Hiromichi Nagao1,2,*, Shin-ichi Ito1,2, Tadashi Kasuya3, Junya Inoue4,3
The International Conference on Computational & Experimental Engineering and Sciences, Vol.22, No.2, pp. 127-127, 2019, DOI:10.32604/icces.2019.05384
Abstract Data assimilation (DA) is a computational technique to integrate numerical simulation models and observational/experimental data based on Bayesian statistics. DA is accepted as an essential methodology for the modern weather forecasting, and is applied to various fields of science including structural materials science. We propose a DA methodology to evaluate unobservable parameters involved in multi-phase-field models with the aim of accurately predicting the observed grain growth, such as in metals and alloys. This approach integrates models and a set of observational image data of grain structures. Since the set of image data is not a… More >