==== Front Sci Rep Sci Rep Scientific Reports 2045-2322 Nature Publishing Group UK London 35619 10.1038/s41598-023-35619-1 Article Advanced characterization-informed machine learning framework and quantitative insight to irradiated annular U-10Zr metallic fuels Xu Fei Cai Lu Salvato Daniele Dilemma Fidelma Capriotti Luca Yao Tiankai tiankai.yao@inl.gov grid.417824.c 0000 0001 0020 7392 Idaho National Laboratory, Idaho Falls, ID 83401 USA 30 6 2023 30 6 2023 2023 13 1061611 10 2022 21 5 2023 © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/ Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. U-10Zr Metal fuel is a promising nuclear fuel candidate for next-generation sodium-cooled fast spectrum reactors. Since the Experimental Breeder Reactor-II in the late 1960s, researchers accumulated a considerable amount of experience and knowledge on fuel performance at the engineering scale. However, a mechanistic understanding of fuel microstructure evolution and property degradation during in-reactor irradiation is still missing due to a lack of appropriate tools for rapid fuel microstructure assessment and property prediction based on post irradiation examination. This paper proposed a machine learning enabled workflow, coupled with domain knowledge and large dataset collected from advanced post-irradiation examination microscopies, to provide rapid and quantified assessments of the microstructure in two reactor irradiated prototypical annular metal fuels. Specifically, this paper revealed the distribution of Zr-bearing secondary phases and constitutional redistribution across different radial locations. Additionally, the ratios of seven different microstructures at various locations along the temperature gradient were quantified. Moreover, the distributions of fission gas pores on two types of U-10Zr annular fuels were quantitatively compared. Subject terms Nanoscale materials Nuclear energy http://dx.doi.org/10.13039/100007000 Laboratory Directed Research and Development 22A1059-094FP issue-copyright-statement© Springer Nature Limited 2023 ==== Body pmcIntroduction Machine-learning (ML), a type of artificial intelligence (AI) methods to predict an outcome based on input data, is graining attractions in materials science. ML models have been used to predict the crystal structures and/or properties of materials and build the relationships of material microstructure—processing—property—performance for different material systems1,2. ML models have the potential to accelerate the research, development, qualification, and commercial licensing of nuclear materials3. The research and eventually deployment of nuclear materials for commercial use is an expensive and time consuming process. To ensure the function of nuclear component under irradiation at extreme environments (high temperature and/or high pressure and/or highly corrosive environments, etc.), a large number of out-of-pile experiments, in-reactor (irradiation) tests, and post-irradiation examination (PIE) and testing matrix are necessary to achieve a good understanding of degradation mechanism and predictable material performance during service4. The goal of present study is to use ML models to analyze a large number of electron microscopy images and spectrum datasets to facilitate the developing of  mechanistic understanding the irradiation induced microstructure and phase change of U-10wt% Zr (U-10Zr) fuel, a primary candidate for next generation sodium cooled fast reactors (SFRs)5. Our long term goal is to accelerate fuel qualification and licensing for commercial use. U-10Zr fuels were tested rigorously in test reactors, such as Experimental Breeder Reactor II (EBR-II) and Fast Flux Test Facility (FFTF) from the 1960s to the 1990s6,7. Advanced PIE data at the sub-nanometer to micrometer scale has been recently collected and analyzed 8–10. Two most fuel performance related phenomena of this fuel type are fuel-cladding chemical interaction (FCCI) 11and fuel constitutional redistribution (in this paper, we are focusing on Zr redistribution)12 FCCI is a chemical reaction between nuclear fuel and cladding (a thin-walled metal as first barrier for retention of fission products and actinides), which creates an important challenge to overcome for metallic fuels7,13. FCCI is generally dominated by the reactions between lanthanide fission products and iron-based cladding, resulting in eroded cladding with deteriorated mechanical properties, which adversely impacts the fuel integrity and performance. Lanthanides are transported from the hottest fuel center to the cold inner cladding through interconnected pores filled by fission gas, with the help from liquid Cs (a fission product with melting temperature of ~28 °C) and sodium if present in case of solid