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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Nanoscale clustering and fission product segregation in irradiated annular U-10Zr fuel

Zirconium (Zr) is added to uranium (U) to improve the performance of metallic fuel for fast reactor applications. This study employs transmission electron microscopy (TEM) and atom probe tomography (APT) to investigate nanoscale clustering of U and Zr, as well as segregation of fission products (FPs), in annular U-10Zr (in weight) metallic fuel irradiated at the Advanced Test Reactor (ATR). The results reveal variations in the shape, size, and chemical composition of clusters at different radial locations within the irradiated fuel cross-section. Zr-rich clusters exhibit higher concentration of FPs compared to U-rich clusters, potentially due to the co-precipitation of Zr and FPs in the fuel matrix during cooling at the end-of-life. In conclusion, this work complements the study of fuel constituents and fission product distribution across multiple length scales in irradiated U-10Zr metallic fuel.

Atom probe tomography↗
DOE OSTI2024

Advanced Electron Microscopy Characterization of the Fuel-Cladding Chemical Interaction Region in a High Burnup U-10Zr Fuel

To support the development of U-10Zr metallic fuel, advanced characterization techniques have been applied to the fuel-cladding chemical interaction (FCCI) region in a Na-bonded solid U-10Zr fuel cross-section that was irradiated to a burnup of ~13.1 at.% at the Fast Flux Test Facility. 17 phases were identified in the FCCI region through a combination of high-resolution scanning transmission electron microscopy (STEM), STEM based energy dispersive X-ray spectroscopy (STEM-EDS), and TEM based selective area electron diffraction (TEM-SAED) analysis. In this talk, we will also discuss the implications of results on metallic fuel FCCI by focusing on the formation of Zr rind, fission product migration, and HT-9 cladding integrality under the investigated thermal irradiation conditions. This work complements our previous study on the TEM characterization of the fuel region of this high burnup fuel sample and serves as scientific basis to support metallic fuel development and qualification.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Characterization of Fuel Cladding Chemical Interaction on a High Burnup U-10Zr Metallic Fuel via Electron Energy Loss Spectroscopy Enhanced by Machine Learning

Fuel cladding chemical interaction (FCCI) is one of the main performance limiting factors for metallic nuclear fuels. The interaction destabilizes the martensitic microstructure and deteriorates mechanical properties of HT-9 cladding. The detection of low atomic number elements (Z<10) and overlapping of elemental peaks can be problematic in interpreting energy dispersive X-ray spectroscopy (EDS) data. Electron energy loss spectroscopy (EELS) provides precise elemental edge energy values and can detect elements with a low atomic number. This work utilizes EELS to study the distribution of lanthanides and light elements at the interaction region. The sample was prepared from the FCCI region of a U-10Zr (wt.%) solid fuel with HT-9 cladding, irradiated to a burnup of 13.2 at.%. Processing the EELS data included three major steps: 1) enhance the signal to noise ratio by denoising the spectrum with principal component analysis (PCA) method, removing background and performing deconvolution; 2) identify chemical elements with core energy loss edges; 3) confirm different phases using a popular machine learning method, K-means. This work presents qualitative assessment of lanthanides and light elements like carbon (C) and oxygen (O) enhanced by the application of machine learning algorithms. By comparing with EDS elemental maps, EELS provides higher resolution chemical maps, reveals the distribution of carbon at the interaction region supporting the formation of zirconium carbide, a rind-like microstructure feature that was proposed to mitigate the chemical interaction. Furthermore, the plasmon peak map was also found to indicate an energy shift associated with the formation of phases/compounds. K-means clustering method was used on the processed electron energy loss (EEL) spectrum to automatically reveal different phases. The resulting clustered maps from K-means clustering align well with elemental maps confirming certain phases, especially Fe-Ce and Zr-C, in the FCCI region.

