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2019 Fiscal Year Final Research Report

Development of innovative prediction method of material microstructure by quantitative coupling of large-scale simulation and in situ observation

Research Project

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Project/Area Number 17H01237
Research Category

Grant-in-Aid for Scientific Research (A)

Allocation TypeSingle-year Grants
Section一般
Research Field Materials/Mechanics of materials
Research InstitutionKyoto Institute of Technology

Principal Investigator

Takaki Tomohiro  京都工芸繊維大学, 機械工学系, 教授 (50294260)

Co-Investigator(Kenkyū-buntansha) 大野 宗一  北海道大学, 工学研究院, 教授 (30431331)
澁田 靖  東京大学, 大学院工学系研究科(工学部), 准教授 (90401124)
安田 秀幸  京都大学, 工学研究科, 教授 (60239762)
青木 尊之  東京工業大学, 学術国際情報センター, 教授 (00184036)
Project Period (FY) 2017-04-01 – 2020-03-31
Keywordsフェーズフィールド法 / 分子動力学法 / その場観察 / データサイエンス / 凝固 / 粒成長 / 材料組織
Outline of Final Research Achievements

In material development, accurate prediction and control of material microstructures are extremely important. In this study, we tried to construct an innovative mesoscale material structure prediction approach by combining three cutting-edge researches of phase-field (PF) simulation, in-situ observation by SPring-8, and molecular dynamics (MD) simulation through data science. We successfully developed the data assimilation to calculate the interfacial physical properties by PF and EnKF using the MD results as observation data. Moreover, to obtain the material physical properties and field information, we constructed a frame of data assimilation by PF and EnKF using the results of SPring-8 in-situ observation as observation data. In addition, we constructed the high-performance PF simulation schemes and performed large-scale PF simulations for solidification and grain growth to predict the material microstructures and clarify important mechanisms in microstructure formation process.

Free Research Field

機械材料・材料力学

Academic Significance and Societal Importance of the Research Achievements

人々の生活が多様性を増す中で,ものづくりの根幹である材料開発の時間短縮が喫緊の課題となっている.材料開発は,素材の開発に加え,メゾスケールの組織制御が極めて重要である.本研究では,大規模フェーズフィールド計算,大型放射光施設によるその場観察,大規模分子動力学計算の,最先端の3つの研究をデータサイエンスを用いて融合する高精度材料組織予測法の開発を試み,その基盤を構築した.当該分野における日本の最先端研究の融合,実験と計算の融合を推し進めたことに学術的意義がある.本研究で構築した融合研究の基盤をさらに発展させ,多様なものづくりを支える材料開発に貢献することが可能となる.

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Published: 2021-02-19  

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