Dataset of tensile properties for sub-sized specimens of nuclear structural materials

Longze Li; John W. Merickel; Yalei Tang; Rongjie Song; Joshua E. Rittenhouse; Aleksandar Vakanski; Fei Xu

doi:10.1038/s41597-024-04329-2

Dataset of tensile properties for sub-sized specimens of nuclear structural materials

Longze Li, John W. Merickel, Yalei Tang, Rongjie Song, Joshua E. Rittenhouse, Aleksandar Vakanski Fei Xu

Scientific Data, Vol.12(1), 48

01/11/2025

: https://doi.org/10.1038/s41597-024-04329-2

639/166/988

639/301/1023/1026

Data Descriptor

Humanities and Social Sciences

multidisciplinary

Science

Science (multidisciplinary)

Mechanical testing with sub-sized specimens plays an important role in the nuclear industry, facilitating tests in confined experimental spaces with lower irradiation levels and accelerating the qualification of new materials. The reduced size of specimens results in different material behavior at the microscale, mesoscale, and macroscale, in comparison to standard-sized specimens, which is referred to as the “specimen size effect.” Although analytical models have been proposed to correlate the properties of sub-sized specimens to standard-sized specimens, these models lack broad applicability across different materials and testing conditions. The objective of this study is to create the first large public dataset of tensile properties for sub-sized specimens used in nuclear structural materials. We performed an extensive literature review of relevant publications and extracted over 1,000 tensile testing records comprising 55 columns including material type and composition, manufacturing information, irradiation conditions, specimen dimensions, and tensile properties. The dataset can serve as a valuable resource to investigate the specimen size effect and develop computational methods to correlate the tensile properties of sub-sized specimens.

: Dataset of tensile properties for sub-sized specimens of nuclear structural materials
: Longze Li - Department of Computer Science, University of Idaho
John W. Merickel - Idaho National Laboratory
Yalei Tang - Idaho National Laboratory
Rongjie Song - Idaho National Laboratory
Joshua E. Rittenhouse - Idaho National Laboratory
Aleksandar Vakanski - Department of Computer Science, University of Idaho
Fei Xu - Idaho National Laboratory
: Scientific Data, Vol.12(1), 48
: Nature Publishing Group UK
: DE-AC07-05ID14517; DE-AC07-05ID14517; DE-AC07-05ID14517; DE-AC07-05ID14517; DE-AC07-05ID14517; DE-AC07-05ID14517; DE-AC07-05ID14517 / U.S. Department of Energy (DOE) (https://doi.org/10.13039/100000015)
: 996761640201851
: Institute for Modeling Collaboration and Innovation; Nuclear Engineering and Industrial Management; Computer Science
: English
: Journal article