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At least 1,171 records · Page 65
All sourcesNASA NTRSDOE OSTI
NASA NTRS1993

Development, refinement, and testing of a short term solar flare prediction algorithm

Progress toward performance of the tasks and accomplishing the goals set forth in the two year Research Grant included primarily analysis of digital data sets and determination of methodology associated with the analysis of the very large, unique, and complex collection of digital solar magnetic field data. The treatment of each magnetogram as a unique set of data requiring special treatment was found to be necessary. It is determined that a person familiar with the data, the analysis system, and logical, coherent outcome of the analysis must conduct each analysis, and interact with the analysis program(s) significantly sometimes many iterations for successful calibration and analysis of the data set. With this interaction, the data sets yield valuable, coherent analyses. During this period, it was also decided that only data sets taken inside heliographic longitudes (Central Meridian Distance) East and West 30 degrees (within 30 degrees of the Central Meridian of the Sun). If the total data set is then found to be numerically inadequate for the final analysis, 30 - 45 degrees Central Meridian Distance data will then be analyzed. The Optical Data storage system (MSFC observatory) was found appropriate for use both in intermediate storage of the data (preliminary to analysis), and for storage of the analyzed data sets for later parametric extraction.

Smith, Jesse B., Jr.↗
DOE OSTI2022

Fast Gaussian Process Estimation for Large-Scale In Situ Inference using Convolutional Neural Networks

Exascale computing will bring with it significant I/O limitations. One foreseeable consequence of such restrictions is that the user can save only a small fraction of complex simulation data to disk for subsequent analysis. An alternative is to fit statistical models to data in situ, that is, inside the simulation as it runs. This option requires extremely fast statistical estimation to avoid slowing down the simulation. Gaussian processes (GPs) have state-of-the-art predictive performance for modeling spatial data. However, standard estimation methods for GPs scale quite poorly to large data sets as parameter estimation requires inverting a covariance matrix to the size of the data set. In the presented work, we use a convolutional neural network (CNN) to predict the GP parameters for a spatial data set, from a simulation or otherwise, rather than optimize the parameters directly. Here, our presented case study models spatial data from E3SM, the Department of Energy’s Exascale climate model. The CNN is trained on synthetic data simulated from GP models with known parameters and then applied to data from the climate simulation. In the presented examples, the neural network scheme produces parameter estimates that compare well with standard methods such as maximum likelihood estimation in predictive performance but is obtained four orders of magnitude faster.

big data↗
DOE OSTI2025

Day-Ahead Probabilistic Forecasting of Net-Load and Demand Response Potentials with High Penetration of Behind-the-Meter Solar-plus-Storage

The goal of this project is to develop advanced methods for day-ahead net-load forecasting, by leveraging the state-of-the-art machine learning techniques. The developed models produce both point and probabilistic forecasts for a variety of use cases, and are versatile to work with different types of data sets. The innovation lies in the novel design of the architectures, leveraging the most recent advances in machine learning that have not been explored in power systems, accompanied by techniques in the broader artificial intelligence fields such as fuzzy systems. This project has achieved the following accomplishments: (1) preprocessing of over 10 data sets covering varying geographical regions, time horizons, and system levels, which form a robust foundation for training and evaluating forecasting models across a wide range of realistic grid scenarios; (2) development of an interactive web app that enables exploratory analysis of load and generation data, and supports better understanding of data trends, anomalies, and correlations, facilitating model development and stakeholder engagement; (3) implementation of over 10 benchmark models for point and probabilistic forecasting, which include a mix of conventional machine learning methods and state-of-the-art deep learning approaches, providing a comprehensive baseline for performance comparison and validation of the proposed models; (4) development of a fuzzy system based gradient boosting model, tailored for small (less than 3 years) data sets, which achieves a mean absolute percentage error (MAPE) of 4% for point forecasting and a 20% improvement in average pinball loss for probabilistic forecasting; (5) development of a Transformer (a state-of-the-art deep learning architecture) based neural network model, tailored for large (3 years or more) data sets, which achieves a MAPE of 2% for point forecasting and a 20% improvement in average pinball loss for probabilistic forecasting; (6) development of a methodology for quantifying DR potential, and extensions of the previous models for multi-target forecasting of net load and DR potential, which achieve a MAPE of 10% for DR potential.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Ancient Rapid Radiation Explains Most Conflicts Among Gene Trees and Well-Supported Phylogenomic Trees of Nostocalean Cyanobacteria

