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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 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Protection settings optimizer

SAND2023-06672O The Protection Settings Optimizer (PSO) uses system and fault data as inputs to formulate the problem of calculating relay settings as a mixed integer, nonlinear optimization problem (MINLP). The MINLP is solved using a genetic algorithm-based optimizer that attempts to find settings to reduce the relay operating times. The PSO protects the power system by using the steady-state fault voltages and currents, which then calculates the optimal device setting to protect the power system. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Patel, Trupal↗
DOE OSTI2021

An Extreme-Preserving Long-Term Gridded Daily Precipitation Data Set for the Conterminous United States

Extreme daily precipitation contributes to flooding that can cause significant economic damages, and so is important to properly capture in gridded meteorological data sets. This work examines precipitation extremes, the mean precipitation on wet days, and fraction of wet days in two widely used gridded data sets over the conterminous U.S. (CONUS). When compared to the underlying station observations, the gridded data show a 27% reduction in annual 1-day maximum precipitation, 25% increase in wet day fraction, 1.5 to 2.5 day increase in mean wet spell length, 30% low bias in 20-year return values of daily precipitation, and 25% decrease in mean precipitation on wet days. It is shown these changes arise primarily from the time-adjustment applied to put the precipitation gauge observations into a uniform time frame, with the gridding process playing a lesser role. A new daily precipitation data set is developed that omits the time-adjustment (as well as extending the gridded data by 7 years) and is shown to perform significantly better in reproducing extreme precipitation metrics. When the new data set is used to force a land surface model, annually averaged 1-day maximum runoff increases 38% compared to the original data, annual mean runoff increases 17%, evapotranspiration drops 2.3%, and fewer wet days leads to a 3.3% increase in estimated solar insolation. Consequently, these changes are large enough to affect portrayals of flood risk and water balance components important for ecological and climate-change applications across the CONUS.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Predicting kinase inhibitors using bioactivity matrix derived informer sets

Prediction of compounds that are active against a desired biological target is a common step in drug discovery efforts. Virtual screening methods seek some active-enriched fraction of a library for experimental testing. Where data are too scarce to train supervised learning models for compound prioritization, initial screening must provide the necessary data. Commonly, such an initial library is selected on the basis of chemical diversity by some pseudo-random process (for example, the first few plates of a larger library) or by selecting an entire smaller library. These approaches may not produce a sufficient number or diversity of actives. An alternative approach is to select an informer set of screening compounds on the basis of chemogenomic information from previous testing of compounds against a large number of targets. We compare different ways of using chemogenomic data to choose a small informer set of compounds based on previously measured bioactivity data. We develop this Informer-Based-Ranking (IBR) approach using the Published Kinase Inhibitor Sets (PKIS) as the chemogenomic data to select the informer sets. We test the informer compounds on a target that is not part of the chemogenomic data, then predict the activity of the remaining compounds based on the experimental informer data and the chemogenomic data. Through new chemical screening experiments, we demonstrate the utility of IBR strategies in a prospective test on three kinase targets not included in the PKIS.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

A Multimodal Event Catalog and Waveform Data Set That Supports Explosion Monitoring from Nevada, U.S.A.

Multimodal, curated data sets and nuisance event catalogs remain rare in the explosion monitoring community relative to curated seismic data sets. The source of this relative absence is the difficultly in deploying multimodal receivers that sense the seismic, acoustic, and other modalities from multiphysics sources. We provide such a data set in this study that delivers seismic, infrasound, and electromagnetic (magnetometer) sensor records collected over a two–week period, within 255 km of a 10 ton buried chemical explosion called DAG–4 that was located at 37.1146°, –116.0693° on 22 June 2019 21:06:19.88 UTC. This catalog includes 485 seismic, seismoacoustic, and infrasound–only events that an expert analyst manually built by reviewing waveforms from 29 seismic and infrasound sensors. Our data release includes waveforms from these 29 seismic, infrasound, and seismoacoustic stations and two magnetometer stations and their station metadata. We deliver these waveforms in NNSA KB Core CSS.w format (i4) with a corresponding wfdisc table that provides the header information. Here, we expect that this data set will provide a valuable, benchmark resource to develop signal processing algorithms and explosion monitoring methods against manual, human observations.

