A novel ensemble Kalman filter based data assimilation method with an adaptive strategy for dendritic crystal growth

Abstract

A novel ensemble Kalman filter based data assimilation method with an adaptive strategy is presented in this research work. The phase field dendritic crystal growth model is an effective tool to simulate the microstructural evolutions of dendritic crystal growth, while numerous simulation parameters must be determined to reproduce the experimentally observed microstructures. The ensemble Kalman filter (EnKF) method can be flexibly applied in phase field dendritic crystal growth simulation and achieve the inverse estimation of the simulation parameters, while it suffers from the issues of high computational cost and storage requirement. In this work, we integrate an adaptive strategy with the EnKF data assimilation. We define an adaptive narrow band domain as a neighboring region of the interface, which can accurately resolve the interfacial transition layer of the phase field. The local and low-dimensional observation data can be extracted from the narrow domain. By combining the adaptive strategy with the EnKF data assimilation, we reduce the high computational cost and storage requirement for the estimation of simulation parameters. We perform various twin experiments for both two- and three-dimensional phase field simulation of dendritic growth to assess the performance of our algorithm. The results reveal that the present method can achieve the desired estimation results using the low-dimensional observation data.