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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
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Final Report for the Workshop for Applied Nuclear Data Activities

The Workshop for Applied Nuclear Data Activities (WANDA) was held at the Elliot School of International Affairs at George Washington University from 22 to 25 January 2019. The purpose of WANDA was to bring subject matter experts from the national laboratories, universities and industry together with government program managers and their advisors to develop collaborative plans of action (e.g., roadmaps ) to address outstanding issues in nuclear data that effect applications ranging from nuclear energy to national security and nonproliferation to isotope production. WANDA was the latest in a series of collaborative, cross-programmatic workshops focused on nuclear data needs for applications that started in 2015 with the Nuclear Data Needs and Capabilities for Applications Workshop (NDNCA) and continuing with the Nuclear Data Roadmapping Enhancement Workshop (NDREW) in 2018 focused on nuclear data needs for applications. NDREW and WANDA played an important role in providing guidance for the recently created Nuclear Data Interagency Working Group (NDIAWG) comprised of program managers from a panoply government agencies. The NDIAWG issued two Funding Opportunity Announcements (FOA) in FY2017 and FY2018 which are supporting collaborative efforts to address high-priority nuclear data needs.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2012

Uranium Enrichment Standards of the Y-12 Nuclear Detection and Sensor Testing Center

The Y-12 National Security Complex has recently fabricated and characterized a new series of metallic uranium standards for use in the Nuclear Detection and Sensor Testing Center (NDSTC). Ten uranium metal disks with enrichments varying from 0.2 to 93.2% {sup 235}U were designed to provide researchers access to a wide variety of measurement scenarios in a single testing venue. Special care was taken in the selection of the enrichments in order to closely bracket the definitions of reactor fuel at 4% {sup 235}U and that of highly enriched uranium (HEU) at 20% {sup 235}U. Each standard is well characterized using analytical chemistry as well as a series of gamma-ray spectrometry measurements. Gamma-ray spectra of these standards are being archived in a reference library for use by customers of the NDSTC. A software database tool has been created that allows for easier access and comparison of various spectra. Information provided through the database includes: raw count data (including background spectra), regions of interest (ROIs), and full width half maximum calculations. Input is being sought from the user community on future needs including enhancements to the spectral database and additional Uranium standards, shielding configurations and detector types. A related presentation are planned for the INMM 53rd Annual Meeting (Hull, et al.), which describe new uranium chemical compound standards and testing opportunities at Y-12 Nuclear Detection and Sensor Testing Center (NDSTC).

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2011

NGNP Data Management and Analysis System Analysis and Web Delivery Capabilities

Projects for the Very High Temperature Reactor (VHTR) Technology Development Office provide data in support of Nuclear Regulatory Commission licensing of the very high temperature reactor. Fuel and materials to be used in the reactor are tested and characterized to quantify performance in high-temperature and high-fluence environments. The NGNP Data Management and Analysis System (NDMAS) at the Idaho National Laboratory has been established to ensure that VHTR data are (1) qualified for use, (2) stored in a readily accessible electronic form, and (3) analyzed to extract useful results. This document focuses on the third NDMAS objective. It describes capabilities for displaying the data in meaningful ways and for data analysis to identify useful relationships among the measured quantities. The capabilities are described from the perspective of NDMAS users, starting with those who just view experimental data and analytical results on the INL NDMAS web portal. Web display and delivery capabilities are described in detail. Also the current web pages that show Advanced Gas Reactor, Advanced Graphite Capsule, and High Temperature Materials test results are itemized. Capabilities available to NDMAS developers are more extensive, and are described using a second series of examples. Much of the data analysis efforts focus on understanding how thermocouple measurements relate to simulated temperatures and other experimental parameters. Statistical control charts and correlation monitoring provide an ongoing assessment of instrument accuracy. Data analysis capabilities are virtually unlimited for those who use the NDMAS web data download capabilities and the analysis software of their choice. Overall, the NDMAS provides convenient data analysis and web delivery capabilities for studying a very large and rapidly increasing database of well-documented, pedigreed data.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