58 GEOSCIENCES↗
DOE OSTI2016

Development of a MODIS-Derived Surface Albedo Data Set: An Improved Model Input for Processing the NSRDB

A significant source of bias in the transposition of global horizontal irradiance to plane-of-array (POA) irradiance arises from inaccurate estimations of surface albedo. The current physics-based model used to produce the National Solar Radiation Database (NSRDB) relies on model estimations of surface albedo from a reanalysis climatalogy produced at relatively coarse spatial resolution compared to that of the NSRDB. As an input to spectral decomposition and transposition models, more accurate surface albedo data from remotely sensed imagery at finer spatial resolutions would improve accuracy in the final product. The National Renewable Energy Laboratory (NREL) developed an improved white-sky (bi-hemispherical reflectance) broadband (0.3-5.0 ..mu..m) surface albedo data set for processing the NSRDB from two existing data sets: a gap-filled albedo product and a daily snow cover product. The Moderate Resolution Imaging Spectroradiometer (MODIS) sensors onboard the Terra and Aqua satellites have provided high-quality measurements of surface albedo at 30 arc-second spatial resolution and 8-day temporal resolution since 2001. The high spatial and temporal resolutions and the temporal coverage of the MODIS sensor will allow for improved modeling of POA irradiance in the NSRDB. However, cloud and snow cover interfere with MODIS observations of ground surface albedo, and thus they require post-processing. The MODIS production team applied a gap-filling methodology to interpolate observations obscured by clouds or ephemeral snow. This approach filled pixels with ephemeral snow cover because the 8-day temporal resolution is too coarse to accurately capture the variability of snow cover and its impact on albedo estimates. However, for this project, accurate representation of daily snow cover change is important in producing the NSRDB. Therefore, NREL also used the Integrated Multisensor Snow and Ice Mapping System data set, which provides daily snow cover observations of the Northern Hemisphere for the temporal extent of the NSRDB (1998-2015). We provide a review of validation studies conducted on these two products and describe the methodology developed by NREL to remap the data products to the NSRDB grid and integrate them into a seamless daily data set.

14 SOLAR ENERGY↗
DOE OSTI2019

Density Functional Theory Applied to Transition Metal Elements and binaries: Development Application and Results of the V-DM/16 Test Set

Density functional theory (DFT) is undergoing a shift from a descriptive to a predictive tool in the field of solid state physics, heralded by a spike in “high-throughput” studies. However, methods to rigorously evaluate the validity and accuracy of these studies is lacking, raising serious questions when simulation and experiment disagree. In response, we have developed the V-DM/16 test set, designed to evaluate the experimental accuracy of DFT’s various implementations for pe riodic transition metal solids. Our test set evaluates 26 transition metal elements and 80 transition metal alloys across three physical observables: lattice constants, elastic coefficients, and formation energy of alloys. Whether or not a functional can accurately evaluate the formation energy offers key insights into whether the relevant physics are being captured in a simulation, an especially impor tant question in transition metals where active d-electrons can thwart the accuracy of an otherwise well-performing functional. Our test set captures a wide variety of cases where the unique physics present in transition metal binaries can undermine the effectiveness of “traditional” functionals. By application of the V/DM-16 test set, we aim to better characterize the performance of existing functionals on transition metals, and to offer a new tool to rigorously evaluate the performance of new functionals in the future.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2023

Defining the Minimal Set of Microbial Genes Required for Valorization of Lignin Biomass (Final Report)

