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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 127 records · Page 7
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DOE OSTI2021

Inverse Biot–Savart Optimization for Superconducting Accelerator Magnets

Superconducting (SC) magnets for accelerator concepts are often synthesized by numerically optimizing magnetic field waveforms, a process that requires a subsequent solution of a constrained inverse problem to identify suitable SC magnet windings. When the desired field distribution is intuitive, the inverse process is facilitated by seeding preconceived coil distributions into design optimization methods for refinement. With more complex magnetic field distributions, an initial design may be unknown, and topology optimization tools are required to synthesize current distributions without a priori guidance from a subject matter expert. In this work, we develop a constrained inverse Biot-Savart topology optimization methodology that synthesizes optimal distributions of current density in racetrack-like SC coils. The problem structure is exploited through a computationally efficient quadratic programming formulation, and the method is applied to recently published magnetic field waveforms for a recirculating proton phase shifter, a proton therapy gantry, and dipole magnets with sharp field transitions. The method and results herein identify novel winding configurations that can help magnet designers bring accelerator concepts to fruition.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

Progress toward a universal biomedical data translator

Clinical, biomedical, and translational science has reached an inflection point in the breadth and diversity of available data and the potential impact of such data to improve human health and well-being. However, the data are often siloed, disorganized, and not broadly accessible due to discipline-specific differences in terminology and representation. To address these challenges, the Biomedical Data Translator Consortium has developed and tested a pilot knowledge graph-based “Translator” system capable of integrating existing biomedical data sets and “translating” those data into insights intended to augment human reasoning and accelerate translational science. Having demonstrated feasibility of the Translator system, the Translator program has since moved into development, and the Translator Consortium has made significant progress in the research, design, and implementation of an operational system. Herein, we describe the current system’s architecture, performance, and quality of results. We apply Translator to several real-world use cases developed in collaboration with subject-matter experts. Finally, we discuss the scientific and technical features of Translator and compare those features to other state-of-the-art, biomedical graph-based question-answering systems.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

A Neural Differential Equation Formulation for Modeling Atmospheric Effects in Hyperspectral Images

Atmospheric correction is the process for removing atmospheric effects from spectral data; a necessary step for recovering salient spectral properties. The complex interactions between the atmosphere and light are dominated by absorbance and scattering physics. Existing methods for modeling atmospheric interactions typically rely on deep knowledge of relevant environmental conditions and high-fidelity numerical simulations of the governing physics in order to obtain accurate estimates of these effects. Additionally, existing approaches often require a subject matter expert for pre/post-processing of the data. Model-based approaches for removing atmospheric effects struggle in situations where such domain expertise is not available, and require significant human effort and computational power even when that expertise is available. In contrast, we propose a data-driven approach the uses Neural Differential Equations (NDEs) to accurately learn the interactions between electromagnetic radiation and the atmospheric without access to location specific environmental information. Once trained, the NDE can be applied bi-directionally; to apply or remove atmospheric effects. We demonstrate the effectiveness and utility of these techniques on an example multi-spectral scene.

Koch, James V.↗
DOE OSTI2026

Asi Nuclear Energy Sensors Data Portal Chatbot And Data Structuring Tool

The Idaho National Laboratory (INL) is advancing the development of an AI-powered chatbot and data structuring tool specifically designed to accelerate data mining processes for sensor-related information and seamlessly integrate the results into the ASI Sensors Data Portal (https://nes.energy.gov/). By doing so, the software aims to enhance the accessibility, usability, and organization of sensor data for nuclear energy applications. The software initial phase focuses on retrieving comprehensive datasets, prioritizing the past five years of publicly available information from the Office of Scientific and Technical Information (OSTI). These datasets will be meticulously processed to ensure compatibility, employing cleaning and preprocessing steps to eliminate irrelevant, incomplete, or corrupted information, thus establishing a robust foundation for subsequent AI use. The data will serve as the backbone for training an AI model and chatbot, which will act as an interactive tool enabling users to ask complex, context-specific questions and receive accurate, validated answers derived from constrained literature. In parallel, the project incorporates a data structuring process supported by AI to organize sensor information from multiple sources into a standardized format. This structured data will include detailed sensor specifications, such as measurement range, applications, accuracy, and operating conditions, generated and documented with AI. These specifications will be systematically integrated into the sensor portal. To maintain the highest levels of accuracy and relevance, all AI-generated outputs will be reviewed and validated by subject matter experts (SMEs), with additional fields or parameters added as needed. Future stages of the project aim to expand the dataset beyond OSTI to include other sources and potentially incorporate unclassified controlled information (UCI) with restricted access protocols to address security and confidentiality requirements.