INTEGRATION OF DATA ANALYTICS WITH SYSTEM HEALTH PROGRAMS

Industry equipment reliability and asset management programs are essential elements that help ensure the safe and economical operation of nuclear power plants. The effectiveness of these programs is addressed in several industry developed and regulatory programs. However, these programs have proven to be labor intensive and expensive. There is an opportunity to significantly enhance the collection, analysis, and use of this information to provide more cost-effective plant operation. Additionally, there is an acute industry need to leverage advanced technology to reduce costs and improve operational effectiveness. The goal of this paper is to provide effective and efficient analytical methods and tools to support risk-informed decisions for the equipment reliability and asset management programs at nuclear power plants. This is accomplished by creating a direct bridge between component health/lifecycle data and decision making (e.g., maintenance scheduling and project prioritization). Here we are supporting typical system engineer decisions regarding maintenance activity scheduling and component ageing management. This is performed in a risk-informed context where herein the term “risk” is broadly constructed to include both plant reliability and economics. This framework combines data analytics tools to analyze equipment reliability data with risk-informed methods designed to support system engineer decisions (e.g., maintenance and replacement schedules, optimal maintenance posture) in a customizable workflow. A challenge is that the structure of this workflow strongly depends on the decision that needs to be made, the type of data available, and the constraints that need to be considered. Current methods are designed to provide specific answers to specific problems; however, these methods might prove to be inadequate even when problem settings slightly change (e.g., different types of requirements, additional dependencies between system reliability and economics). We tackled this challenge by designing framework in a flexible and modular fashion such that the user can assemble and customize his/her own workflow that integrates SSC economic lifecycle models (e.g., maintenance and replacement costs), system reliability models, and optimization methods.

97 - MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Opportunities for an Integrated Web-Based Workbench for Data Access and Analysis - 20244

Management of environmental issues can require integration of multiple types of data and information, conducting data analysis and interpretation, and providing data visualization for effective communications. These data elements are important for site management to support regulator interactions and provide defensibility for remedial decisions. Databases and information repositories are core elements of managing data; however, efficient data access and analysis also enable effective site management. The U.S. Department of Energy (DOE) Hanford Site is an example of a complex site with a voluminous quantity of environmental data and a need for efficient site management. Different tiers of data and information tools have been developed and deployed to address site needs. These tools are configured for ready access via the web site interfaces and meet the rigorous quality requirements for environmental site management. Evolving efforts are focused on an integrated platform to meet site environmental management needs. In this platform, users can access site information at multiple levels of detail based on their need and permissions, so that data and associated analyses are presented within the context of the site mission and the user's management or technical needs. This concept is not only applicable at individual sites like Hanford but also applicable at other sites within the DOE complex. An integrated web-based architecture that links data visualization, data analytics, and management tools can provide holistic access to large data sets, minimize complexity, and maximize interactivity and technical communication. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2019

Data from Rossi-alpha and pulsed neutron prompt neutron time decay measurements at the Oak Ridge Critical Experiments Facility

This report briefly describes the Rossi-alpha and pulsed neutron prompt neutron time decay measurements performed at the Oak Ridge Critical Experiments Facility. This description includes the measurement equipment, type of data, and how to access the actual data from Records Management Services at Oak Ridge National Laboratory and the Nuclear Criticality Safety Benchmark Program (NCSBEP) of the Idaho National Laboratory. Most of the data were acquired between 1960 and 1975 on weapons-grade enriched uranium metal assemblies. Also included are measurements of the same type performed at Los Alamos National Laboratory by Oak Ridge National Laboratory with the JEZEBEL and FLATTOP criticality assemblies and other plutonium metal parts. The purpose of this report is to allow future mining of the data for publication of additional information that can be used to verify calculational methods and nuclear cross sections. These data can be used to create reactor physics benchmarks for the Nuclear Energy Agency’s International Handbook of Evaluated Reactor Physics Experiments. Present calculational methods can be used to directly calculate the measured data. The prompt neutron decay is more neutron spectrum sensitive than other measured quantities, particularly for interacting fissile metal systems where the flight time of neutrons between interacting sections is a large part of the neutron lifetime.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2015

