Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “data aggregation”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 541 records · Page 30
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Error Analysis of CM Data Products Sources of Uncertainty

This goal of this project is to address the current inability to assess the overall error and uncertainty of data products developed and distributed by DOE’s Consequence Management (CM) Program. This is a widely recognized shortfall, the resolution of which would provide a great deal of value and defensibility to the analysis results, data products, and the decision making process that follows this work. A global approach to this problem is necessary because multiple sources of error and uncertainty contribute to the ultimate production of CM data products. Therefore, this project will require collaboration with subject matter experts across a wide range of FRMAC skill sets in order to quantify the types of uncertainty that each area of the CM process might contain and to understand how variations in these uncertainty sources contribute to the aggregated uncertainty present in CM data products. The ultimate goal of this project is to quantify the confidence level of CM products to ensure that appropriate public and worker protections decisions are supported by defensible analysis.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2001

NASA's Information Power Grid: Large Scale Distributed Computing and Data Management

Large-scale science and engineering are done through the interaction of people, heterogeneous computing resources, information systems, and instruments, all of which are geographically and organizationally dispersed. The overall motivation for Grids is to facilitate the routine interactions of these resources in order to support large-scale science and engineering. Multi-disciplinary simulations provide a good example of a class of applications that are very likely to require aggregation of widely distributed computing, data, and intellectual resources. Such simulations - e.g. whole system aircraft simulation and whole system living cell simulation - require integrating applications and data that are developed by different teams of researchers frequently in different locations. The research team's are the only ones that have the expertise to maintain and improve the simulation code and/or the body of experimental data that drives the simulations. This results in an inherently distributed computing and data management environment.

Johnston, William E.↗
DOE OSTI2017

A Workflow for Visual Diagnostics of Binary Classifiers using Instance-Level Explanations

In this paper, we propose a visual analytics workflow to help data scientists explore, diagnose, and understand the decisions made by a binary classifier. The approach leverages “instance-level explanations”, measures of local feature relevance that explain single instances and uses them to build a set of visual representations that guide the users in their investigation. The workflow is based on three main visual representations and steps: one based on aggregate statistics to see how data distributes across correct or incorrect decisions; one based on explanations to understand which features are used to make these decisions; and one based on raw data, to derive insights on potential root causes for the observed patterns. The work is validated through a long-term collaboration with a group of healthcare professionals who used our method to make sense of machine learning models they developed. The case studies from this collaboration demonstrate that the proposed method helps experts derive useful knowledge about the model and the phenomena it describes and also generates useful hypotheses on how a model can be improved.

Krause, Josua↗
DOE OSTI2023

Assessing individual movement, habitat use, and behavior of non-breeding marine birds in relation to prey availability in the US Atlantic

Resource availability is a key factor driving marine bird movements and distributions, but direct information on prey availability is difficult to obtain at relevant scales. We present novel methods for describing multi-scale trophic associations, combining movement analyses of marine birds with estimates of forage fish surface aggregations from digital aerial survey data and species occupancy from bottom trawl survey data. We analyzed satellite telemetry data from northern gannets Morus bassanus , red-throated loons Gavia stellata , and long-tailed ducks Clangula hyemalis in the US Atlantic during the non-breeding period. Using discrete-time hidden Markov models to distinguish area-restricted (i.e. putative foraging) from transit movements, we examined how environmental factors influence movement, and how forage fish species distributions and surface aggregations influence habitat use by gannets and loons that have greater dietary reliance. Our results suggest that chlorophyll a concentration significantly affected movement behavior across species, highlighting the importance of higher-productivity areas around estuaries during colder months when regional productivity is low. Though variable across species and seasons, spatial cross-correlation analysis revealed that herring species (Family Clupeidae), including Atlantic menhaden Brevoortia tyrannus , may be important resources; it also showed positive spatial correlations with forage fish aggregations. This suggests that prey patch dynamics and factors driving aggregation formation may be as important as species composition. However, spatial patterns were generally low (<0.3), suggesting a mismatch in spatiotemporal resolution, exemplifying the challenges in quantifying trophic relationships in marine systems. Disentangling predator-prey relationships is critical to understanding the mechanisms driving marine bird behavior in rapidly changing marine systems.