Mapes, NormanJ. [Idaho National Laboratory (INL), ↗
DOE OSTI2020

Jefferson Laboratory C100 Superconducting Radio-Frequency Cavity Fault Data, 2020

The dataset was created to train machine learning models for the task of identifying the (1) cavity and (2) fault type from C100-type cryomodules at the Thomas Jefferson National Accelerator Facility (Jefferson Lab), thereby replacing the time-consuming efforts of a subject matter expert. Superconducting radio-frequency (SRF) cavity trips represent a significant source of accelerator downtime. Real-time – rather than post-mortem – identification of the offending cavity and classification of the fault type would give control room operators valuable feedback for corrective action planning. The anticipated benefit is increased beam-on-target time for users and provides performance metrics that can be used to improve future cavity designs. A series of 17 RF signals are recorded for each of the 8 cavities in a C100 cryomodule every time a cavity trips. These time-series signals are written to file using a specially designed data acquisition system. The dataset represents fault events recorded during Continuous Electron Beam Accelerator Facility (CEBAF) beam operations between January 18, 2019 and March 9, 2020. The following filtering steps were applied to collected data; (1) only 4 of the 17 signals per cavity are retained (GMES, GASK, CRFP, DETA2) (2) only events with data from each of the eight cavities in the cryomodule are kept, (3) only events that were sampled at 5 kHz were kept, (4) events from cryomodule 0L04 were neglected, (5) events occurring between February 4, 3PM and February 5, 12PM were neglected. As a result of preprocessing, the dataset is comprised of 2,375 unique events. The full dataset is comprised of three files: features.csv, cavity_labels.csv, fault_labels.csv. Each instance in faults.csv includes a timestamp (“date_time”), a label for the cryomodule which experienced the trip (“zone_label”), and 192 features (“feature_1”, “feature_2”... “feature_192”). The features correspond to 6 autoregressive features for each of 4 signals per cavity for each of the 8 cavities (6 × 4 signals/cavity × 8 cavities/cryomodule = 192). To deal with the large variation of signal amplitudes, time-series standardization via the z-score (standard score) function was applied prior to computing the features. For each instance, there is an associated label for the (1) cavity which faulted first (cavity_labels.csv) and (2) the type of fault that caused the trip (fault_labels.csv). The cavity identification can take values of [0, 1, 2, 3, 4, 5, 6, 7, 8] and the fault type can take values of [‘Microphonics’, ‘Quench_100ms’, ‘Controls_Fault’, ‘E_Quench’, ‘Quench_3ms’, ‘Single_Cav_Turn_Off’ , ‘Heat_Riser_Choke’, ‘Multi_Cav_Turn_Off’].

43 PARTICLE ACCELERATORS↗
DOE OSTI2021

Oilwell Conversion (Well API 121913310501) to Geothermal Heat Storage Well for Flexible Electricity Storage