Project Goals: Lignin is the second most abundant biopolymer on earth and represents a critically underutilized biomass resource for hydrocarbon feedstocks. Despite substantial effort, there is still no efficient process to convert lignin to useable carbon-based platform chemicals and materials. The goal of this project is identify a minimal set of microbial enzymes necessary for lignin breakdown and sufficient for the synthesis of valuable chemical intermediates from lignin isolated as a byproduct of lignocellulosic ethanol production. These genes will be then used to engineer functional whole cell biocatalysts for tunable lignin metabolism. To date, although a number of enzymes have been associated with lignin degradation, most have been tested in isolation (as individual enzymes) and on drastically different substrates -- often dyes that are not related to lignin. In contrast, lignin utilization in nature likely occurs by microbial consortia with multiple enzymes acting synergistically. We propose to examine two separate stages of lignin breakdown carried out by the microbes that do it best: (1) early breakdown of native polymeric lignin into soluble fragments by a set of sequenced wood-rotting fungal species, and (2) downstream metabolism of these soluble lignin fragments to useful chemical intermediates by a panel of sequenced soil saprophytes. Our approach involves testing sets of genes that will be assayed combinatorially in the context of a heterologous expression host. The resulting engineered strains will be systematically assayed using soluble lignin fragments, synthetic defined polymeric lignin, and finally lignin directly sourced from lignocellulosic processing streams. In addition to resulting in a functional whole cell biocatalyst for lignin utilization, we anticipate that this approach will allow us to address key unanswered questions about lignin metabolism in nature, including: (1) Why does the Trametes versicolor genome contain 25 different class II peroxidases? (2) What is the role of laccases in lignin metabolism? Why do some aggressive lignin degraders have many laccases (e.g. >7 in T. versicolor) while others have none (e.g. P. chrysosporium)? (3) How is peroxide provided in a controlled manner to drive peroxidase activity without causing the enzyme inhibition that is so often observed in vitro? (4) What strategies do microbial lignin degraders use to avoid the problem of repolymerization during active lignin degradation? and (5) Can microbial lignin metabolism be diverted for high level production of defined aromatics? A final critical question is whether combining key minimal sets of enzymes from a wide range of organisms will result in engineered strains capable of highly efficient, streamlined pathways for lignin utilization that can be tuned for a specific carbon output. This effort will leverage DOE investments in microbial genome sequencing, and secure a critical channel for lignin biomass utilization that will also help to render lignocellulosic a viable feedstock for the production of renewable liquid biofuels.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

NOW-23: The 2023 National Offshore Wind Data Set

In this report, we present the latest wind resource data specifically tailored for offshore regions in the United States. The data set, known as the 2023 National Offshore Wind data set (NOW-23), has been developed by the National Renewable Energy Laboratory (NREL) and its partners, and surpasses the previous resource data set, the Wind Integration National Dataset (WIND) Toolkit, which was released approximately ten years ago, in its offshore component. The WIND Toolkit has been widely utilized by stakeholders involved in wind resource assessments across the continental United States. However, with significant advancements in numerical weather prediction modeling over the past decade, the NOW-23 data set incorporates the latest research and development progress to provide stakeholders with an updated and cutting-edge resource for offshore wind analysis.

17 WIND ENERGY↗
DOE OSTI2024

Combining compositional data sets introduces error in covariance network reconstruction

Microbial communities are diverse biological systems that include taxa from across multiple kingdoms of life. Notably, interactions between bacteria and fungi play a significant role in determining community structure. However, these statistical associations across kingdoms are more difficult to infer than intra-kingdom associations due to the nature of the data involved using standard network inference techniques. We quantify the challenges of cross-kingdom network inference from both theoretical and practical points of view using synthetic and real-world microbiome data. We detail the theoretical issue presented by combining compositional data sets drawn from the same environment, e.g. 16S and ITS sequencing of a single set of samples, and we survey common network inference techniques for their ability to handle this error. We then test these techniques for the accuracy and usefulness of their intra- and interkingdom associations by inferring networks from a set of simulated samples for which a ground-truth set of associations is known. We show that while the two methods mitigate the error of cross-kingdom inference, there is little difference between techniques for key practical applications including identification of strong correlations and identification of possible keystone taxa (i.e. hub nodes in the network). Furthermore, we identify a signature of the error caused by transkingdom network inference and demonstrate that it appears in networks constructed using real-world environmental microbiome data.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

An Updated Global Grid Point Surface Air Temperature Anomaly Data Set: 1851-1990 (revised 1991) (NDP-020)