EELS↗
DOE OSTI2025

TEM characterization of two variants of fuel cladding chemical interaction in a HT-9 Clad U-10Zr Fuel. Variant 1: FCCI with a Zr Rind

Here, this study investigated the fuel cladding chemical interaction (FCCI), a key factor that limits operational temperature and burnup, in an HT-9 clad U-10Zr nuclear fuel sample irradiated to a high burnup of 13.1 at.% at a time-averaged peak inner cladding temperature (PICT) of 530 °C. Previous results showed this fuel sample exhibited two distinct levels of FCCI at d. This paper analyzed the FCCI at an azimuthal position showing an interdiffusion layer of <10 µm using transmission electron microscopy to examine chemical and crystallographic nature of phases at the fuel-cladding interface at the nanoscale level. A ZrC layer and a Zr 3 Si phase were identified at the interface; these, along with the relatively low local temperature, potentially contributed to limit interdiffusion, behaving as inhibitors for deleterious interactions. Lanthanides (Ln) partially consumed the ZrC layer and interacted with Fe, forming a Zr-Ln compound and a (Zr,Ce)Fe 2+x phase while also infiltrating up to 4 µm into the cladding. Neither U nor Zr were observed in the cladding, whereas Fe diffused up to 3–5 µm in the fuel. Fe infiltration formed a ternary U-Zr-Fe ε-phase and likely promoted the precipitation of a Cr-rich α’ phase on the cladding interface. Additionally, a Cr-rich χ-phase, likely formed by the dissociation of pre-existing M 23 C 6 carbide precipitates, was identified about 2–5 µm from the fuel-cladding interface. Irradiation-induced nano-voids were also observed in the HT-9 bulk. These findings provide critical insights into FCCI mechanisms at representative irradiation conditions, essential for developing models simulating in-pile metallic fuel behaviors for next-generation reactors.

36 - MATERIALS SCIENCE↗
DOE OSTI2026

FIB-ToF-SIMS characterization of irradiated U-10Zr

Post-irradiation examination (PIE) is critical for the performance assessment and qualification of nuclear fuels. Secondary ion mass spectrometry (SIMS) is a powerful materials characterization technique that allows for elemental and isotopic mapping with a depth resolution greater than EDS and EPMA. However, it has not yet been applied to PIE of metallic nuclear fuel. Here, in this work, we characterize an fast neutron spectrum irradiated U-10Zr fuel sample using a time-of-flight SIMS (ToF-SIMS) system connected to a FIB/SEM system, which allows for flexible sample analysis compared to a dedicated ToF-SIMS instrument. Analysis of the resulting hyperspectral micrograph data was aided by the development of an unsupervised machine learning (ML) algorithm that iterates on existing methods to segment the 3D micrographic datasets based on the similarity of mass spectra. The results showed that the FIB-ToF-SIMS instrument was potentially capable of spatially resolving closed fission gas bubbles in 3D by continued ion sputtering of the analyzed volume. Additionally, the ML algorithm proved useful in revealing the chemical segregation of light fission products (those with an atomic mass between approximately 85–105 amu, such as ruthenium and rhodium) plus matrix zirconium, heavy fission products (those with an atomic mass between approximately 135–150 amu, such as the lanthanides) and uranium. Future studies are planned to conduct FIB-ToF-SIMS analysis on more irradiated U-Zr samples to study the constituent redistribution.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2026

Site specific porosity-thermal performance correlations in neutron irradiated U-10Zr fuel

In this work, we present a site-specific three-dimensional analysis of porosity evolution in neutron-irradiated U–10Zr metallic fuel using high-resolution synchrotron X-ray tomography. Focused ion beam cubic lift-outs from four radial positions—fuel center, middle, edge, and fuel-cladding interaction (FCCI) zones—were imaged, reconstructed, and segmented to quantify pore volume, density, morphology, and pore connectivity. Porosity increased modestly from 5.5% at the center to 10.5% at the edge, yet pore number density increased by over two orders of magnitude in the fuel portion near the FCCI interface (from 4.8 x 10 4 to 6.0 x 10 6 pores/mm 3 ). Morphological classification revealed a progression from small spherical pores at the center to equiaxed and tortuous networks at the periphery, with enhanced orientation along the radial direction. Connectivity and permeability analysis reveal that the FCCI region maintains dense, highly interconnected pores despite a moderate volume fraction, enabling rapid fission gas transport and lanthanide migration. Effective thermal conductivity models incorporating sodium logging confirm that these site-specific pore features critically influenced heat transport during reactor irradiation. These findings demonstrate that pore topology—not just porosity fraction—controls thermal performance and thermal pathways in U–10Zr fuels, impacting fuel thermal performance in a reactor.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

