A modified ensemble Kalman filter method with an adaptive ensemble strategy for multiphase flow systems

초록

The ensemble Kalman Filter (EnKF) method plays a vital role in data assimilation and parameter estimation for multiphase flow systems within the immiscible fluid framework. Despite the widespread adoption, a major challenge of the EnKF method lies in the high computational cost. The expense is associated with the large ensemble size required to ensure the accuracy and reliability of the assimilation results. To address the challenge, we establish a modified EnKF method featuring an adaptive ensemble strategy to replace the fixed ensemble size in the traditional approach. We employ a localization strategy to extract low-dimensional observational data from the narrow-band domain in the interfacial region, where substantial numerical variations are observed. The present strategy adjusts the ensemble size dynamically according to the reduced dimensionality of the observational data. Consequently, the adjustment decreases the overall computational cost. A series of twin experiments are conducted to assess the efficiency and accuracy of the proposed strategy for data assimilation and parameter estimation in multiphase flow systems. The results demonstrate that the computational cost is significantly reduced while preserving the accuracy of the assimilation process.

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