2021 Fiscal Year Final Research Report
Data assimilation-assisted multi-scale modelling for controlling material microstructures during thermomechanical processes
Project/Area Number |
20K22393
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Research Category |
Grant-in-Aid for Research Activity Start-up
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Allocation Type | Multi-year Fund |
Review Section |
0301:Mechanics of materials, production engineering, design engineering, fluid engineering, thermal engineering, mechanical dynamics, robotics, aerospace engineering, marine and maritime engineering, and related fields
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Research Institution | Tokyo University of Agriculture and Technology |
Principal Investigator |
Miyoshi Eisuke 東京農工大学, 工学(系)研究科(研究院), 助教 (70880962)
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Project Period (FY) |
2020-09-11 – 2022-03-31
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Keywords | 材料微視組織 / 再結晶 / 粒成長 / 粒界 / フェーズフィールド法 / 分子動力学 / データ同化 / HPC |
Outline of Final Research Achievements |
This study was conducted to improve the prediction of material microstructures by solving the following problems of conventional simulation methods for recrystallization and grain growth: physical properties, recrystallization nucleation, and computational scale. Specifically, by integrating continuum-scale phase-field (PF) models and atomic-scale molecular dynamics (MD) calculations based on data science and high-performance computing techniques, we developed a new method to extract unknown physical properties of grain boundaries. Furthermore, we succeeded in accurately quantifying the conditions for abnormal grain growth, which is one of the dominant mechanisms for recrystallization nucleation, by performing systematic evaluations of the phenomenon using very large-scale PF simulations.
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Free Research Field |
計算材料科学
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Academic Significance and Societal Importance of the Research Achievements |
再結晶・粒成長に伴う材料組織変化の高精度予測・制御は,合金添加に頼らず材料の性能を引き出す「材料組織設計」において極めて重要である.しかしながら,従来の数値手法は「物性値」「再結晶核生成」「計算規模」に課題があり,実組織予測への適用は停滞していた.本研究では,PF法・MD法・データ科学・高性能計算を横断的に用いることで,物性値取得法の確立や核生成条件の同定を推し進め,上記課題に解決方策を与えた.これらは,材料学・計算科学における先端的研究の融合を通じて,組織設計技術の高度化による材料開発加速に向けた基盤を提示したものであり,学術・産業面での貢献が期待できる.
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