Environmental Sciences & Ecology↗
NASA NTRS1994

The effects of temperature and NaCl concentration on tetragonal lysozyme face growth rates

Measurements were made of the (110) and (101) face growth rates of the tetragonal form of hen egg white lysozyme at 0.1M sodium acetate buffer, pH 4.0, from 4 to 22 C and with 3.0%, 5.0%, and 7.0% NaCl used as the precipitating salt. The data were collected at supersaturation ratios ranging from approximately 4 to approximately 63. Both decreasing temperature and increasing salt concentrations shifted plots of the growth rate versus C/C(sat) to the right, i.e. higher supersaturations were required for comparable growth rates. The observed trends in the growth data are counter to those expected from the solubility data. If tetragonal lysozyme crystal growth is by addition of ordered aggregates from the solution, then the observed growth data could be explained as a result of the effects of lowered temperature and increased salt concentration on the kinetics and equilibrium processes governing protein-protein interactions in solution. The data indicate that temperature would be a more tractable means of controlling the growth rate for tetragonal lysozyme crystals contrary to the usual practice in, e.g., vapor diffusion protein crystal growth, where both the precipitant and protein concentrations are simultaneously increased. However, the available range for control is dependent upon the protein concentration, with the greatest growth rate control being at the lower concentration.

Forsythe, Elizabeth↗
DOE OSTI2024

A consistent dataset for the net income distribution for 190 countries and aggregated to 32 geographical regions from 1958 to 2015

Abstract. Data on income distributions within and across countries are becoming increasingly important for informing analysis of income inequality and understanding the distributional consequences of climate change. While datasets on income distribution collected from household surveys are available for multiple countries, these datasets often do not represent the same concept of inequality (or income concept) and therefore make comparisons across countries, over time and across datasets difficult. Here, we present a consistent dataset of income distributions across 190 countries from 1958 to 2015 measured in terms of net income. We complement the observed values in this dataset with values imputed from a summary measure of the income distribution, specifically the Gini coefficient. For the imputation, we use a recently developed nonparametric principal-component-based approach that shows an excellent fit to data on income distributions compared to other approaches. We also present another version of this dataset aggregated from the country level to 32 geographical regions. Our dataset is developed for the purpose of calibrating models such as integrated human–Earth system models with detailed data on income distributions. This dataset will enable more robust analysis of income distribution at multiple scales. The latest version of our data are available on Zenodo: https://doi.org/10.5281/zenodo.7093997 (Narayan et al., 2022b).

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

An Equivalent Time-Variant Storage Model to Harness EV Flexibility: Forecast and Aggregation

The demand for vehicle charging will necessitate large investments in power distribution, transmission, and generation. However, this demand is often also flexible in time, and can be actively managed to reduce the needed investments, and to better integrate renewable electricity. Harnessing this flexibility requires forecasting and controlling electric vehicle (EV) charging at thousands of stations. In this work, we address the problem of forecasting and management of the aggregate flexible demand from tens to thousands of EV supply equipment (EVSEs). First, it presents an equivalent time-variant storage model for flexible demand at an aggregation of EVSEs. The proposed model is generalizable to different markets, and also to different flexible loads. Model parameters representing multiple EVSEs can be easily aggregated by summation, and forecasted using autoregressive models. The forecastability of uncontrolled demand and storage parameters is evaluated using data from 1341 nonresidential EVSEs located in Northern California. The median coefficient of variation is as low as 24% for the forecast of uncontrolled demand at the highest aggregation and 10-15% for the storage parameters. The benefits of aggregation and forecastability are demonstrated using an energy arbitrage scenario. Purchasing energy day ahead is less expensive than in the real-time market, but relies on a uncertain forecast of charging availability. The results show that the forecastability significantly improves for larger aggregations. This helps the aggregator make a better forecast, and decreases the cost of charging in comparison to an uncontrolled case by 60% with respect to an oracle scenario.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2018

Annular Core Research Reactor Pulse History Report: 2003-2017

This report documents and describes the tabulation and analysis of historical pulse operation data from the Annular Core Research Reactor (ACRR) at Sandia National Laboratories (SNL). The pulse data was obtained from a combination of pulse log files generated at the control console and pulse diagnostics system data. The pulses presented were performed within the time period of April 2003 and December 2017. A brief analysis of the data is included to characterize the aggregate behavior of ACRR pulses with respect to theoretical treatments based on the point reactor kinetics model. It is expected that the data presented will provide an organized and consolidated resource to reference historical pulse data at the ACRR for use in analyses, verification and validation, and general understanding of the machine. A comprehensive set of data is presented to the reader in the appendices.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
NASA NTRS1996

Packaging and distributing ecological data from multisite studies

Studies of global change and other regional issues depend on ecological data collected at multiple study areas or sites. An information system model is proposed for compiling diverse data from dispersed sources so that the data are consistent, complete, and readily available. The model includes investigators who collect and analyze field measurements, science teams that synthesize data, a project information system that collates data, a data archive center that distributes data to secondary users, and a master data directory that provides broader searching opportunities. Special attention to format consistency is required, such as units of measure, spatial coordinates, dates, and notation for missing values. Often data may need to be enhanced by estimating missing values, aggregating to common temporal units, or adding other related data such as climatic and soils data. Full documentation, an efficient data distribution mechanism, and an equitable way to acknowledge the original source of data are also required.

