Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Average Accuracy”

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.

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

At least 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
NASA NTRS1983

Spaceborne SAR data for land-cover classification and change detection

Supervised maximum-likelihood classifications of Seasat, SIR-A, and Landsat pixel data demonstrated that SIR-A data provided the most accurate discrimination (72 percent) between five land-cover categories. Spatial averaging of the SAR data improved classification accuracy significantly due to a reduction in both fading and within-field variability. The best multichannel classification accuracy (97.5 percent) was achieved by combining the SIR-A data with two Seasat images (ascending and descending orbits). In addition, semiquantitative analysis of Seasat-A digital data shows that orbital SAR imagery can be successfully used for multitemporal detection of change related to hydrologic and agronomic conditions by using simple machine processing techniques.

Brisco, B.↗
NASA NTRS1986

Accurate measurement of mean sea level changes by altimetric satellites

A technique for monitoring changes in global mean sea levels using altimeter data from a well-tracked satellite is examined. The usefulness of this technique is evaluated by analyzing Seasat altimeter data obtained during July-September 1978. The effects of orbit errors, geoid errors, sampling intervals, tides, and atmosphere refraction on the calculation of the mean sea level are investigated. The data reveal that the stability of an altimeter can be determined with an accuracy of + or - 7 cm using globally averaged sea surface height measurements. The application of this procedure to the US/French Ocean Topography Experiment is discussed.

Born, G. H.↗
NASA NTRS1993

Satellite altimeter measurements of surface wind

Recent analyses of wind speed measurements by the Geosat altimeter showed that the radar cross section is affected by oceanographic factors, particularly by the degree of sea development, which are not directly accounted for in the geophysical model functions (GMF). In the present work, two new GMFs which account for the effects of the actual degree of sea development are proposed. Along with the radar cross section, these models use significant wave height information. One particular version is recommended for applications in oceanographic and climate studies where wind speed (or wind stress) data have to be binned (i.e., averaged over time and/or space intervals). The accuracy of this GMF (overall bias of 0.1 m/s and rms error of about 1.6 m/s) is higher than the accuracy of commonly employed GMFs, while the wave-age-related trend is reduced to a geophysically insignificant level. Finally, the wind speed histograms for the collocated data set are derived and compared with the ground truth data as well as with the histograms yielded by presently known GMFs. It is also shown that the accuracy of altimeter measurements could be increased even further if some additional information on the wave field were available from independent sources (e.g., the dominant wavelength from synthetic aperture radar images).

Glazman, Roman E.↗
NASA NTRS2001

ICESat's Laser Measurements of Polar Ice, Atmosphere, Ocean, and Land

The Ice, Cloud and Land Elevation Satellite (ICESat) mission will measure changes in elevation of the Greenland and Antarctic ice sheets as part of NASA's Earth Observing System (EOS) of satellites. Time-series of elevation changes will enable determination of the present-day mass balance of the ice sheets, study of associations between observed ice changes and polar climate, and estimation of the present and future contributions of the ice sheets to global sea level rise. Other scientific objectives of ICESat include: global measurements of cloud heights and the vertical structure of clouds and aerosols; precise measurements of land topography and vegetation canopy heights; and measurements of sea ice roughness, sea ice thickness, ocean surface elevations, and surface reflectivity. The Geoscience Laser Altimeter System (GLAS) on ICESat has a 1064 nm laser channel for surface altimetry and dense cloud heights and a 532 nm lidar channel for the vertical distribution of clouds and aerosols. The accuracy of surface ranging is 10 cm, averaged over 60 m diameter laser footprints spaced at 172 m along-track. The orbital altitude will be around 600 km at an inclination of 94 deg with a 183-day repeat pattern. The onboard GPS receiver will enable radial orbit determinations to better than 5 cm, and star-trackers will enable footprints to be located to 6 m horizontally. The spacecraft attitude will be controlled to point the laser beam to within +/- 35 m of reference surface tracks at high latitudes. ICESat is designed to operate for 3 to 5 years and should be followed by successive missions to measure ice changes for at least 15 years.

