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基于多相场模型上实施四维变分（4DVar）的数据同化的晶粒生长预测。

Grain growth prediction based on data assimilation by implementing 4DVar on multi-phase-field model.

作者信息

Ito Shin-Ichi, Nagao Hiromichi, Kasuya Tadashi, Inoue Junya

机构信息

Earthquake Research Institute, The University of Tokyo, Tokyo, Japan.

Graduate School of Information Science and Technology, The University of Tokyo, Tokyo, Japan.

出版信息

Sci Technol Adv Mater. 2017 Oct 30;18(1):857-869. doi: 10.1080/14686996.2017.1378921. eCollection 2017.

DOI:10.1080/14686996.2017.1378921
PMID:29152018
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC5678441/
Abstract

We propose a method to predict grain growth based on data assimilation by using a four-dimensional variational method (4DVar). When implemented on a multi-phase-field model, the proposed method allows us to calculate the predicted grain structures and uncertainties in them that depend on the quality and quantity of the observational data. We confirm through numerical tests involving synthetic data that the proposed method correctly reproduces the true phase-field assumed in advance. Furthermore, it successfully quantifies uncertainties in the predicted grain structures, where such uncertainty quantifications provide valuable information to optimize the experimental design.

摘要

我们提出了一种基于数据同化的方法，通过使用四维变分方法（4DVar）来预测晶粒生长。当在多相场模型上实施时，该方法使我们能够计算取决于观测数据质量和数量的预测晶粒结构及其不确定性。通过涉及合成数据的数值测试，我们证实该方法能够正确再现预先假设的真实相场。此外，它成功地量化了预测晶粒结构中的不确定性，这种不确定性量化为优化实验设计提供了有价值的信息。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8f67/5678441/6f8e73b2637c/TSTA_A_1378921_F0006_OC.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8f67/5678441/0bdb6465d509/TSTA_A_1378921_UF0001_OC.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8f67/5678441/456aca53d386/TSTA_A_1378921_F0001_OC.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8f67/5678441/cbb1fa790c48/TSTA_A_1378921_F0002_OC.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8f67/5678441/869f42185802/TSTA_A_1378921_F0003_OC.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8f67/5678441/7d552846673c/TSTA_A_1378921_F0004_OC.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8f67/5678441/64cab63c711a/TSTA_A_1378921_F0005_OC.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8f67/5678441/6f8e73b2637c/TSTA_A_1378921_F0006_OC.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8f67/5678441/0bdb6465d509/TSTA_A_1378921_UF0001_OC.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8f67/5678441/456aca53d386/TSTA_A_1378921_F0001_OC.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8f67/5678441/cbb1fa790c48/TSTA_A_1378921_F0002_OC.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8f67/5678441/869f42185802/TSTA_A_1378921_F0003_OC.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8f67/5678441/7d552846673c/TSTA_A_1378921_F0004_OC.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8f67/5678441/64cab63c711a/TSTA_A_1378921_F0005_OC.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8f67/5678441/6f8e73b2637c/TSTA_A_1378921_F0006_OC.jpg

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本文引用的文献

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Phys Rev E. 2016 Oct;94(4-1):043307. doi: 10.1103/PhysRevE.94.043307. Epub 2016 Oct 14.
2
Computer simulations of two-dimensional and three-dimensional ideal grain growth.二维和三维理想晶粒生长的计算机模拟。
Phys Rev E Stat Nonlin Soft Matter Phys. 2006 Dec;74(6 Pt 1):061605. doi: 10.1103/PhysRevE.74.061605. Epub 2006 Dec 27.