Prokaryotic genomes are often considered to be mosaics of genes that do not necessarily share the same evolutionary history due to widespread horizontal gene transfers (HGTs). Consequently, representing evolutionary relationships of prokaryotes as bifurcating trees has long been controversial. However, studies reporting conflicts among gene trees derived from phylogenomic data sets have shown that these conflicts can be the result of artifacts or evolutionary processes other than HGT, such as incomplete lineage sorting, low phylogenetic signal, and systematic errors due to substitution model misspecification. Here, we present the results of an extensive exploration of phylogenetic conflicts in the cyanobacterial order Nostocales, for which previous studies have inferred strongly supported conflicting relationships when using different concatenated phylogenomic data sets. We found that most of these conflicts are concentrated in deep clusters of short internodes of the Nostocales phylogeny, where the great majority of individual genes have low resolving power. We then inferred phylogenetic networks to detect HGT events while also accounting for incomplete lineage sorting. Our results indicate that most conflicts among gene trees are likely due to incomplete lineage sorting linked to an ancient rapid radiation, rather than to HGTs. Moreover, the short internodes of this radiation fit the expectations of the anomaly zone, i.e., a region of the tree parameter space where a species tree is discordant with its most likely gene tree. In this work, we demonstrated that concatenation of different sets of loci can recover up to 17 distinct and well-supported relationships within the putative anomaly zone of Nostocales, corresponding to the observed conflicts among well-supported trees based on concatenated data sets from previous studies. Our findings highlight the important role of rapid radiations as a potential cause of strongly conflicting phylogenetic relationships when using phylogenomic data sets of bacteria. We propose that polytomies may be the most appropriate phylogenetic representation of these rapid radiations that are part of anomaly zones, especially when all possible genomic markers have been considered to infer these phylogenies.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Physical discovery in representation learning via conditioning on prior knowledge

Recent advances in electron, scanning probe, optical, and chemical imaging and spectroscopy yield bespoke data sets containing the information of structure and functionality of complex systems. In many cases, the resulting data sets are underpinned by low-dimensional simple representations encoding the factors of variability within the data. The representation learning methods seek to discover these factors of variability, ideally further connecting them with relevant physical mechanisms. However, generally, the task of identifying the latent variables corresponding to actual physical mechanisms is extremely complex. Here, we present an empirical study of an approach based on conditioning the data on the known (continuous) physical parameters and systematically compare it with the previously introduced approach based on the invariant variational autoencoders. The conditional variational autoencoder (cVAE) approach does not rely on the existence of the invariant transforms and hence allows for much greater flexibility and applicability. Interestingly, cVAE allows for limited extrapolation outside of the original domain of the conditional variable. However, this extrapolation is limited compared to the cases when true physical mechanisms are known, and the physical factor of variability can be disentangled in full. We further show that introducing the known conditioning results in the simplification of the latent distribution if the conditioning vector is correlated with the factor of variability in the data, thus allowing us to separate relevant physical factors. We initially demonstrate this approach using 1D and 2D examples on a synthetic data set and then extend it to the analysis of experimental data on ferroelectric domain dynamics visualized via piezoresponse force microscopy.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
NASA NTRS2010

