SEARCH · Engineering Papers

Results for “variance components”

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.

SourceResearch connectionFrom yearThrough year

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

At least 109 records · Page 6

All sources NASA NTRS DOE OSTI

NASA NTRS1982

LANDSAT-4 image data quality analysis

Work done on evaluating the geometric and radiometric quality of early LANDSAT-4 sensor data is described. Band to band and channel to channel registration evaluations were carried out using a line correlator. Visual blink comparisons were run on an image display to observe band to band registration over 512 x 512 pixel blocks. The results indicate a .5 pixel line misregistration between the 1.55 to 1.75, 2.08 to 2.35 micrometer bands and the first four bands. Also a four 30M line and column misregistration of the thermal IR band was observed. Radiometric evaluation included mean and variance analysis of individual detectors and principal components analysis. Results indicate that detector bias for all bands is very close or within tolerance. Bright spots were observed in the thermal IR band on an 18 line by 128 pixel grid. No explanation for this was pursued. The general overall quality of the TM was judged to be very high.

Anuta, P. E.↗

NASA NTRS1979

Evaluation of SLAR and thematic mapper MSS data for forest cover mapping using computer-aided analysis techniques

The spatial characteristics of the data were evaluated. A program was developed to reduce the spatial distortions resulting from variable viewing distance, and geometrically adjusted data sets were generated. The potential need for some level of radiometric adjustment was evidenced by an along track band of high reflectance across different cover types in the Varian imagery. A multiple regression analysis was employed to explore the viewing angle effect on measured reflectance. Areas in the data set which appeared to have no across track stratification of cover type were identified. A program was developed which computed the average reflectance by column for each channel, over all of the scan lines in the designated areas. A regression analysis was then run using the first, second, and third degree polynomials, for each channel. An atmospheric effect as a component of the viewing angle source of variance is discussed. Cover type maps were completed and training and test field selection was initiated.

Hoffer, R. M.↗

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↗

NASA NTRS1983

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

Two fully processed test tapes were enhanced and evaluated at scales up to 1:10,000, using both hardcopy output and interactive screen display. A large scale, the Detroit, Michigan scene shows evidence of an along line data slip every sixteenth line in TM channel 2. Very large scale products generated in false color using channels 1,3, and 4 should be very acceptable for interpretation at scales up to 1:50,000 and useful for change mapping probably up to scale 1:24,000. Striping visible in water bodies for both natural and color products indicates that the detector calibration is probably performing below preflight specification. For a set of 512 x 512 windows within the NE Arkansas scene, the variance-covariance matrices were computed and principal component analyses performed. Initial analysis suggests that the shortwave infrared TM 5 and 6 channels are a highly significant data source. The thermal channel (TM 7) shows negative correlation with TM 1 and 4.

Everett, J. R.↗

DOE OSTI2020

The Effect Of Ionization Variance On Nuclear-recoil Dark Matter Searches

Direct detection dark matter experiments are increasingly interested in the low-mass dark matter parameter space, but zero-background low-mass searches require event separation between the electron and nuclear recoil bands, which requires a proper understanding of detector energy reconstruction. Previous simulations have shown that we do not entirely understand the ionization efficiency (yield) for electron and nuclear recoils, as the assumption that the distribution for the yield is normally distributed for a true recoil energy is violated. Since the yield distribution may directly affect dark matter low-mass limits, it is crucial we understand how the yield is distributed. A component to understanding the yield distribution is the variance in the number of electron-hole pairs produced or ionization variance. This effect has been studied relatively infrequently as experiments have been interested in large energy deposits (10 - 100 keV) and could accurately separate electron and nuclear recoil events. For electron recoils, the ionization variance is described by a “Fano” factor. For nuclear recoils the effect can be parameterized by an “effective” Fano factor, which has similar definition but a different physical origin. The nuclear recoil “effective” Fano factor is shown to be much larger than the electron-recoil version above around 10 keV deposited energy.