Zwally, H. J.↗
NASA NTRS2007

Strain Dependence of Photoluminescense of Individual Carbon Nanotubes

We have investigated strain dependence of photoluminescense (PL) spectra of single wall carbon nanotubes (SWNT). Nanotubes were sparsely dispersed in a thin PMMA film applied to acrylic bar, and strained in both compression and extension by bending this bar in either direction in a homebuilt four-point bending rig. The average surface strain was measured with high accuracy by a resistive strain gage applied on top of the film. The near infrared imaging and spectroscopy were performed on the inverted microscope equipped with high numerical aperture reflective objective lens and InGaAs CCD cameras. PL was excited with a diode laser at either 658, 730 or 785 nm, linearly polarized in the direction of the strain. We were able to measure (n,m) types and orientation of individual nanotubes with respect to strain direction and strain dependence of their PL maxima. It was found that PL peak shifts with respect to the values measured in SDS micelles are a sum of three components. First, a small environmental shift due to difference in the dielectric constant of the surrounding media, that is constant and independent of the nanotube type. Second, shift due to isotropic compression of the film during drying. Third, shifts produced by the uniaxial loading of the film in the experiment. Second and third shifts follow expression based on the first-order expansion of the TB hamiltonian. Their magnitude is proportional to the nanotube chiral angle and strain, and direction is determined by the nanotube quantum number. PL strain dependence measured for a number of various nanotube types allows to estimate TB carbon-carbon transfer integral.

Nikolaev, Pavel N.↗
DOE OSTI2021

Does Bayesian model averaging improve polynomial extrapolations? Two toy problems as tests

We assess the accuracy of Bayesian polynomial extrapolations from small parameter values, x, to large values of x. We consider a set of polynomials of fixed order, intended as a proxy for a fixed-order effective field theory (EFT) description of data. We employ Bayesian model averaging (BMA) to combine results from different order polynomials (EFT orders). Our study considers two 'toy problems' where the underlying function used to generate data sets is known. We use Bayesian parameter estimation to extract the polynomial coefficients that describe these data at low x. A 'naturalness' prior is imposed on the coefficients, so that they are $\mathcal{O}(1)$. We BMA different polynomial degrees by weighting each according to its Bayesian evidence and compare the predictive performance of this BMA with that of the individual polynomials. In conclusion, the credibility intervals on the BMA forecast have the stated coverage properties more consistently than does the highest evidence polynomial, though BMA does not necessarily outperform every polynomial.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Integration and validation of some modules for modelling of high-speed chemically reactive flows in two-phase gas-droplet mixtures

Three modules are integrated into the built-in OpenFOAM rhoCentralFoam solver towards accurate and efficient modelling of high-speed chemically reactive flows in two-phase gas-droplet mixtures within the OpenFOAM 10.0 framework. The first module is the mixture-averaged diffusion model. The second module is the built-in OpenFOAM Lagrangian solver coupled with optimised droplet drag coefficient and convective heat transfer coefficient sub-models. The last module is a sparse stiff chemistry solver based on dynamic adaptive hybrid integration (AHI-S). The optimised droplet sub-models are first verified in correct implementation for subsequent simulations in this work. Further, they show good accuracy against experimental and analytical data in the modelling of ammonia droplet acceleration and cooling in the flowing and/or low-temperature air. The accuracy and efficiency gains related to the mixture-averaged diffusion model and the AHI-S chemistry solver are examined by simulating 1-D detonation propagation in ammonia droplet-free/laden ammoniaoxygen mixtures. Numerical results of detonation propagation speed, gaseous temperature, density, and species distributions around the induction zone show good agreement with experimental data and analytical solutions. Compared to the built-in OpenFOAM diffusion model, the mixture-averaged diffusion model provides different numerical predictions of pulsating instabilities in detonation propagation. It shows better accuracy in depicting the detonation structure within the droplet-free section attributed to improved multi-component diffusion modelling. Compared to the built-in OpenFOAM solver EulerImplicit (backward Euler), the AHI-S chemistry solver reduces the computational cost by around 50%. It achieves satisfactory accuracy in calculating detonation propagation speed within the droplet-free section with the optimal efficiency when the safety factor, β, equals 0.5.

