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DOE OSTI2021

Report on High Energy Arcing Fault Experiments: Experimental Results from Open Box Enclosures

This report documents an experimental program designed to investigate High Energy Arcing Fault (HEAF) phenomena. The experiments focus on providing data to better characterize the arc to improve the prediction of arc energy emitted during a HEAF event. An open box experiment allow for direct observation of the arc, which allows diagnostic instrumentation to record the phenomenological data needed for better characterization of the arc energy source term. The data collected supports characterization of the arc and arc jet, enclosure breach, material loss, and electrical properties. These results will be used to better characterizing the hazard for improvements in fire probabilistic risk assessment (PRA) realism. The experiments were performed at KEMA Labs located in Chalfont, Pennsylvania. The experimental design, setup, and execution were completed by staff from the NRC, the National Institute of Standards and Technology (NIST), Sandia National Laboratories (SNL) and KEMA Labs. In addition, representatives from the Electric Power Research Institute (EPRI) observed some of the experimental setup and execution. The HEAF experiments were performed between August 22, 2020 and September 18, 2020 on near-identical 51 cm (20 in) cube metal boxes suspended from a Unistrut support structure. The three-phase arcing fault was initiated at the ends of the conductors oriented vertically and located at the center of the box. Either aluminum or copper conductors were used for the conductors. The low-voltage experiments used 1 000 volts AC, while the medium-voltage experiments used 6 900 volts AC consistent with other recently completed experiments. Durations of the experiment ranged from 1 s to 5 s with fault currents ranging from 1 kA to 30 kA. Real-time electrical operating conditions, including voltage, current and frequency, were measured during the experiments. Heat fluxes and incident energies were measured with plate thermometers, radiometers, and slug calorimeters at various locations around the electrical enclosures. The experiments were documented with normal and high-speed videography, infrared imaging and photography.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2024

Materials Characterization, Prediction and Control Project: Summary Report on Data Analytics Framework

This report summarizes the activities performed under the data analytics Vertex in the Materials Characterization, Prediction and Control Project funded under laboratory directed research and development at Pacific Northwest National Laboratory. The data analytics Vertex developed models for associating global or local process parameters, microstructural features, and performance properties of friction-stir-processed 316L stainless steel plates. Statistical, machine learning, and deep learning models, as well as generative artificial intelligence approaches, were used to develop the associations between the process-structure-property data streams. These associations formed the basis for predicting global properties of parts manufactured under different process envelopes, providing a basis for predicting performance using data driven as well as physics-informed and physics-constrained approaches. Additionally, the associations were used to predict local process parameters and microstructural features of the product, predictive relationships that have the potential to form the basis of a control framework that could eventually modulate a friction-stir process to maintain product quality.

316L stainless steel↗

DOE OSTI2023

Multi-decade high-resolution regional hindcasts for wave energy resource characterization in U.S. coastal waters

Long-term, high-resolution, regional wave hindcast datasets were generated using unstructured-grid Simulating WAves Nearshore (SWAN) models for the U.S. coastal waters to support nearshore wave energy development in the U.S. including those bordering U.S. territorial islands. The model domains resolved the entire U.S. exclusive economic zones, with a spatial resolution of approximately 200 m nearshore. The regional SWAN models were driven by the global WAVEWATCH III® model outputs and run for a 42-year period from 1979 to 2020. Extensive model validations were performed using buoy observations and altimeter data. Regional resource characterization was performed based on hindcast data points at 2 km from shore and along the 100 m isobath. Aggregations of wave resource parameters were produced, and spatial and seasonal variations were analyzed for all the regions. Wave resource metrics recommended by international standards, including a 3-hour time series of six resource parameters, hourly frequency- and directionally resolved wave spectra at selected “virtual buoy” locations, and average-annual values of omni-directional wave power, significant wave height, and energy period are publicly disseminated through an Amazon Web Service and a Marine Energy Atlas web application tool to facilitate wave energy research and a wide range of coastal ocean applications.

