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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Comparison and investigation of production of specific nuclides by different fission yield data

Generally, passive gamma spectroscopy (PGS) is known as a useful technique for nondestructive measurement of burnup and nuclide production in normal spent nuclear fuel. However, PGS can be used not only for normal spent fuel, but also for molten fuel generated in severe accidents involving core meltdowns. In such cases, it is estimated by the radioactivity ratios of specific nuclides. At the time of the decommissioning of the Three Mile Island's unit 2 reactor, fission products (FPs) such as Cs{sup 134}, Cs{sup 137}, Ce{sup 144}, Eu{sup 154}, Ru{sup 106}, and Sb{sup 125} were concerned. The radioactivity ratios are estimated by numerical simulations. However, there are various uncertainties in numerical simulations. In this study, we evaluated the effect of the difference in fission yield data. We compared the productions of concerned nuclides of Three Mile Island's unit 2 reactor by using Tokyo-tech and JENDL FP Fission Yields Data File 2011(FPY- 2011). Tokyo-tech is newly created by Tsubakihara et al. at Tokyo Institute of Technology. FPY-2011 is currently the latest fission yield data in Japan. In the concerned nuclides, the relative difference (Tokyo-tech/FPY-2011 -1) in the production of Sb{sup 125} is 4.5% and that of Eu{sup 154} is -5.6%. The relative difference in the production of Sb{sup 125} is found to be caused by the difference in the independent yields of Sn{sup 125}, In{sup 125}, Cd{sup 125}, Ag{sup 125}, and Pd{sup 125}. The relative difference in Eu{sup 154} is found to be caused by the relative difference in the production of Sm{sup 152} and the relative difference in the cumulative yield of Eu{sup 153}.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2023

Overcoming the field-of-view to diameter trade-off in microendoscopy via computational optrode-array microscopy

High-resolution microscopy of deep tissue with large field-of-view (FOV) is critical for elucidating organization of cellular structures in plant biology. Microscopy with an implanted probe offers an effective solution. However, there exists a fundamental trade-off between the FOV and probe diameter arising from aberrations inherent in conventional imaging optics (typically, FOV < 30% of diameter). Here, we demonstrate the use of microfabricated non-imaging probes (optrodes) that when combined with a trained machine-learning algorithm is able to achieve FOV of 1x to 5x the probe diameter. Further increase in FOV is achieved by using multiple optrodes in parallel. With a 1 × 2 optrode array, we demonstrate imaging of fluorescent beads (including 30 FPS video), stained plant stem sections and stained living stems. Our demonstration lays the foundation for fast, high-resolution microscopy with large FOV in deep tissue via microfabricated non-imaging probes and advanced machine learning.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Innovative Separations Research and Development Needs for Advanced Fuel Cycles

Deployment of advanced nuclear reactors will inevitably introduce new challenges for devising and implementing an efficient, safe, and economical nuclear fuel cycle that meets society’s need for clean energy and expectations for environmental stewardship. The growing urgency for decarbonizing the US and global economies makes such technological challenges all the more compelling. The Office of Materials and Chemical Technologies within US Department of Energy’s Office of Nuclear Energy stewards the capabilities and knowledge relied upon by government policy makers to make informed decisions regarding nuclear fuel cycle options. Such decisions in turn rely on the development of efficient and economical separation methods that can accept the used nuclear fuel containing actinides and fission products (FPs) to recycle selected actinides, recover valuable by-products, and deliver waste streams that are suitable for disposal. To help guide the future direction of fuel cycle separations research, taking into account emerging technologies, the Office of Materials and Chemical Technologies sponsored the Innovative Separations R&D Needs for Advanced Fuel Cycles workshop, held virtually August 30–September 1, 2021. Based upon 60 contributed white papers, 6 plenary lectures, and 3 days of discussions, the outcome of the workshop and subsequent deliberations was the generation of this report identifying seven future research directions (FRDs) plus three crosscutting areas of research.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

Big Data Synchrophasor Monitoring and Analytics for Resiliency Tracking (BDSMART)