Information Systems↗
NASA NTRS2012

Contribution of National near Real Time MODIS Forest Maximum Percentage NDVI Change Products to the U.S. ForWarn System

This presentation reviews the development, integration, and testing of Near Real Time (NRT) MODIS forest % maximum NDVI change products resident to the USDA Forest Service (USFS) ForWarn System. ForWarn is an Early Warning System (EWS) tool for detection and tracking of regionally evident forest change, which includes the U.S. Forest Change Assessment Viewer (FCAV) (a publically available on-line geospatial data viewer for visualizing and assessing the context of this apparent forest change). NASA Stennis Space Center (SSC) is working collaboratively with the USFS, ORNL, and USGS to contribute MODIS forest change products to ForWarn. These change products compare current NDVI derived from expedited eMODIS data, to historical NDVI products derived from MODIS MOD13 data. A new suite of forest change products are computed every 8 days and posted to the ForWarn system; this includes three different forest change products computed using three different historical baselines: 1) previous year; 2) previous three years; and 3) all previous years in the MODIS record going back to 2000. The change product inputs are maximum value NDVI that are composited across a 24 day interval and refreshed every 8 days so that resulting images for the conterminous U.S. are predominantly cloud-free yet still retain temporally relevant fresh information on changes in forest canopy greenness. These forest change products are computed at the native nominal resolution of the input reflectance bands at 231.66 meters, which equates to approx 5.4 hectares or 13.3 acres per pixel. The Time Series Product Tool, a MATLAB-based software package developed at NASA SSC, is used to temporally process, fuse, reduce noise, interpolate data voids, and re-aggregate the historical NDVI into 24 day composites, and then custom MATLAB scripts are used to temporally process the eMODIS NDVIs so that they are in synch with the historical NDVI products. Prior to posting, an in-house snow mask classification product is computed for the current compositing period and integrated into the change images to account for snow related NDVI drops. The supplemental snow classification product was needed because other available QA cloud/snow mask typically underestimates snow cover. MODIS true and false color composites were also computed from eMODIS reflectance data and the true color RGBs are also posted on ForWarn?s FCAV; this data is used for assessing apparent occasional quality issues on the change products due to residual unmasked cloud cover. New forest change products are posted with typical latencies of 1-2 days after the last input eMODIS data collection date for a given 24 day compositing period.

Spruce, Joseph P.↗
DOE OSTI2018

NBIC Biofeeds: Deploying a New, Digital Tool for Open Source Biosurveillance across Federal Agencies