Matheny, Mitchell Douglas↗

NASA NTRS1991

Anisotropy and minimum variance directions of solar wind fluctuations in the outer heliosphere

Voyager 2 magnetic field and plasma data are examined over time intervals of 1 to 12 hours in the heliospheric range of 1 to 10 AU to study the evolution of the anisotropy of solar wind fluctuations. Consistent with previous results, the directions of minimum variance vectors of magnetic fluctuations are found to be close to the mean magnetic field direction with an increasing component along the field at larger scales. At large radial distances there is more spread in the minimum variance directions than at smaller radial distances. The power in smaller-scale fluctuations in the magnetic field components perpendicular to the local mean field B(0) is in the ratio of about 5:1 near 1 AU at the scale of 1 hour but decreases to about 3:1 further out. No evidence for selective enhancement of out-of-the-ecliptic components of fluctuations is found. In contrast to results for field fluctuations, analysis of velocity fluctuations shows that the minimum variance direction systematically remains more radially oriented and becomes increasingly less oriented along B(0) with increasing heliocentric distance. The velocity fluctuations are generally more isotropic than the magnetic fluctuations. The observations cannot be explained by a superposed wave picture, and thus are consistent with the view that nonlinear turbulent evolution is responsible for the anisotropy in the fluctuations.

Klein, Larry W.↗

NASA NTRS2004

An Empirical Cumulus Parameterization Scheme for a Global Spectral Model

Realistic vertical heating and drying profiles in a cumulus scheme is important for obtaining accurate weather forecasts. A new empirical cumulus parameterization scheme based on a procedure to improve the vertical distribution of heating and moistening over the tropics is developed. The empirical cumulus parameterization scheme (ECPS) utilizes profiles of Tropical Rainfall Measuring Mission (TRMM) based heating and moistening derived from the European Centre for Medium- Range Weather Forecasts (ECMWF) analysis. A dimension reduction technique through rotated principal component analysis (RPCA) is performed on the vertical profiles of heating (Q1) and drying (Q2) over the convective regions of the tropics, to obtain the dominant modes of variability. Analysis suggests that most of the variance associated with the observed profiles can be explained by retaining the first three modes. The ECPS then applies a statistical approach in which Q1 and Q2 are expressed as a linear combination of the first three dominant principal components which distinctly explain variance in the troposphere as a function of the prevalent large-scale dynamics. The principal component (PC) score which quantifies the contribution of each PC to the corresponding loading profile is estimated through a multiple screening regression method which yields the PC score as a function of the large-scale variables. The profiles of Q1 and Q2 thus obtained are found to match well with the observed profiles. The impact of the ECPS is investigated in a series of short range (1-3 day) prediction experiments using the Florida State University global spectral model (FSUGSM, T126L14). Comparisons between short range ECPS forecasts and those with the modified Kuo scheme show a very marked improvement in the skill in ECPS forecasts. This improvement in the forecast skill with ECPS emphasizes the importance of incorporating realistic vertical distributions of heating and drying in the model cumulus scheme. This also suggests that in the absence of explicit models for convection, the proposed statistical scheme improves the modeling of the vertical distribution of heating and moistening in areas of deep convection.