42 ENGINEERING↗
NASA NTRS1974

The long-term prediction of artificial satellite orbits

Survey of averaging and multirevolution methods for long-term orbit prediction. A technical approach with the following features is recommended: (1) averaged variation-of-parameter equations, (2) analytical expressions for oblateness and third-body effects, (3) definite integrals for atmospheric drag and lunar effects (for long-period orbits), (4) nonsingular equinoctial element formulation, (5) multistep numerical integration processes, and (6) precise osculating-to-mean element transformation. Several orbital predictions illustrate the contribution of this technical approach to overall accuracy and efficiency. Future development of the analytical averaging method in nonsingular coordinates by automated manipulation of literal series is discussed.

Cefola, P. J.↗
DOE OSTI2024

Enhancing Solar Power Forecasting with Regularized Constrained Quantile Regression Averaging and Bootstrapping Techniques

Probabilistic solar power forecasting (SPF) plays an essential role in optimizing power-grid operations by quantifying the forecast uncertainty. To improve the accuracy and robustness of probabilistic SPF, this paper introduces the regularized constrained quantile regression averaging (rCQRA) method to combine outputs from multiple PSPF models. In addition, a bootstrapping method was used to quantify model uncertainty, providing insights into the reliability and significance of each ensemble component. To evaluate its efficacy, the proposed rCQRA method is used to integrate four PSPF methods. The resulting SPF models are trained and validated using a real-world six-year dataset from a rooftop solar plant in the USA. The performance of the proposed rCQRA method is evaluated and compared with two benchmark methods under three categories of weather conditions. It is shown that the rCQRA method has superior performance in its forecast reliability, sharpness, and accuracy.

Ensemble learning, probabilistic solar power forec↗
NASA NTRS1994

OSSE observations of the Crab pulsar

We present results of the Compton Gamma-Ray Observatory (CGRO) Oriented Scintillation Spectrometer Experiment (OSSE) observations of the Crab pulsar, made during MJD 48373-48406 (1991 April 27 - 1991 May 30) and MJD 48798-48804 (1992 June 25 - 1992 July 1). Pulsar light curves and spectra over the approximately 0.05 to 10 MeV range are presented. The arrival time of the gamma-ray peak and the radio peak agree to within 30 microsec which is better than the approximately 300 microsec accuracy of the measurements. The overall pulse phase averaged spectrum in the 0.1 - 10 MeV range is well-fit by a power law of the form 0.05 x (E/0.13 MeV)exp(-(1.99 +/- 0.03)) photons/sq cm/s. The outer-gap model (with gap parameter equal to 0.46) provided to us by Ho agrees with the data to better than 20%. The spectra of the bridge and second peak are slightly harder than the first peak as measured by the hardness ratio (approximately 110 - 220 keV)/(approximately 50 - 105 keV): P1 = 0.54 +/- 0.01, P2 = 0.63 +/- 0.01, bridge = 0.68 +/- 0.03. The phase of the two peaks in the light curve is constant over the 50 - 550 keV range to within the accuracy of the measurements (better than 0.02 in phase). No evidence was found for variability of the light curve on timescales from 2 minutes (less than a factor of 1.8) to 1 year (less than a factor of 1.06), where these are 3 sigma upper limits. However, when we examine the historical database, we find, in agreement with Nolan et al. (1993), that there is evidence for a 13 year variation in the ratio of the intensity of peak 2 to peak 1. We show that if this is interpreted as being due to precession (which changes the relative view of the intrinsic gamma-ray pulse as seen on earth), the variation is consistent with models of neutron star structure. The optical data may be in conflict with the interpretation however. We found no statistically significant lines in the 50 - 550 keV range in the spectrum. The average 3 sigma upper limits in 10(exp -3) photons/sq cm/s for lines at 0.073, 0.078, 0.4, 0.44, 0.511, and 0.545 MeV are 0.3, 0.5, 0.6, 0.5, and 0.1.