This NDP presents land-based monthly surface-air-temperature anomalies (departures from a 1951-1970 reference period mean) on a 5° latitude by 10° longitude global grid. Monthly surface-air-temperature anomalies (departures from a 1957-1975 reference period mean) for the Antarctic (grid points from 65°S to 85°S) are presented in a similar way as a separate data set. The data were derived primarily from the World Weather Records and from the archives of the United Kingdom Meteorological Office. This long-term record of temperature anomalies may be used in studies addressing possible greenhouse-gas-induced climate changes. To date, the data have been employed in producing regional, hemispheric, and global time series for determining whether recent (i.e., post-1900) warming trends have taken place. The present updated version of this data set is identical to the earlier version for all records from 1851-1978 except for the addition of the Antarctic surface-air-temperature anomalies beginning in 1957. Beginning with the 1979 data, this package differs from the earlier version in several ways. Erroneous data for some sites have been corrected after a review of the actual station temperature data, and inconsistencies in the representation of missing values have been removed. For some grid locations, data have been added from stations that had not contributed to the original set. Data from satellites have also been used to correct station records where large discrepancies were evident. The present package also extends the record by adding monthly surface-air-temperature anomalies for the Northern (grid points from 85°N to 0°) and Southern (grid points from 5°S to 60°S) Hemispheres for 1985-1990. In addition, this updated package presents the monthly-mean-temperature records for the individual stations that were used to produce the set of gridded anomalies. The periods of record vary by station. Northern Hemisphere data have been corrected for inhomogeneities, while Southern Hemisphere data are presented in uncorrected form.For access to the data files, click this link to the CDIAC data transition website: http://cdiac.ess-dive.lbl.gov/ndps/ndp020.html

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Primary Velocity and Orbital Phase Effects on Planetary Detectability from Small Epoch Number Data Sets

Cross-correlation analyses of high-resolution spectroscopic data have recently shown great success in directly detecting planetary signals and enabling the characterization of their atmospheres. One such technique aims to observe a system at multiple epochs and combine the measured planetary radial velocities from each epoch into a measurement of the planetary Keplerian orbital velocity K {sub p}, constituting a direct detection of the planetary signal. Recent work has shown that in few-epoch (∼5) data sets, unintended structure can arise at a high level, obscuring the planetary detection. In this work, we look to simulations to examine whether there are ways to reduce this structured noise in few-epoch data sets by careful planning of observations. The choice of observation date allows observers to select the primary (stellar) velocity through a set systemic velocity and chosen barycentric velocity and the planetary orbital phase so we focus on the effects of these two parameters. We find that epochs taken when the primary velocity is near zero and the stellar lines remain relatively fixed to the telluric rest-frame greatly reduce the level of structured noise and allow for much stronger planetary detections, on average more than twice the significance of detections made with epochs using randomly selected primary velocities. Following these results, we recommend that observers looking to build up high-resolution multi-epoch data sets target nights when their system has a near-zero primary velocity.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

The 2023 National Offshore Wind data set (NOW-23)

Abstract. This article introduces the 2023 National Offshore Wind data set (NOW-23), which offers the latest wind resource information for offshore regions in the United States. NOW-23 supersedes, for its offshore component, the Wind Integration National Dataset (WIND) Toolkit, which was published a decade ago and is currently a primary resource for wind resource assessments and grid integration studies in the contiguous United States. By incorporating advancements in the Weather Research and Forecasting (WRF) model, NOW-23 delivers an updated and cutting-edge product to stakeholders. In this article, we present the new data set which underwent regional tuning and performance validation against available observations and has data available from 2000 through, depending on the region, 2019–2022. We also provide a summary of the uncertainty quantification in NOW-23, along with NOW-WAKES, a 1-year post-construction data set that quantifies expected offshore wake effects in the US Mid-Atlantic lease areas. Stakeholders can access the NOW-23 data set at https://doi.org/10.25984/1821404 (Bodini et al., 2020).

17 WIND ENERGY↗
DOE OSTI2017

The compression–error trade-off for large gridded data sets

The netCDF-4 format is widely used for large gridded scientific data sets and includes several compression methods: lossy linear scaling and the non-lossy deflate and shuffle algorithms. Many multidimensional geoscientific data sets exhibit considerable variation over one or several spatial dimensions (e.g., vertically) with less variation in the remaining dimensions (e.g., horizontally). On such data sets, linear scaling with a single pair of scale and offset parameters often entails considerable loss of precision. We introduce an alternative compression method called "layer-packing" that simultaneously exploits lossy linear scaling and lossless compression. Layer-packing stores arrays (instead of a scalar pair) of scale and offset parameters. An implementation of this method is compared with lossless compression, storing data at fixed relative precision (bit-grooming) and scalar linear packing in terms of compression ratio, accuracy and speed. When viewed as a trade-off between compression and error, layer-packing yields similar results to bit-grooming (storing between 3 and 4 significant figures). Bit-grooming and layer-packing offer significantly better control of precision than scalar linear packing. Relative performance, in terms of compression and errors, of bit-groomed and layer-packed data were strongly predicted by the entropy of the exponent array, and lossless compression was well predicted by entropy of the original data array. Layer-packed data files must be "unpacked" to be readily usable. The compression and precision characteristics make layer-packing a competitive archive format for many scientific data sets.