Rajendran, K.↗

DOE OSTI2024

Godiva IV Simulated Radiation Field Characterization and Variance Reduction

Godiva IV is a system comprised of highly enriched uranium alloyed with molybdenum in the form of fuel plate rings. The reactor, along with its predecessors, was designed with the unique ability to satisfy interests in the super-prompt-critical reactor operation space. Originally, the reactor was part of the Los Alamos Critical Experiments Facility (LACEF) at Technical Area-18 (TA-18). The radiation field around Godiva at this facility was well characterized and understood. As a fast neutron system, the neutron spectrum in and around Godiva was close to a Watt Fission spectrum. The Kiva where Godiva IV was located at LACEF was made of thin, sheet metal walls which did not contribute significantly to the neutron spectrum. Following the transition of LACEF to the National Critical Experiments and Research Center (NCERC) in Nevada, Godiva-IV was moved from TA-18 to the Device Assembly Facility (DAF) at the Nevada National Security Site (NNSS). Part of this move brought renewed interest in radiation field characterization. The new facility introduced significant changes to the environment surrounding Godiva, and preliminary foil irradiation results suggested that the room contribution to the neutron spectrum was significant. Unlike at TA-18, a large thermal neutron signature was added to the fast spectrum from Godiva due to significant room return. A primary goal due to the additional complexity that the room return adds to the Godiva IV radiation emission spectrum was the development of an efficient Monte Carlo N-Particle (MCNP) calculation capable of characterizing the neutron spectrum anywhere in the room around Godiva. A campaign of activation foil irradiations and analysis were completed to support the validation of the MCNP model. The modeling of these foils in MCNP can be easily done with a standard volumetric neutron flux tally. However, given the multitude of locations and reaction rates to be modeled, further steps must be taken to increase the efficiency of these calculations in MCNP. During this study, a benchmark model currently under development for Godiva IV was used. A qualitative assessment of the thermal neutron contributors was performed using spatial neutron distribution plots. Additional detail was added to the model based on the qualitative results showing the thermal spectrum’s large sensitivity to hydrogenous material. Neutron energy spectra was evaluated at discrete locations in the room around Godiva to quantify the relative contribution of various components. It was discovered that the concrete walls are the largest contributor to the thermal signature, with minor contributions from plastic components surrounding Godiva. Following these results, two different variance reduction techniques were implemented to improve the problem efficiency in these calculations. In the first approach, an F5 point detector tally was implemented in the standard Godiva IV criticality problem. The second approach involved a weight-window generator implementation with an F5 point detector tally in a fixed source problem. The weight window implementation reduced the runtime from 42739.55 minutes to 1803.34 minutes (computer time), compared to the F5 KCODE implementation.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

NASA NTRS2019

The Cognition and Fine Motor Skills Test Batteries: Normative Data and Interdependencies

Space mission success and safety relies upon astronaut functional state. Since spaceflight stressors affect cognitive processing and fine motor skills, NASA requires that measures of performance of these things remain within clinically accepted values (NASA STD 3001). NASA is in the process of developing two test batteries for the assessment of crew cognitive and fine motor skills before, during and after spaceflight. Toward that goal, the current project collected normative scores in 91 “astronaut-like” military and civilian pilots. The Cognition Test Battery (CTB) contains ten sub-tests that measure a range of cognitive abilities. For five of the ten CTB sub-tests, we propose scores to improve the battery’s sensitivity. Among the ten sub-tests, response times were more highly correlated than accuracy scores. Principle component analysis of the correlations revealed that the first response time factor could explain over 40% of the total variance and appeared to represent the tendency of observers to try to respond more quickly. The first accuracy factor (explaining only 20%) gave a high weight to the higher level cognitive sub-tests and a negative weight to tasks associated with motor and lower level cognitive processing. The Fine Motor Skills (FMS) test battery contains four sub-tests (Tracking, Pointing, Tracing, Rotating) performed on an Apple iPad tablet computer. Principle component analysis on the sub-test response time correlations revealed that the first two factors accounted for ~80% of the variance in performance. The first component captured overall speed on all four of the sub-tests. The second factor separated the sub-tasks into two groups (Drag-Point vs Trace-Rotate). Previous work found the first group response times correlated with that of a standard peg board task, while those of the other group did not. Correlations were computed between the first FMS factor and the response time and accuracy scores from each CTB sub-task. Performing fine motor behaviors rapidly was significantly correlated with the ability to perform many of the CTB sub-tests rapidly. This ability cannot be simple motor speed since scores on the Psychomotor Vigilance Test (PVT) subtask did not correlate with the ability to perform the other tasks rapidly. Speed on fine motor skills correlated significantly with accuracy on the short-term-memory sub-test. We hypothesize that eye movements, which can be regarded as a fine motor skill, may explain this relationship.

cognition↗

DOE OSTI2023

Structuring Nutrient Yields throughout Mississippi/Atchafalaya River Basin Using Machine Learning Approaches

