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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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23 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Physicochemical evolution of uranium nitride kernel microstructure with varying carbon distribution for advanced TRISO fuel forms

Uranium nitride (UN) has emerged as a fuel candidate for advanced nuclear reactor concepts due to its superior uranium density, thermal conductivity, and high melting temperature. However, the fabrication route for converting UO 2 to UN is complex and difficult to standardize. Although the chemistry of this conversion process is well-studied, more insight into the physicochemical dynamics of this conversion using advanced characterization techniques can help further our understanding of this material system. This work leveraged thermogravimetric analysis (TGA), X-ray diffraction (XRD), and nondestructive 3D X-ray computed tomography (XCT) to characterize dynamic microstructural changes in the UO 2 → UCO → UN fabrication pathway for two kernels with a varying carbon distribution in the starting composition. TGA and XRD were used to quantify changes in the mass, density, and chemical composition of the two kernels, while three-dimensional image processing and segmentation of XCT data were used to quantify the volume, surface area, and spatial distribution of features within each kernel for multiple steps along the fabrication pathway. The analysis indicates distinct differences between the two kernels that are correlated to downstream conversion efficiency. In conclusion, this work is among the first to perform 3D quantification of physicochemical evolution during UN conversion, providing quantitative correlation between processing, properties, and expected fuel performance.

Nuclear fuel↗
DOE OSTI2026

RU Net for Automatic Characterization of TRISO Fuel Cross Sections

TRistructural ISOtropic (TRISO) particle fuel is a type of nuclear fuel known for its high-temperature and high-burnup performance. Each sub-millimeter diameter TRISO particle consists of uranium-oxycarbide (UCO) or UO2 fuel kernel, coated with buffer, inner pyrolytic carbon (IPyC), silicon carbide (SiC), and outer pyrolytic carbon (OPyC) layers. The SiC layer acts as the main containment barrier for the TRISO particle to retain the fission products, while the IPyC and OPyC layers provide additional barriers to the release of fission products, especially fission gases. During irradiation, phenomena like kernel swelling, buffer densification, and IPyC fracture may impact fuel performance. Post-irradiation microscopy on entire compact cross sections or samples of individual particles deconsolidated from compacts is often used to identify these irradiation-induced changes in morphology. However, each fuel compact generally contains thousands of TRISO particles. To get statistical information on these phenomena, it is cumbersome work if done manually. For example, to get information about swelling/densification behaviors of different layers or kernels after irradiation, researchers previously manually measured the perimeter of each TRISO layer in hundreds of particles after four rounds of iterative grinding and polishing encompassing more than 2000 cross-section images for a total of four fuel compacts. To attempt to reduce the subjectivity inherent in that process and accelerate data analysis, we conducted a study on the automatic TRISO layer segmentation on cross-sectional microscopic images using Convolutional Neural Networks (CNNs). CNNs are a class of machine learning algorithms specifically designed for processing structured grid data that have gained popularity in recent years due to their remarkable performance in various computer vision tasks, including image classification, object detection, and image segmentation. In this research, we have generated the large irradiated TRISO layer dataset with more than 2000 cross-section TRISO microscopic images and the corresponding annotated images. Based on these annotated images, we have employed different CNNs for automatic segmentation of different TRISO layers. These include RU-Net (developed in this study), as well as three existing architectures: U-Net, Residual Network (ResNet), and Attention U-Net. The preliminary results show that the model based on RU-Net has the best performance in terms of intersection-over-union (IoU). Through the aid of these CNN models, we can expedite the analysis of TRISO particle cross-sections, significantly reducing the manual labor involved and improving the objectivity of the segmentation results.

