Understanding fission gas bubble distribution, lanthanide transportation, and thermal conductivity degradation in neutron-irradiated α-U using machine learning

Cai, Lu; Xu, Fei; Lemma, Fidelma Giulia Di; Giglio, Jeffrey J.; Benson, Michael; Murray, Daniel J.; Adkins, Cynthia A.; Kane, Joshua J.; Xian, Min; Capriotti, Luca; Yao, Tiankai

doi:10.1016/j.matchar.2021.111657

Cited by 17 publications

(17 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Moreover, this work demonstrated the applicability of the ML model that is built from AF1 to classify the fission gas bubbles' categories by successfully applying the model to the new advanced U-10Zr fuel, AF2. 17 Additionally, the following conclusive findings were obtained:…”

Section: Discussionmentioning

confidence: 86%

“…The more UZr2, the higher the frequency value of the peak. Therefore, to compare the distributions in [17], we generated the area A with a radius range [665 µm, 1,065µm] from the fuel center and detected the bubbles using the method in [17]. The result is shown in Figure 9(c) with more than 17,700 bubbles.…”

Section: Bubble Detection and Classificationmentioning

confidence: 99%

“…The Zr redistributes across the pin radius and forms up to three distinctive zones by different U/Zr contents. 17 In our previous study, machine-learning has been successfully applied to characterize and classify pores and gain insights into the lanthanide transportation of an advanced U-10Zr annular fuel (AF1). 17 In this paper, another advanced U-10Zr fuel, named AF2 was investigated.…”

Section: Introductionmentioning

confidence: 99%

“…17 In our previous study, machine-learning has been successfully applied to characterize and classify pores and gain insights into the lanthanide transportation of an advanced U-10Zr annular fuel (AF1). 17 In this paper, another advanced U-10Zr fuel, named AF2 was investigated. The pipeline of the proposed method is shown in Figure 1.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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

Cai

Salvato

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Discussionmentioning

confidence: 86%

Section: Bubble Detection and Classificationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

Cai

Salvato

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…With high velocity image data generating method, such as FIB/SEM, an automatic way to extract the microstructural information quantitively can better serve the needs from post irradiation characterization. A trained machine learning model, named Decision Tree, is employed to generate a bubble classifier and to categorize bubbles into three categories: isolated bubble, connected without lanthanides, and connected with lanthanides bubbles [3]. This work presents a showcase of this approach on six regions of a fuel cross-section along the radial temperature gradient.…”

mentioning

confidence: 99%