The National Biosurveillance Integration Center (NBIC) is deploying a scalable, flexible open source data collection, analysis, and dissemination tool to support biosurveillance operations by the U.S. Department of Homeland Security (DHS) and its federal interagency partners.IntroductionNBIC integrates, analyzes, and distributes key information about health and disease events to help ensure the nation’s responses are well-informed, save lives, and minimize economic impact. To meet its mission objectives, NBIC utilizes a variety of data sets, including open source information, to provide comprehensive coverage of biological events occurring across the globe. NBIC Biofeeds is a digital tool designed to improve the efficiency of analyzing large volumes of open source reporting and increase the number of relevant insights gleaned from this dataset. Moreover, the tool provides a mechanism to disseminate tailored, electronic message notifications in near-real time so that NBIC can share specific information of interest to its interagency partners in a timely manner. NBIC intends to implement operational use of the capability in FY 2018. The core components of the system are data collection, curation, and dissemination of information deemed important by NBIC subject matter experts. NBIC Biofeeds has captured information from more than 70,000 unique sources published from around the globe and presents, on average, 9,000 new biosurveillance-relevant articles to users each day. NBIC leverages a variety of data feeds, including third party aggregators like Google and subscription-based feeds such as HealthMap, as well as Really Simple Syndication (RSS) feeds and web-scraping of highly relevant sources. The NBIC biosurveillance taxonomy imbedded in the tool consists of more than 600 metadata targets that cover key information for understanding the significance of an active biological event, including etiologic agents, impact to humans and animals (e.g., infection severity, healthcare workers involved, type of host), social disruption, infrastructure strain, countermeasures engaged, and ‘red flag’ characteristics (e.g., pathogen appearance in a new geographic area, unusual clinical signs). This taxonomy serves as a foundation for data curation and can be tailored by NBIC partners to more directly meet their own mission objectives. At this time, metadata is predominately captured by NBIC analysts, who manually tag information, which triggers the population of three automatically-disseminated products from the tool: 1) the NBIC Daily Biosurveillance Review, 2) immediate and daily summary email notifications, and 3) custom-designed RSS feeds. These products are meant for individual recipients in the federal interagency and for consumption by other biosurveillance information technology systems, such as the Department of Defense, Defense Threat Reduction Agency (DTRA) Biosurveillance Ecosystem (BSVE). NBIC is working in partnership with DTRA to integrate NBIC Biofeeds as an application directly into the BSVE and further develop the BSVE as an all-in-one platform for biosurveillance data analytics. To improve the efficiency and effectiveness of gaining insights using NBIC Biofeeds, developers of the tool at the Pacific Northwest National Laboratory (PNNL) are researching and testing a variety of advanced analytics techniques focused on: 1) article relevancy ratings to improve the review of queried data, 2) significance ratings to elucidate the perceived severity of an event based on reported characteristics, 3) full-text article retrieval and storage for improved machine-tagging, and 4) anomaly detection for emerging threats. Testing and implementation of new analytic capabilities in NBIC Biofeeds is planned for this fiscal year. NBIC Biofeeds was developed to serve as a sophisticated and powerful open source biosurveillance technology of value to the federal government by providing information to stakeholders conducting open source biosurveillance as well as those consuming biosurveillance information. In FY 2018, NBIC Biofeeds will begin operational use by NBIC and an initial set of users in various federal agencies. User accounts for testing purposes will be available to other federal partners, and a broad scope of federal stakeholders can receive products directly from NBIC Biofeeds based on their interests. NBIC Biofeeds is expected to enable more rapid recognition and enhanced analysis of emerging biological events by NBIC analysts. NBIC anticipates other federal agencies with biosurveillance missions will find this technology of value and intends to offer use of the platform to those federal partners that can benefit from access to the tool and information generated from NBIC Biofeeds.

NBIC↗
DOE OSTI2023

Development of a Benchmark Eddy Flux Evapotranspiration Dataset for Evaluation of Satellite-Driven Evapotranspiration Models Over the CONUS

A large sample of ground-based evapotranspiration (ET) measurements made in the United States, primarily from eddy covariance systems, were post-processed to produce a benchmark ET dataset. The dataset was produced primarily to support the intercomparison and evaluation of the OpenET satellite-based remote sensing ET (RSET) models and could also be used to evaluate ET data from other models and approaches. OpenET is a web-based service that makes field-delineated and pixel-level ET estimates from well-established RSET models readily available to water managers, agricultural producers, and the public. The benchmark dataset is composed of flux and meteorological data from a variety of providers covering native vegetation and agricultural settings. Flux footprint predictions were developed for each station and included static flux footprints developed based on average wind direction and speed, as well as dynamic hourly footprints that were generated with a physically based model of upwind source area. The two footprint prediction methods were rigorously compared to evaluate their relative spatial coverage. Data from all sources were post-processed in a consistent and reproducible manner including data handling, gap-filling, temporal aggregation, and energy balance closure correction. The resulting dataset included 243,048 daily and 5,284 monthly ET values from 194 stations, with all data falling between 1995 and 2021. We assessed average daily energy imbalance using 172 flux sites with a total of 193,021 days of data, finding that overall turbulent fluxes were understated by about 12% on average relative to available energy. Multiple linear regression analyses indicated that daily average latent energy flux may be typically understated slightly more than sensible heat flux. This dataset was developed to provide a consistent reference to support evaluation of RSET data being developed for a wide range of applications related to water accounting and water resources management at field to watershed scales.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1998

A Conceptual Approach to Assimilating Remote Sensing Data to Improve Soil Moisture Profile Estimates in a Surface Flux/Hydrology Model: Aggregation - 2

This is a follow up on the preceding presentation by Crosson. The grid size for remote microwave measurements is much coarser than the hydrological model computational grids. To validate the hydrological models with measurements we propose mechanisms to aggregate the hydrological model outputs for soil moisture to allow comparison with measurements. Weighted neighborhood averaging methods are proposed to facilitate the comparison. We will also discuss such complications as misalignment, rotation and other distortions introduced by a generalized sensor image.

