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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 973 records · Page 54
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Raccoon density estimation from camera traps for raccoon rabies management

Abstract Density estimation for unmarked animals is particularly challenging, yet density estimates are often necessary for effective wildlife management. Raccoons ( Procyon lotor ) are the primary terrestrial wildlife reservoir for Lyssavirus rabies within the United States. The raccoon rabies variant (RRVV) is actively managed at landscape scales using oral rabies vaccination (ORV) within the eastern United States. To effectively manage RRVV, it is important to know the density of raccoons to appropriately scale the density of ORV baits distributed on the landscape. We compared methods to estimate raccoon densities from camera‐trap data versus more intensive capture‐mark‐recapture (CMR) estimates across 2 land cover types (upland pine and bottomland hardwood) in the southeastern United States during 2019 and 2020. We evaluated the effect of alternative camera configurations and durations of camera trapping on density estimates and used an N‐mixture model to estimate raccoon densities, including covariates on abundance and detection. We further compared different methods of scaling camera‐based counts, with the maximum number of raccoons seen on any given image within a day best explaining density. Camera‐trap density estimates were moderately correlated with CMR estimates ( r = 0.56). However, densities from camera‐trap data were more reliable when classifying category of density as an index used to inform management (83% correct when compared to CMR estimates), although the densities in our study fell into the 2 lowest density classes only. Using more cameras reduced bias and uncertainty around density estimates; however, if ≤6 camera traps were used at a site, a line transect approach proved less biased than a grid design. Camera trapping should be conducted for at least 3 weeks for more accurate estimates of raccoon population density in our study area (<5% bias). We show that camera‐trap data can be used to assign raccoon densities to management‐relevant density index bins, but more studies are needed to ensure reliability across a greater range of environmental conditions and raccoon densities.

Davis, Amy J.↗
DOE OSTI2022

ORNL Neutron Cross Section Measurements of 90 Zr

Nuclear criticality modeling and simulations rely on the quality of the existing evaluated nuclear data libraries such as Evaluated Nuclear Data File (ENDF)/B, the Joint Evaluated Fission and Fusion (JEFF) nuclear data library, or the Japanese Evaluated Nuclear Data Library (JENDL). In some cases, the cross-section evaluations of those libraries were found to be deficient in describing criticality benchmarks accurately. More than two decades ago, the US Nuclear Criticality Safety Program (NCSP) established a Nuclear Data (ND) task which encompassed experiments and evaluations. In response to this, the Oak Ridge National Laboratory (ORNL) formed a Nuclear Criticality and Data group which performed ND experiments, data analysis, and evaluations to produce ENDF files for the ND libraries as identified in the NCSP Five-Year Plan. Before being submitted to the ENDF library, files were processed and tested for performance by running benchmark calculations. This procedure was centralized in the ORNL group and is now often referred to as the ND pipeline. NCSP collaborates with the Joint Research Center (JRC) of the European Commission in Geel, Belgium, to perform high-resolution neutron-induced cross section measurements at the Geel Linear Accelerator (GELINA). The objective is to address emerging ND problems in criticality calculations. Difficulties with ND include insufficient neutron energy range, missing covariances, and previously unrecognized inaccuracies with experiments. New neutron total and capture cross sections of 90 Zr in the neutron energy range from 100 eV to several hundred keV were recently performed. These measured data will be used, together with existing high-resolution transmission data from a metallic 90 Zr sample, to improve representation of the cross sections.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Ion Clusters Reveal the Sources, Impacts, and Drivers of Freshwater Salinization