ICESat Lidar and Global Digital Elevation Models: Application to DESDynI

Geodetic control is extremely important in the production and quality control of topographic data sets, enabling elevation results to be referenced to an absolute vertical datum. Global topographic data with improved geodetic accuracy achieved using global Ground Control Point (GCP) databases enable more accurate characterization of land topography and its change related to solid Earth processes, natural hazards and climate change. The multiple-beam lidar instrument that will be part of the NASA Deformation, Ecosystem Structure and Dynamics of Ice (DESDynI) mission will provide a comprehensive, global data set that can be used for geodetic control purposes. Here we illustrate that potential using data acquired by NASA's Ice, Cloud and land Elevation Satellite (ICEsat) that has acquired single-beam, globally distributed laser altimeter profiles (+/-86deg) since February of 2003 [1, 2]. The profiles provide a consistently referenced elevation data set with unprecedented accuracy and quantified measurement errors that can be used to generate GCPs with sub-decimeter vertical accuracy and better than 10 m horizontal accuracy. Like the planned capability for DESDynI, ICESat records a waveform that is the elevation distribution of energy reflected within the laser footprint from vegetation, where present, and the ground where illuminated through gaps in any vegetation cover [3]. The waveform enables assessment of Digital Elevation Models (DEMs) with respect to the highest, centroid, and lowest elevations observed by ICESat and in some cases with respect to the ground identified beneath vegetation cover. Using the ICESat altimetry data we are developing a comprehensive database of consistent, global, geodetic ground control that will enhance the quality of a variety of regional to global DEMs. Here we illustrate the accuracy assessment of the Shuttle Radar Topography Mission (SRTM) DEM produced for Australia, documenting spatially varying elevation biases of several meters in magnitude.

Carabajal, Claudia C.↗
DOE OSTI2023

Deep Learning for Multigroup Cross-Section Representation in Two-Step Core Calculations

Here we investigate using deep learning, a type of machine-learning algorithm employing multiple layers of artificial neurons, for the mathematical representation of multigroup cross sections for use in the Griffin reactor multiphysics code for two-step deterministic neutronics calculations. A three-dimensional fuel element typical of a high-temperature gas reactor as well as a two-dimensional sodium-cooled fast reactor lattice are modeled using the Serpent Monte Carlo code, and multigroup macroscopic cross sections are generated for various state parameters to produce a training data set and a separate validation data set. A fully connected, feedforward neural network is trained using the open-source PyTorch machine-learning framework, and its accuracy is compared against the standard piecewise linear interpolation model. Additionally, we provide in this work a generic technique for propagating the cross-section model errors up to the k eff using sensitivity coefficients with the first-order uncertainty propagation rule. Quantifying the eigenvalue error due to the cross-section regression errors is especially practical for appropriately selecting the mathematical representation of the cross sections. We demonstrate that the artificial neural network model produces lower errors and therefore enables better accuracy relative to the piecewise linear model when the cross sections exhibit nonlinear dependencies; especially when a coarse grid is employed, where the errors can be halved by the artificial neural network. However, for linearly dependent multigroup cross sections as found for the sodium-cooled fast reactor case, a simpler linear regression outperforms deeper networks.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2021

The kinematic properties of Milky Way stellar halo populations

ABSTRACT In the Gaia era, stellar kinematics are extensively used to study Galactic halo stellar populations, to search for halo structures, and to characterize the interface between the halo and hot disc populations. We use distribution function-based models of modern data sets with 6D phase space data to qualitatively describe a variety of kinematic spaces commonly used in the study of the Galactic halo. Furthermore, we quantitatively assess how well each kinematic space can separate radially anisotropic from isotropic halo populations. We find that scaled action space (the ‘action diamond’) is superior to other commonly used kinematic spaces at this task. We present a new, easy to implement selection criterion for members of the radially anisotropic Gaia–Enceladus merger remnant. Assuming a 1:1 ratio of Gaia–Enceladus stars to more isotropic halo, we find our selection achieves a sample purity of 86 per cent in our models with respect to contamination from the more isotropic halo. We compare this criterion to literature criteria, finding that it produces the highest purity in the resulting samples, at the expense of a modest reduction in completeness. We also show that selection biases that underlie nearly all contemporary spectroscopic data sets can noticeably impact the E−Lz distribution of samples in a manner that may be confused for real substructure. We conclude by providing recommendations for how authors should use stellar kinematics in the future to study the Galactic stellar halo.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

2025 Annual INMM Graph and Tables for High Purity Germanium Detector Normalization Presentation

The data set includes gamma spectroscopy peak data for measurements taken with two different high purity germanium detectors using a mixed nuclide source and a U-235 fuel rod. There are a total of 5 specific energy peaks that were analyzed for the mixed nuclide source stemming from Am-241, Cd-109, Cs-137, and Co-60. There are a total of 3 specific energy peaks that were analyzed for the U-235 fuel rod. The data set includes the calculations and results from using a linear correction factor, absolute efficiency curve, and relative efficiency curve to compare the net peaks counts from two different detectors.

