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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

Station-based Surface Data Value-Added Product

This report describes the Atmospheric Radiation Measurement (ARM) Best Estimate (ARMBE) station-based surface data (ARMBESTNS) value-added product. It is a twin data product of the ARMBE 2-Dimensional gridded (ARMBE2DGRID) data set. Unlike the ARMBE2DGRID data set, ARMBESTNS data are reported at the original site locations and show the original information (except for the interpolation over time). Therefore, the users have the flexibility to process the data with the approach more suitable for their applications. This document provides information about the input data, quality control (QC) method, and output format of this data set. As much of the information is identical to that of the ARMBE2DGRID data, this document will emphasize more on the different aspects of these two data sets.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Consolidated Hydropower Data Repository: Value and Opportunities

Hydropower is one of several types of generating assets that provides energy, capacity, and services to electric power systems. It does so under rubrics and objectives—market driven and regulated, internal and external to asset and fleet owners—that address reliability, cost, price, and, increasingly, flexibility of output. The aggregation of data from multiple hydropower units can provide insights into asset operations and maintenance practices and needs and assist in meeting hydropower objectives. This paper examines the concept and potential benefits of aggregating hydropower asset data—primarily supervisory control and data acquisition (SCADA) information—with examples of insights developed from data aggregated by the Hydropower Research Institute (HRI). Data aggregation as discussed herein, and as implemented by the HRI, extends beyond multiple units in a powerhouse and beyond multiple hydropower facilities in an electric utility fleet or river system. Examples of research and analytics from such aggregated datasets range from unit load dependency analyses to modeling sensor measurements to detect and diagnose anomalies in assets. These examples showed the benefits of utilizing the entire dataset for insights into how the sensor layout of a single unit or set of units compares to the hydropower industry overall. Such insights include whether additional sensors are needed to complete analyses or to make decisions. In addition, utilizing multiple sensors of the same kind within a unit can provide an indication of possible current or upcoming problems with equipment. Although other analyses are possible, their use requires the development of complex models and, potentially, access to types of data that are currently not included with the example dataset used in this study. However, the examples studied herein confirmed the value of data aggregation in the fleet and unit contexts, and the value extends beyond multiple units in a powerhouse and beyond multiple hydropower facilities in an electric utility fleet or river system. The assessments also provided insights into potential extensions to the data aggregation concept that could further add to their value to the hydropower community, and these are included in this document as a set of recommendations.

13 HYDRO ENERGY↗
DOE OSTI2015

2D Gridded Surface Data Value-Added Product

This report describes the Atmospheric Radiation Measurement (ARM) Best Estimate (ARMBE) 2-dimensional (2D) gridded surface data (ARMBE2DGRID) value-added product. Spatial variability is critically important to many scientific studies, especially those that involve processes of great spatial variations at high temporal frequency (e.g., precipitation, clouds, radiation, etc.). High-density ARM sites deployed at the Southern Great Plains (SGP) allow us to observe the spatial patterns of variables of scientific interests. The upcoming megasite at SGP with its enhanced spatial density will facilitate the studies at even finer scales. Currently, however, data are reported only at individual site locations at different time resolutions for different datastreams. It is difficult for users to locate all the data they need and requires extra effort to synchronize the data. To address these problems, the ARMBE2DGRID value-added product merges key surface measurements at the ARM SGP sites and interpolates the data to a regular 2D grid to facilitate the data application.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Efficient Data Query for Gaussian Process Compressed Data through Value Range Estimation [Slides]

When the resolution of the data increases, data reduction methods are applied to simulation output, including Gaussian process, neural representation and compression algorithms. Lots of data analysis/visualization techniques requires data query, but data query from reduced representation is still challenging. This report will provide examples and provide possible answers to why data query from reduced representation is still challenging.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Temporal link encoding

Temporal link encoding, including: identifying a data type of a data value to be transmitted; determining that the data type is included in one or more data types for temporal encoding; and transmitting the data value using temporal encoding.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2012

A scalable messaging system for accelerating discovery from large scale scientific simulations