Geothermal growth is limited by a lack of geographically dispersed high-temperature thermal resources and high initial upfront investment in characterization and well construction. This project intended to address the challenges of energy supply intermittency and enhance grid resilience, reliability, and energy security by storing energy provided from currently available renewable resources in the subsurface to harvest it a later time during at-peak energy demand. This project intended to improve geothermal adoption, reduce initial project risk, and improve price competitiveness through utilizing existing oil and gas infrastructure such as non-productive wells, non-economic fields, dry holes, and orphaned wells. The project also intended to address the lack of geographically dispersed thermal resources and enhance grid resilience, reliability, and energy security by introducing an economical method for storing energy from currently available renewable resources in the subsurface for usage during at-peak energy demand. During this research, the project furthered the understanding of the feasibility of utilizing abandoned oil and gas wells as geothermal heat storage wells. The project team investigated the heat storage and hydrogeological characteristics of subsurface reservoirs in the Illinois Basin to evaluate their response to heat injection for determining the evolution of temperature profiles and heat losses over time using existing and available data sets. The project team then performed modeling and simulation to evaluate the heat losses of returning fluids during heat extraction. The outputs were used to select an optimal candidate reservoir and location in Southern Illinois. The team designed and performed a small-scale field test in an existing oil well to refine the model and to demonstrate the permitting and regulatory pathways necessary for the conversion of oil and gas assets to geothermal use. The field test also serves as a proof of concept and can guide the procedures for future research and implementation. Additionally, the project team, conducted initial market research and customer discovery to develop a go to market strategy for an Advanced Geothermal Energy Storage (AGES) system. The project team in this research also identified the parameters to be refined in future research, to improve the current go to market strategy economic model. To this end several subject matter experts were also identified to assist in future research with geothermal infrastructure setup, energy storage policy and law, energy storage market demand, potential siting based on demand etc. Future research will involve further sophistication of the site commercial modeling, implementing a larger-scale test, and further refinement of the thermodynamic modeling/simulation process. The output will be lifecycle costs and economics suitable for comparison to alternative approaches from a validated full-scale demonstration for venture capital investment into this technology. The project successfully demonstrated the ability to leverage existing oilfield infrastructure, permits, and land access and leasing agreements, to enable geothermal storage projects to come online faster and cheaper than a greenfield development could. This technology could allow for greater energy independence and security through long-term energy storage solutions. The longer duration allows for greater storage for renewables currently limited by hours-long storage durations of lithium-ion. The AGES system would support the growth of renewable energy farms, and provide greater opportunities for a cleaner energy infrastructure.

15 GEOTHERMAL ENERGY↗
DOE OSTI2023

Steptoe Valley NV Data Compilation: Understanding a Stratigraphic Hydrothermal Resource through Geophysical Imaging

Sandia National Laboratories partnered with a multi-disciplinary group of subject matter experts to evaluate a stratigraphic geothermal resource in Steptoe Valley, Nevada using both established and novel geophysical imaging techniques. Provided here are a compilation of newly acquired data over the area and select modeling efforts. This encompasses a 3D geological model (inclusive of full Leapfrog files, Leapfrog viewer files, and XYZ data for faults and stratigraphy) with embedded geophysical modeling, controlled-source electromagnetic (CSEM) and magnetotelluric (MT) data packages, aqueous spring geochemistry data, seismic reflection interpretations, and a gravity data package. The stratigraphic reservoir in Steptoe Valley was previously discovered during oil and gas exploration. Subsequent studies, such as the Nevada Play Fairway Analysis, added data which further highlighted potential resource targets in the basin. Geophysical surveys, complimented with refined geologic mapping and geochemical sampling, were deployed to further characterize the resource. The resulting 3D geologic interpretation, conceptual model refinements, and reservoir simulations suggest that a power-capable reservoir is economically accessible in the Paleozoic carbonates of the deep/central basin. Additional geophysical characterization and exploration drilling efforts are recommended to calibrate interpretation and determine where/how to potentially develop the Steptoe resource. The geophysical tools, interpretations, lessons learned, and publicly available data generated by this study establish an exploration methodology to inform decisions for successful development of stratigraphic reservoirs.

