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Title: Automated X-Ray Diffraction of Irradiated Materials

Abstract

Synchrotron-based X-ray diffraction (XRD) and small-angle Xray scattering (SAXS) characterization techniques used on unirradiated and irradiated reactor pressure vessel steels yield large amounts of data. Machine learning techniques, including PCA, offer a novel method of analyzing and visualizing these large data sets in order to determine the effects of chemistry and irradiation conditions on the formation of radiation induced precipitates. In order to run analysis on these data sets, preprocessing must be carried out to convert the data to a usable format and mask the 2-D detector images to account for experimental variations. Once the data has been preprocessed, it can be organized and visualized using principal component analysis (PCA), multi-dimensional scaling, and k-means clustering. In conclusion, from these techniques, it is shown that sample chemistry has a notable effect on the formation of the radiation induced precipitates in reactor pressure vessel steels.

Authors:
 [1];  [2];  [3];  [3];  [2]
  1. Syracuse Univ., NY (United States). College of Engineering and Computer Science
  2. Brookhaven National Lab. (BNL), Upton, NY (United States). Computational Science Initiative
  3. Brookhaven National Lab. (BNL), Upton, NY (United States). Nuclear Science and Technology Department
Publication Date:
Research Org.:
Brookhaven National Laboratory (BNL), Upton, NY (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Nuclear Physics (NP) (SC-26); USDOE Office of Nuclear Energy (NE); USDOE Office of Science (SC), Workforce Development for Teachers and Scientists (WDTS) (SC-27)
OSTI Identifier:
1426469
Report Number(s):
BNL-203331-2018-JAAM
Grant/Contract Number:
SC0012704
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
Scientific Data Summit (NYSDS), 2017 New York
Additional Journal Information:
Journal Name: Scientific Data Summit (NYSDS), 2017 New York; Conference: Scientific Data Summit (NYSDS), 2017 New York , New York, NY, USA, 6/6/2017 - 6/9/2017
Country of Publication:
United States
Language:
English
Subject:
73 NUCLEAR PHYSICS AND RADIATION PHYSICS; 46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND TECHNOLOGY

Citation Formats

Rodman, John, Lin, Yuewei, Sprouster, David, Ecker, Lynne, and Yoo, Shinjae. Automated X-Ray Diffraction of Irradiated Materials. United States: N. p., 2017. Web. doi:10.1109/NYSDS.2017.8085053.
Rodman, John, Lin, Yuewei, Sprouster, David, Ecker, Lynne, & Yoo, Shinjae. Automated X-Ray Diffraction of Irradiated Materials. United States. doi:10.1109/NYSDS.2017.8085053.
Rodman, John, Lin, Yuewei, Sprouster, David, Ecker, Lynne, and Yoo, Shinjae. Thu . "Automated X-Ray Diffraction of Irradiated Materials". United States. doi:10.1109/NYSDS.2017.8085053.
@article{osti_1426469,
title = {Automated X-Ray Diffraction of Irradiated Materials},
author = {Rodman, John and Lin, Yuewei and Sprouster, David and Ecker, Lynne and Yoo, Shinjae},
abstractNote = {Synchrotron-based X-ray diffraction (XRD) and small-angle Xray scattering (SAXS) characterization techniques used on unirradiated and irradiated reactor pressure vessel steels yield large amounts of data. Machine learning techniques, including PCA, offer a novel method of analyzing and visualizing these large data sets in order to determine the effects of chemistry and irradiation conditions on the formation of radiation induced precipitates. In order to run analysis on these data sets, preprocessing must be carried out to convert the data to a usable format and mask the 2-D detector images to account for experimental variations. Once the data has been preprocessed, it can be organized and visualized using principal component analysis (PCA), multi-dimensional scaling, and k-means clustering. In conclusion, from these techniques, it is shown that sample chemistry has a notable effect on the formation of the radiation induced precipitates in reactor pressure vessel steels.},
doi = {10.1109/NYSDS.2017.8085053},
journal = {Scientific Data Summit (NYSDS), 2017 New York},
number = ,
volume = ,
place = {United States},
year = {Thu Oct 26 00:00:00 EDT 2017},
month = {Thu Oct 26 00:00:00 EDT 2017}
}

Journal Article:
Free Publicly Available Full Text
This content will become publicly available on October 26, 2018
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