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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Error and Correction Analysis for the FFA@CEBAF Energy Upgrade

An energy upgrade design for the Continuous Electron Beam Accelerator Facility (CEBAF) is under development, using fixed field alternating gradient (FFA) return arcs to recirculate electron beam up to an additional five times through the accelerating structures at CEBAF. A necessary component of any large accelerator is a beam steering and optical correction system. Small environmental changes and system errors can lower beam quality or even shut down the machine; and in pursuit of the scientific mission of JLab, high quality electron beams must be delivered to the experimental halls on a predictable schedule. Correction in the novel FFA arcs of the current upgrade design is complicated by several factors. These complexities inform the choice of correction algorithm structure and parameter values. A baseline algorithm in addition to diagnostic and correction hardware configuration is presented. The effect of this correction protocol is shown with respect to estimated errors, and several possible extensions of the algorithm are discussed. This work presents an important proof of concept for the FFA@CEBAF design effort, and provides a functional correction strategy which may be simply adjusted and optimized for future design changes.

Coxe, Alex [Old Dominion Univ., Norfolk, VA (Unite↗
DOE OSTI2022

Description and Genome Characterization of Three Novel Fungal Strains Isolated from Mars 2020 Mission-Associated Spacecraft Assembly Facility Surfaces—Recommendations for Two New Genera and One Species

National Aeronautics and Space Administration’s (NASA) spacecraft assembly facilities are monitored for the presence of any bacteria or fungi that might conceivably survive a transfer to an extraterrestrial environment. Fungi present a broad and diverse range of phenotypic and functional traits to adapt to extreme conditions, hence the detection of fungi and subsequent eradication of them are needed to prevent forward contamination for future NASA missions. During the construction and assembly for the Mars 2020 mission, three fungal strains with unique morphological and phylogenetic properties were isolated from spacecraft assembly facilities. The reconstruction of phylogenetic trees based on several gene loci (ITS, LSU, SSU, RPB, TUB, TEF1) using multi-locus sequence typing (MLST) and whole genome sequencing (WGS) analyses supported the hypothesis that these were novel species. Here we report the genus or species-level classification of these three novel strains via a polyphasic approach using phylogenetic analysis, colony and cell morphology, and comparative analysis of WGS. The strain FJI-L9-BK-P1 isolated from the Jet Propulsion Laboratory Spacecraft Assembly Facility (JPL-SAF) exhibited a putative phylogenetic relationship with the strain Aaosphaeria arxii CBS175.79 but showed distinct morphology and microscopic features. Another JPL-SAF strain, FJII-L3-CM-DR1, was phylogenetically distinct from members of the family Trichomeriaceae and exhibited morphologically different features from the genera Lithohypha and Strelitziana. The strain FKI-L1-BK-DR1 isolated from the Kennedy Space Center facility was identified as a member of Dothideomycetes incertae sedis and is closely related to the family Kirschsteiniotheliaceae according to a phylogenetic analysis. The polyphasic taxonomic approach supported the recommendation for establishing two novel genera and one novel species. The names Aaosphaeria pasadenensis (FJI-L9-BK-P1 = NRRL 64424 = DSM 114621), Pasadenomyces melaninifex (FJII-L3-CM-DR1 = NRRL 64433 = DSM 114623), and Floridaphiala radiotolerans (FKI-L1-BK-DR1 = NRRL 64434 = DSM 114624) are proposed as type species. Furthermore, resistance to ultraviolet-C and presence of specific biosynthetic gene cluster(s) coding for metabolically active compounds are unique to these strains.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

A platform to measure isentropes from proton-heated warm dense matter on short pulse laser facilities

We describe the development of an experimental platform that measures the release isentrope of materials heated isochorically to temperatures of a few electron volts, using short-pulse laser-produced protons to heat the sample and long-pulse laser-produced x rays to perform streaked x-ray radiography. The density profiles derived from the radiography data are integrated to generate pressure–density isentropes, independent of prior knowledge of the equation of state of the sample material. In order to understand the sensitivities of isentrope extraction from radiography data, we analyze synthetic radiographs generated by a radiation hydrodynamics code. Noise reduction and high spatial resolution are critical for isentrope reconstruction, as demonstrated by the analysis of a proof-of-principle shot day on the OMEGA-EP facility. In conclusion, the data demonstrate the feasibility of the platform for characterizing isentropes, and we discuss the necessary improvements to enhance precision in differentiating between equation-of-state models.