This report contains key findings from a project titled Big Data Synchrophasor Monitoring and Analytics for Resiliency Tracking (BDSMART), which was carried out through a collaborative effort of a team of researchers from Texas A&M Engineering Experiment Station, Temple University, and Quanta Technology, LLC. The in-kind support came from OSIsoft (acquired by AVEVA), which provided their PI Historian software to demonstrate the use case of streaming PMU data. The first section of the report describes the project goals and objectives related to the development of Machine Learning (ML) models capable of detecting and classifying events by processing phasor measurements captured in the field by Phasor Measurement Units (PMUs). The data for this study was contributed by the utilities/ISOs from the Western and Eastern interconnects and ERCOT, further referred to as Interconnect B (IC B), Interconnect A (IC A), and Interconnect C (IC C), respectively. The approach that the BDSMART Research Team proposed and the key research tasks defined by the team are outlined in this section. The next section describes the technical approach. We first discuss the data constraints related to the PMU measurements and data interpretation constraints imposed by the data contributors. They provided neither the topological information of the grid nor PMU placement locations and captured recorded data at very few locations in the system with the reporting rate of either 30 or 60 fps. The recordings are mostly positive sequence voltage, frequency, and ROCOF, and in some limited cases, three-phase voltages and currents. We then reflect on the bad data issues that stem from poor recording practices and vague definitions of the PMU status bits to supposedly be used for bad data identification. Finally, the data discovery points to imprecise time stamps with incomplete event start/end time, as well as inconsistent and incomplete event labeling, which combined make the implementation of the data models using supervising learning quite challenging. Following the data discovery study, we hypothesize that because the IC B data has the most complete labels, we should focus our model development on that data and then test it on data from other interconnects. We also define the common metrics used to evaluate the results from the ML algorithm tests. We concluded this section by summarizing the common ML models we used and explaining how we implemented and tested them. The issues from this section are expanded in the Training Dataset Report from this project. The final section of this report deals with the accomplishments and conclusions. As the accomplishments, we formulate the problem we are solving and what is achieved by solving the problem. We then reflect on each of the analytics tools we developed and point out the performance of each tool when applied to solving the mentioned problems. We reference this work for further details to the papers we published on each tool. In the conclusions, we give recommendations on how to improve future PMU recording practices to facilitate the ML algorithm implementation and guidance for the future standardization work aimed at clarifying the ambiguities associated with the PMU status bits. We finally list future tasks that can bring about further improvements in the proposed algorithms. The issues from this section are expanded in the Training, and Test Dataset Report filed at the project completion date.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Report on the DANCE/FP14 Beam Profile and plans for the CY23 LANSCE run cycle

During the beam outage at the beginning of calendar year 2022, the Lujan Center Spallation Target was replaced with the Mark-IV design. Most notably, this included a rotated tungsten disk (for neutron production) in the direct line of sight of the upper tier flight-paths (FPs 12-15), which include DANCE, DICER, and the general-purpose flight path 12, all focused on nuclear physics measurements. This change was made in order to significantly improve both the neutron flux and the time-of-flight resolution for neutrons above 1 keV. Details of the design and motivation can be found in Zavorka et al. 2018. Due to the changes in the spallation target, significant work was required to provide new shutters and shielding for the upper tier flight paths. While the planned beam deliver was for September of 2022, because of delays in the installation process, first beam to DANCE was received the afternoon of 22 Nov 2022. Due to accelerator reliability issues, a total of only 18 days of beam were available for the run cycle. Approximately 90 days of development were planned for understanding the neutronic proper<es of the new spallation target. Most of this work could not be completed. Two measurements were completed, of which one will be discussed here.

43 PARTICLE ACCELERATORS↗
DOE OSTI2023

Quantum Search Approaches to Sampling-Based Motion Planning

In this paper, we present a novel formulation of traditional sampling-based motion planners as database-oracle structures that can be solved via quantum search algorithms. We consider two complementary scenarios: for simpler sparse environments, we formulate the Quantum Full Path Search Algorithm (q-FPS), which creates a superposition of full random path solutions, manipulates probability amplitudes with Quantum Amplitude Amplification (QAA), and quantum measures a single obstacle free full path solution. For dense unstructured environments, we formulate the Quantum Rapidly Exploring Random Tree algorithm, q-RRT, that creates quantum superpositions of possible parent-child connections, manipulates probability amplitudes with QAA, and quantum measures a single reachable state, which is added to a tree. As performance depends on the number of oracle calls and the probability of measuring good quantum states, we quantify how these errors factor into the probabilistic completeness properties of the algorithm. We then numerically estimate the expected number of database solutions to provide an approximation of the optimal number of oracle calls in the algorithm. We compare the q-RRT algorithm with a classical implementation and verify quadratic run-time speedup in the largest connected component of a 2D dense random lattice. We conclude by evaluating a proposed approach to limit the expected number of database solutions and thus limit the optimal number of oracle calls to a given number.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Path to Automated Validation of ENDF/B-VIII.1 [Slides]

We need to expand the variety of applications to rigorously test libraries. Advanced reactors: – Decreasing reactivity for 8.1b2 compared to 8.0, some unexpected nuclides causing major differences (F-19, Cr), no clear performance difference when compared to experiment. Depletion RCA: – High impact isotopes closer to 7.1 – Small improvement on average (U-5, Pu-9, BC FPs), worse for Am and Cm. Fuel reactivity: – 8.1b2 is higher reactivity at high burnups than 8.0, but likely under predicting k eff for PWRs at high burnup.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Finding ways to reduce nuclear waste: searching for the unknown one step at a time