16 TIDAL AND WAVE POWER↗

DOE OSTI2023

System and method for characterization of air leakage in building using data from communicating thermostats and/or interval meters

Systems and methods for characterization of retrofit opportunities are described. Some embodiments are directed to methods for determining the air leakage rate of a building, and accordingly, for determining suitability of sealing of air leaks to improve the energy efficiency of a building. The methods may comprise computing, using at least one computing device disposed remote from a building and based at least in part on heating, ventilation and air conditioning (HVAC) runtime data associated with the building, one or more thermal characteristics of the building. The HVAC runtime data may be computed based on data received from a thermostat or a meter, such as an electric or a gas meter. To isolate the impact of air leakage, subsets of the HVAC runtime data at time intervals selected to have substantially the same conditions, but different wind speeds, may be computed.

Zeifman, Michael↗

DOE OSTI2016

Oak Ridge Reservation: Annual Site Environmental Report for 2015

The Oak Ridge Reservation Annual Site Environmental Report is prepared annually and presents summary environmental data to (1) characterize environmental performance, (2) summarize environmental occurrences reported during the year, (3) confirm compliance with environmental standards and requirements, and (4) highlight significant program activities. The report fulfills the requirement contained in DOE Order 231.1A, Environment, Safety and Health Reporting (DOE 2004) that an integrated annual site environmental report be prepared. The results summarized in this report are based on data collected prior to and through 2015. This report is not intended to nor does it present the results of all environmental monitoring associated with the ORR. Data collected for other site and regulatory purposes, such as environmental restoration/remedial investigation reports, waste management characterization sampling data, and environmental permit compliance data, are presented in other documents that have been prepared in accordance with applicable DOE guidance and/or laws and are referenced herein as appropriate. Environmental monitoring on the ORR consists primarily of two major activities: effluent monitoring and environmental surveillance. Effluent monitoring involves the collection and analysis of samples or measurements of liquid and gaseous effluents at the points of release to the environment; these measurements allow the quantification and official reporting of contaminant levels, assessment of radiation and chemical exposures to the public, and demonstration of compliance with applicable standards and permit requirements. Environmental surveillance consists of direct measurements and collection and analysis of samples taken from the site and its environs exclusive of effluents; these activities provide information on contaminant concentrations in air, water, groundwater, soil, foods, biota, and other media. Environmental surveillance data support determinations regarding environmental compliance and, when combined with data from effluent monitoring, support chemical and radiation dose and exposure assessments of the potential effects of ORR operations, if any, on the local environment.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1991

Materials sub-panel

The following subject areas are covered: general issues (unique operating/storage environments, adapt existing materials/develop rocket materials, long lead time for new materials, high cost, integration of materials development and manufacturing technology, and availability of materials data); technical issues (materials characterization for operating and storage environments, advanced materials development, availability and dissemination of materials properties, advanced materials tests facilities, fire hazards, and propellants); major objectives (materials characterization, advanced materials development, and materials data base development/maintenance); and candidate programs (materials characterization, advanced materials development, and aerospace materials data base).

Bhat, Bil↗

NASA NTRS1973

A criterion based on an information theoretic measure for goodness of fit between classifier and data base

A criterion for characterizing an iteratively trained classifier is presented. The criterion is based on an information theoretic measure that is developed from modeling classifier training iterations as a set of cascaded channels. The criterion is formulated as a figure of merit and as a performance index to check the appropriateness of application of the characterized classifier to an unknown data base and for implementing classifier updates and data selection respectively.

Eigen, D. J.↗

NASA NTRS1974

A criterion based on an information theoretic measure for goodness of fit between classifier and data base

Eigen, D. J.↗

DOE OSTI2018

Simulations of carbon dioxide push-pull into a conjugate fault system modeled after Dixie Valley—Sensitivity analysis of significant parameters and uncertainty prediction by data-worth analysis

Characterizing the faults and fractures that provide flow pathways for efficient geothermal energy production is critical for design of sustainable geothermal energy production. Both natural faults and stimulated fractures in enhanced geothermal systems (EGS) are difficult to image and map by seismic methods because hot brine filling the fractures and faults does not create a strong seismic property contrast relative to surrounding rock. We investigate here the technical feasibility of using supercritical CO 2 (scCO 2 ) injection into faults in a single-well push-pull scenario to characterize the hydraulic properties of the fault zone by emplacing scCO 2 that can serve as a contrast fluid for seismic monitoring. We develop a conceptual and numerical reservoir model of two intersecting faults based on the Dixie Valley geothermal system in Nevada, USA.