Equations of state↗
DOE OSTI2021

High-fidelity wind farm simulation methodology with experimental validation

The complexity and associated uncertainties involved with atmospheric-turbine-wake interactions produce challenges for accurate wind farm predictions of generator power and other important quantities of interest (QoIs), even with state-of-the-art high-fidelity atmospheric and turbine models. A comprehensive computational study was undertaken with consideration of simulation methodology, parameter selection, and mesh refinement on atmospheric, turbine, and wake QoIs to identify capability gaps in the validation process. For neutral atmospheric boundary layer conditions, the massively parallel large eddy simulation (LES) code Nalu-Wind was used to produce high-fidelity computations for experimental validation using high-quality meteorological, turbine, and wake measurement data collected at the Department of Energy/Sandia National Laboratories Scaled Wind Farm Technology (SWiFT) facility located at Texas Tech University’s National Wind Institute. The wake analysis showed the simulated lidar model implemented in Nalu-Wind was successful at capturing wake profile trends observed in the experimental lidar data.

17 WIND ENERGY↗
DOE OSTI2023

Economic and environmental feasibility of recycling flexible plastic packaging from single stream collection

As demand for plastic increases, there is an urgent need to ramp up its collection and recycling rate. This study reports results of a pilot study in the United States to recycle flexible plastic packaging (FPP) from single stream curbside collection focusing on both the economic feasibility and carbon footprint. To explore the marketability of recycled FPP, four down-stream market pathways were studied, including roof coverboard, plastic pellets, pallets, and film. In this work, results indicated that (1) the cleaning and pelletizing process at reclaimers contributed the most to the total greenhouse gas (GHG) emissions of recycled FPP products, and (2) the GHG emissions of recycled FPP products in all four pathways are lower than the comparator products. Therefore, using a higher percentage of recycled FPP substituted in a product can result in greater GHG emission reductions. All the recycled FPP products also showed favorable economics compared to their direct competitors.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

A 3-Year Sample of Almost 1,600 Elves Recorded Above South America by the Pierre Auger Cosmic-Ray Observatory

Elves are a class of transient luminous events, with a radial extent typically greater than 250 km, that occur in the lower ionosphere above strong electrical storms. We report the observation of 1,598 elves, from 2014 to 2016, recorded with unprecedented time resolution (100 ns) using the fluorescence detector (FD) of the Pierre Auger Cosmic-Ray Observatory. The Auger Observatory is located in the Mendoza province of Argentina with a viewing footprint for elve observations of 3∙10sup>6 km ₂, reaching areas above the Pacific and Atlantic Oceans, as well as the Córdoba region, which is known for severe convective thunderstorms. Primarily designed for ultrahigh energy cosmic-ray observations, the Auger FD turns out to be very sensitive to the ultraviolet emission in elves. The detector features modified Schmidt optics with large apertures resulting in a field of view that spans the horizon, and year-round operation on dark nights with low moonlight background, when the local weather is favorable. The measured light profiles of 18% of the elve events have more than one peak, compatible with intracloud activity. Within the 3-year sample, 72% of the elves correlate with the far-field radiation measurements of the World Wide Lightning Location Network. The Auger Observatory plans to continue operations until at least 2025, including elve observations and analysis. To the best of our knowledge, this observatory is the only facility on Earth that measures elves with year-round operation and full horizon coverage.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Rapid 235 U/ 238 U determination by matrix assisted ionization–time-of-flight mass spectrometry