Consequence analyses of sabotage-induced radiological releases in sodium-cooled fast microreactors

Analysis of three sodium-cooled fast microreactors (SFMs) with thermal powers of 10, 30, and 50 MWt showed that smaller reactors result in lower radiological consequences during a postulated sabotage-induced event because of their reduced core inventory. All SFMs used U-10Zr metal fuel enriched to 15 wt% high-assay low-enriched uranium and operated until their respective effective multiplication factor (k eff ) reduced to less than 1 or until the end of their operational lifespan. Sabotage scenarios were simulated at this point, when the fuel inventory within the core contains the highest-level of radioactivity. Radionuclide core inventories were calculated using the SCALE code at shutdown and 3 days post-shutdown. Dose consequence analyses were performed for three sabotage scenarios using the RASCAL tool. As microreactor developers plan for minimal on-site or complete off-site emergency response, it remains essential to evaluate their physical protection needs and potential hazards, including assessing postulated sabotage-induced events that could become more relevant. SFM licensees should identify a credible worst-case, major accident, estimate release source terms, and perform dose consequence analyses to evaluate site-specific physical protection measures. In conclusion, this recommendation supports a risk-informed, performance-based approach, aligning with applicable regulatory requirements, i.e., 10 CFR Parts 100 and 53 rulemaking in the United States.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2026

Patch-Based Convolutional Neural Networks for Multiple Microstructural Features Detection in FIB-SEM Micrographs of Irradiated Nuclear Fuel

Focused ion beam scanning electron microscopy (FIB-SEM) tomography has increasingly been utilized for acquiring three-dimensional (3D) microstructure features at the sub-micron scale in irradiated nuclear materials. This technique involves sequential ion beam slicing followed by electron beam imaging and compositional mapping using energy dispersive spectroscopy (EDS). Despite its growing use, several challenges persist. These include the time-intensive nature of data collection of EDS data, difficulties in distinguishing between various microstructures, and issues with image alignment. These challenges currently limit the broader application of FIB-SEM tomography in the field. To overcome these limitations, we propose using convolutional neural networks (CNNs) to automate microstructure identification in SEM images. Our study introduces a new framework for identifying microstructures in irradiated U-10Zr (wt. %) metallic fuel with limited annotated data. The framework includes the creation of a reliable annotated dataset with paired SEM and ground truth data from EDS maps, the applications of CNNs for microstructure identification, and the validation of model performance. Specifically, we employed the Segment Anything Model (SAM) to align SEM images with corresponding EDS maps and focused ion beam (FIB) tomography SEM data. We evaluate several models, including Patch-based U-Net, Attention U-Net, and Residual U-Net, finding that patch-based U-Net exhibits superior segmentation performance and consistency. This approach reduces reliance on EDS detectors and aids in accelerating nuclear material analysis process, highlighting the potential of advanced deep learning techniques to improve microstructural understanding in nuclear material. This is the first framework to integrate SAM and Patch-based CNN models for semantic segmentation of irradiated nuclear materials, with potential applicability to other tomography datasets.

