driven method allows analysts to infer data from the Bayesian approach and analyze the correlation between the predicting model and the relevant knowledge from experimental measurements. In the fluid-dynamics field, the challenging question is how to incorporate available data from fluid flow and temperature measurements into the available models, and how to take into account the discrepancy between the model prediction and the knowledge of the scenario coming from the data. In this sense, the use of data assimilation is a strong potential tool to increase the robustness and reliability of the analysis with a compatible computational burden. At the state of art, the integration of data assimilation in computational fluid dynamics is recently investigated in a Materials 2018, 11, 2222; doi:10.3390/ma11112222 www.mdpi.com/journal/materials
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