To minimize the eutrophication pressure along the Gulf of Mexico or reduce the size of the hypoxic zone in the Gulf of Mexico, it is important to understand the underlying temporal and spatial variations and correlations in excess nutrient loads, which are strongly associated with the formation of hypoxia. This study’s objective was to reveal and visualize structures in high-dimensional datasets of nutrient yield distributions throughout the Mississippi/Atchafalaya River Basin (MARB). For this purpose, the annual mean nutrient concentrations were collected from thirty-three US Geological Survey (USGS) water stations scattered in the upper and lower MARB from 1996 to 2020. Eight surface water quality indicators were selected to make comparisons among water stations along the MARB over the past two decades. Principal component analysis (PCA) was used to comprehensively evaluate the nutrient yields across thirty-three USGS monitoring stations and identify the major contributing nutrient loads. The results showed that all samples could be analyzed using two main components, which accounted for 81.6% of the total variance. The PCA results showed that yields of orthophosphate (OP), silica (SI), nitrate–nitrites (NO 3 -NO 2 ), and total suspended sediment (TSS) are major contributors to nutrient yields. It also showed that land-planted crops, density of population, domestic and industrial discharges, and precipitation are fundamental causes of excess nutrient loads in MARB. These factors are of great significance for the excess nutrient load management and pollution control of the Mississippi River. It was found that the average nutrient yields were stable within the sub-MARB area, but the large nitrogen yields in the upper MARB and the large phosphorus yields in the lower MARB were of great concern. t-distributed stochastic neighbor embedding (t-SNE) revealed interesting nonlinear and local structures in nutrient yield distributions. Clustering analysis (CA) showed the detailed development of similarities in the nutrient yield distribution. Moreover, PCA, t-SNE, and CA showed consistent clustering results. This study demonstrated that the integration of dimension reduction techniques, PCA, and t-SNE with CA techniques in machine learning are effective tools for the visualization of the structures of the correlations in high-dimensional datasets of nutrient yields and provide a comprehensive understanding of the correlations in the distributions of nutrient loads across the MARB.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1980

Ozone data and mission sampling analysis

A methodology was developed to analyze discrete data obtained from the global distribution of ozone. Statistical analysis techniques were applied to describe the distribution of data variance in terms of empirical orthogonal functions and components of spherical harmonic models. The effects of uneven data distribution and missing data were considered. Data fill based on the autocorrelation structure of the data is described. Computer coding of the analysis techniques is included.

Robbins, J. L.↗

NASA NTRS1981

Effect of cloud-radiation feedback on the climate of a general circulation model

Experiments are described which show significant changes in the simulated large-scale dynamical circulation of a global model. Fixed clouds acting as zonally asymmetric radiative heat sources increase the generation of eddy available potential energy (EAPE) and the energy's conversion to eddy kinetic energy. Generation of EAPE by net radiative heating increases by 50% (0.11 W/sq m) for the fixed cloud experiment. The increase caused by the stationary component is much larger (approximately 100%), but it is partially compensated by a decrease caused by the transient component. A substantial increase is found in the variances of the planetary-scale stationary waves and the medium-scale waves of 2.7 day period. Although the sea surface temperatures are prescribed identically in both integrations, the changes in evaporation and precipitation are found to be much larger over the oceans than over the land.

Shukla, J.↗

NASA NTRS2015

Drought Variability in the Eastern Australia and New Zealand Summer Drought Atlas (ANZDA, CE 1500-2012) Modulated by the Interdecadal Pacific Oscillation

Agricultural production across eastern Australia and New Zealand is highly vulnerable to drought, but there is a dearth of observational drought information prior to CE (Christian Era) 1850. Using a comprehensive network of 176 drought-sensitive tree-ring chronologies and one coral series, we report the first Southern Hemisphere gridded drought atlas extending back to CE 1500. The austral summer (December-February) Palmer drought sensitivity index reconstruction accurately reproduces historically documented drought events associated with the first European settlement of Australia in CE 1788, and the leading principal component explains over 50 percent of the underlying variance. This leading mode of variability is strongly related to the Interdecadal Pacific Oscillation tripole index (IPO), with a strong and robust antiphase correlation between (1) eastern Australia and the New Zealand North Island and (2) the South Island. Reported positive, negative, and neutral phases of the IPO are consistently reconstructed by the drought atlas although the relationship since CE 1976 appears to have weakened.