36 - MATERIALS SCIENCE↗
DOE OSTI2025

Using FIPD and OPTD to Benchmark Metallic Fuel Performance

This report serves as an introduction, tutorial, and benchmark specification for out-of-pile tests on metallic fuel. It introduces a new user to the EBR-II legacy fuel performance test program and the fast reactor fuel performance databases built to preserve the records. It then details the information stored in each database and how to find it. A benchmark specification is included for a small set of out-of-pile tests on U-10Zr fuel to function as a tutorial demonstrating how the legacy fuel performance data sets stored in the FIPD and OPTD databases can be used together to benchmark fuel performance models for steady-state and transient performance.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2026

A Multiphysics Evaluation of Annular Uranium-Zirconium Metallic Fuels [Poster]

This study examines the performance of U-10Zr annular metallic fuel rodlets which were experimentally evaluated as part of the Advanced Fuels Campaign (AFC). The AFC mission is to develop novel fuel technologies and facilitate the implementation of those technologies by industry partners. A key objective is to improve steady-state and transient performance over current fuel types. The experiments of interest in this study included annular metallic U-Zr fuel rodlets within HT-9 cladding which were placed in SS-316 capsules and inserted in the Advanced Test Reactor (ATR). Certain mechanical and thermal conditions cannot be directly evaluated through experiments and fuel performance modeling is used to shed light on this evolution over time. In this study, BISON Multiphysics simulations are leveraged to investigate the state of the fuel system throughout and after the experimental conditions.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2026

Overview of IMPACT Data Acquisition System and Data Reduction Process

This report documents the development of the data acquisition system (DAS) and data reduction methodologies for the Irradiated Material Property Accelerated Characterization Test (IMPACT) experiment at the Advanced Test Reactor (ATR). The IMPACT experiment is designed to enable in-pile measurement of thermal conductivity in metallic nuclear fuels, specifically U-10Zr, using an instrumented thermal conductivity probe. The DAS supports both passive temperature monitoring and active thermal interrogation of the probe through controlled AC and DC excitation. Significant modifications to laboratory-scale systems were required to accommodate the higher resistance paths associated with the in-pile application. Custom electronics and relay-controlled measurement sequencing were developed to enable the measurement and sufficient power delivery to the sensing region. A reduced-order, axisymmetric thermal model based on the thermal quadrupoles method is presented to support data interpretation. This model enables efficient evaluation of transient heat transfer behavior and facilitates solution of the inverse problem required to extract thermal properties from measured signals. Multiple boundary condition formulations are discussed to address varying experimental time scales and geometries. Additionally, machine learning techniques are introduced to support data reduction and improve confidence in inverse solutions. Convolutional neural networks are applied to identify the presence of gas gaps and other evolving geometric features that significantly impact thermal response during irradiation. These efforts contribute to the broader integration of digital twin frameworks and real-time modeling capabilities within the Advanced Fuels Campaign.

46 - INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AN↗
DOE OSTI2025

Pre-Transient Characterization of MLOF-1 Test Pin

This study focuses on the pre-transient characterization of U-10Zr test and sibling fuel pins for the THOR-M-LOF test series. Using neutron radiography, element contact profilometry (ECP), precise gamma scan (PGS), and gas assay, sampling, and recharge (GASR) analysis, it was confirmed that the fuel pins were intact and suitable for testing. Key fuel behaviors quantified include axial elongation, diametral strain, fluff structure geometry, axial isotope distribution, and fission gas release. Any deviations from historically expected behaviors were investigated and attributed to factors other than the irradiation behavior of the fuel pin. These pre-transient measurements establish a baseline for future post-transient analysis, which will be used to inform fuel performance models and safety criteria for sodium-cooled fast reactors (SFRs). The results will enhance understanding of transient fuel behavior and expand limited data on LOF scenarios.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

SEM Milestone Report for MARMOT Validation Project

MARMOT is a meso-scale fuel code under development by NEAMS designed to predict the microstructure evolution of fuel materials to determine updated thermal and mechanical property models. These updated models can be used by engineering scale codes such as BISON. To support its development and validation the MARMOT Validation Project (MVP) was initiated and consists of extensive microstructure and thermal characterization of U-10Zr fuel material before and at discreet levels of irradiation. This report summarizes the SEM and EDS characterization of the lowest burnup samples (1.5% FIMA) and how they compare to pre-irradiation SEM and EDS.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

An integrated approach to examine fuel-cladding chemical interaction in HT9/U-10Zr metallic fast reactor fuels: Coupling machine learning with electron microscopy and local mechanical properties analysis

