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Published May 10, 2020 | public
Journal Article

Flow state estimation in the presence of discretization errors

Abstract

Ensemble data assimilation methods integrate measurement data and computational flow models to estimate the state of fluid systems in a robust, scalable way. However, discretization errors in the dynamical and observation models lead to biased forecasts and poor estimator performance. We propose a low-rank representation for this bias, whose dynamics is modelled by data-informed, time-correlated processes. State and bias parameters are simultaneously corrected online with the ensemble Kalman filter. The proposed methodology is then applied to the problem of estimating the state of a two-dimensional flow at modest Reynolds number using an ensemble of coarse-mesh simulations and pressure measurements at the surface of an immersed body in a synthetic experiment framework. Using an ensemble size of 60, the bias-aware estimator is demonstrated to achieve at least 70 % error reduction when compared to its bias-blind counterpart. Strategies to determine the bias statistics and their impact on the estimator performance are discussed.

Additional Information

© The Author(s), 2020. Published by Cambridge University Press. Received 1 April 2019; revised 16 December 2019; accepted 2 February 2020. Published online by Cambridge University Press: 11 March 2020. This study has been supported in part by a grant from AFOSR (FA9550-14-1-0328) with Dr D. Smith as program manager, and in part by the Coordenação de aperfeiçoamento de Pessoal de Nível Superior – Brasil (CAPES) – Finance Code 001 (grant no. BEX 12966/13-4). The authors also acknowledge Professors D. Williams (Illinois Institute of Technology), J. Eldredge (UCLA) and A. Stuart (Caltech) for helpful discussions of this work. The authors report no conflict of interests.

Additional details

Created:
August 19, 2023
Modified:
October 20, 2023