Automation of environmental compliance data acquisition and management - 15267

Regulatory sampling for environmental compliance at the Savannah River Site (SRS), Aiken, South Carolina employs several different types of sampling techniques at various sampling frequencies. These sampling events result in the collection of large quantities of data and generate substantial data-related documents. The use of paper documents resulted in the need for manual input of data collected in the field and distribution of documents from one group to the next. To efficiently and effectively manage the extensive amounts of data and documents, elements of the sampling process were automated using hand-held field tablets and an electronic application. The tablets allow for easier distribution of sampling documents, and the application allows for digital transfer of sampling information and collected field data. The transition to automated data acquisition and management has resulted in a more efficient and reliable manner for conducting business at SRS. This system benefits numerous organizations at SRS by utilizing a centralized database for efficient management of all environmental data collected. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2016

An XML-hierarchical data structure for ENSDF

A data structure based on an eXtensible Markup Language (XML) hierarchy according to experimental nuclear structure data in the Evaluated Nuclear Structure Data File (ENSDF) is presented. A Python-coded translator has been developed to interpret the standard one-card records of the ENSDF datasets, together with their associated quantities defined according to field position, and generate corresponding representative XML output. The quantities belonging to this mixed-record format are described in the ENSDF manual. Of the 16 ENSDF records in total, XML output has been successfully generated for 15 records. An XML-translation for the Comment Record is yet to be implemented; this will be considered in a separate phase of the overall translation effort. Continuation records, not yet implemented, will also be treated in a future phase of this work. Several examples are presented in this document to illustrate the XML schema and methods for handling the various ENSDF data types. However, the proposed nomenclature for the XML elements and attributes need not necessarily be considered as a fixed set of constructs. Indeed, better conventions may be suggested and a consensus can be achieved amongst the various groups of people interested in this project. The main purpose here is to present an initial phase of the translation effort to demonstrate the feasibility of interpreting ENSDF datasets and creating a representative XML-structured hierarchy for data storage.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

BNL NCSP Succession Planning Efforts in FY 2020

This document fulfills a technical support element milestone (TS6:Q4) given in the Five-Year Plan. This document describes BNL succession planning efforts in FY-2020 for the Nuclear Data task area.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2011

NGNP Risk Management Database: A Model for Managing Risk

The Next Generation Nuclear Plant (NGNP) Risk Management System (RMS) is a database used to maintain the project risk register. The RMS also maps risk reduction activities to specific identified risks. Further functionality of the RMS includes mapping reactor suppliers Design Data Needs (DDNs) to risk reduction tasks and mapping Phenomena Identification Ranking Table (PIRTs) to associated risks. This document outlines the basic instructions on how to use the RMS. This document constitutes Revision 1 of the NGNP Risk Management Database: A Model for Managing Risk. It incorporates the latest enhancements to the RMS. The enhancements include six new custom views of risk data - Impact/Consequence, Tasks by Project Phase, Tasks by Status, Tasks by Project Phase/Status, Tasks by Impact/WBS, and Tasks by Phase/Impact/WBS.

42 ENGINEERING↗
DOE OSTI2021

Dr. Cecil Lubitz (1925–2021)

Dr. Cecil Lubitz passed away on March 5, 2021 at his home in upstate New York. A graduate of the Class of 1945 from the Naval Academy, Cecil served in the US Navy until 1947 after which he earned a MS in electrical engineering (1949) and a PhD in Physics (1960) from the University of Michigan. Following graduate school, Cecil came to work at Knolls Atomic Power Laboratory (KAPL) on applied nuclear physics in the Naval Nuclear Propulsion Program (NNPP). Cecil worked at KAPL for 54 years and had an impactful career with many substantial contributions to the NNPP, national and global nuclear data communities.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2022

Nuclear Criticality Safety Program (NA-ESH) [Slides]

This presentation provides an overview of the Nuclear Criticality Safety Program, including its history, mission, vision, organization, measurement and evaluation priorities for FY23, 5-year plan, and cross-cutting nuclear data work.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