15 GEOTHERMAL ENERGY↗
DOE OSTI2022

GeoThermalCloud: Machine Learning for Geothermal Resource Exploration

Geothermal is a renewable energy source that can provide reliable and flexible electricity generation for the world. In the past decade, the U.S. Geological Survey's resource assessments, Play Fairway Analyses (PFA), and GeoVision report by the U.S. Department of Energy's Geothermal Technologies Office provided insights on enormous untapped potential for geothermal energy to contribute to the U.S. domestic energy needs. The past studies identified that geothermal resources without surface expression (e.g., blind/hidden hydrothermal systems) comprise a huge potential. These blind systems can significantly increase power generation. But a primary challenge is locating and quantifying these hidden resources, which do not have any thermal manifestations on the surface. PFA has successfully identified some blind systems in the western USA (e.g., specific locations in the Great Basin region within Nevada). However, a comprehensive search for these blind systems can be time-consuming, expensive, and resource-intensive with a low probability of success. Accelerated discovery of these blind resources is needed with growing energy needs and higher chances of exploration success. Recent advances in machine learning (ML) have shown promise in shortening the timeline for this discovery. This paper presents a novel ML-based methodology for geothermal exploration towards PFA applications. Our methodology is provided through our open-source ML framework called GeoThermalCloud \url{https://github.com/SmartTensors/GeoThermalCloud.jl}. GeoThermalCloud uses a series of unsupervised, supervised, and physics-informed ML methods available in SmartTensors AI platform \url{https://github.com/SmartTensors}. Here, the presented analyses are performed using our unsupervised ML algorithm called NMF$k$, which is available in the SmartTensors AI platform. Our ML algorithm facilitates the discovery of new phenomena, hidden patterns, and mechanisms that helps us to make informed decisions. Moreover, the GeoThermalCloud enhances the collected PFA data and discovers signatures representative of geothermal resources. Through GeoThermalCloud, we were able to identify hidden patterns in the geothermal field data needed for the efficient discovery of blind systems. Crucial geothermal signatures often overlooked in traditional PFA are extracted using GeoThermalCloud and analyzed by the subject matter experts to provide ML-enhanced PFA, which is informative for efficient exploration. We applied our ML methodology on various open-source geothermal datasets within the U.S. (some of these are collected by past PFA work), and the results provide valuable insights on resource types within those explored regions. This ML-enhanced workflow makes GeoThermalCloud attractive for the geothermal community to improve existing datasets and extract valuable information often unnoticed during geothermal exploration.

machine learning (ML), geothermal energy↗
DOE OSTI2021

Superconducting Radio-frequency Cavity Fault Classification Using Machine Learning at Jefferson Laboratory

We report on the development of machine learning models for classifying C100 superconducting radiofrequency (SRF) cavity faults in the Continuous Electron Beam Accelerator Facility (CEBAF) at Jefferson Lab. Of the 418 SRF cavities in CEBAF, 96 are designed with a digital low-level RF system configured such that a cavity fault triggers recordings of RF signals for each of eight cavities in the cryomodule. Subject matter experts analyze the collected time-series data and identify which of the eight cavities faulted first and classify the type of fault. This information is used to find trends and strategically deploy mitigations to problematic cryomodules. However, manually labeling the data is laborious and time-consuming. By leveraging machine learning, near real-time - rather than postmortem - identification of the offending cavity and classification of the fault type has been implemented. We discuss the performance of the machine learning models during a recent physics run. We also discuss efforts for further insights into fault types through unsupervised learning techniques and present preliminary work on cavity and fault prediction using data collected prior to a failure event.

Tennant, C. D.↗
DOE OSTI2025

ICAT: The Interactive Corpus Analysis Tool

The Interactive Corpus Analysis Tool (ICAT) is a Python library for creating dashboards to explore textual datasets and build simple binary classification models to help filter through them and focus on entries of interest. This tool uses a form of interactive machine learning (IML), a paradigm of “machine teaching” (Simard et al., 2017) that sits at the intersection of the fields of human computer interaction (HCI), visual analytics, and machine learning. The intent of ICAT is to allow subject matter experts (SME) with limited to no experience in machine learning to benefit from an iterative human-in-the-loop (HITL) approach to building their own model without needing to understand the details of the underlying algorithm. This interactivity is achieved by allowing the user to create features, label data points, and visually manipulate a representation of the features to manually cluster and investigate data, while a model is trained on the fly based on these actions. ICAT is built on top of the Panel (Holoviz, 2018) library, using a combination of Vega, a custom IPyWidget using D3, and ipyvuetify, and is intended to be used inside of a Jupyter environment.

