Microstructure evolution in U-10Zr alloy irradiated by swift Xe ions at 700 ∘C

Miao, Yinbin; Mo, Kun; Bhattacharya, Sumit; Jamison, Laura; Oaks, Aaron; Kim, Yeon Soo; Yacout, Abdellatif M.

doi:10.1016/j.jnucmat.2020.152470

Cited by 8 publications

(3 citation statements)

References 27 publications

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“…This melting temperature is much higher than what has been observed for uranium-zirconium solid solution fuels, ex. U-10Zr fast reactor fuel [26]. This can be understood as follows.…”

Section: Figure 9: Phase Diagram Of the Zr-h System With Isobars Of E...mentioning

confidence: 99%

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MARVEL Reactor Fuel Performance Report

Evans

Keiser

Sweet

2023

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The Microreactor Applications Research Validation and EvaLuation (MARVEL) project is producing a high temperature liquid metal-cooled nuclear test bed at the Idaho National Laboratory (INL) to ultimately improve integration of microreactors to end-user applications. This ambitious effort seeks to design, authorize, construct, test, and operate the reactor within five years. In order to construct and operate the MARVEL reactor in a timely manner, the system will utilize materials and component designs which have already been used, qualified, or licensed from previous reactors. The MARVEL reactor will be located at the INL Transient Reactor Test (TREAT) facility in the north high-bay equipment pit and will use the existing 304 stainless steel-clad U-ZrH 1.6 pin-type fuel system developed by General Atomics and purchased from TRIGA International. This fuel has been previously qualified under the United States Department of Energy's Reduced Enrichment for Research and Test Reactors (RERTR) Program.Even though the regulator of the MARVEL reactor is the United States Department of Energy, the standards and approach recommended by the Nuclear Regulatory Commission is well-defined and utilized here. Following NUREG-1537 regulatory guidance, this report documents the authorization case for the MARVEL fuel system's application to MARVEL and establishes stable and predictable fuel performance during the most thermophysically unfavorable conditions achievable in the MARVEL reactor. To that end, this report provides a comprehensive survey of the known thermophysical properties, performance, and quantitative relationships associated with the MARVEL reactor fuel element and uses this information to determine its mechanical integrity and risk of reaching unacceptable conditions during the most extreme accident scenarios predicted for the reactor using the most conservative assumptions available. The information contained herein is compiled from a combination of historical reports and peer reviewed scientific publication manuscripts. Known mechanisms under which the fuel is susceptible to failure are highlighted and compared to conditions that could exist in the MARVEL reactor during an unanticipated transient or accident scenario.The two scenarios considered for analysis in this report are (1) an unprotected loss of flow accident and (2) a hypothetical unprotected loss of coolant accident during the loss of flow accident. Preliminary 2D steady-state analyses herein indicate that both fuel-cladding chemical interactions and fuel-cladding mechanical interactions are negligible throughout the fuel's operational cycle under both normal and high temperature accident scenario conditions. Although higher fidelity 3D time-dependent modeling and simulations are planned, the following may be concluded presently. The MARVEL fuel element maintains its geometric stability and structural integrity during the most extreme accident scenarios predicted for the MARVEL reactor. The hoop stress during the unprotected loss of flow accident reaches a...

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“…This melting temperature is much higher than what has been observed for uranium-zirconium solid solution fuels, ex. U-10Zr fast reactor fuel [26]. This can be understood as follows.…”

Section: Figure 9: Phase Diagram Of the Zr-h System With Isobars Of E...mentioning

confidence: 99%

“…For example, experiments have shown that the fuel melting temperature is closer to 1230 °C for U-ZrH x clad in Hastelloy-N. This occurs because the zirconium hydride forms a eutectic with the Hastelloy-N somewhat above the melting point of the Hastelloy when they are in contact [26].…”

Section: Figure 9: Phase Diagram Of the Zr-h System With Isobars Of E...mentioning

confidence: 99%

MARVEL Reactor Fuel Performance Report

Evans

Keiser

Sweet

2023

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“…For example, the γ2 phase (a Zr-rich body centered cubic solid solution between U and Zr) is surprisingly present at Zone 3b [12]. The γ2 seems to have low Xe (one type of fission gas) diffusivity [41].…”

Section: Porosity Analysis Using the Fuel Cross-sectional Imagingmentioning

confidence: 99%

A Fine Pore-preserved Deep Neural Network for Porosity Analytics of a High Burnup U-10Zr Metallic Fuel

Wang

Cai

et al. 2023

Preprint

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U-10 wt.% Zr (U-10Zr) metallic fuel is the leading candidate for next-generation sodium-cooled fast reactors. Porosity is one of the most important factors that impacts the performance of U-10Zr metallic fuel. The pores generated by the fission gas accumulation can lead to changes in thermal conductivity, fuel swelling, Fuel-Cladding Chemical Interaction (FCCI) and Fuel-Cladding Mechanical Interaction (FCMI). Therefore, it is crucial to accurately segment and analyze porosity to understand the U-10Zr fuel system to design future fast reactors. To address the above issues, we introduce a data processing workflow to produce multi-source Scanning Electron Microscope (SEM) image data. Moreover, an encoder-decoder-based, deep fully convolutional network is proposed to segment pores accurately by integrating the residual unit and the densely connected units. Two SEM 250× field of view image datasets with different formats are utilized to evaluate the new proposed model’s performance. Sufficient comparison results demonstrate that our method quantitatively outperforms two popular deep fully convolutional networks. Furthermore, we conducted experiments on the third SEM 2500 × field of view image dataset, and the transfer learning results show the potential capability to transfer the knowledge from low-magnification images to high-magnification images. Finally, we use a pre-trained network to predict SEM images in the whole cross-sectional image and obtain quantitative porosity analysis. Our findings will guide the SEM microscopy data collection efficiently, provide a mechanistic understanding of the U-10Zr fuel system and bridge the gap between advanced characterization to fuel system design.

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