Drumm, Natalie Daphne [Sandia National Laboratorie↗
DOE OSTI2023

WIND Toolkit Raw Data Reformatted

Wind Integration National Dataset Toolkit The Wind Integration National Dataset (WIND) Toolkit is an update and expansion of the Eastern Wind Integration Data Set and Western Wind Integration Data Set. It supports the next generation of wind integration studies.

17 WIND ENERGY↗
NASA NTRS1983

Methodology for interpretation of SST retrievals using the AVHRR split window algorithm

Intercomparisons of sea surface temperature (SST) products derived from the operational NOAA-7 AVHRR-II algorithm and in situ observations are made. The 1982 data sets consist of ship survey data during the winter from the Mid-Atlantic Bight (MAB), ship and buoy measurements during April and September in the Gulf of Mexico and shipboard observations during April off the N.W. Spanish coast. The analyses included single pixel comparisons and the warmest pixel technique for 2 x 2 pixel and 10 x 10 pixel areas. The reason for using multi-pixel areas was for avoiding cloud contaminated pixels in the vicinity of the field measurements. Care must be taken when applying the warmest pixel technique near oceanic fronts. The Gulf of Mexico results clearly indicate a persistent degradation in algorithm accuracy due to El Chichon aerosols. The MAB and Spanish data sets indicate that very accurate estimates can be achieved if care is taken to avoid clouds and oceanic fronts.

Barbieri, R. W.↗
NASA NTRS1993

Rossby-gravity waves in tropical total ozone data

Evidence for Rossby-gravity waves in tropical data fields produced by the European Center for Medium Range Weather Forecasts (ECMWF) was recently reported. Similar features are observable in fields of total column ozone from the Total Ozone Mapping Spectrometer (TOMS) satellite instrument. The observed features are episodic, have zonal (east-west) wavelengths of 6,000-10,000 km, and oscillate with periods of 5-10 days. In accord with simple linear theory, the modes exhibit westward phase progression and eastward group velocity. The significance of finding Rossby-gravity waves in total ozone fields is that (1) the report of similar features in ECMWF tropical fields is corroborated with an independent data set and (2) the TOMS data set is demonstrated to possess surprising versatility and sensitivity to relatively smaller scale tropical phenomena.

Stanford, J. L.↗
NASA NTRS1993

Rossby-gravity waves in tropical total ozone

Randel (1992) has recently reported evidence for Rossby-gravity waves in tropical data fields produced by the European Centre for Medium Range Weather Forecasts (ECMWF). The purpose of this paper is to show that similar features are observable in fields of total column ozone from the Total Ozone Mapping Spectrometer (TOMS) satellite instrument. The observed features are episodic, have zonal (east-west) wavelengths of approximately 6,000-10,000 km and oscillate with periods of 5-10 days. The modes exhibit westward phase progression and eastward group velocity. A simple linear model is used to estimate the magnitude of total ozone perturbations induced by Rossby-gravity waves. The model is able to account for the magnitude of observed TOMS features as well as their asymmetry about the equator. The significance of finding Rossby-gravity waves in total ozone fields is that: (1) the report of similar features in ECMWF tropical fields is corroborated with an independent data set; and (2) the TOMS data set is demonstrated to possess surprising versatility and sensitivity to relatively smaller scale tropical phenomena.