Emerging scientific and engineering simulations running at scale on leadership-class High End Computing (HEC) environments are producing large volumes of data, which has to be transported and analyzed before any insights can result from these simulations. The complexity and cost (in terms of time and energy) associated with managing and analyzing this data have become significant challenges, and are limiting the impact of these simulations. Recently, data-staging approaches along with in-situ and in-transit analytics have been proposed to address these challenges by offloading I/O and/or moving data processing closer to the data. However, scientists continue to be overwhelmed by the large data volumes and data rates. In this paper we address this latter challenge. Specifically, we propose a highly scalable and low-overhead associative messaging framework that runs on the data staging resources within the HEC platform, and builds on the staging-based online in-situ/in- transit analytics to provide publish/subscribe/notification-type messaging patterns to the scientist. Rather than having to ingest and inspect the data volumes, this messaging system allows scientists to (1) dynamically subscribe to data events of interest, e.g., simple data values or a complex function or simple reduction (max()/min()/avg()) of the data values in a certain region of the application domain is greater/less than a threshold value, or certain spatial/temporal data features or data patterns are detected; (2) define customized in-situ/in-transit actions that are triggered based on the events, such as data visualization or transformation; and (3) get notified when these events occur. The key contribution of this paper is a design and implementation that can support such a messaging abstraction at scale on high- end computing (HEC) systems with minimal overheads. We have implemented and deployed the messaging system on the Jaguar Cray XK6 machines at Oak Ridge National Laboratory and the Lonestar system at the Texas Advanced Computing Center (TACC), and we present the experimental performance evaluation using these HEC platforms in the paper.

Jin, Tong↗
DOE OSTI2020

Simulation of the Fast Reactor Fuel Assembly Duct-Bowing Reactivity Effect Using Monte Carlo Neutron Transport and Finite Element Analysis

This paper discusses a new method of simulating the fuel assembly duct-bowing reactivity coefficient for EBR-II run 138B. Quantification of the fuel assembly duct-bowing reactivity effect in liquid metal–cooled fast reactors has been a persistent problem since they were first designed and operated. Simulation of the duct-bowing reactivity effect is difficult because the level of detail required to simulate the effect has exceeded most modeling capabilities. The new method outlined in this paper utilizes the finite element analysis code ANSYS to analyze the thermal and structural components. Here, the displacement of the fuel assembly duct due to thermal expansion and mechanical interaction was calculated by ANSYS using recorded EBR-II run 138B temperature and power boundary value data. The displacement values were incorporated into to a Monte Carlo model of EBR-II run 138B and keff was calculated. Multiple Monte Carlo calculations were performed with duct displacement values corresponding to different reactor temperatures. Using the calculated keff values associated with the different duct displacement results allowed calculation of the duct-bowing reactivity coefficient. The duct-bowing reactivity coefficient was calculated to be –14.5 × 10 –4 $/°C/ ± 4.4%.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2016

Spatial Support Vector Regression to Detect Silent Errors in the Exascale Era

As the exascale era approaches, the increasing capacity of high-performance computing (HPC) systems with targeted power and energy budget goals introduces significant challenges in reliability. Silent data corruptions (SDCs) or silent errors are one of the major sources that corrupt the executionresults of HPC applications without being detected. In this work, we explore a low-memory-overhead SDC detector, by leveraging epsilon-insensitive support vector machine regression, to detect SDCs that occur in HPC applications that can be characterized by an impact error bound. The key contributions are three fold. (1) Our design takes spatialfeatures (i.e., neighbouring data values for each data point in a snapshot) into training data, such that little memory overhead (less than 1%) is introduced. (2) We provide an in-depth study on the detection ability and performance with different parameters, and we optimize the detection range carefully. (3) Experiments with eight real-world HPC applications show thatour detector can achieve the detection sensitivity (i.e., recall) up to 99% yet suffer a less than 1% of false positive rate for most cases. Our detector incurs low performance overhead, 5% on average, for all benchmarks studied in the paper. Compared with other state-of-the-art techniques, our detector exhibits the best tradeoff considering the detection ability and overheads.

Subasi, Omer↗
DOE OSTI2017

MACORD: Online Adaptive Machine Learning Framework for Silent Error Detection

Future HPC systems with ever-increasing resource capacity (such as compute cores, memory and storage) may significantly increase the risks on reliability. Silent data corruptions (SDCs) or silent errors are one of the major sources that corrupt HPC execution results. Unlike fail-stop errors, SDCs are rather harmful and dangerous in that they cannot be detected by hardware. We propose an online machine-learning based silent data corruption detection framework (abbreviated as MACORD) for detecting SDCs in HPC applications. In particular, we comprehensively investigate the prediction ability of a multitude of machine-learning algorithms in our study, and enable the detector to automatically select the best-fit algorithms at runtime to adapt to the data dynamics. Our learning framework exhibits low memory overhead (less than 1%), since it takes only spatial features (i.e., neighboring data values for each data point in the current time step) into the training data. Experiments based on real-world scientific applications/benchmarks show that our framework can get the detection sensitivity (i.e., recall) up to 99% while the false positive rate is limited to 0.1% in most cases, which is one order of magnitude improvement compared with the latest state-of-art spatial technique.