Palmer, Jonathan G.↗

NASA NTRS2019

FIGS: spectral fitting constraints on the star formation history of massive galaxies since the cosmic noon

We constrain the stellar population properties of a sample of 52 massive galaxies – with stellar mass log (M(s)/M(⊙)) ≳ 10.5 – over the redshift range 0.5 < z < 2 by use of observer-frame optical and near-infrared slitless spectra from Hubble Space Telescope’s ACS and WFC3 grisms. The deep exposures (∼100 ks) allow us to target individual spectra of massive galaxies to F160W = 22.5 AB. Our spectral fitting approach uses a set of six base models adapted to the redshift and spectral resolution of each observation, and fits the weights of the base models, including potential dust attenuation, via a Markov Chain Monte Carlo method. Our sample comprises a mixed distribution of quiescent (19) and star-forming galaxies (33). We quantify the width of the age distribution (Δt) that is found to dominate the variance of the retrieved parameters according to principal component analysis. The population parameters follow the expected trend towards older ages with increasing mass, and Δt appears to weakly anticorrelate with stellar mass, suggesting a more efficient star formation at the massive end. As expected, the redshift dependence of the relative stellar age (measured in units of the age of the Universe at the source) in the quiescent sample rejects the hypothesis of a single burst (aka monolithic collapse). Radial colour gradients within each galaxy are also explored, finding a wider scatter in the star-forming subsample, but no conclusive trend with respect to the population parameters.

Ignacio Ferreras↗

NASA NTRSDate not supplied

Monitoring and Assessing Heat Vulnerability to Identify Locations for Heat Mitigation Efforts in Sarasota, Florida

Located in the coastal subtropical region, Florida’s Sarasota County receives plenty of sunlight for more than half of the year. However, the ongoing rise in summer temperatures attributed to climate change poses an increasing vulnerability to extreme heat for the residents. Moreover, the area's high relative humidity exacerbates the discomfort of high temperatures, thereby intensifying the severity of heat events and elevating the risks of heat-related illnesses. Our collaborations with Sarasota County Sustainability underscore shared concerns about the impact of urban heat island (UHI) on the community. We utilized Earth observation data from NASA Landsat 8 and Landsat 9’s Thermal Infrared Sensors (TIRS) and the International Space Station’s Ecosystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) to model UHI effects within the county during the summer from 2019 to 2023. By implementing the open-source Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) Urban Cooling software model, ArcGIS ModelBuilder, and principal component analysis, we identified the land surface temperature variance within the county and areas that are least capable of mitigating the effects of UHI. In addition, we use socioeconomic and demographic data as indicators to quantify vulnerability at the census tract level. The results revealed that heat intensity varies significantly across Sarasota County, with the highest temperatures being in the more developed western part of the region. We pinpointed that at least three vulnerable communities reside in high-heat regions: North Sarasota, Venice, and North Port. These areas demonstrate a confluence between socioeconomic sensitivity and environmental hazard, indicating a high priority in future heat mitigation efforts.

Theresia Phoa↗

DOE OSTI2020

Leveraging Machine Learning Capabilities for the Characterization of Irradiated Uranium: A Case Study of Analysis Methods for Nuclear Safeguards and Nuclear Forensics

