Back to Search Start Over

Machine learning powered predictive modelling of complex residual stress for nuclear fusion reactor design

Authors :
Bin Zhu
Nathanael Leung
Brandon Steel
David England
Yinglong He
Andrew J. London
Hannah Zhang
Michael Gorley
Yiqiang Wang
Mark J. Whiting
Tan Sui
Source :
Materials & Design, Vol 248, Iss , Pp 113449- (2024)
Publication Year :
2024
Publisher :
Elsevier, 2024.

Abstract

Fusion In-vessel components, assembled and maintained using laser welding, one of the most promising techniques, exhibit complex distributions of residual stress, microstructures, and material properties. These residual stresses can compromise structural integrity and lifespan of critical components. Although using advanced experimental measurements can evaluate the residual stress for individual case, extending the measurements to massive number of components are costly and time-consuming. Traditional machine learning (ML) models struggle to account for the heterogeneity and anisotropy of these stress distributions. Here, we develop a novel ML framework based on the Eurofer97 steel, the structural material for in-vessel components. The ML framework is trained on high-resolution residual stress data derived from recently-developed evaluation techniques. Combining with microstructures, the model enables prediction of heterogenous and anisotropic residual stress distribution. It successfully predicts the compressive residual stress in fusion zone (∼−200 MPa) balanced by tensile residual stress in heat affected zone (∼300 MPa), aligning closely with experimental results with the R-squared value of 0.989 and the mean square error of 10−4. Unlike experiments that take hours, the ML model provides predictions within seconds. It offers valuable insights into residual stress prediction for various joints, enhancing the reliability and lifetime prediction of in-vessel components.

Details

Language :
English
ISSN :
02641275
Volume :
248
Issue :
113449-
Database :
Directory of Open Access Journals
Journal :
Materials & Design
Publication Type :
Academic Journal
Accession number :
edsdoj.036ca9bfc0de49a8805d405cba5f433b
Document Type :
article
Full Text :
https://doi.org/10.1016/j.matdes.2024.113449