Matrix-assisted ionization (MAI) of inorganic analytes is a nascent research domain that holds promise for rapid, potentially facility-deployable analytical applications. Here, we present results of MAI uranium isotopic analysis ( 235 U/ 238 U) obtained on the timescale of minutes utilizing simple sample preparation and an ambient ionization time-of-flight mass spectrometer (ToF MS). Experimental MAI-ToF MS characterization of uranium Certified Reference Materials (CRMs) was used to establish method calibration and validate quantitative 235 U/ 238 U determination spanning depleted, natural, and low-enriched uranium isotopic compositions. Secondary standard analyses with total uranium mass loadings of 5–500 ng per analysis yield accurate calibrated 235 U/ 238 U results and relative uncertainties of 4.7–17.2% (approx. ±95% confidence level), with weighted-mean uncertainties approaching 1.5%. This method permits accurate determination of uranium isotopic composition in a sample with uranium content as low as 200 pg for equal atom 235 U: 238 U. Instrument detection limits constrain the minimum uranium mass required to identify the presence of highly enriched uranium (HEU ≥20% 235 U) as only 500 pg using the method presented here. MAI-ToF MS quantitation of relatively extreme isotope ratios ( 235 U/ 238 U ≤ 0.01) is limited by detection of minor 235 U (LoD 100 pg 235 U/analysis ≈ 10 ng total U/analysis), and subsequent method optimization is anticipated to further reduce these limits. These findings underscore the potential of MAI-ToF MS for isotopic characterization of uranium and other inorganic species for both basic and applied science.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

MetFish: a Metabolomics Pipeline for Studying Microbial Communities in Chemically Extreme Environments

Metabolites have essential roles in microbial communities, including as mediators of nutrient and energy exchange, cell-to-cell communication, and antibiosis. However, detecting and quantifying metabolites and other chemicals in samples having extremes in salt or mineral content using liquid chromatography-mass spectrometry (LC-MS)-based methods remains a significant challenge. Here, we report a facile method based on in situ chemical derivatization followed by extraction for analysis of metabolites and other chemicals in hypersaline samples, enabling for the first time direct LC-MS-based exometabolomics analysis in sample matrices containing up to 2 M total dissolved salts. The method, MetFish, is applicable to molecules containing amine, carboxylic acid, carbonyl, or hydroxyl functional groups, and it can be integrated into either targeted or untargeted analysis pipelines. In targeted analyses, MetFish provided limits of quantification as low as 1 nM, broad linear dynamic ranges (up to 5 to 6 orders of magnitude) with excellent linearity, and low median interday reproducibility (e.g., 2.6%). MetFish was successfully applied in targeted and untargeted exometabolomics analyses of microbial consortia, quantifying amino acid dynamics in the exometabolome during community succession; in situ in a native prairie soil, whose exometabolome was isolated using a hypersaline extraction; and in input and produced fluids from a hydraulically fractured well, identifying dramatic changes in the exometabolome over time in the well.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Hanford Site Composite Analysis Data Package: Exposure Scenarios and Radionuclide Specific Dose Conversion Factors

This data package summarizes the exposure assumptions, equations, and methods used to calculate radionuclide-specific unit dose factors and the radiological doses for both groundwater and atmospheric pathways as a part of the updated Hanford Site Composite Analysis (CA). An All Pathways Representative Person exposure scenario is considered to evaluate exposure via both groundwater and atmospheric transport pathways. The radiological dose assessments for both groundwater and atmospheric pathways are included in the performance assessments (PAs) for various Waste Management Areas at the Hanford Site. This data package calculates exposure route-specific and total unit dose factors for CA-specific radionuclides of concern based on the exposure assumptions used in the CA and PAs. This data package also presents the results and comparison of the radionuclide-specific unit dose factors based on the exposure assumptions used in the revised Hanford Site Composite Analysis and Hanford Site performance assessments for low-level waste disposal facilities.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2020

Evaluation of PETSc on a Heterogeneous Architecture, the OLCF Summit System: Part II - Basic Communication Performance

Nearest-neighbor communication is at the heart of many high-performance parallel computations. We report on the performance of such communication on the Oak Ridge Leadership Computing Facility system Summit in the context of the PETSc communication module. The analysis in this report includes basic Ping-Pong point-to-point communication and regular and irregular nearest-neighbor communication.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Safeguards Considerations for Coated Particle Fuel Fabrication Facilities

