Recent trends in metallic fast reactor fuels research

Aitkaliyeva, Assel

doi:10.1016/j.jnucmat.2021.153377

Cited by 19 publications

(8 citation statements)

References 55 publications

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“…In the last years, over 10,000 U-10Zr fuel pins have been irradiated in the test reactors and new advanced Post-Irradiation Examination (PIE) data has been collected at Idaho National Laboratory (INL). The data includes observations on fuel swelling, Zr redistribution, FCCI, and FCMI phenomena, obtained through various characterization techniques such as neutron radiography, pin profilometry, gamma scan, fission gas release analysis, metallography, micro-hardness, isotopic chemical analysis, and high-resolution/micro-nano scale characterization [8][9][10][11][12][13][14][15][16][17]. New advanced characterization techniques employ multi-source data, including Scanning Electron Microscopy (SEM) images, Energy-Dispersive Spectroscopy (EDS) images, and Scanning Transmission Electron Microscopy (STEM), which provide qualitative material information from the optical scale, overall microstructure and pore distribution from SEM and EDS microscale, and phase/crystal structure identification from STEM nanoscale.…”

Section: Introductionmentioning

confidence: 99%

“…Advanced PIE technologies have enabled researchers to gain qualitative understanding of the irradiation behavior of U-10Zr fuel, including Zr redistribution, FCMI, and FCCI [8][9][10][11][12][13][14][15][16][17]. For example, Salvato et al [12] investigated the Zr redistribution on a U-10Zr fuel cross-section, named FA, that was irradiated to a burnup of approximately 13.2at.%.…”

Section: Introductionmentioning

confidence: 99%

“…based machine learning model for fission gas pore classification on annular U-10Zr fuels [24,25]; however, the tree-based method is non-robust and sensitive to the SEM images at different formats and length scales. Recently, convolutional neural networks (CNNs) achieved advanced performance and robustness in segmenting, recognizing, and classifying natural and microscopy images [26][27][28][29][30][31][32][33][34][35][36][37]. However, there remain challenges for CNNs to accurately detect pores from the SEM images.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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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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“…6,7 A lot of new PIE data within the sub-nanometer to micrometer scale has been recently generated. [8][9][10] Two main challenges of this fuel material are zirconium redistribution (Fuel Constituent Migration), 11 and fuel-cladding chemical interactions (FCCI). 12 FCCI is a chemical reaction between nuclear fuel and cladding (a thin-walled metal as first barrier for retention of fission products), which creates an important challenge to overcome in metallic fuels.…”

Section: Introductionmentioning

confidence: 99%

“…7,10 It is essential to achieve a comprehensive understanding of the zirconium redistribution (phase distribution) in order to better predict fuel performance. 11 A U-10Zr fuel cross-section would host Zr redistribution in three major phases: 𝛾 phase (a continuous body centered cubic solid solution between U and Zr), noted as the (U, Zr) matrix; 𝛼 -U matrix; and 𝛼 -U + UZr2. The Zr redistributes across the pin radius and forms up to three distinctive zones by different U/Zr contents.…”

Section: Introductionmentioning

confidence: 99%

Advanced Characterization-Informed Framework and Quantitative Insight to Irradiated Annular U-10Zr Metallic Fuels

Cai

Salvato

et al. 2022

Preprint

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U-10Zr-based metallic nuclear fuel is a promising fuel candidate for next-generation sodium-cooled fast reactors. Idaho National Laboratory’s research experience for this type of fuel dates back to the 1960s. Idaho National Laboratory researchers have accumulated a considerable amount of experience and knowledge regarding fuel performance at the engineering scale. The limitation of advanced characterization and lack of proper data analysis tools prevented a mechanistic understanding of fuel microstructure evolution and properties degradation during irradiation. This paper proposed a new workflow, coupled with domain knowledge obtained by advanced post-irradiation examination methods, to provide unprecedented and quantified insights into the fission gas bubbles and pores, and lanthanide distribution in an annular fuel irradiated in the Advanced Test Reactor. In the study, researchers identify and confirm that the Zr-bearing secondary phases exist and generate the quantitative ratios of seven microstructures along the thermal gradient. Moreover, the distributions of fission gas bubbles on two samples of U-10Zr advanced fuels were quantitatively compared. Conclusive findings were obtained and allowed for evaluation of the lanthanide transportation through connected bubbles based on approximately 67,000 fission gas bubbles of the two advanced samples.

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Automating selective area electron diffraction phase identification using machine learning

Mika,

Tomczak,

Finney

et al. 2024

Journal of Materiomics

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Recent trends in metallic fast reactor fuels research

Cited by 19 publications

References 55 publications

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

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

Advanced Characterization-Informed Framework and Quantitative Insight to Irradiated Annular U-10Zr Metallic Fuels

Automating selective area electron diffraction phase identification using machine learning

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