Population growth, land use change, climate change, and natural resource extraction are driving the salinization of freshwater resources worldwide. Reversing these trends will require data-centric approaches that identify salt sources, environmental drivers, and ecosystem responses. In this study, we applied principal component analysis and hierarchical clustering to identify ion covariance patterns, or “ion clusters,” in Broad Run, an urban stream in the Mid-Atlantic United States. These clusters correspond to distinct hydrologic regimes and reveal specific salinization risks: (1) phosphorus pollution mobilized during summer storms (Cluster 1); (2) elevated concentrations of sulfate and bicarbonate during baseflow (Cluster 2), likely reflecting groundwater discharge; and (3) elevated specific conductance and sodium, chloride, and potassium ion concentrations during snowmelt and rain-on-snow events (Cluster 3), driven by deicer and anti-icer wash-off. These ion fingerprints offer a transferable framework for diagnosing salt sources, assessing ecological risk, and identifying management targets. Our findings underscore the need for next-generation stormwater infrastructure and smart growth policies to protect aquatic life in rapidly urbanizing watersheds.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

AmeriFlux MX-Tes Tesopaco, secondary tropical dry forest

This is the AmeriFlux version of the carbon flux data for the site MX-Tes Tesopaco, secondary tropical dry forest. Site Description - This tower was located in the "Ejido la Estrella" where the eddy covariance tower is located 7.5 km east of the town Rosario de Tesopaco, in southeastern Sonora, Mexico. The tower was positioned in the center of a homogeneous flat terrain at the foothills of the Madre Occidental. This ecosystem lies in a highly seasonal region under the influence of the North American Monsoon that brings about 70% of the rains from July to September. The site is dominated by leguminous trees. Some of the dominant species are Lysiloma divaricatum, Ipomoea arborescens, Acacia cochliacantha, Haematoxylum brasiletto, and Celtis reticulata. The mean basal area at the study site is 14.83 ± 3.3m2 ha-1, and the aboveground biomass is estimated to be 43.20±9.5Mgha-1. These values and the high counts of Acacia cochliacantha indicate that the area is a secondary forest with significant management activities in the past.

Yepez, Enrico A.↗
NASA NTRS1975

Stochastic process approximation for recursive estimation with guaranteed bound on the error covariance

An approach, is proposed for the design of approximate, fixed order, discrete time realizations of stochastic processes from the output covariance over a finite time interval, was proposed. No restrictive assumptions are imposed on the process; it can be nonstationary and lead to a high dimension realization. Classes of fixed order models are defined, having the joint covariance matrix of the combined vector of the outputs in the interval of definition greater or equal than the process covariance; (the difference matrix is nonnegative definite). The design is achieved by minimizing, in one of those classes, a measure of the approximation between the model and the process evaluated by the trace of the difference of the respective covariance matrices. Models belonging to these classes have the notable property that, under the same measurement system and estimator structure, the output estimation error covariance matrix computed on the model is an upper bound of the corresponding covariance on the real process. An application of the approach is illustrated by the modeling of random meteorological wind profiles from the statistical analysis of historical data.

Menga, G.↗
NASA NTRS1985

Binning of satellite magnetic anomalies

Crustal magnetic anomaly signals over satellite orbits were simulated to investigate numerical averaging as an anomaly estimator. Averaging as an anomaly estimator involves significant problems concerning spatial and amplitude smoothing of the satellite magnetic observations. The results of simulations suggest that the error of numerical averaging constitutes a small and relatively minor component of the total error-budget of higher orbital anomaly estimates, whereas for lower orbital estimates numerical averaging error increases substantially. As an alternative to numerical averaging, least-squares collocation was investigated and observed to produce substantially more accurate anomaly estimates, particularly as the orbital elevation of prediction was decreased towards the crustal sources. In contrast to averaging, collocation is a significantly more resource-intensive procedure to apply because of the practical, but surmountable problems related to establishing and inverting the covariance matrix for accurate anomaly prediction. However, collocation may be much more effectively used to exploit the anomaly details contained in the lower orbital satellite magnetic data for geologic analysis.