58 GEOSCIENCES↗
NASA NTRS1975

Life analysis of helical gear sets using Lundberg-Palmgren theory

A mathematical model is developed for surface fatigue life of helical gears. The expected fatigue life of a pinion, gear, or gear set may be calculated from the model. An equation for the dynamic capacity of a gear set was also derived. Dynamic capacity is the transmitted tangential load which gives a 90 percent probability of survival of the gear set for one million pinion revolutions. The equations, when simplified by setting the helix angle to zero, reduce to the results which were previously developed for spur gears. A sample calculation is given which illustrates the use of the new fatigue life model.

Coy, J. J.↗
NASA NTRS1975

The geometric approach to sets of ordinary differential equations and Hamiltonian dynamics

The calculus of differential forms is used to discuss the local integration theory of a general set of autonomous first order ordinary differential equations. Geometrically, such a set is a vector field V in the space of dependent variables. Integration consists of seeking associated geometric structures invariant along V: scalar fields, forms, vectors, and integrals over subspaces. It is shown that to any field V can be associated a Hamiltonian structure of forms if, when dealing with an odd number of dependent variables, an arbitrary equation of constraint is also added. Families of integral invariants are an immediate consequence. Poisson brackets are isomorphic to Lie products of associated CT-generating vector fields. Hamilton's variational principle follows from the fact that the maximal regular integral manifolds of a closed set of forms must include the characteristics of the set.

Estabrook, F. B.↗
NASA NTRS1980

Geometric representation methods for multi-type self-defining remote sensing data sets

Efficient and convenient representation of remote sensing data is highly important for an effective utilization. The task of merging different data types is currently dealt with by treating each case as an individual problem. A description is provided of work which is carried out to standardize the multidata merging process. The basic concept of the new approach is that of the self-defining data set (SDDS). The creation of a standard is proposed. This standard would be such that data which may be of interest in a large number of earth resources remote sensing applications would be in a format which allows convenient and automatic merging. Attention is given to details regarding the multidata merging problem, a geometric description of multitype data sets, image reconstruction from track-type data, a data set generation system, and an example multitype data set.

Anuta, P. E.↗
NASA NTRS1981

SPAR data set contents

The contents of the stored data sets of the SPAR (space processing applications rocket) finite element structural analysis system are documented. The data generated by each of the system's processors are stored in a data file organized as a library. Each data set, containing a two-dimensional table or matrix, is identified by a four-word name listed in a table of contents. The creating SPAR processor, number of rows and columns, and definitions of each of the data items are listed for each data set. An example SPAR problem using these data sets is also presented.

Cunningham, S. W.↗
NASA NTRS1984

A calibration adjustment technique combining ERB parameters from different remote sensing platforms into a long-term data set

Earth-radiation-budget (ERB) experiments on board the Nimbus-6 (ERB 6) and Nimbus-7 (ERB 7) spacecraft have measured wide-field-of-view total (0.2 to 50 micron), shortwave (0.2 to 3.8 micron), and near-infrared (0.7 to 2.8 micron) terrestrial irradiances for a joint lifetime of over 8 years. Though the spectral characteristics of both experiments are nearly identical, instrument degradation and altitude differences introduce discrepancies between the two data sets. ERB parameters from these two observing platforms may be combined into a scientifically meaningful data set only after these discrepancies are eliminated. To facilitate the creation of a long-term ERB data set, comparisons of the ERB 6 experiment irradiances with, and calibration adjustments with respect to, the corresponding ERB 7 irradiances have been performed. Two calibration methods were developed and applied to the irradiance data from 28 pairs of collocated orbits. The differential effects of altitude, illumination, albedo, and scene inhomogeneities were applied. The result is a set of calibration adjustments that adjust the ERB 6 data to match the ERB 7 data.

Ardanuy, P. E.↗