Microstructural Evolution of the Interdiffusion Zone between U-9 Wt Pct Mo Fuel Alloy and Zr-1 Wt Pct Nb Cladding Alloy Upon Annealing

Neogy, S.; Laik, A.; Saify, M.T.; Jha, Santosh Kumar; Srivastava, D.; Dey, G.K.

doi:10.1007/s11661-017-4033-x

Cited by 6 publications

(3 citation statements)

References 57 publications

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“…This conversion from HEU to LEU fuel is necessary in order to minimize, and ultimately eliminate, nuclear proliferation risks associated with continued manufacturing and operation of HEU fuel systems, which typically contain greater than 85% 235 U (relative to all U isotopes). LEU fuels have significantly less 235 U, where LEU is defined by the International Atomic Energy Association (IAEA) and the United States Nuclear Regulatory Commission (NRC) as having less than 20% 235 U (relative to all U isotopes) [3,4,5,6,7,8,9].…”

Section: Introductionmentioning

confidence: 99%

A machine learning approach to thermal conductivity modeling: A case study on irradiated uranium-molybdenum nuclear fuels

Kautz

Hagen

Johns

et al. 2019

Computational Materials Science

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Section: Introductionmentioning

confidence: 99%

A machine learning approach to thermal conductivity modeling: A case study on irradiated uranium-molybdenum nuclear fuels

Kautz

Hagen

Johns

et al. 2019

Computational Materials Science

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“…U-10Mo is currently under investigation as a candidate low enriched uranium (LEU) fuel system in order to replace highly enriched uranium (HEU) fuels currently used in several research and radioisotope production facilities worldwide. Replacement of HEU with a LEU fuel system will reduce proliferation concerns associated with continued operation with HEU fuels [6,7,8,9,10,11,12].…”

Section: Introductionmentioning

confidence: 99%

An image-driven machine learning approach to kinetic modeling of a discontinuous precipitation reaction

Kautz¹,

Ma²,

Jana³

et al. 2019

Preprint

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Micrograph quantification is an essential component of several materials science studies. Machine learning methods, in particular convolutional neural networks, have previously demonstrated performance in image recognition tasks across several disciplines (e.g. materials science, medical imaging, facial recognition). Here, we apply these well-established methods to develop an approach to microstructure quantification for kinetic modeling of a discontinuous precipitation reaction in a case study on the uranium-molybdenum system. Prediction of material processing history based on image data (classification), calculation of area fraction of phases present in the micrographs (segmentation), and kinetic modeling from segmentation results were performed. Results indicate that convolutional neural networks represent microstructure image data well, and segmentation using the k-means clustering algorithm yields results that agree well with manually annotated images. Classification accuracies of original and segmented images are both 94% for a 5-class classification problem. Kinetic modeling results agree well with previously reported data using manual thresholding. The image quantification

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“…As a part of U.S. high performance research reactor conversion program, there is an effort to replace all the highly enriched uranium fuel currently used in research reactors to low enriched nuclear fuels. Development of such a LEU fuel has significant implications to international nuclear non-proliferation, safeguards, and health and environmental contamination risks associated with continued handling of highly enriched uranium (HEU) fuels [18,[25][26][27][28][29][30]. Metallic low enriched fuel made of Uranium-10wt% Molybdenum alloys (LEU-10Mo) with less than 20% enrichment is a leading candidate chosen for this effort.…”

mentioning

confidence: 99%

Nanoscale Spatially Resolved Mapping of Uranium Enrichment in Actinide-Bearing Materials

Kautz¹,

Burkes²,

Joshi³

et al. 2019

Microsc Microanal

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Spatially resolved analysis of uranium isotopes in small volumes of actinide-bearing materials is critical for a variety of technical disciplines, including earth and planetary sciences, environmental monitoring, bioremediation, and the nuclear fuel cycle. However, achieving sub-nanometer scale spatial resolution for such isotopic analysis is currently a challenge. By using atom probe tomography, a three dimensional nanoscale characterization technique, we demonstrate unprecidented nanoscale mapping of uranium isotopic enrichment with high sensitivity across various microstructural interfaces within small volumes (100 nm 3 ) of depleted and low enriched uranium alloyed with 10 wt % molybdenum with different nominal enrichments of 0.20 and 19.75% 235 U respectively. The approach presented here can be applied to study nanoscale variations of isotopic abundances in the broad class of actinide-bearing materials, providing unique insights into their origin and thermo-mechanical processing routes.

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Microstructural Evolution of the Interdiffusion Zone between U-9 Wt Pct Mo Fuel Alloy and Zr-1 Wt Pct Nb Cladding Alloy Upon Annealing

Cited by 6 publications

References 57 publications

A machine learning approach to thermal conductivity modeling: A case study on irradiated uranium-molybdenum nuclear fuels

A machine learning approach to thermal conductivity modeling: A case study on irradiated uranium-molybdenum nuclear fuels

An image-driven machine learning approach to kinetic modeling of a discontinuous precipitation reaction

Nanoscale Spatially Resolved Mapping of Uranium Enrichment in Actinide-Bearing Materials

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