This study aims to identify where existing fuel fabrication safeguards are limited in application to coated particle fuel fabrication facilities; perform a review of techniques that may be appropriate to address these limitations; and identify where further study is required. The purpose is to ensure the necessary technology and safeguards developments are established to facilitate safeguards-by-design for coated particle fuel fabrication facilities, analogous to the research and development (R&D) efforts in the advanced reactor space, including those requiring coated particle fuels and progress to higher technology readiness levels. Finally, this study will also help meet high priority objectives set forth by the IAEA in establishing safeguards R&D precedence and measurement/procedural standards at emerging fuel fabrication facilities. To achieve these objectives, the report is formatted to (1) distill relevant background information on fuel fabrication of oxide and coated particle fuels; (2) provide an overview of the fuel fabrication techniques for a reference oxide fuel fabrication facility and two selected TRISO fuel fabrication facilities; (3) develop facility models of safeguards approaches for the reference facility and the two TRISO facilities; and (4) identify any observed technology needs or gaps from this analysis.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

Legacy Metallic Fuel U(Pu)Zr Data Qualification

The Metallic Fuels Irradiation and Physics Database (FIPD) is an organized collection of legacy metallic fuel U(Pu)Zr data, measurements, and reactor conditions from the Experimental Breeder Reactor II (EBR-II) and the Fast Flux Test Facility (FFTF). The database provides a wealth of information and is easily accessed and utilized by the U.S. nuclear industry and the Nuclear Regulatory Commission (NRC). The database contains three categories of data. The first category covers fuel pin fabrication specifications, including fuel slug diameter and length, cladding diameter, smeared density, etc. The second consists of operational conditions, which include axial distributions of power, temperature, fluence, burnup, isotopic density, etc. The third category contains post-irradiation examination (PIE) results, largely collected at facilities now part of the Idaho National Laboratory (INL), formerly part of Argonne National Laboratory -West (ANL-W) and at the Alpha-Gamma Hot Cell Facility (AGHCF) at Argonne National Laboratory, comprising fission gas release and chemistry analysis, profilometry measurements, neutron radiography data, etc. All these data have been organized and preserved in FIPD.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

Assessing the Potential Impact of Fugitive Methane Emissions on Offshore Platform Safety

One of the biggest risks to safety on offshore platform safety is the ignition of high-pressure natural gas streams. Currently, the size and number of fugitive emissions on offshore platforms is unknown and methods used to detect fugitives have significant shortcomings. To investigate the frequency, size, and potential impact of fugitives, a data collection exercise was conducted using incidents reported, leak survey data, and independent measurements. The size and number of fugitives on offshore facilities were simulated to investigate likely areas of safety concern. Incident reports indicate in 2021 there were 113 reports of gas leaks on 1119 offshore facilities, suggesting 0.02 fugitives per Type 1 facility (older, shallow-water platforms) and 0.31 fugitives per Type 2 facility (larger deeper-water facilities). Leak survey data report 12 fugitives per Type 1 facility (average emission 0.6 kg CH 4 h −1 leak −1 ) and 15 fugitives per Type 2 facility (average emission 1.5 kg CH 4 h −1 leak −1 ). Reconciliation of direct measurements with a bottom-up model suggests that the number of fugitive emissions generated from the leak report data is an underestimate for Type 1 platforms (44 fugitives facility −1 ; average emission 0.6 kg CH 4 h −1 leak −1 ) and in general agreement for the Type 2 platforms (15 fugitives facility −1 ; average emission 1.5 kg CH 4 h −1 leak −1 ). Analysis of the fugitive emission rates on an offshore platform suggests that gas will not collect to explosive concentration if any air movement is present (>0.36 mph); however, large volumes of air (~600 m 3 ) near representative leaks on the working deck could become explosive in hour-long zero-wind conditions. We suggest that wearable technology could be employed to indicate gas build up, safety regulations amended to consider low-wind conditions and real-world experiments are conducted to test assumptions of air mixing on the working deck.

explosion↗
DOE OSTI2022

Snowmass2021 Cosmic Frontier CF6 White Paper: Multi-Experiment Probes for Dark Energy - Transients