Enhancing nuclear data validation analysis by using machine learning

In this work, we demonstrate how machine learning (ML) techniques can be used as an auxiliary tool for nuclear data validation analysis. The output of the ML analysis can inform evaluators and validators on the quality, or lack thereof, of specific nuclear data and benchmark experiments with respect to simulating these experimental benchmark values. To this end, measured and simulated effective neutron multiplication factors, k eff , of 875 selected ICSBEP critical assemblies and the latter's sensitivities with respect to nuclear data as well as benchmarks' features (e.g., material nuclide, core geometry) are used as input for a random forest (RF) regression model. The RF is used to encode the complex inter-dependencies between thousands of nuclear data features (i.e., sensitivity profiles and aspects describing the measurements) and 875 simulated and experimental k eff values in order to understand what nuclear data features are most informative for predicting bias. The complexity of relationships and high-dimensional space is difficult-to-impossible to search fully using simply expert judgment. As a first proof-of-concept—step, it is demonstrated that this technique is able to correctly trace large discrepancies between simulated and experimental k eff back to fabricated shortcomings in nuclear data that were perturbed to simulated k eff values. In a second, real-case scenario, step, the RF algorithm is used to validate the ENDF/B-VIII.0 library in comparison to ENDF/B-VII.1 nuclear data. One case is showcased where the chosen ML algorithms highlighted nuclear data (the 19 F(n,inl) cross section from 0.4–0.9 MeV) that are shown to be problematic by comparing them to associated differential experimental data and nuclear data from other libraries. In addition to that, it is shown that the RF results point towards poor benchmark experiments and associated underestimated uncertainties (e.g., the PU-SOL-THERM-028 series). However, using the RF algorithm for validating nuclear data with respect to k eff is currently limited to pinpointing groups of questionable nuclear data due to the inherent correlations between features introduced by the nuclear data themselves and how k eff is simulated. Due to this, we recommend that the ML methods presented be used to augment—rather than replace—the expert knowledge of evaluators and validators.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2018

Explicitly Accounting for Multiple Spatial Scales in Data Informing Kd Distribution Development - 18414

This work provides an example of incorporating information about spatial scale into input parameter distribution development for probabilistic performance assessment (PA) models and highlights the importance of having an interdisciplinary team working together in the distribution development process. The novel approach is demonstrated for contaminant partitioning coefficients for water/rock (Kds), providing a specific context for discussing the approach and challenges, but the strategy is general and applies to many other PA model inputs. In this example, a PA model of a radioactive waste disposal site is assumed to have large cells representing spatial scales of up to thousands of square meters in area and several to tens of meters in depth. Spatial variability in Kds is expected to occur at scales much smaller than the units in the model domain over which values are applied, and therefore values are intended to represent possible averages over the modeled volume (i.e., spatial scale represented by a PA model cell). Information about possible Kd values for each material category comes from a variety of literature sources, and the reported results used to inform the distribution are available at a wide range of scales (e.g., individual batch experiment results from small-scale samples, averages based on samples collected over site-scales, averages based on samples from large regions, etc.). Effectively addressing the mismatch in spatial scale between available information and the PA Model is a challenge within PA distribution development and it takes effort from database managers, subject matter experts, modelers, and statisticians. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2026

Metadata Standards for the NSE: Extended Field Standards

This standard presents a set of optional metadata fields for managed digital objects within the Nuclear Security Enterprise (NSE) and provides a deeper look at data representation in metadata by looking at the representation of 1) Records Management required metadata, and 2) common representations of technical/scientific data. Metadata standardization is a critical enabler for effectively sharing data, documents, and other digital objects between NSE sites, and for tracing the digital thread at the object level. Standardization is necessary for both schemas and vocabularies, meaning that both field standards and value standards must be specified. This document serves as a complementary field standard, recommending an optional set of fields that should be uniformly built for all managed digital objects within the NSE. This document specifically focuses on extending the shared discovery layer defined in the first white paper by introducing additional descriptive and data representation fields that improve cross-site search and interpretation.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2018

Nevada National Security Site 2017 Waste Management Monitoring Report Area 3 and Area 5 Radioactive Waste Management Sites

Environmental monitoring data are collected at and around the Area 3 and Area 5 Radioactive Waste Management Sites (RWMSs) within the Nevada National Security Site (NNSS). These data include direct radiation exposure, as well as radiation from the air, groundwater, meteorology, and vadose zone. This report summarizes the 2017 environmental data.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2016