In my home country, Venezuela, research has been stagnant. Due to the political turmoil and the crisis, many educated people have left the country in search for a better life. This has caused a deficit in any technological and scientific advances, making Venezuela one of the first South American countries to have its rate of publications decline by 29% in 2013. Currently, Venezuela lacks the infrastructure and the means to keep up with the research progress as compared to other countries in South America, such as Brazil. Since coming to the United States (US), and currently working for a national laboratory, the active research environment endorses a wide range of careers and engineering programs that allow researchers to thrive at any given field. Researchers have access to funds and tools to succeed in developing materials for the future. There are 17 national laboratories in the US, and all of these have a different research focus/objective. As examples, Los Alamos National Laboratory and Sandia National Laboratory focus is on national homeland security, weapon science, radiation effects, among others. Argonne National Laboratory focuses on nuclear energy, energy storage, high performance computing, etc. At Idaho National Laboratory (INL) the research focuses on innovating nuclear energy and clean energy resources, critical infrastructure materials, along with fuel cycle solutions to manage, dispose and find ways to recycle current and future radiological waste. Compared to other national laboratories, INL focuses slightly more on applied processes and how nuclear energy can be innovated to next reactor design and technologies. The research being conducted at INL made me apply for a Seaborg distinguished postdoctoral position. For the position itself, the researcher must submit a proposal related to actinide chemistry on a research field area. In this position, 50% of my time will be focused on my own proposal. The proposal that I am working on is focused on the innovation of nuclear energy and fuel cycle recycling, which is why I was mainly interested on working at this national laboratory. To give a bit more context of what my proposal is about, a little bit of background is necessary: After the nuclear fuel (UO2) is used in a reactor, the fuel matrix is then characterized by various fission products (FP). Among these FP (including rare earth elements, alkali/alkaline earths, and actinides), many can potentially be recovered through nuclear reprocessing technologies. In pyroprocessing, the used nuclear fuel undergoes electrochemical dissolution into a molten chloride salt mixture in an electrorefiner. Initially, uranium is reduced onto an inert cathode by applied potentials. However, numerous remaining FPs accumulate in the melt and pose challenges for recovery by an inert electrode, particularly the rare earth elements (e.g., Nd, Gd, Pr, Sm) due to their multivalent oxidation states and tendencies toward side reactions, leading to their dissolution in the electrolyte. These recovery challenges result in inefficiencies and necessitate the continual discarding of the molten chloride salt, thereby generating additional waste. Furthermore, the presence of rare earth elements and other fission products in the molten salt electrolyte alters its physical and chemical properties, affecting both uranium recovery efficiency and the longevity of the molten chloride salt. To improve the recovery efficiency of the FP, specifically rare earth elements, I am investigating the fundamental interactions between rare earth elements in the molten chloride salt and their metallic form. The kinetic pathways and the chemical reactions of these elements will give insights on how the recovery efficiency can be improved. The interactions and speciation of these elements are being studied by spectro-electrochemistry at high temperature environments in quartz and other ceramic materials (e.g., alumina crucibles). Some of the challenges I am facing specifically relates the reactivity of some of these elements with different glass and crucible materials. Although my research focuses on fundamental science, it will benefit the applied process by generating new scientific knowledge and closing the gap for an efficient recycling of the waste: one step at a time.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

Automated Waterbox Inspection for Nuclear Power Plants Using Computer Vision - Based Change Detection

Nuclear power plant waterboxes require regular inspection for leaks, missing components, and structural damage during maintenance outages. Traditional manual inspection is time-consuming and poses safety risks from confined space entry. We developed an automated computer vision system for drone-based waterbox inspection in partnership with Florida Light and Power. Our approach uses feature detection and matching to identify critical changes between baseline and current inspection images, automatically flagging additions (leaks/debris), removals (missing plugs), and translations (displaced components) while compensating for drone movement and environmental variations. We systematically evaluated six feature matching methods, from classical approaches (SIFT+BF) to state-of-the-art neural networks (SuperPoint+SuperGlue), using both standard benchmarks (HPatches) and waterbox-specific validation with real-world augmentations. SuperPoint+SuperGlue achieved superior performance with 7.82 pixels RMSE and 100% success rate—2.8x better accuracy than our baseline. While the pre-trained model has commercial licensing restrictions for nuclear deployment, our findings validate this architecture for custom training. We implemented a real-time GUI demonstrating the SIFT+BF approach for immediate deployment, processing drone feeds at 30 FPS with color-coded change visualization. Future work includes training a custom SuperPoint+SuperGlue model on waterbox data and integrating Vision-Language Models for automated reporting and maintenance guidance.

46 - INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AN↗