Data-driven prediction of the probability of creep-fatigue crack initiation in 316H stainless steel
File(s)
Accepted version
Author(s)
Chavoshi, Saeed Zare
Tagarielli, Vito L
Type
Journal Article
Abstract
Stainless steel components in advanced gas-cooled reactors (AGRs) are susceptible to creep–fatigue cracking at high temperatures. Quantifying the probability of creep–fatigue crack initiation requires probabilistic numerical simulations; these are complex and computationally intensive. Here, we present a data-driven approach to develop fast probabilistic surrogate models of creep–fatigue crack initiation in 316H stainless steel. We perform a set of Monte Carlo simulations based on the R5V2/3 high temperature assessment procedure and determine the sensitivity of the probability of crack initiation to loads and operating conditions. The data are used to train different supervised machine learning models considering Bayesian hyperparameter optimization. We discuss the relative performance of such models and show that a gradient tree boosting algorithm results in surrogate models with the highest accuracy.
Date Issued
2023-01
Date Acceptance
2022-09-22
Citation
Fatigue and Fracture of Engineering Materials and Structures, 2023, 46 (1), pp.212-227
ISSN
1460-2695
Publisher
Wiley
Start Page
212
End Page
227
Journal / Book Title
Fatigue and Fracture of Engineering Materials and Structures
Volume
46
Issue
1
Copyright Statement
Copyright © 2022 Owner. This is the peer reviewed version of the following article: Chavoshi, SZ, Tagarielli, VL. Data-driven prediction of the probability of creep–fatigue crack initiation in 316H stainless steel. Fatigue Fract Eng Mater Struct. 2023; 46( 1): 212- 227. doi:10.1111/ffe.13858
, which has been published in final form at [Link to final article using the DOI]. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions. This article may not be enhanced, enriched or otherwise transformed into a derivative work, without express permission from Wiley or by statutory rights under applicable legislation. Copyright notices must not be removed, obscured or modified. The article must be linked to Wiley’s version of record on Wiley Online Library and any embedding, framing or otherwise making available the article or pages thereof by third parties from platforms, services and websites other than Wiley Online Library must be prohibited.
, which has been published in final form at [Link to final article using the DOI]. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions. This article may not be enhanced, enriched or otherwise transformed into a derivative work, without express permission from Wiley or by statutory rights under applicable legislation. Copyright notices must not be removed, obscured or modified. The article must be linked to Wiley’s version of record on Wiley Online Library and any embedding, framing or otherwise making available the article or pages thereof by third parties from platforms, services and websites other than Wiley Online Library must be prohibited.
Identifier
https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000869309700001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
Subjects
Science & Technology
Technology
Engineering, Mechanical
Materials Science, Multidisciplinary
Engineering
Materials Science
creep-fatigue
machine learning
probabilistic assessment
stainless steel
LIFETIME MANAGEMENT
NUCLEAR BOILERS
VARIABLES
Publication Status
Published
Date Publish Online
2022-10-17