Ulmer, M. P.↗
DOE OSTI2020

Collagen Turnover in Relation to Risk Factors and Hemodynamics in Human Intracranial Aneurysms

Determinants for molecular and structural instability, that is, impending growth or rupture, of intracranial aneurysms (IAs) remain uncertain. To elucidate this, we endeavored to estimate the actual turnover rates of the main molecular constituent in human IA (collagen) on the basis of radiocarbon ( 14 C) birth dating in relation to IA hemodynamics. Collagen turnover rates in excised human IA samples were calculated using mathematical modeling of 14 C birth dating data of collagen in relation to risk factors and histological markers for collagen maturity/turnover in selected IA. Hemodynamics were simulated using image-based computational fluid dynamics. Correlation, logistic regression, and receiver operating characteristic analyses were performed. Collagen turnover rates were estimated in 46 IA (43 patients); computational fluid dynamics could be performed in 20 IA (20 patients). The mean collagen turnover rate (γ) constituted 126% (±1% error) per year. For patients with arterial hypertension, γ was greater than 2600% annually, whereas γ was distinctly lower with 32% (±1% error) per year for patients without risk factors, such as smoking and hypertension. There was a distinct association between histological presence of rather immature collagen in human IA and the presence of modifiable risk factors. Spatial-temporal averaged wall shear stress predicted rapid collagen turnover (odds ratio, 1.6 [95% CI, 1.0–2.7]). Receiver operating characteristic analysis demonstrated a good test accuracy (area under the curve, 0.798 [95% CI, 0.598–0.998]) for average wall shear stress with a threshold ≥4.9 Pa for rapid collagen turnover. Our data indicate that turnover rates and stability of collagen in human IA are strongly associated with the presence of modifiable risk factors and aneurysmal hemodynamics. Finally, these findings underline the importance of strict risk factor modification in patients with unruptured IA. Future should include more detailed risk factor data to establish a more causal understanding of hemodynamics and the rupture risk of individual IA.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Local Bayesian Dirichlet mixing of imperfect models

Abstract To improve the predictability of complex computational models in the experimentally-unknown domains, we propose a Bayesian statistical machine learning framework utilizing the Dirichlet distribution that combines results of several imperfect models. This framework can be viewed as an extension of Bayesian stacking. To illustrate the method, we study the ability of Bayesian model averaging and mixing techniques to mine nuclear masses. We show that the global and local mixtures of models reach excellent performance on both prediction accuracy and uncertainty quantification and are preferable to classical Bayesian model averaging. Additionally, our statistical analysis indicates that improving model predictions through mixing rather than mixing of corrected models leads to more robust extrapolations.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1982

Combining remotely sensed and other measurements for hydrologic areal averages

A method is described for combining measurements of hydrologic variables of various sampling geometries and measurement accuracies to produce an estimated mean areal value over a watershed and a measure of the accuracy of the mean areal value. The method provides a means to integrate measurements from conventional hydrological networks and remote sensing. The resulting areal averages can be used to enhance a wide variety of hydrological applications including basin modeling. The correlation area method assigns weights to each available measurement (point, line, or areal) based on the area of the basin most accurately represented by the measurement. The statistical characteristics of the accuracy of the various measurement technologies and of the random fields of the hydrologic variables used in the study (water equivalent of the snow cover and soil moisture) required to implement the method are discussed.