Development of a Smart Signal Detection Method for Cyclic Voltammetry via Artificial Neural Intelligence

The electrorefiner (ER) is the heart of pyroprocessing technology which is a high-temperature method for separating uranium from Used Nuclear Fuel (UNF). It is important to improve this technology with respect to nuclear materials detection and accountability. Artificial Neural Intelligence (ANI) is a novel data analysis and simulation method that can be applied to electrochemical data sets. This computational code, which has been performed using the commercial software MATLAB, can be trained to generalize adequate electrical current and potential simulated data sets for the unseen data with a high accuracy of prediction. For this purpose, a massive collection of cyclic voltammetry (CV) data sets by Hoover (2014), for 0.5, 1, 2.5, and 5 wt% of zirconium chloride in LiCl-KCl molten salt with different scan rates at 773 K has been considered. The computer is trained via ANI to predict the unseen data after providing suitable hidden layers and validation numbers. In addition, this work can trace the CV plot for a blind condition by interpolating between two simulated data set. The different hidden layers with various neurons (from 5 to 30) at several validation numbers (from 5 to 30) has been studied and the average percent error between experimental and theoretical data for 0.5 wt% with 200 and 450 mV/s has been calculated. Preliminary results demonstrate that if the number of hidden layers increases from one to three, the average error falls down from 44% to around 8%. The best condition which gives a minimum average percent error has been discovered and the simulated CV graph has been compared with the experimental data. (authors)

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2015

Development of Improved Graphical Displays for an Advanced Outage Control Center, Employing Human Factors Principles for Outage Schedule Management

The long-term viability of existing nuclear power plants in the United States (U.S.) is dependent upon a number of factors, including maintaining high capacity factors, maintaining nuclear safety, and reducing operating costs, particularly those associated with refueling outages. Refueling outages typically take 20-30 days, and for existing light water NPPs in the U.S., the reactor cannot be in operation during the outage. Furthermore, given that many NPPs generate between $1-1.5 million/day in revenue when in operation, there is considerable interest in shortening the length of refueling outages. Yet refueling outages are highly complex operations, involving multiple concurrent and dependent activities that are somewhat challenging to coordinate; therefore, finding ways to improve refueling outage performance, while maintaining nuclear safety has proven to be difficult. The Advanced Outage Control Center (AOCC) project is a research and development (R&D) demonstration activity under the LWRS Program. LWRS is an R&D program that works closely with industry R&D programs to establish technical foundations for the licensing and managing of long-term, safe, and economical operation of current fleet of NPPs. As such, the LWRS Advanced Outage Control Center project has the goal of improving the management of commercial NPP refueling outages. To accomplish this goal, INL is developing an advanced outage control center (OCC) that is specifically designed to maximize the usefulness of communication and collaboration technologies for outage coordination and problem resolution activities. The overall focus is on developing an AOCC with the following capabilities that enables plant and OCC staff to; Collaborate in real-time to address emergent issues; Effectively communicate outage status to all workers involved in the outage; Effectively communicate discovered conditions in the field to the OCC; Provide real-time work status; Provide automatic pending support notifications; Provide real-time requirements monitoring; Maximize their collective situational awareness to improve decision-making; and Leverage macro data to better support resource allocation. INL has partnered with several commercial NPP utilities to develop a number of advanced outage management technologies. These outage management technologies have focused on both collaborative technologies for control centers and developing mobile technologies for NPP field workers. This report describes recent efforts made in developing a suite of outage technologies to support more effective schedule management. Currently, a master outage schedule is created months in advance using the plant’s existing scheduling software (e.g., Primavera P6). Typically, during the outage, the latest version of the schedule is printed at the beginning of each shift. INL and its partners are developing technologies that will have capabilities such as Automatic Schedule Updating, Automatic Pending Support Notifications, and the ability to allocate and schedule outage support task resources on a sub-hour basis (e.g., outage Micro-Scheduling). The remaining sections of this report describe in more detail the scheduling challenges that occur during outages, how the outage scheduling technologies INL is developing helps address those challenges, and the latest developments on this task (e.g., work accomplished to date and the path forward)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