This invited Snowmass 2021 White Paper highlights the power of joint-analysis of astronomical transients in advancing HEP Science and presents research activities that can realize the opportunities that come with current and upcoming projects. Transients of interest include gravitational wave events, neutrino events, strongly-lensed quasars and supernovae, and Type~Ia supernovae specifically. These transients can serve as probes of cosmological distances in the Universe and as cosmic laboratories of extreme strong-gravity, high-energy physics. Joint analysis refers to work that requires significant coordination from multiple experiments or facilities so encompasses Multi-Messenger Astronomy and optical transient discovery and distributed follow-up programs.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Machine Learning for Anomaly Detection in Neural Network Security and SRF Cavities

This dissertation explores the development and deployment of machine learning approaches to address critical challenges in anomaly detection across two distinct domains: neural network security in federated learning settings and cavity behavior analysis in particle accelerator operations at Jefferson Lab in Newport News, Virginia. Anomaly detection identifies deviations from expected patterns, safeguarding systems in cybersecurity, industry, and research against malicious activities and failures. This dissertation demonstrates how our machine learning approaches enhance detection accuracy and efficiency in both neural network security and industrial applications. First, we investigate vulnerabilities in deep neural networks deployed in federated learning. Although federated learning preserves user privacy by training models locally, it remains vulnerable to backdoor attacks, in which malicious participants embed hidden triggers that induce targeted misbehavior. We propose a self-supervised contrastive learning framework to detect and mitigate such backdoor attacks. In our experiments, this method achieves higher detection accuracy and lower false positive rates than existing defenses, while operating without access to local model updates or original training data and thus preserving the privacy guarantees of the federated setting. Second, we address the operational reliability of superconducting radio-frequency (SRF) cavities at the Continuous Electron Beam Accelerator Facility (CEBAF). Our research leverages an unsupervised learning approach, combined with Principal Component Analysis (PCA) and k-means clustering, to identify anomalous behaviors in SRF cavities. Our method detects subtle anomalous behavior by analyzing SRF signal data. This knowledge allows for the early detection and resolution of potential faults, significantly improving the efficiency and reliability of operations. Third, we extend these insights to time-series anomaly detection more broadly. We design a contrastive-learning based model tailored to increasingly dynamic environments and academic research. This model improves detection accuracy in settings that require real-time monitoring and predictive maintenance. Our research underscores the broader applicability and impact of advanced machine learning techniques in anomaly detection. By extracting meaningful patterns from complex data, machine learning can significantly enhance security in distributed neural networks and improve the efficiency of particle accelerator operations. This dissertation serves as a stepping stone for future investigations into the vast possibilities of anomaly detection, inspiring further exploration and development of machine learning techniques in this field.

Ferguson, Hal [Old Dominion University]↗
DOE OSTI2021

FY2020 performance assessment annual review for the E-area low-level waste facility

The Savannah River Site (SRS) E-Area Low-Level Waste Facility (ELLWF) consists of six types of disposal units described in the Performance Assessment (PA) (WSRC, 2008): Low Activity Waste Vault (LAWV), Intermediate Level Vault (ILV), Trenches [Slit Trenches (STs), Engineered Trenches (ETs), and Component-in-Grout (CIG) Trenches], and Naval Reactor Component Disposal Areas (NRCDAs). The ELLWF is a part of the Solid Waste Management Facility (SWMF). SWMF is managed and operated by the SRS Management and Operations prime contractor, Savannah River Nuclear Solutions (SRNS). Within SRNS, the Solid Waste Management (SWM) organization is responsible for operating the SWMF, and the Savannah River National Laboratory (SRNL) is the technical agency responsible for preparing and maintaining the PA. SWMF operations have been performed at SRS since 1952. The mission of the SWMF is to provide storage, processing, disposal, and shipment of radioactive, hazardous, and mixed waste. The SWMF is committed to treat, store, and dispose of these waste products in a manner that protects the environment and the health and safety of the facility worker, the co-located worker, and the offsite general public. Wastes handled in the SWMF include low level waste, transuranic waste, hazardous waste, Toxic Substances Control Act waste, and mixed waste (containing both hazardous and radioactive constituents). The FY2020 PA Annual Review for the ELLWF affirms that the disposal facility continued to operate within the bounds of the current PA and Composite Analysis (CA) baseline and the subsequent SA’s and satisfied all the requirements, conditions, and limitations identified in the 2008 DAS (DOE 2008a), RWMB SRNL_STI-2020-00588 Revision 0 vi (McGill, 2020), and ELLWF Low-Level Waste Acceptance Criteria (SRS-1S, 2014). This annual review affirms that the supporting studies performed in FY2020 do not alter the conclusions of the ELLWF PA (WSRC, 2008) and that there is a reasonable expectation that the ELLWF will meet the performance objectives delineated in DOE Manual 435.1-1 (DOE 2011)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2023