Subasi, Omer↗
DOE OSTI2023

DEEPEN 3D PFA Index Models for Exploration Datasets at Newberry Volcano

DEEPEN stands for DE-risking Exploration of geothermal Plays in magmatic ENvironments. As part of the development of the DEEPEN 3D play fairway analysis (PFA) methodology for magmatic plays (conventional hydrothermal, superhot EGS, and supercritical), index models needed to be developed to map values in geoscientific exploration datasets to favorability index values. This GDR submission includes those index models. Index models were created by binning values in exploration datasets into chunks based on their favorability, and then applying a number between 0 and 5 to each chunk, where 0 represents very unfavorable data values and 5 represents very favorable data values. To account for differences in how exploration methods are used to detect each play component, separate index models are produced for each exploration method for each component of each play type. Index models were created using histograms of the distributions of each exploration dataset in combination with literature and input from experts about what combinations of geophysical, geological, and geochemical signatures are considered favorable at Newberry. This is in attempt to create similar sized bins based on the current understanding of how different anomalies map to favorable areas for the different types of geothermal plays (i.e., conventional hydrothermal, superhot EGS, and supercritical). For example, an area of partial melt would likely appear as an area of low density, high conductivity, low vp, and high vp/vs. This means that these target anomalies would be given high (4 or 5) index values for the purpose of imaging the heat source. To account for differences in how exploration methods are used to detect each play component, separate index models are produced for each exploration method for each component of each play type. Index models were produced for the following datasets: - Geologic model - Alteration model - vp/vs - vp - vs - Temperature model - Seismicity (density*magnitude) - Density - Resistivity - Fault distance - Earthquake cutoff depth model

15 GEOTHERMAL ENERGY↗
DOE OSTI2018

FACE-MDS Phase 2: Data from Six US-Located Elevated CO2 Experiments

This dataset contains measured ecosystem data from four Free Air CO2 Enrichment (FACE) experiments–Duke, Oak Ridge (ORNL), and Rhinelander -- and one Open Top Chamber (OTC) experiment -- Kennedy Space Center (KSCO). The sites were located in the United States and contain data from 1996-2009. These sites were the study sites of the FACE Model Data Synthesis (FACE-MDS) project phase 2. The file naming convention can be found in the file 'facemds-file-table-sitedata.xml' and information about variable naming convention can be found in the file 'facemds-standard-name-table-modvars.xml'. We followed the ALMA v3 convention for meteorological variables and developed a hierarchical naming scheme for ecosystem variables. Additional info can be found in the protocols in the accompanying meteorological dataset (Walker et al. 2018: doi:10.15485/1480328). We replaced the original naming convention mentioned in the protocol with the one described in 'facemds-standard-name-table-modvars.xml'. The outputs of the FACE-MDS phase 2 are in Walker et al (2018: doi:10.15485/1480327). The dataset contains 18 data files that are available in comma-separate (*.csv), and 18 R Data Serialization (*.rds) format. 2 XML (*.xml) companion files and additional metadata are provided: 2 data dictionaries and a file-level metadata file in comma-separate (.csv) format and a user guide in PDF (*.pdf) format. This dataset was updated on 2019-06-28 to fix daily files at Duke and ORNL were incorrectly labelled and were standard error files instead of plot level data. Now corrected and all three (plot, mean, standard error) daily data files for both sites have been added. This dataset was updated on 2026-06-08 to add a user guide, data dictionary file, and a file-level metadata file. DUKE_ax_v2.csv was added to match the no data value format of other files in this dataset. DUKE_ax.csv is maintained as a data file. No previously released data values were changed.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Systems and methods for optical physical unclonable parameters

An article of manufacture includes a substrate and a security primitive deposited on the substrate. The security primitive includes a transition metal dichalcogenide having a varying thickness. According to various embodiments, the transition metal dichalcogenide comprises a chalcogen atom (X) selected from the group consisting of S, Se, and Te and a transition metal (M) selected from the group consisting of Mo, W, Hf, and Zr. The security primitive is pixelated into a plurality of discrete regions having different luminescence. A security primitive key includes a first set of data values corresponding to a first set of coordinates of a first region and a second set of data values corresponding to a second set of coordinates of a second region. In some embodiments, the security primitive key is digitally captured through an optical reader and verified by querying a database.

Shahrjerdi, Davood↗
DOE OSTI2011

DENSITY ESTIMATION FOR PROJECTED EXOPLANET QUANTITIES

Exoplanet searches using radial velocity (RV) and microlensing (ML) produce samples of 'projected' mass and orbital radius, respectively. We present a new method for estimating the probability density distribution (density) of the unprojected quantity from such samples. For a sample of n data values, the method involves solving n simultaneous linear equations to determine the weights of delta functions for the raw, unsmoothed density of the unprojected quantity that cause the associated cumulative distribution function (CDF) of the projected quantity to exactly reproduce the empirical CDF of the sample at the locations of the n data values. We smooth the raw density using nonparametric kernel density estimation with a normal kernel of bandwidth {sigma}. We calibrate the dependence of {sigma} on n by Monte Carlo experiments performed on samples drawn from a theoretical density, in which the integrated square error is minimized. We scale this calibration to the ranges of real RV samples using the Normal Reference Rule. The resolution and amplitude accuracy of the estimated density improve with n. For typical RV and ML samples, we expect the fractional noise at the PDF peak to be approximately 80 n{sup -log2}. For illustrations, we apply the new method to 67 RV values given a similar treatment by Jorissen et al. in 2001, and to the 308 RV values listed at exoplanets.org on 2010 October 20. In addition to analyzing observational results, our methods can be used to develop measurement requirements-particularly on the minimum sample size n-for future programs, such as the microlensing survey of Earth-like exoplanets recommended by the Astro 2010 committee.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