Nondestructively determining the initial enrichment of irradiated uranium is a complex and laborious multivariable problem due to the presence of fission products. This work demonstrates the capabilities of machine learning to analyze gamma-ray spectral data to determine initial enrichment without knowledge of the decay time of the sample. The approach developed is agnostic to the particular scenario and is applicable to a wide variety of applications in nuclear forensics and nuclear safeguards. We irradiated 5 mg uranium standard reference materials at discrete enrichment values ranging from 0.02% to 97% 235 U (weight percent) in UT Austin’s Nuclear Engineering Teaching Laboratory TRIGA Mark II 1.1 MW research reactor, allowed each to decay for 8 hours, and then measured each sample via gamma-ray spectrometry for 50 hours post-irradiation yielding 1,400 individual gamma-ray spectra discretized into 8,192 energy bins. We then trained decision trees models to analyze individual gamma-ray spectra and estimate the associated initial enrichment without knowledge of the time since end of irradiation. We evaluated the performance of the models with a reserved test set not used for training or calibrating the model. A decision tree model constructed with this procedure achieved a mean absolute error in initial enrichment determination of 2.3% (weight percent 235 U). Next, we implemented a principal component analysis pre-processing routine of the gamma-ray spectrometry data to reduce the dimensionality of the dataset from 8,192 channels in the spectrum to 10 principal components while retaining over 99% of the inherent variance in the data. Decision tree models constructed with these data demonstrated decreased mean absolute error in enrichment determination, reduced computational time, and decreased complexity. A single decision tree model constructed with this procedure achieved a mean absolute error in initial enrichment determination of 0.05% (weight percent 235 U). Furthermore, we analyzed these models with learning curves to ensure that overfitting did not occur. The capabilities provided by these models can be naturally extended to other application-focused measurements in the fields of nuclear safeguards, nuclear forensics, and nuclear non-proliferation.

Drescher, Adam↗

NASA NTRS2012

Gravity Wave Variances and Propagation Derived from AIRS Radiances

As the first gravity wave (GW) climatology study using nadir-viewing infrared sounders, 50 Atmospheric Infrared Sounder (AIRS) radiance channels are selected to estimate GW variances at pressure levels between 2-100 hPa. The GW variance for each scan in the cross-track direction is derived from radiance perturbations in the scan, independently of adjacent scans along the orbit. Since the scanning swaths are perpendicular to the satellite orbits, which are inclined meridionally at most latitudes, the zonal component of GW propagation can be inferred by differencing the variances derived between the westmost and the eastmost viewing angles. Consistent with previous GW studies using various satellite instruments, monthly mean AIRS variance shows large enhancements over meridionally oriented mountain ranges as well as some islands at winter hemisphere high latitudes. Enhanced wave activities are also found above tropical deep convective regions. GWs prefer to propagate westward above mountain ranges, and eastward above deep convection. AIRS 90 field-of-views (FOVs), ranging from +48 deg. to −48 deg. off nadir, can detect large-amplitude GWs with a phase velocity propagating preferentially at steep angles (e.g., those from orographic and convective sources). The annual cycle dominates the GW variances and the preferred propagation directions for all latitudes. Indication of a weak two-year variation in the tropics is found, which is presumably related to the Quasi-biennial oscillation (QBO). AIRS geometry makes its out-tracks capable of detecting GWs with vertical wavelengths substantially shorter than the thickness of instrument weighting functions. The novel discovery of AIRS capability of observing shallow inertia GWs will expand the potential of satellite GW remote sensing and provide further constraints on the GW drag parameterization schemes in the general circulation models (GCMs).

AIRS↗

DOE OSTI2024

Sensitivity Analysis for the Component Design App: Analysis of Success Assured Data

A new tool has been developed to perform variance-based global sensitivity analysis (VBGSA) on data from a set-based concurrent engineering software called Success Assured (SA). The tool is part of a digital component design app, which is currently in production as an Accelerated Digital Engineering Pathfinder at Sandia National Laboratories. When working with complex digital models, it is important to understand relationships between inputs and outputs, i.e., how “sensitive” model outputs are to changes in model inputs. After extensive research and trials of various sensitivity analysis methods, it was determined that estimation of Sobol’ indices for VBGSA with Monte Carlo simulation, paired with simple surrogate models, produces the best results for SA data. This tool increases understanding of SA models and streamlines the creation of SA datasets. This report details the methodology and implementation of this sensitivity analysis tool so others can understand it and implement it.

97 MATHEMATICS AND COMPUTING↗