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DOE OSTI · 3001641

Bridging multimodal microscopy for advanced characterization on nuclear fuel using machine learning

Manda, Haarika [Idaho National Laboratory (INL), Idaho Falls, ID (United States); Univ. of California, Santa Barbara, CA (United States)]·Zhao, Liang [Idaho National Laboratory (INL), Idaho Falls, ID (United States)]·Reddy Kancharla, Rahul [Idaho National Laboratory (INL), Idaho Falls, ID (United States)]·Xiao, Xianghui [Brookhaven National Laboratory (BNL), Upton, NY (United States). National Synchrotron Light Source II (NSLS-II)]·Purushotham, Charith [Idaho National Laboratory (INL), Idaho Falls, ID (United States); Univ. of Colorado, Boulder, CO (United States)]·Tang, Yalei [Idaho National Laboratory (INL), Idaho Falls, ID (United States)]·Xu, Peng [Idaho National Laboratory (INL), Idaho Falls, ID (United States)]·Yao, Tiankai [Idaho National Laboratory (INL), Idaho Falls, ID (United States)]·Xu, Fei [Idaho National Laboratory (INL), Idaho Falls, ID (United States); Univ. of Texas at El Paso, TX (United States)]
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Abstract

Uranium dioxide (UO 2 ), widely used as driver fuel in light water reactors, experiences microstructure and property change by nuclear fission reactions. This paper bridges the characterization of fresh UO 2 fuel at different length scales, serving as a baseline for future post irradiation examination of irradiated UO 2 fuel. To characterize the microstructural change of nuclear fuel, modern approaches cover a wide range of length scales through different characterization techniques, such as mm scale for Synchrotron-based X-ray computed tomography (SXCT) and microscale for focused ion beam (FIB) and scanning electron microscopy (SEM). It is challenging to bridge the data and knowledge of the same sample in different length scales. This paper proposed a deep learning framework leveraging transfer learning to detect microstructural defects, trained from a sparse FIB, SEM, and SXCT images. The proposed model achieved superior performance in defect segmentation on multiscale microscopic data compared to four of the latest deep learning models.

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36 MATERIALS SCIENCEdeep learningmultimodal microscopynuclear fuelstransfer learninguranium dioxide (UO2)

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Manda, Haarika [Idaho National Laboratory (INL), Idaho Falls, ID (United States); Univ. of California, Santa Barbara, CA (United States)], Zhao, Liang [Idaho National Laboratory (INL), Idaho Falls, ID (United States)], Reddy Kancharla, Rahul [Idaho National Laboratory (INL), Idaho Falls, ID (United States)], Xiao, Xianghui [Brookhaven National Laboratory (BNL), Upton, NY (United States). National Synchrotron Light Source II (NSLS-II)], Purushotham, Charith [Idaho National Laboratory (INL), Idaho Falls, ID (United States); Univ. of Colorado, Boulder, CO (United States)], Tang, Yalei [Idaho National Laboratory (INL), Idaho Falls, ID (United States)], Xu, Peng [Idaho National Laboratory (INL), Idaho Falls, ID (United States)], Yao, Tiankai [Idaho National Laboratory (INL), Idaho Falls, ID (United States)], Xu, Fei [Idaho National Laboratory (INL), Idaho Falls, ID (United States); Univ. of Texas at El Paso, TX (United States)]. 2025-10-15. Bridging multimodal microscopy for advanced characterization on nuclear fuel using machine learning. https://doi.org/10.3389/fmech.2025.1619834

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DOE OSTI2026

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The transition to a flexible and reliable energy infrastructure, using electro-thermal energy generation technologies such as geothermal, concentrated solar power, and nuclear, usually demands simultaneous advancement of thermal energy storage (TES) to support on-demand electricity generation and industrial applications while mitigating the inherent intermittency of renewable energy sources and power outages from direct energy generation. Among TES technologies, thermochemical energy storage (TCES) based on calcium looping emerges as a compelling high-power energy storage candidate due to its high reaction enthalpy, compatibility with elevated operating temperatures, and abundance of low-cost materials. However, the long-term durability of calcium-based sorbents for TCES is hindered by surface sintering and particle aggregation, leading to performance degradation over repeated thermal cycles. This study explores a moisture hydration-based strategy to regenerate a degraded calcium sorbent and mitigate performance degradation for long duration TCES. The addition of moisture transforms calcium oxide into calcium hydroxide and produces intercalation water layers, associated with a regenerated surface area and reduced calcium oxide crystallite size. Both these effects are beneficial in restoring the sorbents' reactivity for carbonization. Additionally, an optimized hydration-assisted reactivation protocol balances the recovered energy storage capacity with heating penalty required for moisture removal from hydrated samples, resulting in an enhanced energy storage capacity up to 176% compared to benchmark sorbents that undergo cycling without reactivation after 60 cycles. In conclusion, these results highlight the potential of hydration-assisted reactivation to enhance the long-term performance of TCES, providing an effective pathway to advancing electro-thermal storage technologies.

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