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Low-cost operator splitting based parallel data assimilation methods with the application in phase-field simulation
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DOI：10.1016/j.jcp.2025.114580.png)
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Abstract
En 中文
The phase-field simulation is quite sensitive to the settings of model parameters and initial conditions, as different settings may yield significantly different simulation results. However, in practical phase-field simulation, model errors may arise from the initial input to the model. Therefore, utilizing available real-time observations for data assimilation to enhance the accuracy of numerical simulation has become an important research topic. Traditional data assimilation methods, such as three-dimensional variational data assimilation and Ensemble Kalman Filter, face challenges due to the need for computing high-dimensional covariance matrices or solving high-dimensional optimization problems, which result in low computational efficiency and high storage requirements. To address these challenges, this paper proposes and compares three parallel data assimilation methods based on the operator splitting method: nudging, three-dimensional variational data assimilation, and Ensemble Kalman Filter. Although the three methods all reduce computational costs and storage requirements, each has its own specific advantages. By comparing the three methods and integrating their strengths, this paper further proposes a more comprehensive hybrid data assimilation method, which significantly improves simulation accuracy and avoids the limitations of using a single data assimilation method. Meanwhile, the effectiveness of the hybrid method in improving simulation accuracy is validated using the phase-field dendritic growth model as a test case.
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