Framework development for a SAVY-4000 nuclear material storage container structural integrity surveillance tool

被引：0

作者：

Hafen, Joseph ^{[1
,2
]}

Teague, Jon ^{[1
]}

Fleming, Brandon ^{[1
]}

Ruthstrom, Justin ^{[1
]}

Moore, Murray ^{[1
]}

Lukow, Steven ^{[1
]}

Suazo, Julio ^{[1
]}

Grow, David ^{[1
]}

Choudhury, Samrat ^{[2
]}

Gigax, Jonathan ^{[1
]}

机构：

[1] Los Alamos Natl Lab, Los Alamos, NM 87545 USA

[2] Univ Mississippi, Mech Engn Dept, University, MS 38677 USA

来源：

NUCLEAR ENGINEERING AND DESIGN | 2025年 / 439卷

关键词：

Nuclear storage containers; Machine Learning (ML); Plutonium; Finite Element Analysis (FEA); Stress; Corrosion; MODEL;

D O I：

10.1016/j.nucengdes.2025.114064

中图分类号：

TL [原子能技术]; O571 [原子核物理学];

学科分类号：

0827 ; 082701 ;

摘要：

This work presents the preliminary design of an automated surveillance tool to assess the health of SAVY-4000 nuclear material storage containers. This tool is designed by training several machine learning (ML) regression models to predict maximum residual stress in plain dents on the container sidewall. The model is trained on an experimentally validated Finite Element Analysis (FEA) model built in Abaqus FEA. The accuracy of each ML model is compared. The potential for application as well as model shortcomings are assessed. Necessary FEA model improvements are outlined and the various ML models are proposed.

引用

页数：12

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