The metallic U-Zr nuclear fuel alloy has garnered renewed interest as a promising candidate for next-generation sodium-cooled fast reactors. Recent studies and technology assessments have identified several areas requiring improvements, enhanced knowledge, and reliable data to strengthen the U-Zr fuel design basis for qualification and commercial applications. One of the most challenging phenomena impacting this fuel system’s performance is fuel-cladding chemical interaction (FCCI). This work aimed to harvest FCCI data by examining selected HT9/U-10Zr (wt. %) fuel samples of prototypic full-length fuel pins through an integrated approach. This approach integrated scanning electron microscopy (SEM) microstructure characterization with localized mechanical properties examination to deepen understanding of FCCI phenomenon in HT9/U-10Zr fuel system. Particularly, this study focused on MFF fuel pins irradiated at Fast Flux Test Facility (FFTF), which aimed to qualify metallic fuel as a driver fuel for FFTF and to assess its viability for larger-scale fast reactors. Electron microscopy provided high confidence in detecting and distinguishing the different FCCI layers, while small-scale mechanical testing (SSMT) probed the mechanical properties of these layers. SEM examination of a MFF-2 pin 192167, with a time averaged inner cladding temperature (TICT) slightly over 500°C, revealed minimal cladding-side FCCI (cladding wastage). In contrast, significantly thicker cladding wastage comprising two distinct sublayers was observed in samples from the thermally hot MFF-3 pin 193045 and MFF-5 pin 195011 where the TICT ranged from 610-635°C. SSMT indicated complete embrittlement in the sublayer adjacent to the fuel and a tendency toward embrittlement in the other sublayer. Additionally, a new machine learning method was developed, validated, and used to quantify cladding wastage thickness. The machine learning method reliably predicted the wastage thickness across various fuel pins and sample cross-sections. Furthermore, the available cladding wastage data from HT9/U-10Zr fuel system demonstrated a strong temperature dependency. However, the dataset remains small, and ongoing research activities are essential to further understand the FCCI phenomenon and develop a reliable FCCI model for enhanced fuel performance simulation under various conditions.

36 - MATERIALS SCIENCE↗
DOE OSTI2024

Segmentation and Classification of Fission as Pores in Reactor Irradiated Annular U–10Zr Metallic Fuel Using Machine Learning Models

Metallic fuels, particularly U—10Zr, are promising candidates for next-generation sodium-cooled fast reactors. Irradiation of nuclear fuels in reactors can lead to the formation of solid and gas fission product which subsequently forms microstructural pores, deteriorating fuel performance. Due to the massive amount of pores and complex phases formed, a quantitative description of fission gas pores is not yet available, preventing the development of microstructure-informed fuel performance modeling for fuel qualification. This paper applied a pre-trained deep learning model to ~10,260 high magnification scanning electron microscopy images. This method increased the accuracy of fission gas pore segmentation and allows statistical features to be extracted which cannot be achieved manually. A pre-trained decision tree model worked on the segemenation results and further classified the pores into different categories to produce a correlation between the pores, movement of lanthanides, and temperature gradient during irradiation. Finally, this paper emphasizes the potentials of machine learning models to accelerate fuel research, development, and qualification for advanced reactors.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Resumption of SFR Overpower Testing and Post-Transient Examination of the THOR-C-2 Irradiation Test

Transient overpower testing for fuel safety research and development has resumed at Idaho National Laboratory. The THOR-C-2 commissioning test has been performed and post-transient examination has been performed revealing the intended creep rupture failure near the top of the fuel zone. This paves the way for future testing on previously irradiated fuels that will support fuel development and qualification.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Resumption of SFR Overpower Testing and Post-Transient Examination of the THOR-C-2 Irradiation Test

Transient overpower testing for fuel safety research and development has resumed at Idaho National Laboratory. The THOR-C-2 commissioning test has been performed and post-transient examination has been performed revealing the intended failure near the top of the fuel zone. This paves the way for future testing on previously irradiated fuels that will support fuel development and qualification.

advanced reactors↗