Goyal, H. K.↗
NASA NTRS2017

Assimilation of AERONET and MODIS AOT Observations Using Variational and Ensemble Data Assimilation Methods and Its Impact on Aerosol Forecasting Skill

Data assimilation of Aerosol Robotic Network (AERONET) and Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol optical thickness (AOT) for aerosol forecasting was tested within the Navy Aerosol Analysis Prediction System (NAAPS) framework, using variational and ensemble data assimilation methods. Navy aerosol forecasting currently makes use of a deterministic NAAPS simulation coupled to Navy Variational Data Assimilation System for aerosol optical depth, a two-dimensional variational data assimilation system, for MODIS AOT assimilation. An ensemble version of NAAPS (ENAAPS) coupled to an ensemble adjustment Kalman filter (EAKF) from the Data Assimilation Research Testbed was recently developed, allowing for a range of data assimilation and forecasting experiments to be run with deterministic NAAPS and ENAAPS. The main findings are that the EAKF, with its flow-dependent error covariances, makes better use of sparse observations such as AERONET AOT. Assimilating individual AERONET observations in the two-dimensional variational system can increase the analysis errors when observations are located in high AOT gradient regions. By including AERONET with MODIS AOT assimilation, the magnitudes of peak aerosol events (AOT> 1) were better captured with improved temporal variability, especially in India and Asia where aerosol prediction is a challenge. Assimilating AERONET AOT with MODIS had little impact on the 24 h forecast skill compared to MODIS assimilation only, but differences were found downwind of AERONET sites. The 24 h forecast skill was approximately the same for forecasts initialized with analyses from AERONET AOT assimilation alone compared to MODIS assimilation, particularly in regions where the AERONET network is dense; including the United States and Europe, indicating that AERONET could serve as a backup observation network for over-land synoptic-scale aerosol events.

Rubin, Juli I.↗
DOE OSTI2021

Bounding uncertainty in functional data: A case study

Functional data are fast becoming a preeminent source of information across a wide range of industries. A particularly challenging aspect of functional data is bounding uncertainty. In this unique case study, we present our attempts at creating bounding functions for selected applications at Sandia National Laboratories (SNL). The first attempt involved a simple extension of functional principal component analysis (fPCA) to incorporate covariates. Though this method was straightforward, the extension was plagued by poor coverage accuracy for the bounding curve. This led to a second attempt utilizing elastic methodology which yielded more accurate coverage at the cost of more complexity.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1983

Study of LANDSAT-D thematic mapper performance as applied to hydrocarbon exploration

Analysis of the tapes of the Detroit, Michigan scene, which were received in fully processed format with geometric and radiometric correction, shows evidence of an along line data slip every sixteenth line in TM channel 2. Very large scale products were therefore generated in false color using channels 1, 3, and 4. Subjective evaluation of these enhanced scenes indicates that they are acceptable for interpretation at scales up to 1:50,000 and should be useful for change mapping probably up to 1:24,000 scale. The significant striping visible in water bodies for both the natural color and false color products indicates that the detector calibration is probably performing below the preflight specification. Variance-covariance matrices were computed and principal component analysis were performed for a set of 512 x 512 windows within the Arkansas scene. Initial analysis shows the shortwave infrared channels (TM 5 and 6) are a highly significant data source. The thermal channel (TM 7) shows negative correlation with TM 1 through 4.

Source record↗
DOE OSTI2022

Scalable Gaussian Processes, GPyTorch Application Benchmarking, and Targeted Adaptive Design (TAD) on ThetaGPU