Martindale, Nathan [Oak Ridge National Laboratory ↗
DOE OSTI2020

RadResponder Nationwide Drill 2020: FRMAC participation After Action Report

On February 11th, 2020 FRMAC participated in the 2020 RadResponder nationwide drill involving survey data review and approval. During this drill, dozens of organizations from across the country participated in uploading simulated survey results to their own RadResponder event and reviewing those data given some "best-practice" guidelines. FRMAC participated in this drill to learn how this process is carried out in RadResponder and to determine some areas of improvement that will make FRMACs use of RadResponder more effective and efficient. As drill participants, the four FRMAC staff each set up their own RadResponder events so they could work independently to work through each step. Each were given an import file with 60 survey measurements to import and review. At the end of the drill a hotwash was held where users were able to ask questions and receive feedback from Chainbridge and FRMAC participants. In summary, several areas for improvement have been identified and will be categorized as items that need immediate attention (Bugs), areas for improvement in the near-term (change requests), and ideas on how the overall process can be improved (feature requests). The items presented in this report by no means reflect all the required changes FRMAC Monitoring and Sampling and Assessment will need to carry out their procedures in RadResponder but do represent a step in the right direction. A full review and exercise of the FRMAC process will need to be conducted with more subject matter experts to discover all the required features FRMAC needs to carry out their mission.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Los Alamos Recapitalization Project Prioritization—Summary Using Generic Project Names

The purpose of this document is to provide a standard method of prioritizing proposals for NNSA’s NA-50 Office of Safety, Infrastructure, and Operations recapitalization projects under the auspices of the Los Alamos Weapons Infrastructure Program Office (WI-PO). The framework described here has the goal of providing a consistent method by which all proposals are ranked from a pre-determined set of criteria. The results of this study are not expected to be definitive but rather provide input for management decisions. The top-ranked projects can be pursued further to become the focus for an appropriate fiscal year NA-50 funding cycle. A recapitalization team of subject matter experts (SMEs) and managers used multi-attribute decision analysis to determine the top priority NA-50 projects for FY2022. Ratings of seven criteria against twelve projects were combined with four weighting perspectives to provide the final results.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2020

Observations Regarding Face Shield Designs Using Commonly Available Materials

A Sandia COVID-19 LDRD effort, the Sandia E-PiPEline Team, systematically evaluated design options for face shields constructed from commonly available materials (CAMs). This study is not focused on face shields for medical applications, and as such, has excluded labeling and flammability considerations suggested by the FDA. Design options for face shields were analyzed with subject matter expert input considering the design's effectiveness (seal around face), reusability (compatibility with solvents, degree of inertness), producibility (ability to obtain materials, build time), cost, and comfort (fit around head, contact surface interface). Observations for the design of face shields using CAMS are provided here.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Observations Regarding Face Covering Designs Using Commonly Available Materials

The Center for Disease Control has recommended that the public should wear cloth face coverings in public settings'. A Sandia COVID-19 LDRD effort, the Sandia E-PiPEline Team, systematically evaluated design options for face coverings constructed from commonly available materials (CAMS). The design options were analyzed with subject matter expert input considering the design's effectiveness (metric fiber density, material construction, and water saturation), reusability (degree of inertness), producibility (ability to obtain materials, build time), cost, and comfort (fit on face, breathability). Observations for the design of face coverings using CAMs are provided here.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Evaluation of Engineered Barrier Systems (FY19 Report)

