Computational approach to grain boundary segregation engineering of nickel-base superalloys.

Grain boundary (GB) strengthening elements, such as B, C, and Zr have been added in small amounts to nickel-base superalloys. However, their strengthening effects have not been quantified and no specific design principles for GB chemistry have been reported. In this study, we propose a practical computational approach for the GB segregation engineering of nickel-base superalloys.

A Generalized Framework of Multifidelity Max-Value Entropy Search Through Joint Entropy.

Bayesian optimization (BO) is a popular method for expensive black-box optimization problems; however, querying the objective function at every iteration can be a bottleneck that hinders efficient search capabilities. In this regard, multifidelity Bayesian optimization (MFBO) aims to accelerate BO by incorporating lower-fidelity observations available with a lower sampling cost. In our previous work, we proposed an information-theoretic approach to MFBO, referred to as multifidelity max-value entropy search (MF-MES), which inherits practical effectiveness and computational simplicity of the well-known max-value entropy search (MES) for the single-fidelity BO.

Computational thermodynamics and microstructure simulations to understand the role of grain boundary phase in Nd-Fe-B hard magnets.

To control the coercivity of Nd hard magnets efficiently, the thermal stability of constituent phases and the microstructure changes observed in hard magnets during thermal processes should be understood. Recently, the CALPHAD method and phase-field method have been recognized as promising approaches to realize phase stability and microstructure developments in engineering materials. Thus, we applied these methods to understand the thermodynamic feature of the grain boundary phase and the microstructural developments in Nd-Fe-B hard magnets.

Estimation of material parameters based on precipitate shape: efficient identification of low-error region with Gaussian process modeling.

In this study, an efficient method for estimating material parameters based on the experimental data of precipitate shape is proposed. First, a computational model that predicts the energetically favorable shape of precipitate when a d-dimensional material parameter (x) is given is developed. Second, the discrepancy (y) between the precipitate shape obtained through the experiment and that predicted using the computational model is calculated.

Data assimilation for massive autonomous systems based on a second-order adjoint method.

Data assimilation (DA) is a fundamental computational technique that integrates numerical simulation models and observation data on the basis of Bayesian statistics. Originally developed for meteorology, especially weather forecasting, DA is now an accepted technique in various scientific fields. One key issue that remains controversial is the implementation of DA in massive simulation models under the constraints of limited computation time and resources.