We aim at showcasing the scalability of Gaussian Process (GP). The naive GP implementation scales cubically with data size, which can be prohibitive, so GP has not heretofore been considered suitable for very large-scale problem settings. We take advantage of GPyTorch, a library for scalable GPs built on top of PyTorch that incorporates GPU acceleration. With GPyTorch, one can achieve nearly linear scaling with structured kernel interpolation (SKI) and constant-time predictive covariances computation with LanczOs Variance Estimates (LOVE) while preserving accuracy. We also take advantage of the computational power of ThetaGPU, a supercomputer of Argonne Leadership Computing Facility (ALCF). In addition, we implement a scalable, GPU-ready version of Targeted Adaptive Design (TAD), a GP-based data-driven algorithm that efficiently searches the control space of an advanced manufacturing experiment for settings capable of producing a required design within a specified tolerance, despite the poorly known mapping from control settings to design. We finally show our benchmarking for GPyTorch and TAD performance on CPU vs. ThetaGPU and discuss the results and implications.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Developing a Continuous Nuclear Data Evaluation Method for the Resolved and Unresolved Resonance Regions With FITAPI and SAMMY [Poster]

SAMMY is an R-matrix Bayesian fitting program used in the analysis of nuclear cross section data. SAMMY produces separate cross section models in the resolved and unresolved resonance regions. This project develops a new method of continuous evaluation joining both energy regions with FITAPI. Full energy spectrum model introduces cross covariance between physics models allowing more accurate description of uncertainty. Consistency developed between physics models by the use of a unique set of prior parameters. An unresolved region cross section developed with sensitivity to resolved cross section. Incorporation into ORNL FITAPI provides access to greater number of parameter estimation algorithms.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
NASA NTRS1984

Preliminary Comparisons of the Information Content and Utility of TM Versus MSS Data

Comparisons were made between subscenes from the first TM scene acquired of the Washington, D.C. area and a MSS scene acquired approximately one year earlier. Three types of analyses were conducted to compare TM and MSS data: a water body analysis, a principal components analysis and a spectral clustering analysis. The water body analysis compared the capability of the TM to the MSS for detecting small uniform targets. Of the 59 ponds located on aerial photographs 34 (58%) were detected by the TM with six commission errors (15%) and 13 (22%) were detected by the MSS with three commission errors (19%). The smallest water body detected by the TM was 16 meters; the smallest detected by the MSS was 40 meters. For the principal components analysis, means and covariance matrices were calculated for each subscene, and principal components images generated and characterized. In the spectral clustering comparison each scene was independently clustered and the clusters were assigned to informational classes. The preliminary comparison indicated that TM data provides enhancements over MSS in terms of (1) small target detection and (2) data dimensionality (even with 4-band data). The extra dimension, partially resultant from TM band 1, appears useful for built-up/non-built-up area separation.

Markham, B. L.↗
DOE OSTI2022

Aggregation bias and its drivers in large‐scale flood loss estimation: A Massachusetts case study

Abstract Large‐scale estimations of flood losses are often based on spatially aggregated inputs. This makes risk assessments vulnerable to aggregation bias, a well‐studied, sometimes substantial outcome in analyses that model fine‐grained spatial phenomena at coarse spatial units. To evaluate this potential in the context of large‐scale flood risk assessments, we use data from a high‐resolution flood hazard model and structure inventory for over 1.3 million properties in Massachusetts and examine how prominent data aggregation approaches affect the magnitude and spatial distribution of flood loss estimates. All considered aggregation approaches rely on aggregate structure inventories but differ in whether flood hazard is also aggregated. We find that aggregating only structure inventories slightly underestimates overall losses (−10% bias), and when flood hazard data is spatially aggregated to even relatively small spatial units (census block), statewide aggregation bias can reach +366%. All aggregation‐based procedures fail to capture the spatial covariation of inputs distributions in the upper tails that disproportionately generate total expected losses. Our findings are robust to several key assumptions, add important context to published risk assessments and highlight opportunities to improve flood loss estimation uncertainty quantification.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

