# A universal Bayesian inference framework for complicated creep constitutive equations

https://mdr.nims.go.jp/datasets/e763780c-2a9d-4958-86c4-dd2d13e6b9ca

## File

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## Id

e763780c-2a9d-4958-86c4-dd2d13e6b9ca

## Local identifier



## Visibility

open_to_public

## State

published

## Created at

2021-08-05T16:24:15.909449Z

## Updated at

2024-01-05T13:13:52.288958Z

## Published at

2022-10-31T02:55:41.969251Z

## Doi



## First published url

https://doi.org/10.1038/s41598-020-65945-7

## Date published

2020-06-26

## Recorded date published



## Resource type

journal_article

## Manuscript type

vor

## Collection



## Title

- title: A universal Bayesian inference framework for complicated creep constitutive
    equations
  title_type: original
  lang: en

## Description

- description: Evaluating the creep deformation process of heat-resistant steels is
    important for improving the energy efficiency of power plants by increasing the
    operating temperature. There is an analysis framework that estimates the rupture
    time of this process by regressing the strain–time relationship of the creep process
    using a regression model called the creep constitutive equation. Because many
    creep constitutive equations have been proposed, it is important to construct
    a framework to determine which one is best for the creep processes of different
    steel types at various temperatures and stresses. A Bayesian model selection framework
    is one of the best frameworks for evaluating the constitutive equations. In previous
    studies, approximate-expression methods such as the Laplace approximation were
    used to develop the Bayesian model selection frameworks for creep. Such frameworks
    are not applicable to creep constitutive equations or data that violate the assumption
    of the approximation. In this study, we propose a universal Bayesian model selection
    framework for creep that is applicable to the evaluation of various types of creep
    constitutive equations. Using the replica exchange Monte Carlo method, we develop
    a Bayesian model selection framework for creep without an approximate-expression
    method. To assess the effectiveness of the proposed framework, we applied it to
    the evaluation of a creep constitutive equation called the Kimura model, which
    is difficult to evaluate by existing frameworks. Through a model evaluation using
    the creep measurement data of Grade 91 steel, we confirmed that our proposed framework
    gives a more reasonable evaluation of the Kimura model than existing frameworks.
    Investigating the posterior distribution obtained by the proposed framework, we
    also found a model candidate that could improve the Kimura model.
  description_type: abstract
  lang: en

## Creator

- name: Izuno, Hitoshi
  role: author
  orcid: https://orcid.org/0000-0003-0503-3621
  organization: National Institute for Materials Science
  department: Research and Services Division of Materials Data and Integrated System
  ror: https://ror.org/026v1ze26
- name: Mototake, Yoh-ichi
  role: author
  organization: The Institute of Statistical Mathematics
- name: Nagata, Kenji
  role: author
  orcid: https://orcid.org/0000-0001-9894-4461
  organization: National Institute for Materials Science
  department: Research and Services Division of Materials Data and Integrated System
  ror: https://ror.org/026v1ze26
- name: Demura, Masahiko
  role: author
  orcid: https://orcid.org/0000-0002-7308-3041
  organization: National Institute for Materials Science
  department: Research and Services Division of Materials Data and Integrated System
  ror: https://ror.org/026v1ze26
- name: Okada, Masato
  role: author
  organization: University of Tokyo
  department: Graduate School of Frontier Sciences

## Contact agent



## Publisher

organization: Springer Nature

## Managing organization



## Keyword

- subject: Materials science
  schema: not_defined
- subject: Metals and alloys
  schema: not_defined
- subject: Structural materials
  schema: not_defined

## Rights

- description: Creative Commons BY Attribution 4.0 International
  identifier: https://creativecommons.org/licenses/by/4.0/

## Other identifier(s)



## Data origin

- data_origin_type: other

## Embargo



## Journal

- title: Scientific Reports
  issn: '20452322'
  volume: '10'
  article_number: '10437'

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## Fileset

- id: 1d723e4a-81df-4a94-9f56-6083db504ca2
  filename: 10.1038_s41598-020-65945-7.pdf
  content_type: application/pdf
  size: 4342146
  md5: efccef426e91a40a44ff4b8fb3fce752

## Thumbnail

fileset_id: 1d723e4a-81df-4a94-9f56-6083db504ca2
filename: 10.1038_s41598-020-65945-7.pdf