15 GEOTHERMAL ENERGY↗

NASA NTRS2019

NASA EOSDIS Data Usage Metrics- Insight and Assessment

NASA's Earth Science Data and Information System (ESDIS) Project collects Earth science data usage metrics on a daily basis through the ESDIS Metrics System (EMS). This includes metrics on distribution of data products, users, data volumes, and number of files, which are key parameters in evaluating system-level performance of any of the Distributed Active Archive Centers (DAACs) encompassed by NASA's Earth Observing System (EOS) Data and Information System (EOSDIS). EOSDIS data usage metrics illustrate the benefits of making NASA data openly available to the public and show a rapid growth in data distribution to a worldwide user community. In fact, each year since 2014 the EOSDIS has distributed over one billion data files of products from EOS satellite, airborne, and in situ observations. An assessment of the long-term trends of data usage metrics and user characterization provides insights into data usability.This study will focus on describing the EMS as a metrics collection tool and will provide a comprehensive analysis of EOSDIS data usage metrics over the last 10 years. This study will also characterize the product distribution metrics by various tools and services, such as Giovanni, the Open-source Project for a Network Data Access Protocol (OPeNDAP), and subsets, to address how these tools/services have extended the usage of data in the EOSDIS collection. Data usage patterns based on discipline and study area will further assist in understanding how EOSDIS data user needs have evolved over time. Results from this study will provide useful information for the DAACs that can help them improve the functionality of their tools and services as well as more efficiently allocate the resources necessary for enhanced access and availability of their data products. Knowledge of these metrics may also benefit user discovery of data in the EOSDIS collection, promote research collaboration, and stimulate new ideas from work and research conducted using specific datasets and data collections.

Kafle, Durga N.↗

NASA NTRS2008

Landsat Data Continuity Mission Calibration and Validation

The primary payload for the Landsat Data Continuity Mission (LDCM) is the Operational Land Imager (OLI), being built by Ball Aerospace and Technologies, under contract to NASA. The OLI has spectral bands similar to the Landsat-7 ETM+, minus the thermal band and with two new bands, a 443 nm band and 1375 nm cirrus detection band. On-board calibration systems include two solar diffusers (routine and pristine), a shutter and three sets of internal lamps (routine, backup and pristine). Being a pushbroom opposed to a whiskbroom design of ETM+, the system poses new challenges for characterization and calibration, chief among them being the large focal plane with 75000+ detectors. A comprehensive characterization and calibration plan is in place for the instrument and the data throughout the mission including Ball, NASA and the United States Geological Survey, which will take over operations of LDCM after on-orbit commissioning. Driving radiometric calibration requirements for OLI data include radiance calibration to 5% uncertainty (1 q); reflectance calibration to 3% uncertainty (1 q) and relative (detector-to-detector) calibration to 0.5% (J (r). Driving geometric calibration requirements for OLI include bandto- band registration of 4.5 meters (90% confidence), absolute geodetic accuracy of 65 meters (90% CE) and relative geodetic accuracy of 25 meters (90% CE). Key spectral, spatial and radiometric characterization of the OLI will occur in thermal vacuum at Ball Aerospace. During commissioning the OLI will be characterized and calibrated using celestial (sun, moon, stars) sources and terrestrial sources. The USGS EROS ground processing system will incorporate an image assessment system similar to Landsat-7 for characterization and calibration. This system will have the added benefit that characterization data will be extracted as part of the normal image data processing, so that the characterization data available will be significantly larger than for Landsat-7 ETM+.