This report describes research and development (R&D) activities conducted during fiscal year 2019 (FY19) specifically related to the Engineered Barrier System (EBS) R&D Work Package in the Spent Fuel and Waste Science and Technology (SFWST) Campaign supported by the United States (U.S.) Department of Eneregy (DOE). The R&D activities focus on understanding EBS component evolution and interactions within the EBS, as well as interactions between the host media and the EBS. A primary goal is to advance the development of process models that can be implemented directly within the Genreric Disposal System Analysis (GDSA) platform or that can contribute to the safety case in some manner such as building confidence, providing further insight into the processes being modeled, establishing better constraints on barrier performance, etc.The FY19 EBS activities involved not only modeling and analysis work, but experimental work as well. The report documents the FY19 progress made in seven different research areas as follows: (1) thermal analysis for the disposal of dual purpose canisters (DPCs) in sedimentary host rock using the semianalytical method, (2) tetravalent uranium solubility and speciation, (3) modeling of high temperature, thermal-hydrologic-mechanical-chemical (THMC) coupled processes, (4) integration of coupled thermalhydrologic- chemical (THC) model with GDSA using a Reduced-Order Model, (5) studying chemical controls on montmorillonite structure and swelling pressure, (6) transmission x-ray microscope for in-situ nanotomography of bentonite and shale, and (7) in-situ electrochemical testing of uranium dioxide under anoxic conditions. The R&D team consisted of subject matter experts from Sandia National Laboratories, Lawrence Berkeley National Laboratory (LBNL), Los Alamos National Laboratory (LANL), Pacific Northwest National Laboratory (PNNL), the Bureau de Recherches Géologiques et Minières (BRGM), the University of California Berkeley, and Mississippi State University. In addition, the EBS R&D work leverages international collaborations to ensure that the DOE program is active and abreast of the latest advances in nuclear waste disposal. For example, the FY19 work on modeling coupled THMC processes at high temperatures relied on the bentonite properties from the Full-scale Engineered Barrier EXperiment (FEBEX) Field Test conducted at the Grimsel Test Site in Switzerland. Overall, significant progress has been made in FY19 towards developing the modeling tools and experimental capabilities needed to investigate the performance of EBS materials and the associated interactions in the drift and the surrounding near-field environment under a variety of conditions including high temperature regimes.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2020

Status Report on ASME Code Development for Nonmetallic Core Components in 2020

The purpose of this report is to provide a status update on the progress and ongoing activities of the current ASME Boiler Pressure Vessel (BPV) Code Section III, Division 5, on nonmetallic core components and assemblies which includes the design rules for graphite and ceramic composite materials. Section III is concerned with the design and construction of nuclear reactor components and Division 5 focuses on high temperature reactors (HTR). The ASME Section III Committee’s function for nuclear items other than pressure-retaining components, is to establish rules of safety related to structural integrity. The term “construction” refers to the all-inclusive effort comprising of materials, design, fabrication, examination, inspection, testing, certification, and pressure relief. The Department of Energy (DOE) supports industry codes and standards development through focused research providing the technical basis for new or modified codes and standards and the participation of subject matter experts in codes and standards committees. The activities for the nonmetallic core components and assemblies committees are the pursuit of two working groups that meet quarterly during ASME BPV Code Week meetings. The two working groups are General Requirements for Graphite and Ceramic Composite Core Components and Assemblies (GR GCCCCA) and Nonmetallic Design and Materials (NDM). The rules on general requirements for nonmetallic core components are discussed in the articles of Subsection HA subpart B. Ceramic composites were recently incorporated within the rules, and the GR GCCCCA working group is now in the process of aligning the requirements with the subsections and articles of ASME BPV Code Section III NCA, the general requirements for Division 1 and 2 of the code. The NDM working group is concerned with the design rules for graphite and composite materials for core components and assemblies discussed in Subsection HH (Class SN Nonmetallic Core Components) subpart A and subpart B which reference graphite and composite materials respectively. The bulk of the progress and work is done within this working group. A significant undertaking, with discussion that started in 2015, is to gain endorsement of ASME Section III, Division 5, by the U.S. Nuclear Regulatory Commission (NRC). In June 2018, ASME formally recommended endorsement to the NRC. The NRC has since initiated the endorsement review process, which is to be concluded early in 2021. ASME task groups on metallic structures and components, as well as non-metallic core support structures, have been formed to define potential pathways and schedules for NRC endorsement of Division 5. NRC determined to perform a review on the 2017 edition of Section III Division 5 as a baseline. As a result, the use of graphite as nonmetallic core components formed part of the review, but it excluded composite materials (which were first introduced in the 2019 edition). This report documents progress in FY 2020 directed toward ASME’s efforts to develop design codes for graphite and composites. Progress includes a major code review by NRC, the first code enquiry, and its solution, an overhaul of the treatment of Weibull Statistics, and the addition of Nonmandatory Appendices to the composites code relating to carbon-carbon composite materials.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2020