U-10Zr9,14. However, porosity distribution regarding lanthanide movement in irradiated U-10Zr remains obscure,  making it difficulty understand lanthanide transportation mechanism and FCCI mitigation. In addition, a clear and quantitative understanding of pore characteristics (distribution, size, etc.) can help better predict the fuel property degradation. For example, fission gas pores can lead to a 35% reduction of thermal conductivity for metallic fuel15. Fission gas pores show different sizes and distribution patterns along a radial thermal gradient inside irradiated U. The gas pores are primarily round and significantly smaller in the hot center zone than the ones near the cladding zone16. How thermal conductivity will be impacted remains poorly understood. Another key factor is constitutional redistribution. Fuel constituent radial redistribution during irradiation changes the local fuel composition and therefore performance critical properties, such as melting temperature8. The temperature gradient within the U-10Zr fuel, as well as irradiation enhanced diffusion, causes zirconium and uranium to migrate in the opposite directions, resulting in different zones with different characteristic microstructures and crystal structures. The zirconium redistribution can be beneficial, because it can increase the solidus temperature in the hot fuel center where Zr migrates to6. However, the zirconium redistribution may also have adverse impacts on the fuel performance as it may influence/degrade the material properties (e.g., thermal conductivity) in the rest of the fuel7,10. It is essential to achieve a comprehensive and quantified understanding of the zirconium redistribution (phase distribution) in order to better predict fuel performance11. 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 contents17. In our previous study, machine learning (ML) model has been successfully applied to characterize and classify pores and gained quantitative insights into the lanthanide transportation of an U-10Zr annular fuel (AF1)17. In this paper, another annular U-10Zr fuel, named AF2 was investigated. The pipeline of the proposed method is shown in Fig. 1. Two major processes were involved in this study: (1) analyzing the distributions of two major elements U and Zr and (2) exploring the fission gas pore distribution in the fuel microstructure from hot region to colder region close to cladding. In the process of fission gas pore analysis, the UZr2 phase is identified; then pores are segmented from the background (detailed in “Phase identification and analysis” and “Pore detection and classification” sections); and then a trained Decision Tree classifier is applied to cluster the pores into categories such as large/intermediate/small size and connected/isolated (as described in “Pore statistics and its implication on nuclear fuel performance” section). Lastly, the quantitative results of the new advanced fuel are generated and used to obtain conclusive findings by comparing the two advanced fuels.Figure 1 Workflow of the proposed advanced characterization informed ML modeling enhanced post irradiation examination method on AF2. Experiments and models Experimental data Idaho National Laboratory (INL) has been the leading national laboratory for research and development (R&D) of metallic fuel. Advanced characterization techniques, e.g., focused ion beam (FIB) sampling and scanning transmission electron microscopy (STEM) characterization, have been applied to investigate U-10Zr fuel samples irradiated in EBR-II and FFTF to gain thorough understanding of fuel microstructures and property evolutions as a function of irradiation burn up. STEM Energy dispersive spectroscopy (EDS) and TEM images of fuel samples were used to analyze element composition and identify new phases. We used scanning electron microscopy (SEM) EDS images for the distributions of chemical elements, and STEM EDS data for more accurate chemical composition for each microstructure. The STEM EDS and selective area electron diffraction patterns (SAED) have successfully identified different phases (crystal structures and compositions) within irradiated U-10Zr8 in the nano-meter scale (see Fig. 2).Figure 2 The micrographic cross-section of AF2 with 4.3% fission per initial metal atom (FIMA) burnup (a); and three significant regions of the cross-section and four selected locations for STEM characterization (b); STEM compositions of elements study at different locations on the fuel with 8 selected phases (c,d). The corresponding phase composition of U and Zr for each phase (e). Microstructure UZr2 is indicated as phases 6 and 8 in locations S3 and S4. The purpose of this study was to quantitatively understand the Zr redistributions and microstructure