Markham, Brian L.↗

DOE OSTI2014

TU-C-12A-07: Characterization of Longitudinal Reproducibility of Quantitative Diffusion Imaging Data Acquired with Four Different Protocols Using a Phantom

Purpose: To characterize and compare the longitudinal reproducibility of diffusion imaging data acquired with four different protocols using a phantom. Methods: The Diffusive Quantitative Imaging Phantom (DQIP) was constructed using fifteen cylindrical compartments within a larger compartment, filled with deionized water doped with CuSO4 and NaCl. The smaller compartments contained arrays of hexagonal or cylindrical glass capillaries of varying inner diameters, for differing restraint of water diffusion. The sensitivity of diffusion imaging metrics to signal-to-noise ratio (SNR) was probed by doping compartments with differing ratios of deuterium oxide to H2O. A cork phantom enclosure was constructed to increase thermal stability during scanning and a cork holder was made to reproduce scanner positioning. Four different protocols of DWI (diffusion weighted imaging) and DTI (Diffusion tensor imaging) imaging were assembled on a GE Excite HDx 3.0T MRI scanner to collect imaging data over 9-10 days. Data was processed with in-house software created in Matlab to obtain fractional anisotropy (FA) and apparent diffusion coefficient (ADC) values. Results: All DTI and DWI sequences showed good longitudinal stability of mean FA and ADC values per compartment, exhibiting low standard deviation ∼9%. A t-test was performed to compare mean FA values from the DTI clinical protocol to those of the DTI special protocol, indicating significantly different values in the majority of compartments. ANOVA performed on ADC values for all DTI and DWI sequences also showed significantly different values in a majority of compartments. Conclusion: This work has the potential for quantifying systemic variations between diffusion imaging sequences from different platforms. Characterization of DWI and DTI performance were done over four sequences with predictable results. This data suggests that the DQIP phantom may be a reliable method of monitoring day-to-day and scan-to-scan variation in diffusion imaging sequences from different platforms. Schott Glass North America and The Phantom Laboratory have donated materials and personnel time to this project.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2014

Thermodynamic Assessment of the Ternary Alloy System Li-Sn-Zn

The goal of this project is to predict thermodynamics and structure-properties relationships of a series of Li-based alloys for the development of advanced tritium breeding materials that exhibit the beneficial properties of pure lithium while mitigating its chemical reactivity issues for fusion applications. This project makes use of phenomenological thermodynamics based on the CALPHAD (CALculation of PHAse Diagrams), and the use of the Thermo-Calc software (see Appendix A), and experimental calorimetric data and structural characterization for the purpose of validating the data. The initial selection of Sn and Zn as solutes is considered in this first report. Originally Li-Sn alloys have shown promise in meeting tritium breeding ratio (TBR) and energy multiplication requirements. However with the addition of Zn, we expect an improvement in the nuclear, thermal, and chemical requirements of the working fluid. Other substitutes will be considered once neutronic calculations are carried out. Hence the objective of this first task is to carry out a thermodynamic assessment of the Li-Sn-Zn alloy system, as part of the development of a self-consistent and validated thermodynamic database that will include other solute elements for optimizing the properties of blanket materials in fusion-based reactors. This obviously involves the thermodynamic assessment of the three binary subsystems, namely Li-Sn, Li-Zn, and Sn-Zn.

36 MATERIALS SCIENCE↗

DOE OSTI2015

Selecting a Classification Ensemble and Detecting Process Drift in an Evolving Data Stream

We characterize the commercial behavior of a group of companies in a common line of business using a small ensemble of classifiers on a stream of records containing commercial activity information. This approach is able to effectively find a subset of classifiers that can be used to predict company labels with reasonable accuracy. Performance of the ensemble, its error rate under stable conditions, can be characterized using an exponentially weighted moving average (EWMA) statistic. The behavior of the EWMA statistic can be used to monitor a record stream from the commercial network and determine when significant changes have occurred. Results indicate that larger classification ensembles may not necessarily be optimal, pointing to the need to search the combinatorial classifier space in a systematic way. Results also show that current and past performance of an ensemble can be used to detect when statistically significant changes in the activity of the network have occurred. The dataset used in this work contains tens of thousands of high level commercial activity records with continuous and categorical variables and hundreds of labels, making classification challenging.