Schamschula, Marius↗
NASA NTRS2021

Perceive: Proactive Exploration of Risky Concept Emergence for Identifying Vulnerabilities & Exposures

National databases that collect various kinds of textual threat reports such as ASRS, CERT, and NVD manually process their reports individually. They then offer data products to disseminate the aggregate information, like newsletters, alerts or individual report searching. The goal of this research is to connect these individual reports thematically and temporally to identify emerging or recurring threats, by analyzing large collections of text, source code, collaboration and communication patterns. This capability, I argue, enables us to identify the emergence and recurrence of such themes, and the contexts in which they re-occur, facilitating faster and more capable mitigation. I propose two models to shed light on this goal: An empirical model of vulnerabilities as bugs, the commit flow model, and one of the vulnerabilities and aviation safety threats as topics, the topic flow model. I use as gold standard existing manual workflows in both domains, reflected in the existing data products by these organizations, and empirically evaluate if the automated model scan match or outperform existing manual practices.

ASRS↗
NASA NTRSDate not supplied

Watching Without Seeing a Tool to Surveil Astronaut Health Outcomes While Maintaining Astronaut Medical Privacy

BACKGROUND The Privacy Act of 1974 regulates the use a nd disclosure of personally identifiable information by US Federal agencies. The Act applies to biographical, financial, a nd other identity-linked information, a s well a s personal health information (PHI). As such, the use of astronaut PHI is limited to authorized personnel for preapproved uses, with data reporting often limited to aggregated information about groups. These limitations on the use a nd reporting of astronaut PHI complicates surveillance efforts, wherein epidemiologists a t the National Aeronautics and Space Administration (NASA)monitor the incidence of targeted health conditions in the astronaut population, or to discover emerging trends of aging and disease. Stratification on one or more covariates –particularly time-period, sex, a nd mission participation –can lead to extremely small datasets such that the reporting of results is potentially attributable to individuals. An additional challenge is the small size of the astronaut population, both in terms of numbers of individuals a s well a s in terms of density of exposure time. Such small datasets yield volatile rate estimates that are difficult to interpret. To a id the epidemiological surveillance efforts, a surveillance tool is required that can (a) satisfy the need for rapid computation of condition-specific incidence and mortality rates; (b) improve the statistical estimates of these estimated rates; and (c) maintain astronaut privacy. Here we describe a nd demonstrate such a tool. METHODS We devised a system that models incidence a nd mortality rates rather than calculating them directly. This ha s the advantage of using all the available data to derive the estimates, lea ding to rates that a re not attributable to any one individual, a nd a re a s numerically stable a s they can be given the extremely limited data. The system models disease endpoints using a Poisson regression model with exposure density (measured in person-years) a s a n offset term. By doing so the model is estimating event counts per person-year, equivalent to modeling the rates directly. It uses a standard (pre-specified)set of covariates; the system does not engage in “model-building” as model parsimony is not the goa l. Instead, it is explicitly recognized that if a covariate is not statistically significant a nd not a confounder then it will likely have very little effect on the estimate of the incidence a nd mortality rates. Users are able to specify the disease endpoint of interest and the covariates over which they would like to stratify. The system then uses the resulting model to compute the estimated rates for the user-chosen configuration of variables as visualizes those either over an age range within a specified time-period, or over time for astronauts with a specified age range. RESULTS The first iteration of the tool computes incidence a nd mortality rates for cardiovascular conditions and cancers. Code ha s been developed to retrieve the appropriate data from the IMPALA analysis platform, compute the models for incidence a nd mortality, a nd then use those models to generate the corresponding rate curves. A companion graphical user interface allows the user to specify the curves and visualize the results. CONCLUSIONS It is important to note that the rapid surveillance tool described here is neither meant to be a definitive assessment of the incidence or mortality of any particular disease or condition in the astronaut population, nor is it meant to be used for research purposes. Rather, it is meant as an early indicator that in-depth investigation may be warranted. By automating a repetitive process and leveraging carefully curated astronaut health outcomes, the tool makes possible a rapid “first look” into known areas of concern, and, if used judiciously, may surface new areas of concern for long-term astronaut health. This work is supported in part by the Translational Research Institute for Space Health (TRISH) through NASA Cooperative Agreement NNX16AO69A.