features of two irradiated annular U-10Zr fuels (AF1 and AF2). AF1 and AF2 were manufactured using a similar process except that there was an extra fabrication step for AF218. The AF2 fuel slug was machined after casting to create a small (< 25 µm) well-controlled gap between the fuel and cladding. However, the AF1 fuel slug was placed in the pin without this machining step and resulted in a significant gap (> 50 um) between the fuel slug and the cladding. In addition, the irradiation capsules containing AF1 had defects that could result in local variations in temperature, which eventually caused the early termination of the irradiation experiment19. The increased gap as well as manufacturing defects caused irregularities on fuel-cladding contact with large voids. Because of helium bonded instead of sodium bonding, the large fission-gas filled voids between fuel and cladding result in significantly higher fuel temperatures during irradiation. Based on BISON calculation, the peak fuel temperature can be 100 °C higher if small voids present on the fuel-cladding interface of AF1 compared to the nominal case where the fuel is fully bonded with the cladding20. On the other hand, the irradiation temperatures experienced by AF2 were more reasonable18. The irradiation condition of the two advanced annular U-10Zr fuels is shown in Table 1 and the optical cross-section images of the two fuels are shown side-by-side in Fig. 3. (U, Zr) matrix exists on the center zones of both fuels. In the outside of (U, Zr) region, AF1 is α-U matrix, while AF2 is α-U matrix with UZr2 secondary phase. Due to polish, the fuel regions with radius greater than 1.7 mm from fuel center are removed and therefore not investigated in this study. Seven types of features exist on the fuel microstructure: 1) four phases, α-Uranium matrix (α-U), U rich with Zirconium (Zr) matrix phase, noted as (U, Zr) matrix, pure Zr, Zr rich with U, noted as UZr2; and 2) three types of pores (isolated pore, connected empty pore, and connected pore with fission products inside). Due to the limitation of the 2-dimensional images and the cutting location, some fission products inside the pores may not be observed. In future, the 3D microstructure from X-ray tomography as well as focused-ion-beam sections will be investigated to couple with SEM images. As shown in Fig. 2a,b, the investigated fuel cross-section could be separated into three concentric zones with different compositions of elements. Three types of pores are major microstructural features in the (U, Zr) matrix Zone A. Three types of pores, (U, Zr) matrix and phase UZr2, consist in Zone B. Zone C mainly contain pores and UZr2 in the α-U matrix. EDS results provided the elemental mapping of U, Zr, and fission products. Table 1 Information of the two advanced U-10Zr fuels. Fuel ID Alloy Fuel form Bond material Nominal smear density (%) Burnup (FIMA, %) Cladding temperature FCCI AF1 U-10Zr Annular Helium 55 3.3 540–600 °C High AF2 U-10Zr Annular Helium 55 4.3 600 °C Low Figure 3 The micrographic cross-sections of two U-10Zr annular fuels. As shown in Fig. 2c,d, multiple phases were present even in nano-meter scale. The EDS in scanning electron microscope (SEM) can provide elemental mapping in the micron-scaled area, but it is impossible to derive the phase information from pure SEM-EDS. It is a challenge to bridge this phase information in a nano-meter scaled area from STEM, with the micron-scaled information from SEM-EDS, to obtain more meaningful statistical insights. In this study, EDS images were collected at six representative locations for a single fuel cross-section (see Fig. 7a). Additionally, STEM images from four locations were collected to identify the phases (see Fig. 2c,d,e). Moreover, a partial cross-section image is generated by stitching 587 high resolutions Back Scattered Electron (BSE) images which were collected using a JEOL JSM-7000F SEM with a 20 keV accelerated electron beam. The images have pixel sizes of 0.05 µm/pixel. In “Phase identification and analysis” section, image processing techniques have been used to identify phases from EDS mapping with the aid from TEM diffraction pattern results. Figures 4 and 5 show two examples of SEM-EDS images. The original SEM-EDS images used the RGB color model, and the color brightness has a linear relationship with the concentration of elements, (i.e., the brighter a pixel is, the higher concentration an element has). Figure 4 shows the ‘Region 1’ SEM image patch and its corresponding three SEM-EDS images (Zr, U and Nd). Figure 5 is the images of ‘Region 6.’ By using SEM-EDS images, this study was able to generate partially annotated gas pores, different types of microstructures, and calculate the statistics.Figure 4 Region 1 of AF2 (close to the hot fuel center) annotated Pure Zr, U, and Nd images. Location with red arrow: pure zirconium (> 90% Zr, < 10% U); orange arrow: pores. Figure 5 Region 6 of AF2 annotated Pure Zr, α-U matrix, UZr2, and Nd images. green arrow: UZr2 (> 50% Zr, < 45% U); white arrow: Nd. Phase identification and analysis As shown in Figs. 4 and 5, the original SEM image is grayscale. In digit image, the smallest element is noted as pixel. Each pixel has its intensity, and higher intensity appears brighter in the image. In grayscale image, the intensity in the image is between [0, 255]. The pure Zr, UZr2 and Nd phases can be easily picked up from SEM-EDS images (refer to the colored arrows in Figs. 4 and 5). The pure Zr, pure Nd, (U, Zr) matrix, and α-U matrix are defined as the pixels with relative higher intensities in the corresponding SEM-EDS mapping images. The phase UZr2 is defined as moderate intensities in both Zr and U images. The pores appear relatively dark in SEM images. As discussed in19, advanced STEM-EDS characterization shows pure Zr is assumed to be more than 90% Zr, alpha-U more than 80% Uranium, and high Nd more than 50% Neodymium in the composition. A multiple threshold segmentation method helps in separating the pixels into different groups according to their intensity levels, but cannot tell the chemical compositon of the group. Therefore, we combine the STEM-EDS characterization results together to generate the initial annotated images of pure Zr, (U, Zr) matrix, α-U matrix, UZr2, Nd, and pores. Tk1∼Tk3 (k can be Zr, U, or Nd) are the threshold values generated by the method on Zr, U and Nd from SEM-EDS images. Since different percentages of Zr exist in the four different microstructures/phases, such as pores with 0% Zr, (U, Zr) matrix or α-U matrix with Zr content less than 40%, UZr2 with 50–80% Zr, and pure Zr with over 90% Zr, we applied the multi-thresh method with 3 threshold values to segment the Zr SEM-EDS image into 4 groups, eventually map into the four microstructures/phases by combining the STEM-EDS Zr content range for different microstructures. From Eq. (1), the (U, Zr) matrix or α-U matrix cannot be distinguished only based on SEM-EDS of element Zr, thus, we need to combine with Eq. (2) which segments out the α-U matrix first. If α-U matrix is not detected from Eq. (2), the other condition of Eq. (1) will obtain the (U, Zr) matrix, verse vise. Uranium and Nd are consisting of two distinguish microstructures/phases respectively, high U (α-U), Nd and pores. Therefore, only one threshold value is needed in Eqs. (2) and (3). For instance, in Zr SEM-EDS image, the pixels are separated into four groups based on the intensities as shown in Eq. (1).1 GZri,j=PureZr,ifIZri,j≥max(Tzr1,δ1∗255)NoZrelement,ifIZri,jTzr3andIZri,j 205 µm2, corresponding to ECD of 16.2 µm calculated by  diameter=2×area/π), the intermediate pores (32 < area ≤ 205 µm2, or ECD 6.4 < ECD ≤ 16.2 µm of round-shaped pores), and small pores (area ≤ 32 µm2, or ECD ≤ 6.4 µm of round-shaped pores). On the other hand, the pores are classified into three categories based on the pore interconnection: isolated pore, connected without fission products, and connected with fission products.Figure 10 The porosity and contribution from different sized pores of AF1 (a) and AF2 (b) as a function of distance from the inner cladding. Both are in the high Zr zone of AF1 and AF2 as shown in Fig. 3. Figure 11 The porosity contribution by three different pore types as a function of distance from the inner cladding of the two fuels. (a) AF1, (b) AF2. Figure 10a,b are an overview of the porosity and contribution by pores from different categories on the fuel cross section for AF1 and AF2 fuels, respectively. Because of different magnification of the SEM images taken for AF1 and AF2, the minimum identified pore size at AF1 is 3 µm2 while it is 0.25 µm2 at AF2, which can contribute to a very small portion of porosity at AF2 but are not detectable at AF1. As mentioned in “Phase identification and analysis” section, the pores of the imaged area in Fig. 6 are manually identified and the calculated porosity is 50.3%. This value matches well with Fig. 10b where the fuel central region of AF2 has a porosity close to 50%. The AF1 and AF2 fuels have very different microstructural features. In the high Zr region of the two fuels (fuel center), AF2 has higher porosity than AF1 (nearly 45% to 50% porosity in AF2, 25% to 40% porosity in AF1). Because the central void of AF1 is more filled than AF2, it is expected that AF1 should have higher overall porosity than AF2. Note here only the porosity in the high Zr region (fuel central region) is compared. Local porosity in the high Zr-region (fuel central region) is higher in AF2 than that in AF1. Another