Isshu Lee, John W Merickel, Yugandhar Kasala Sreenivasulu, Fei Xu, Yalei Tang, Joshua E Rittenhouse, Aleksandar Vakanski, Rongjie Song
{"title":"Comprehensive Toughness Dataset of Nuclear Reactor Structural Materials using Charpy V-Notch Impact Testing.","authors":"Isshu Lee, John W Merickel, Yugandhar Kasala Sreenivasulu, Fei Xu, Yalei Tang, Joshua E Rittenhouse, Aleksandar Vakanski, Rongjie Song","doi":"10.1038/s41597-025-04823-1","DOIUrl":null,"url":null,"abstract":"<p><p>Reactor pressure vessel (RPV) steels are critical for maintaining the structural integrity and safety of nuclear reactors, designed to endure extreme conditions over prolonged operational lifetimes. Evaluating the mechanical properties of RPV steels frequently involves tests with sub-sized specimens, due to size constraints associated with irradiated materials. However, the reduced specimen dimensions introduce a size effect that alters material behavior and requires correlating the test results to full-sized specimens. Although numerous correlation methods have been previously proposed, they are typically applicable to specific test conditions. To address these challenges, this study introduces a public dataset of 4,961 Charpy impact test records for RPV steels. The dataset was compiled through a comprehensive literature review and incorporates data from 109 peer-reviewed publications. It provides detailed information on material composition, manufacturing treatments, specimen dimensions, testing conditions, and test results. The primary objective of the dataset is to advance the understanding of specimen size effect in Charpy impact testing, and support studies for validating existing methods and developing data-driven approaches for test results correlation.</p>","PeriodicalId":21597,"journal":{"name":"Scientific Data","volume":"12 1","pages":"543"},"PeriodicalIF":5.8000,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11961680/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Scientific Data","FirstCategoryId":"103","ListUrlMain":"https://doi.org/10.1038/s41597-025-04823-1","RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MULTIDISCIPLINARY SCIENCES","Score":null,"Total":0}
引用次数: 0
Abstract
Reactor pressure vessel (RPV) steels are critical for maintaining the structural integrity and safety of nuclear reactors, designed to endure extreme conditions over prolonged operational lifetimes. Evaluating the mechanical properties of RPV steels frequently involves tests with sub-sized specimens, due to size constraints associated with irradiated materials. However, the reduced specimen dimensions introduce a size effect that alters material behavior and requires correlating the test results to full-sized specimens. Although numerous correlation methods have been previously proposed, they are typically applicable to specific test conditions. To address these challenges, this study introduces a public dataset of 4,961 Charpy impact test records for RPV steels. The dataset was compiled through a comprehensive literature review and incorporates data from 109 peer-reviewed publications. It provides detailed information on material composition, manufacturing treatments, specimen dimensions, testing conditions, and test results. The primary objective of the dataset is to advance the understanding of specimen size effect in Charpy impact testing, and support studies for validating existing methods and developing data-driven approaches for test results correlation.
期刊介绍:
Scientific Data is an open-access journal focused on data, publishing descriptions of research datasets and articles on data sharing across natural sciences, medicine, engineering, and social sciences. Its goal is to enhance the sharing and reuse of scientific data, encourage broader data sharing, and acknowledge those who share their data.
The journal primarily publishes Data Descriptors, which offer detailed descriptions of research datasets, including data collection methods and technical analyses validating data quality. These descriptors aim to facilitate data reuse rather than testing hypotheses or presenting new interpretations, methods, or in-depth analyses.