R J Reynolds↗
DOE OSTI2021

Simulation of PV Variability as a Function of PV Generation and Plant Size

The deployment of photovoltaic (PV) systems continues to show significant expansion; however, this growth has brought added attention to issues around the variability of the solar resource. Both spatial and temporal variability exist. Temporal scales can range from the sub-second to multiyear, whereas spatial scales can range from a few meters to tens of kilometers. There are multiple methods described in the literature to quantify PV variability at various spatial and temporal scales. This study focuses on short-term temporal variability and uses similar approaches with the addition of PV plant size a parameter to quantify variability. The method employed here incorporates the normalization of clear-and cloudy-sky conditions and PV plant size to quantify nominal variability metrics. The distribution and fluctuations of these metrics provide relevant information that is useful for system operations. The National Solar Radiation Database (NSRDB) is used to simulate PV variability as a function of PV generation and plant size. Hypothetical but realistic system information at 33 locations is used to model PV generation by feeding NSRDB solar irradiance data to the National Renewable Energy Laboratory’s System Advisor Model (SAM). Over the selected region, it is found that the aggregated ramp rates for the 1-minute data are associated with standard deviations ranging from 0.002–0.055 on a daily basis; however, hourly intervals induce higher aggregated ramp rates than the other timescales. Even though minute-to-minute variations are significant for the 1-minute time-scale, the standard deviation aggregated into a daily metric is smaller because of the cancellation of values.

irradiance↗
DOE OSTI2021

Simulation of PV Variability as a Function of PV Generation and Plant Size: Preprint

The deployment of photovoltaic (PV) systems continues to show significant expansion; however, this growth has brought added attention to issues around the variability of the solar resource. Both spatial and temporal variability exist. Temporal scales can range from the sub-second to multiyear, whereas spatial scales can range from a few meters to tens of kilometers. There are multiple methods described in the literature to quantify PV variability at various spatial and temporal scales. This study focuses on short-term temporal variability and uses similar approaches with the addition of PV plant size a parameter to quantify variability. The method employed here incorporates the normalization of clear- and cloudy-sky conditions and PV plant size to quantify nominal variability metrics. The distribution and fluctuations of these metrics provide relevant information that is useful for system operations. The National Solar Radiation Database (NSRDB) is used to simulate PV variability as a function of PV generation and plant size. Hypothetical but realistic system information at 33 locations is used to model PV generation by feeding NSRDB solar irradiance data to the National Renewable Energy Laboratory’s System Advisor Model (SAM). Over the selected region, it is found that the aggregated ramp rates for the 1-minute data are associated with standard deviations ranging from 0.002–0.055 on a daily basis; however, hourly intervals induce higher aggregated ramp rates than the other timescales. Even though minute-to-minute variations are significant for the 1-minute timescale, the standard deviation aggregated into a daily metric is smaller because of the cancellation of values.

41 EE - Solar Energy Technologies Office (EE-4S)↗
DOE OSTI2018

Rock-Forming Minerals Radiation-Induced Volumetric Expansion – Revisiting Literature Data

Neutron radiation-induced volumetric expansion (RIVE) of concrete aggregate is recognized as a major degradation mechanism causing extensive damage to concrete constituents (Hilsdorf et al. 1978; Seeberger and Hilsdorf 1982; Field et al. 2015). Nearly 400 RIVE data obtained in test-reactors on varied rock-forming minerals were collected by Denisov et al. (2012). These data were input into the Oak Ridge National Laboratory (ORNL) irradiated minerals, aggregates and concrete (IMAC) database and were reanalyzed in order to develop a general empirical model for minerals RIVE and interpret the susceptibility of silicates toward expansion. Furthermore, the empirical models best regression coefficient (r2 ≈ 0.95) is obtained by combining two different modeling techniques: (1) an interpolation-like model based on the relative distance to existing data points, and, (2) a nonlinear regression model assuming varied mathematical forms to describe RIVE as a function of the neutron fluence3 and the average irradiation temperature. The susceptibility to develop irradiation-induced expansion greatly varies with the nature of minerals. Silicates, i.e., [SiO4]4– bearing minerals show a wide range of maximum RIVEs, from a few percents to what appears as a bounding value of 17.8% for quartz. The maximum RIVE of varied silicates appears to be governed, macroscopically, by three parameters: (1) Primarily, the dimensionality of silicate polymerization (DOSP), (2) the relative number of Si–O bond per unit cell, and, (3) the relative bonding energy (RBE) of the unit cell.

58 GEOSCIENCES↗