finding is that large pores in AF1 contributes more than 85% of the porosity, while AF2 is dominated by small and medium size pores and the number of large pores on AF2 is less than 20%. In addition, AF2 has more isolated and connected without lanthanides pores as shown in Fig. 11b, while the AF1 has much more connected pores with lanthanides inside as shown in Fig. 11a. These statistical data matches well with the observations that AF2 has much less FCCI, because connected pores are envisioned as the pathway for lanthanides transported from fuel center to the cladding along temperature gradient. The small or isolated pores contribute minimal to the lanthanide movement or may even limit these movement, therefore, there are much fewer pores with lanthanides for AF2 as well as fewer lanthanides transported through the interconnected pores (shortcut) to the inner cladding for FCCI. Due to the lack of annotated data, the porosity analysis for other image patches was not validated; Also, microstructure analysis based on image processing techniques from SEM-EDS data cannot be directly applied to the entire cross-section due to the limitation of time and labor for a whole cross section SEM-EDS. Our future work focus on building a benchmark of pores and microstructures on advanced U-10Zr fuels to accelerate the PIE of irradiated fuel. For this purpose, more accurate and efficient machine learning models are needed to detect and classify the pores and microstructures to better support quantitative analysis on fuel performance. Conclusion This work developed a ML modeling workflow that integrates multi-scaled microscopic images (from millimeter to nanometer) from multiple sources (SEM, STEM images and EDS) to enhance the post irradiation examination of reactor irradiated prototypical annular U-10Zr metallic fuel. To help develop mechanistic understandings for fuel performance, this work provided quantitative results on the distribution of ⍺-U and UZr2 phase as well as the detection and classification of pores. The Following conclusions can be drawn:Phase detection model is developed by integrating SEM image, SEM–EDS, and STEM-EDS data. To our knowledge, this is the first time the cross link of materials characterization methods is used for ML model development. Both annular fuels have high Zr region in the center. However, AF2 shows a region with mixed phase of ⍺-U and UZr2, which is not observed in AF1. The porosity of high Zr region in AF2 is close to 45–50%, which is much higher than that of AF1 (25–40%). More than 85% porosity belongs to the large pores in AF1, while 80% of porosity of AF2 is small and medium size pores. Developed ML models are highly transferable for pore detection and classification in irradiated metallic fuel.  Quantitatively assessment of Zr concentration s and porosity as well as pore detection and classification along the thermal gradient from fuel center region to cladding of irradiated metallic fuel is beyond human’s capability. The new framework accelerates the quantitative analysis and will serve as bridges between PIE observation and fuel performance modeling efforts which will ultimately accelerate fuel qualification. Acknowledgements This work was supported by the U.S. Department of Energy, Office of Nuclear Energy under DOE Idaho Operations Office Contract DE-AC07-05ID14517 and LDRD project of 22A1059-094FP. The authors also acknowledge the support of DOE Advanced Fuel Campaign on the sample preparation and irradiation test. Accordingly, the U.S. Government retains and the publisher, by accepting the article for publication, acknowledges that the U.S. Government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript or allow others to do so, for U.S. Government purposes. Disclaimer This information was prepared as an account of work sponsored by an agency of the U.S. Government. Neither the U.S. Government nor any agency thereof, nor any of their employees, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. References herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise, does not necessarily constitute or imply its endorsement, recommendation, or favoring by the U.S. Government or any agency thereof. The views and opinions of authors expressed herein do not necessarily state or reflect those of the U.S. Government or any agency thereof. Author contributions F.X., L.C. and T.Y. wrote the main manuscript text. D.S. and F. D. prepared the experimental data. L.C. and T. Y. Conceptualization, Formal analysis, Supervision, Validation. All the authors reviewed the manuscript. Data availability The datasets generated and/or analyzed during the current study are not publicly available due to the laboratory policy but are available from the corresponding author on reasonable request. Competing interests The authors declare no competing interests. Publisher's note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. These authors contributed equally: Fei Xu and Lu Cai. ==== Refs References 1. Schmidt J Marques MRG Botti S Marques MAL Recent advances and applications of machine learning in solid-state materials science npj Comput. Mater. 