Data for The Value of Reversible Carbon Storage in a Zero-Emissions World

Atmospheric carbon dioxide removal (CDR) is required to stabilize global temperature. CDR can be achieved via ecosystem-based approaches that are cost-effective but reversible (e.g., soil and forest management) or by more durable but expensive approaches (e.g., direct air capture coupled with geologic storage). Here, we examine trade-offs between these approaches, focusing on timing, climate impacts, and cost. We simulated reversible carbon accrual for a range of CDR contract structures using a general minimalist model of ecosystem carbon cycling, and parameterized it to simulate US agricultural soil management─specifically cover cropping─as a case study. We then quantified the resulting impact on atmospheric carbon and global temperature using a climate model emulator. We find that maintaining a patchwork of reversible CDR projects by replacing lapsed projects with new projects can reduce warming by 22–195 μ°C in 2100 and that the magnitude of this cooling effect depends on how effectively the patchwork is maintained. Long-term maintenance of reversible CDR projects requires institutional stability that cannot be guaranteed over multiple decades. Consequently, effective CDR ultimately requires replacing reversible projects with durable projects. To address this problem, we modeled the cost of replacing reversible agricultural soil CDR with geologic CDR. We found that using reversible CDR as a bridge to durable CDR is potentially more cost-effective as a global cooling strategy (0.20–0.81 billion USD per μ°C avoided) than perpetual maintenance of reversible CDR (0.32–1.31 billion USD per μ°C avoided) or an immediate transition to durable CDR (1.37–2.19 billion USD per μ°C avoided). However, we emphasize that institutional commitments to maintain reversible CDR projects cannot be guaranteed. Reliance on reversible CDR as a bridge to durable CDR therefore carries an unknown amount of risk and will only function if efforts to maintain reversible CDR are robust.

Carbon↗
DOE OSTI2017

Data Challenges in Estimating the Capacity Value of Solar Photovoltaics

We examine the robustness of solar capacity-value estimates to three important data issues. The first is the sensitivity to using hourly averaged as opposed to subhourly solar-insolation data. The second is the sensitivity to errors in recording and interpreting load data. The third is the sensitivity to using modeled as opposed to measured solar-insolation data. We demonstrate that capacity-value estimates of solar are sensitive to all three of these factors, with potentially large errors in the capacity-value estimate in a particular year. If multiple years of data are available, the biases introduced by using hourly averaged solar-insolation can be smoothed out. Multiple years of data will not necessarily address the other data-related issues that we examine. Our analysis calls into question the accuracy of a number of solar capacity-value estimates relying exclusively on modeled solar-insolation data that are reported in the literature (including our own previous works). Our analysis also suggests that multiple years' historical data should be used for remunerating solar generators for their capacity value in organized wholesale electricity markets.

14 SOLAR ENERGY↗
DOE OSTI2026

Updated Commercial Aviation Non-Airport Crash Density Values Using Data Obtained from the U.S. National Transportation Safety Board for 2000 through 2019

This report documents the calculation of the commercial aviation (CA) non-airport crash density for Air Route Traffic Control Center (ARTCC) area associated with the Contiguous United States (CONUS) using the methodology outlined in the Department of Energy (DOE) Standard 3014-2006, Accident Analysis for Aircraft Crash Into Hazardous Facilities. For analytical purposes, the CA aircraft fleet is comprised of aircraft engaged in operations under Title 14 of the Code of Federal Regulations (CFR) Part 121 (i.e., Air Carrier) and Part 135 (i.e., Air Taxi).

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2026

Updated General Aviation Non-Airport Crash Density Values Using Data Obtained from the U.S. National Transportation Safety Board for 2000 through 2019

This report documents the calculation of the General Aviation (GA) non-airport crash density for Department of Energy (DOE) sites and power generating facilities licensed by the Nuclear Regulatory Commission (NRC) using the methodology outlined in DOE-STD-3014-2006, Accident Analysis for Aircraft Crash Into Hazardous Facilities. The results of this report will be used by the American Nuclear Society (ANS) for the development of ANS standard 2.36, Accident Analysis for Aircraft Crash into Reactor and Nonreactor Nuclear Facilities.

99 GENERAL AND MISCELLANEOUS↗