Grain growth prediction based on data assimilation by implementing 4DVar on multi-phase-field model
Source abstract
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.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Solidification and crystal growth phenomena · Metallurgy and Material Forming · Magnetic Properties and Applications
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Earthquake Research Institute, The University of Tokyo, Tokyo, Japan.
Graduate School of Information Science and Technology, The University of Tokyo, Tokyo, Japan.
Graduate School of Engineering, The University of Tokyo, Tokyo, Japan.
Research Center for Advanced Science and Technology, The University of Tokyo, Tokyo, Japan.
Funding
Council for Science, Technology and Innovation (CSTI), Cross-ministerial Strategic Innovation Promotion Program (SIP), ``Structural Materials for Innovation''
References (29)
Show source-provided references
1. 10.2355/isijinternational.36.19410.2355/isijinternational.36.194 ↗
2. 10.2355/isijinternational.44.123010.2355/isijinternational.44.1230 ↗
3. 10.1016/j.matdes.2008.03.00910.1016/j.matdes.2008.03.009 ↗
4. 10.1016/0167-2789(93)90120-P10.1016/0167-2789(93)90120-P ↗
5. 10.1016/0167-2789(95)00298-710.1016/0167-2789(95)00298-7 ↗
6. 10.1016/S0167-2789(99)00129-310.1016/S0167-2789(99)00129-3 ↗
7. 10.1103/PhysRevE.74.06160510.1103/PhysRevE.74.061605 ↗
8. 10.1146/annurev.matsci.32.101901.15580310.1146/annurev.matsci.32.101901.155803 ↗
9. 10.1146/annurev.matsci.32.112001.13204110.1146/annurev.matsci.32.112001.132041 ↗
10. 10.1016/j.commatsci.2012.11.01710.1016/j.commatsci.2012.11.017 ↗
11. 10.1017/CBO978110770680410.1017/CBO9781107706804 ↗
12. 10.1016/S0065-2687(08)60442-210.1016/S0065-2687(08)60442-2 ↗
13. Kalnay E · 2003
14. 10.2151/jmsj.85B.33110.2151/jmsj.85B.331 ↗
15. 10.2151/sola.2007-00210.2151/sola.2007-002 ↗
16. 10.1093/comjnl/bxs13910.1093/comjnl/bxs139 ↗
17. 10.1093/gji/ggw41010.1093/gji/ggw410 ↗
18. 10.1002/2015GL06558810.1002/2015GL065588 ↗
19. 10.1103/PhysRevE.94.04330710.1103/PhysRevE.94.043307 ↗
20. 10.1023/A:100893541003810.1023/A:1008935410038 ↗
21. 10.1201/978158488922910.1201/9781584889229 ↗
22. 10.1175/1520-0493(1998)126\lt0796:DAUAEK\gt2.0.CO;210.1175/1520-0493(1998)126\lt0796:DAUAEK\gt2.0.CO;2 ↗
23. 10.1007/s10236-003-0036-910.1007/s10236-003-0036-9 ↗
24. 10.1111/j.1600-0870.1986.tb00459.x10.1111/j.1600-0870.1986.tb00459.x ↗
25. 10.1002/qj.4971134781210.1002/qj.49711347812 ↗
26. 10.1007/BF0102550110.1007/BF01025501 ↗
27. 10.1023/A:101832130739310.1023/A:1018321307393 ↗
28. 10.1175/1520-0493(2002)130\lt0629:SOIIDA\gt2.0.CO;210.1175/1520-0493(2002)130\lt0629:SOIIDA\gt2.0.CO;2 ↗
29. 10.1137/091606910.1137/0916069 ↗