Convolutional Neural Networks↗
NASA NTRS2004

Optical Tweezer Assembly and Calibration

An Optical Tweezer, as the name implies, is a useful tool for precision manipulation of micro and nano scale objects. Using the principle of electromagnetic radiation pressure, an optical tweezer employs a tightly focused laser beam to trap and position objects of various shapes and sizes. These devices can trap micrometer and nanometer sized objects. An exciting possibility for optical tweezers is its future potential to manipulate and assemble micro and nano sized sensors. A typical optical tweezer makes use of the following components: laser, mirrors, lenses, a high quality microscope, stage, Charge Coupled Device (CCD) camera, TV monitor and Position Sensitive Detectors (PSDs). The laser wavelength employed is typically in the visible or infrared spectrum. The laser beam is directed via mirrors and lenses into the microscope. It is then tightly focused by a high magnification, high numerical aperture microscope objective into the sample slide, which is mounted on a translating stage. The sample slide contains a sealed, small volume of fluid that the objects are suspended in. The most common objects trapped by optical tweezers are dielectric spheres. When trapped, a sphere will literally snap into and center itself in the laser beam. The PSD s are mounted in such a way to receive the backscatter after the beam has passed through the trap. PSD s used with the Differential Interference Contrast (DIC) technique provide highly precise data. Most optical tweezers employ lasers with power levels ranging from 10 to 100 miliwatts. Typical forces exerted on trapped objects are in the pico-newton range. When PSDs are employed, object movement can be resolved on a nanometer scale in a time range of milliseconds. Such accuracy, however, can only by utilized by calibrating the optical tweezer. Fortunately, an optical tweezer can be modeled accurately as a simple spring. This allows Hook s Law to be used. My goal this summer at NASA Glenn Research Center is the assembly and calibration of an optical tweezer setup in the Instrumentation and Controls Division (5520). I am utilizing a custom LabVIEW Virtual Instrument program for data collection and microscope stage control. Helping me in my assignment are the following people: Mentor Susan Wrbanek (5520), Dr. Baha Jassemnejad (UCO) and Technicians Ken Weiland (7650) and James Williams (7650). Without their help, my task would not be possible.

Collins, Timothy M.↗
DOE OSTI2024

DoD Symposium Presentation

TRISO fuel, or TRi-structural ISOtropic, is a ceramic based nuclear fuel capable of operating at high temperatures (up to 1600°C). The fuel, consisting of uranium oxycarbide (UCO) fuel kernels, is coated with three layers of carbon and ceramic (dense silicon carbide) materials to capture and contain radioactive fission products. BWXT, located in Lynchburg, VA, has perfected the manufacturing techniques to produce this fuel and is currently the only US company licensed to produce this irradiation-tested fuel. BWXT is currently producing fuel in support of a demonstration reactor scheduled for startup on the INL site in 2025. The first fuel delivery, estimated at 200 kilograms High Assay Low Enriched Uranium, is targeted for the end of CY2024.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2025

Summary Report Of The FY25 Reactor Physics Verification And Validation Exercises In The Advanced Reactor Technologies - Gas-cooled Reactor Program

Valdiation and verification of numerical tools is critical for ensuring reasonable predictions for design scoping, licensing, and safety analsyis. In this report, two reactor physics verification and validation exercises are presented. The first of these exercises focuses on burnup analysis with data from the Advanced Gas Reactor (AGR) program. Simulations are performed with Monte Carlo N-Particle (MCNP) and are compared with the experimental measurements for the AGR 1 and 2 experiments that utilize both UCO and UO2 fuel. The second exercises utilizes data from the HTR-Proteus experiments to perform reactor physics validation. Specifications of the experimental facility are provdied, along with a demonstration of initial modeling efforts in Serpent for one of the determistic packing experiments. Both cases are part of the Generation-IV international forum (GIF) Very High-Temperature Reactor (VHTR) Computational Methods, Validation, and Benchmarking (CMVB) program, an international collaborative organization dedicated to the verification and validation of High-Temperature Gas-Cooled Reactor (HTGR) analysis. Participation in the CMVB allows the US Department of Energy (DOE) to leverage these existing validation activities to provide extra value through benchmarking activities with other CMVB members.

and Benchmarking (CMVB) program↗