NEONiso R Package, versions greater than 0.5.2

Carbon fluxes in terrestrial ecosystems and their response to environmental change are a major source of uncertainty in the modern carbon cycle. The National Ecological Observatory Network (NEON) presents the opportunity to merge eddy covariance (EC)-derived fluxes with CO2 isotope ratio measurements to gain insights into carbon cycle processes. Collected continuously and consistently across >40 sites, NEON EC and isotope data facilitate novel integrative analyses. However, currently provisioned atmospheric isotope data are uncalibrated, greatly limiting ability to perform cross-site analyses. Here, we present two approaches to calibrating NEON CO2 isotope ratios, along with an R package to calibrate NEON data. We find that calibrating CO2 isotopologues independently yields a lower d13C bias (<0.05‰) and higher precision (<0.40‰) than directly correcting d13C with linear regression (bias: <0.11‰, precision: 0.42‰), but with slightly higher error and lower precision in calibrated CO2 mole fraction. The magnitude of the corrections to d13C and CO2 mole fractions vary substantially by site, underscoring the need for users to apply a consistent calibration framework to data in the NEON archive. Post-calibration data sets show that site mean annual d13C correlates negatively with precipitation, temperature, and aridity, but positively with elevation. Forested and agricultural ecosystems exhibit larger gradients in CO2 and d13C than other sites, particularly during the summer and at night. The overview and analysis tools developed here will facilitate cross-site analysis using NEON data, provide a model for other continental-scale observational networks, and enable new advances leveraging the isotope ratios of specific carbon fluxes.

Fiorella, Richard↗
DOE OSTI2023

Feature Interpretability

The feature interpretability code is a python module that interprets and analyzes neural networks trained on hydrodynamic simulation output in the form of numpy arrays. The code takes trained neural networks and extracts internal model states in the form of images. Additionally, tools for covariance analysis of network weights and predictions are provided. This code is built on the TensorFlow and PyTorch python libraries, and includes trained networks and example input data for demonstration purposes.

Callis, Skylar↗
NASA NTRS1978

Possible measurements of J sub 2 and the sun's angular momentum with the solar probe

The metric tensor is given for describing the dynamical effects of the sun on a solar probe and the gravitational redshift of a probe-based clock in a covariance analysis for the detectability of the solar quadrupole moment and the solar angular momentum. Unknown parameters were determined by least squares fit to the probe tracking data. A polar orbit was assumed with perihelion at 5 solar radii and with an earth-sun-probe angle of 135 deg at perihelion. Tracking was assumed to be Doppler only, with a basic uncertainity of .1 mm/sec in the radial velocity. A drag-free system which could reduce nongravitational forces below the level of 2 times 10 to the minus 12th power was found necessary to preserve this same level of accuracy. Both one way and two way Doppler were supposed in order to determine the gravitational redshift as well as the pure spacecraft dynamics.

Nordtvedt, K.↗
NASA NTRS1977

Multilaterating the GEOS-3 satellite

It is demonstrated by means of rigorous simulations with an operational software system that multilateration of the GEOS-3 satellite is possible. This operation, i.e., the processing of simultaneous range measurements from the satellite to a real world constellation of stations using strictly geometric reduction of the observables, will result in the determination of the interstation coordinates to accuracies approaching those of the data measurements obtained at the stations. The satellite-to-satellite link between the GEOS-3 and ATS-6 can be used to further enhance the station covariance matrix.

Escobal, P. R.↗
NASA NTRS1988

Deriving a unique reference frame for GPS measurements

Two strategies for deriving a unique reference frame for GPS (Global Positioning System) measurements are discussed. The first strategy utilizes the precise relative positions which have been predetermined by VLBI (very long baseline interferometry) to fix the frame orientation and the absolute scaling, while the offset from the geocenter is determined from GPS measurements. Three different cases are presented under this strategy. The second strategy establishes a reference frame by holding only the longitudinal of one of the tracking sites fixed. The absolute scaling is determined by the adopted gravitational constant (GM) of the earth; and the latitude is inferred from the time signature of the earth's rotation in the GPS measurements. The coordinate system thus defined will be a geocentric earth fixed coordinate system. A covariance analysis shows that geometric positioning to an accuracy of a few centimeters can be achieved with just one day of precise GPS pseudorange and carrier phase data.

Malla, Rajendra P.↗