Multi-length scale characterization of microstructure-processing relationships in uranium-molybdenum alloys

Kautz, Elizabeth J.
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Lewis, Daniel J.
Radke, Richard J., 1974-
Chen, Ying
Duquette, David J.
Watson, Bruce
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Materials engineering
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Microstructure and chemistry of nuclear reactor fuel materials have a major influence on material performance, from mechanical and irradiation stability to corrosion resistance. It is therefore essential to thoroughly understand microstructure evolution as a function of processing and irradiation parameters. Here, uranium alloyed with ten weight percent molybdenum was investigated as a low-enriched uranium nuclear fuel system for applications in high performance research reactors, relevant to nuclear non-proliferation efforts worldwide. Characterization efforts performed span multiple length scales and employ a variety of experimental techniques (scanning and transmission electron and focused ion beam microscopy, and atom probe tomography) and data analysis tools (image processing, empirical modeling, integrated visualization and analysis software) to address the overarching goal of improving: (1) fundamental understanding of what microstructure is produced as a function of material processing parameters, and (2) predictive capabilities of relating microstructure to processing. Impurity element concentration was determined to impact volume fraction of precipitate phases, enrichment of the matrix phase, and phase transformation kinetics during sub-eutectoid annealing. Enrichment variation within the fuel microstructure was experimentally measured in different phases for the first time through development of a methodology with high spatial resolution and accuracy, employing atom probe tomography. Microstructure-processing predictive capabilities were further investigated through application of image processing to microstructure quantification. This work highlights the importance of leveraging multi-length scale characterization and data analysis methods for developing capability to predict microstructure from processing parameters.
May 2018
School of Engineering
Dept. of Materials Science and Engineering
Rensselaer Polytechnic Institute, Troy, NY
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