Estimation of solid-state sintering and material parameters using phase-field modeling and ensemble four-dimensional variational method
Estimation of solid-state sintering and material parameters using phase-field modeling and ensemble four-dimensional variational method
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DOI：
10.1088/1361-651x/ac13cd
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发表时间：
2021
影响因子：
1.8
通讯作者：
Akimitsu Ishii;A. Yamanaka;Eisuke Miyoshi;Yuki Okada-;A. Yamamoto
中科院分区：
文献类型：
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作者：
Akimitsu Ishii;A. Yamanaka;Eisuke Miyoshi;Yuki Okada-;A. Yamamoto
烧结是粉末冶金、陶瓷工业和三维打印等增材制造工艺的基础技术。为了提高烧结材料的性能，需要使用数值模拟来预测其微观结构。然而，用于这种预测的物理值和材料参数通常是未知的。数据同化（DA）通过整合模拟结果和观测数据，能够估计未观测状态和未知物质参数。在本文中，我们开发了一种新的模型，耦合基于集成的四维变分（En 4DVar）DA与固态烧结相场模型（En 4DVar-PF模型），以估计烧结材料的状态和多个未知的材料参数。开发的En 4DVar-PF模型进行了验证的数值实验称为孪生实验，其中先验假设真实的初始状态和多个材料参数估计。双胞胎实验的结果表明，仅使用三维形态数据的烧结显微组织，我们开发的En 4DVar-PF模型可以同时和准确地估计颗粒形状，晶界的分布，和材料参数，包括扩散系数和迁移率相关的晶界迁移。此外，我们的工作确定了确定适当的DA条件的标准，如使用我们开发的模型准确估计材料参数所需的观测时间间隔。所开发的En 4DVar-PF模型为获得烧结过程中不可观测的状态和难以测量的材料参数提供了一个很有前途的框架，这对于准确预测烧结过程和开发上级材料至关重要。
Sintering is a fundamental technology for powder metallurgy, the ceramics industry, and additive manufacturing processes such as three-dimensional printing. Improvement of the properties of sintered materials requires prediction of their microstructure using numerical simulations. However, the physical values and material parameters used for such predictions are generally unknown. Data assimilation (DA) enables the estimation of unobserved states and unknown material parameters by integrating simulation results and observational data. In this paper, we develop a new model that couples an ensemble-based four-dimensional variational (En4DVar) DA with a phase-field model of solid-state sintering (En4DVar-PF model) to estimate the state of the sintered material and multiple unknown material parameters. The developed En4DVar-PF model is validated by numerical experiments called twin experiments, in which a priori assumed-true initial state and multiple material parameters are estimated. The results of the twin experiments demonstrate that, using only three-dimensional morphological data of the sintered microstructure, our developed En4DVar-PF model can simultaneously and accurately estimate the particle shape, distribution of grain boundaries, and material parameters, including diffusion coefficients and mobilities related to grain boundary migration. Furthermore, our work identifies criteria for determining appropriate DA conditions such as the observational time interval required to accurately estimate the material parameters using our developed model. The developed En4DVar-PF model provides a promising framework to obtain unobservable states and difficult-to-measure material parameters in sintering, which is crucial for the accurate prediction of sintering processes and for the development of superior materials.