Stanford, J. L.↗
NASA NTRS1997

Dynamic Ground Effect for a Cranked Arrow Wing Airplane

Flight-determined ground effect characteristics for an F-16XL airplane are presented and correlated with wind tunnel predictions and similar flight results from other aircraft. Maneuvers were conducted at a variety of flightpath angles. Conventional ground effect flight test methods were used, with the exception that space positioning data were obtained using the differential global positioning system (DGPS). Accuracy of the DGPS was similar to that of optical tracking methods, but it was operationally more attractive. The dynamic flight determined lift and drag coefficient increments were measurably lower than steady-state wind-tunnel predictions. This relationship is consistent with the results of other aircraft for which similar data are available. Trends in the flight measured lift increments caused by ground effect as a function of flightpath angle were evident but weakly correlated. An engineering model of dynamic ground effect was developed based on linear aerodynamic theory and super-positioning of flows. This model was applied to the F-16XL data set and to previously published data for an F-15 airplane. In both cases, the model provided an engineering estimate of the ratio between the steady-state and dynamic data sets.

Curry, Robert E.↗
NASA NTRS1997

Spherical Harmonics Analysis of the ECMWF Global Wind Fields at the 10-Meter Height Level During 1985: A Collection of Figures Illustrating Results

Half-daily global wind speeds in the east-west (u) and north-south (v) directions at the 10-meter height level were obtained from the European Centre for Medium Range Weather Forecasts (ECMWF) data set of global analyses. The data set covered the period 1985 January to 1995 January. A spherical harmonic expansion to degree and order 50 was used to perform harmonic analysis of the east-west (u) and north-south (v) velocity field components. The resulting wind field is displayed, as well as the residual of the fit, at a particular time. The contribution of particular coefficients is shown. The time variability of the coefficients up to degree and order 3 is presented. Corresponding power spectrum plots are given. Time series analyses were applied also to the power associated with degrees 0-10; the results are included.

Sanchez, Braulio V.↗
NASA NTRS2004

Spaceflight Operations Services Grid Prototype

NASA over the years has developed many types of technologies and conducted various types of science resulting in numerous variations of operations, data and applications. For example, operations range from deep space projects managed by JPL, Saturn and Shuttle operations managed from JSC and KSC, ISS science operations managed from MSFC and numerous low earth orbit satellites managed from GSFC that are varied and intrinsically different but require many of the same types of services to fulfill their missions. Also, large data sets (databases) of Shuttle flight data, solar system projects and earth observing data exist which because of their varied and sometimes outdated technologies are not and have not been fully examined for additional information and knowledge. Many of the applications/systems supporting operational services e.g. voice, video, telemetry and commanding, are outdated and obsolete. The vast amounts of data are located in various formats, at various locations and range over many years. The ability to conduct unified space operations, access disparate data sets and to develop systems and services that can provide operational services does not currently exist in any useful form. In addition, adding new services to existing operations is generally expensive and with the current budget constraints not feasible on any broad level of implementation. To understand these services a discussion of each one follows. The Spaceflight User-based Services are those services required to conduct space flight operations. Grid Services are those Grid services that will be used to overcome, through middleware software, some or all the problems that currently exists. In addition, Network Services will be discussed briefly. Network Services are crucial to any type of remedy and are evolving adequately to support any technology currently in development.

Bradford, Robert N.↗
NASA NTRS2007

Application of NASA Giovanni to Coastal Zone Remote Sensing Search

The Goddard Earth Sciences Data and Information Services Center (GES DISC) Interactive Online Visualization ANd aNalysis Infrastructure (Giovanni) provides rapid access to, and enables effective utilization of, remotely-sensed data that are applicable to investigations of coastal environmental processes. Data sets in Giovanni include precipitation data from the Tropical Rainfall Measuring Mission (TRMM), particularly useful for coastal storm investigations; ocean color radiometry data from the Sea-viewing Wide Field-of-view Sensor (SeaWIFS) and Moderate Resolution Imaging Spectroradiometer (MODIS), useful for water quality evaluation, phytoplankton blooms, and terrestrial-marine interactions; and atmospheric data from MODIS and the Advanced Infrared Sounder (AIRS), providing the capability to characterize atmospheric variables. Giovanni provides a simple interface allowing discovery and analysis of environmental data sets with accompanying graphic visualizations. Examples of Giovanni investigations of the coastal zone include hurricane and storm impacts, hydrologically-induced phytoplankton blooms, chlorophyll trend analysis, and dust storm characterization. New and near-future capabilities of Giovanni will be described.

Acker, James↗