Augmented Monitoring and Condition Assessment Program (AMCAP) Material Test Reactor (MTR) (Fuel Inspection Program Report)

The AMCAP MTR Fuel Inspection Program, a special inspection program comprised of four inspection campaigns, examined a total of ten (10) pre-selected aluminum-clad, aluminum-based fuel core spent nuclear fuel assemblies (ASNF) stored in the L Area Disassembly Basin (L Basin) at the Savannah River Site. A full description of the inspections and the results are reported. The fuel had been stored in bundled-tube storage (Vertical Tube Storage) for periods of 18 to 21 years. The prior service experience of the individual 10 assemblies varies, but all included irradiation followed by wet storage at international research reactor sites prior to shipment to the US and storage in L Basin. The 10 assemblies were expected to be among the “worst"’ in terms of prior corrosion damage of the entire inventory of the direct-bundle-stored ASNF in L Basin. The inventory of MTR ASNF in L Basin will continue to be stored in the bundled-tube configuration or in slug-storage buckets with inserts pending retrieval for ultimate disposition. The MTR fuel inspections focused on collecting information for characterization of the material condition of the ASNF considering various types of aluminum fuel corrosion degradation of its assembled materials in water storage. A custom-designed Fuel Inspection Table was used to stage the fuel for remote, enhanced visual examination (close-up video imaging & recording) with controlled lighting and positioning that enables reproducibility of imaging conditions. The inspections were conducted by fuel subject matter expert staff from Spent Fuel Project Engineering (SFPE) and the Savannah River National Laboratory (SRNL). Stills captured from the video records were used to compare the corrosion evolution from previous records, as available. This evaluation of the inspection results including the comparison to the previous inspection results demonstrate that the water quality and the storage configuration of ASNF in L Basin do not cause aggressive corrosion degradation of the fuel; mitigation of the prior corrosion degradation of the fuel also appears to have been achieved with the good water quality conditions of L Basin. Recommendations are made for future inspection of the fuel to trend corrosion degradation and demonstrate continued safe wet storage of the ASNF in L Basin. The next fuel examination is recommended to be performed in 5 years.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2020

Sandia National Laboratories Early Career University Faculty Mentoring Program in International Safeguards

Recent years have seen a significantly increased focus in the areas of knowledge retention and mentoring of junior staff within the U.S. national laboratory complex. In order to involve the university community in this process, as well, an international safeguards mentoring program was established by Sandia National Laboratories (SNL) for early career university faculty. After a successful experience during 2019, the program continued into 2020 to include two new faculty members who were paired with SNL subject matter experts based on the topic of their individual projects: one to work on advanced laboratory work for physics, technology, and policy of nuclear safeguards and nonproliferation, and the other to look at machine learning applied to international safeguards and nonproliferation. There is a two-pronged purpose to the program: fostering the development of educational resources available for international safeguards and exploring new research topics stemming from the exchange of mentor and mentee. Further, the program as a whole allows for junior faculty members to establish and expand a relationship network within international safeguards. In addition, programs such as this build stronger connections between the academic and the national laboratory community. Thanks to the junior faculty members that now have new connections into the laboratory community and potential for collaboration projects with the laboratories in the future, safeguards knowledge can actually increase far beyond just individually engaging students using this new and efficient avenue.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