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Automated characterization of uranium-molybdenum fuel microstructures
Collette, Ryan A.
Collette, Ryan A.
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2015
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Interpreting the performance of nuclear fuel materials under various irradiation conditions is essential to the qualification of new nuclear fuels. Automated image processing routines have the potential to aid in the fuel performance evaluation process by eliminating judgment calls that may vary from person-to-person or sample-to-sample. This thesis develops several image analysis routines designed for fission gas bubble characterization in irradiated uranium molybdenum (U-Mo) monolithic-type plate fuels. Electron micrographs of uranium-molybdenum fuel samples prepared by Idaho National Laboratory are used as the reference images for algorithm development using CellProfiler and MATLAB's Image Processing Toolbox. The resulting algorithm cleans the input image through pre-processing and subsequently segments the fission gas bubbles from the fuel sample images. The segmented image is then used to determine the bubble count, calculate the bubble size distribution, and estimate the overall sample porosity. In addition to technique development, the project includes verification and validation of the established image processing algorithm, as well as large-scale data extraction and analysis of a stack of U-Mo sample images. This work demonstrates that it is possible to use automated image analysis to extract meaningful fission product data from micrographs of nuclear fuel. In particular, the largely qualitative and visual inspection based methods often used by fuel performance analysts can effectively be replaced by quantitative methods that are faster, more consistent, and at least as accurate as their manual counterparts.
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