A novel estimation method for microstructural evolution based on data assimilation and phase field crystal model

In the simulation of microstructural evolutions, detailed priori knowledge for the model parameters and initial states is difficult to be observed by experiments. For an improved simulation, we present a data assimilation framework for the phase field crystal model with the effect of stochastic noise. A sequential data assimilation method based on the ensemble Kalman filter is applied to integrate phase field simulation and observational data from experiments. Furthermore, we couple the present framework with a second-order accurate unconditional-energy stable scheme, and thus the issue of stability restriction can be avoided. A series of twin experiments are performed to demonstrate the performance of our present framework. The results reveal that our framework can successfully improve the simulation accuracy, even when some parameters are uncertain and the initial value significantly deviates from the observed situation.