paper-with-me

Papers

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

2022-10-17 · Fei Xu, Lu Cai, Daniele Salvato, Fidelma Dilemma, Luca Capriotti, Tiankai Yao

U-10Zr-based metallic nuclear fuel is a promising fuel candidate for next-generation sodium-cooled fast reactors.The research experience of the Idaho National Laboratory 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.

📄 PDF Abstract BibTeX arXiv:2210.09104

Code (0)

등록된 구현이 없습니다.

Methods 이 논문이 사용한 방법론

Test 설명 없음

Similar Papers 제목 키워드 기반

Are We Ready for Robust and Resilient SLAM? A Framework For Quantitative Characterization of SLAM Datasets

2022-02-23 · Islam Ali, Hong Zhang

Reliability of SLAM systems is considered one of the critical requirements in modern autonomous systems. This directed the efforts to developing many state-of-the-art systems, creating challenging datasets, and introduci…

Data-Driven Optical To Thermal Inference in Pool Boiling Using Generative Adversarial Networks

2025-05-01 · Qianxi Fu, Youngjoon Suh, Xiaojing Zhang, Yoonjin Won

Phase change plays a critical role in thermal management systems, yet quantitative characterization of multiphase heat transfer remains limited by the challenges of measuring temperature fields in chaotic, rapidly evolvi…

Data AugmentationGenerative Adversarial Network

Ultra-Strong Gradient Diffusion MRI with Self-Supervised Learning for Prostate Cancer Characterization

2025-12-02 · Tanishq Patil, Snigdha Sen, Kieran G. Foley, Fabrizio Fasano 외 arxiv

Diffusion MRI (dMRI) enables non-invasive assessment of prostate microstructure but conventional dMRI metrics such as the Apparent Diffusion Coefficient in multiparametric MRI and reflect a mixture of underlying tissues …

Self-Supervised Learning

Non-destructive Degradation Pattern Decoupling for Ultra-early Battery Prototype Verification Using Physics-informed Machine Learning

2024-06-01 · Shengyu Tao, Mengtian Zhang, Zixi Zhao, Haoyang Li 외

Manufacturing complexities and uncertainties have impeded the transition from material prototypes to commercial batteries, making prototype verification critical to quality assessment. A fundamental challenge involves de…

AttributePhysics-informed machine learning

Self-Supervised Weighted Image Guided Quantitative MRI Super-Resolution

2025-12-19 · Alireza Samadifardheris, Dirk H. J. Poot, Florian Wiesinger, Stefan Klein 외 arxiv

High-resolution (HR) quantitative MRI (qMRI) relaxometry provides objective tissue characterization but remains clinically underutilized due to lengthy acquisition times. We propose a physics-informed, self-supervised fr…