2019 5 83 10.1038/s41524-019-0221-0 2. Wang AY-T Murdock RJ Kauwe SK Oliynyk AO Gurlo A Brgoch J Persson KA Sparks TD Machine learning for materials scientists: An introductory guide toward best practices Chem. Mater. 2020 32 4954 4965 10.1021/acs.chemmater.0c01907 3. Morgan D Pilania G Couet A Uberuaga BP Sun C Li J Machine learning in nuclear materials research Curr. Opin. Solid State Mater. Sci. 2022 26 100975 10.1016/j.cossms.2021.100975 4. Allen T Busby J Meyer M Petti D Materials challenges for nuclear systems Mater. Today 2010 13 14 23 10.1016/S1369-7021(10)70220-0 5. Janney DE Hayes SL Experimentally known properties of U-10Zr Alloys: A critical review Nucl. Technol. 2018 203 109 128 10.1080/00295450.2018.1435137 6. Carmack WJ Porter DL Chang YI Hayes SL Meyer MK Burkes DE Lee CB Mizuno T Delage F Somers J Metallic fuels for advanced reactors J. Nucl. Mater. 2009 392 139 150 10.1016/j.jnucmat.2009.03.007 7. Ogata, T. In Comprehensive Nuclear Materials, 2nd ed. (eds Konings, R. J. M. & Stoller, R. E.) 1–42 (Elsevier, 2020). 8. Yao TK Capriotti L Harp JM Liu X Wang YC Teng F Murray DJ Winston AJ Gan J Benson MT He LF alpha-U and omega-UZr2 in neutron irradiated U-10Zr annular metallic fuel J. Nucl. Mater. 2020 10.1016/j.jnucmat.2020.152536 9. Benson MT Harp JM Xie Y Yao TK Tolman KR Wright KE King JA Hawari AI Cai QS Out-of-pile and postirradiated examination of lanthanide and lanthanide-palladium interactions for metallic fuel J. Nucl. Mater. 2021 10.1016/j.jnucmat.2020.152727 10. Salvato D Liu X Murray DJ Paaren KM Xu F Pavlov T Benson MT Capriotti L Yao T Transmission electron microscopy study of a high burnup U-10Zr metallic fuel J. Nucl. Mater. 2022 570 153963 10.1016/j.jnucmat.2022.153963 11. Matthews C Unal C Galloway J Keiser DD Hayes SL Fuel-cladding chemical interaction in U-Pu-Zr metallic fuels: A critical review Nucl. Technol. 2017 198 231 259 10.1080/00295450.2017.1323535 12. Aitkaliyeva A Recent trends in metallic fast reactor fuels research J. Nucl. Mater. 2022 558 153377 10.1016/j.jnucmat.2021.153377 13. Keiser DD Fuel cladding chemical interaction in metallic sodium fast reactor fuels: A historical perspective J. Nucl. Mater. 2019 514 393 398 10.1016/j.jnucmat.2018.09.045 14. Zhang, J. & Taylor, C. Studies of Lanthanide Transport in Metallic Fuel. Report No. 14-6482, (The Ohio State University, 2018). 15. Bauer TH Holland JW In-pile measurement of the thermal-conductivity of irradiated metallic fuel Nucl. Technol. 1995 110 407 421 10.13182/Nse110-407 16. Yun D Yacout AM Stan M Bauer TH Wright AE Simulation of the impact of 3-D porosity distribution in metallic U-10Zr fuels J. Nucl. Mater. 2014 448 129 138 10.1016/j.jnucmat.2014.02.002 17. Cai L Xu F Di Lemma FG Giglio JJ Benson MT Murray DJ Adkins CA Kane JJ Xian M Capriotti L Yao T Understanding fission gas bubble distribution, lanthanide transportation, and thermal conductivity degradation in neutron-irradiated α-U using machine learning Mater. Charact. 2022 184 111657 10.1016/j.matchar.2021.111657 18. Harp, J. M., Capriotti, L. & Cappia, F. Baseline Postirradiation Examination of the AFC-3C, AFC-3D, and AFC-4A Experiments (2018). 19. Harp JM Chichester HJM Capriotti L Postirradiation examination results of several metallic fuel alloys and forms from low burnup AFC irradiations J. Nucl. Mater. 2018 509 377 391 10.1016/j.jnucmat.2018.07.003 20. Medvedev, P. G. BISON Investigation of the Effect of the Fuel- Cladding Contact Irregularities on the Peak Cladding Temperature and FCCI Observed in AFC-3A Rodlet 4. Medium: ED; Size: 20 p (2016). 21. Hofman GL Hayes SL Petri MC Temperature gradient driven constituent redistribution in U-Zr alloys J. Nucl. Mater. 1996 227 277 286 10.1016/0022-3115(95)00129-8 22. Liu X Capriotti L Yao T Harp JM Benson MT Wang Y Teng F He L Fuel-cladding chemical interaction of a prototype annular U-10Zr fuel with Fe-12Cr ferritic/martensitic HT-9 cladding J. Nucl. Mater. 2021 544 152588 10.1016/j.jnucmat.2020.152588 23. Xu F Cai L Salvato D Dilemma F Giglio JJ Benson M Murray DJ Adkins CA Kane JJ Xian M Capriotti L Yao T Understanding fission gas bubble distribution and zirconium redistribution in neutron-irradiated U-Zr metallic fuel using machine learning Microsc. Microanal. 2022 28 82 83 10.1017/S1431927622001234 24. Hofman GL Pahl RG Lahm CE Porter DL Swelling behavior of U-Pu-Zr fuel Metall. Trans. A 1990 21 517 528 10.1007/BF02671924