Ensemble classifiers↗

NASA NTRS1986

Estimation of high temperature low cycle fatigue on the basis of inelastic strain and strainrate

Fatigue life at elevated temperature can be predicted by introducing parametric values obtained from monotonic constitutive behavior into the Universal-Slopes Equation. For directionally solidified MAR-M200+HF at 975 C, these parameters are the maximum stress achievable under entirely plastic (time-independent) and purely creep (time-dependent) conditions and the corresponding inelastic strains, as well as the elastic modulus. For materials which exhibit plasticity/creep interaction, two more pairs of monotonic parameters must be evaluated for fatigue life prediction. This life-prediction method based on the Universal-Slopes Equation, resulted from a constitutive model characterizing monotonic and cyclic data as inelastic strainrate as a function of inelastic strain. Characterizing monotonic data is this way, permitted distinction between different material responses such as strain-hardening, strain-softening, and dynamic recovery effects. Understanding and defining the region of influence of each of these effects facilitated formulation of the constitutive model in relation to the mechanical and microstructural processes occurring in the material under cyclic loading.

Berkovits, A.↗

DOE OSTI2021

Characterization of mechanical discontinuities based on data-driven classification of compressional-wave travel times

Wave propagation and diffusive transport phenomena are influenced by the mechanical discontinuities in material. Here this study shows that certain bulk properties of the network of low-velocity mechanical discontinuities (e.g. air-filled cracks) in a material can be characterized by processing compressional-wave travel times using traditional data-driven classification techniques. To that end, we perform three tasks in chronological order: (1) use the discrete fracture network (DFN) method to create two-dimensional (2D) numerical models of crack-bearing material embedded with various types of low-velocity mechanical discontinuities, (2) use the fast marching method (FMM) to simulate the propagation of the wave/diffusion front from a single source through the 2D crack-bearing material to multiple receivers placed on the boundary of the material, and (3) train 9 data-driven classifiers to characterize the crack-bearing materials (i.e. bulk properties of the network of mechanical discontinuities in the crack-bearing material) by learning from the simulations of travel times detected by multiple receivers placed around the crack-bearing material. The classifiers identified the orientation, spatial distribution, and dispersion of the low-velocity mechanical discontinuities. Voting classifier performs the best among the 9 classifiers. For the characterization of bulk dispersion and distribution of discontinuities, the sensors located on the adjacent boundaries are more important; whereas for the characterization of bulk orientation of discontinuities, the sensors located on the opposite side are more important.

42 ENGINEERING↗

DOE OSTI2019

NGEE Arctic Tram: Overview of Tram-mounted Instruments, Co-located Data, Measurement Position, Elevation and Polygon Characterization, Utqiagvik (Barrow), Alaska, 2014-2018

The NGEE-Arctic Tram operated from 2014 - 2018. It was installed on the Barrow Environmental Observatory in Barrow, Alaska in late May of 2014 and operated through mid-November 2014. The subsequent 2015 through 2018 years followed a similar schedule, but with different deployment dates and data gaps, with the goal always to capture the surface dynamics of the snow melt, growing, and freeze-up seasons. This Tram overview dataset has three objectives: (1) provide new data users with a brief description of the Tram system and its research purposes; (2) provide generally useful Tram site characterization details, including measurement position ground surface elevation, characteristics of the Arctic polygonal tundra under the Tram, vegetation community composition (future), and derived image products; and (3) reference all of the data products and archived datasets that were collected with the Tram-mounted sensors and co-located instruments. This dataset contains two csv files, two zipped files, one tif file, and one pdf file. The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

Uncertainty Analysis of Consequence Management Data Products

An interlaboratory effort has developed a probabilistic framework to characterize uncertainty in data products that are developed by the US Department of Energy Consequence Management Program in support of the Federal Radiological Monitoring and Assessment Center. The purpose of this paper is to provide an overview of the probability distributions of input variables and the statistical methods used to propagate and quantify the overall uncertainty of the derived response levels that are used as contours on data products due to the uncertainty in input parameters. Uncertainty analysis results are also presented for several study scenarios. This paper includes an example data product to illustrate the potential real-world implications of incorporating uncertainty analysis results into data products that inform protective action decisions. Data product contours that indicate areas where public protection actions may be warranted can be customized to an acceptable level of uncertainty. Finally, the investigators seek feedback from decision makers and the radiological emergency response community to determine how uncertainty information can be used to support the protective action decision-making process and how it can be presented on data products.

61 RADIATION PROTECTION AND DOSIMETRY↗