Lawrence Livermore National Laboratory Livermore Site: 4200 Block Development Project Soil Sampling and Analysis Plan (June 2023)

This Soil Sampling and Analysis Plan (SAP) was prepared by the Environmental Function Area (EFA)/Technical Services Department (TSD) of the Environment, Safety & Health (ES&H) Directorate for the Project Management Office (PMO) for the proposed Maintenance Shop Facility in the 4200 Block (project). The purpose of the SAP is to describe the procedures for collection and analysis of environmental samples and evaluation of analytical data (chemical and radiological) to determine management options of excavated soil during project construction. This SAP follows criteria established in Lawrence Livermore National Laboratory’s (LLNL) Soils Screening and Management Plan (SSMP) (LLNL 2022), which was formalized in accordance with U.S. Environmental Protection Agency (EPA) guidance for developing Data Quality Objectives for environmental data (EPA 2006) and the Multi-Agency Radiation Survey and Site Investigation Manual (MARSSIM) guidance (U.S. NRC, U.S. EPA, U.S. DOE, and U.S. DOD 2000). The scope of this SAP is based on design information provided by PMO.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Artificial Intelligence Application to D and D - 20492

As aging facilities across the DOE complex await decommissioning, there is an ongoing need to understand any changes in the structural conditions. Many of these facilities were built over 50 years ago and, in some cases, these facilities have gone beyond the expected operational lifetime. Many facilities have been placed in a state of 'cold and dark,' sitting unused and awaiting decommissioning. Especially challenging are the aging facilities that provide unique operational/production capabilities to support critical DOE missions and cannot be shut down. In any of these scenarios, the structural integrity of these facilities may become compromised as time passes. It is critical that adequate inspections be performed on a continual basis and that the data collected undergoes sufficient analysis to support timely identification of any new or worsening structural issues as well as prompt needed maintenance and repairs to maintain the facilities in a safe condition. In recent days, Artificial Intelligence (AI) [1] and its application to various domains are growing at fast speed. FIU is performing research in this area and exploring the associated technologies to solve nuclear decommissioning problems. Artificial intelligence refers to the capability of a program to autonomously act, react and adapt to the working environment. AI enables the machine to behave like humans and perform the cognitive functions such as 'learning' and 'problem solving'. AI systems gradually moving from traditional approaches (algorithms and expert systems) towards more efficient and advanced technologies (machine learning [1] and deep learning [2] [3]). AI is the study of algorithms and statistical models that is being used by computers to perform specific tasks without using explicit instructions. FIU is working to develop a pilot-scale infrastructure to implement structural health monitoring using AI technologies with focus on machine learning, deep learning. This research is focused on Computer Vision/Image Classification area of AI applications. This can also be expanded to other areas of AI related to Object Recognition and Character Recognition in images. In addition to utilizing existing data sets, FIU will collect and investigate image and video data using FIU test-bed mockups to monitor structural health of the facility. Resulting data will be processed and analyzed using machine learning/deep learning technologies. The proposed pilot system is intended to serve as a starting point to engage the DOE field sites on related data sets and their decision making needs. It is anticipated that proposed machine learning/deep learning technologies can be effectively employed using anomaly detection to solve EM challenges in surveillance and maintenance of the D and D facilities. FIU will work with research stakeholders to identify applications at various sites and other DOE facilities. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