Precise prediction of diffusional phase transformation behavior by phase-field method and data assimilation

2015 Fiscal Year Final Research Report

• Project/Area Number: 25630322
• Research Category: Grant-in-Aid for Challenging Exploratory Research
• Allocation Type: Multi-year Fund
• Research Field: Material processing/Microstructural control engineering
• Research Institution: Tokyo University of Agriculture and Technology
• Principal Investigator: Yamanaka Akinori 東京農工大学, 工学(系)研究科(研究院), 准教授 (50542198)
• Project Period (FY): 2013-04-01 – 2016-03-31
• Keywords: フェーズフィールド法 / データ同化 / アンサンブルカルマンフィルター / 鉄鋼材料 / ミクロ組織

Outline of Final Research Achievements

The phase-field (PF) method has attracted much attention as a numerical simulation technique for analyzing microstructure evolutions in various materials. However, parameters and initial condition used in the PF simulation have been estimated by simply comparing the simulation with the experimental results. The purpose of this study is to estimate the parameters efficiently and improve the simulation results by integrating experimental results into the PF simulation on the basis of the data assimilation (DA) method. The results of this study reveals that the ensemble Kalman filter (EnKF) is an appropriate sequential DA algorithm for the PF simulation. Furthermore, we demonstrated that a parameter used in the PF simulation can be estimated adequately using EnKF through numerical experiments called as twin experiments.

Free Research Field

機械材料、材料工学、フェーズフィールド法