Johnson, E. R.↗
NASA NTRS1980

A preliminary assessment of the accuracy of selected meteorological parameters determined from Nimbus 6 satellite profile data

Published rms errors in rawinsonde data and discrepancies between satellite and rawinsonde profile data for temperature, dewpoint temperature, mixing ratio, and wind speed. Satellite rms errors were found to be 2 to 3 times as large as those for rawinsonde data. Gradients of the preceding parameters were computed for both rawinsonde and satellite data and compared with means and near extreme values computed from the AVE 2 and AVE 4 experiments. In all cases, it was found that satellite data can be used to determine with relatively good accuracy the near extreme gradients but not those whose value does not exceed the average. Synoptic charts were prepared to show that patterns of temperature could be determined with relatively good accuracy, while those of dew point were not as good as those for temperature. Winds represented by cloud motion vectors (satellite winds) were compared with rawinsonde winds, and it was found that large gaps exist in satellite values for a given pressure level and that errors in the satellite determined concluded that satellite profile data are very useful in synoptic analysis, particularly in data sparse regions as well as regions where near extreme gradients exist in the measured parameters.

Scoggins, J. R.↗
NASA NTRS2019

Weighted Least-Squares Cell-Average Gradient Construction Methods for the VULCAN-CFD Second-Order Accurate Unstructured-Grid Cell-Centered Finite-Volume Solver

The ability to solve the equations governing the hypersonic turbulent flow of a real gas on unstructured grids using a spatially-elliptic, 2nd-order accurate, cell-centered, finite-volume method has been recently implemented in the VULCAN-CFD code. The construction of cell-average gradients using a weighted linear least-squares method and the use of these gradients in the construction of the inviscid fluxes is the focus of this paper. A comparison of least-squares stencil construction methodologies is presented and approaches to augment the number of cells participating in the stencil while preserving accuracy are explored. Due to our interest in hypersonic flow, a robust multidimensional cell-average gradient limiter procedure that is consistent with the stencil used to construct the cell-average gradients is described and investigated. Canonical problems are computed to illustrate the challenges and investigate the accuracy, robustness and convergence behavior of the cell-average gradient methods on unstructured cell-centered finite-volume grids. Finally, thermally perfect, chemically frozen, Mach 8 turbulent flow of air around a blunt wedge is computed to demonstrate the robustness and convergence behavior of the new method for constructing stencils of use in a weighted linear least-squares gradient method for a hypersonic flow.

White, Jeffery A.↗
NASA NTRS1984

Landsat-4 horizon scanner flight performance

This paper presents an analysis of the flight data from a new design of horizon scanner flown on Landsat-4. The salient features in the data are described and demonstrated by data plots. High frequency noise must be filtered out to achieve good accuracy, but this is effectively done by 128-point averaging. Sun and moon interference effects are identified. The effects of earth oblateness and spacecraft altitude variations are modeled, and the residual systematic errors are analyzed. Most of the residual errors are apparently explained by the effects of earth radiance variation, with the winter polar regions showing the highest variability in the attitude measurements due to winter stratosphere temperature variations. In general, this sensor provides improved accuracy over those flown on previous missions.

Bilanow, S.↗
DOE OSTI2023

Multi-site evaluation of stratified and balanced sampling of soil organic carbon stocks in agricultural fields

Estimating soil organic carbon (SOC) stocks in agricultural fields is essential for environmental and agronomic research, management, and policy. Stratified sampling is a classic strategy for estimating mean soil properties, and has recently been codified in SOC monitoring protocols. However, for the specific task of estimating the SOC stock of an agricultural field, concrete guidance is needed for which covariates to stratify on and how much stratification can improve estimation efficiency. It is also unknown how stratified sampling of SOC stocks compares to modern alternatives, notably doubly balanced sampling. To address these gaps, we collected high-density (average of 7 samples ha -1 ) and deep (average of 75 cm) measurements of SOC stocks at eight commercial fields under maize-soybean production in two US Midwestern states. We combined these measurements with a Bayesian geostatistical model to evaluate stratified and balanced sampling strategies that use a set of readily-available geographic, topographic, spectroscopic, and soil survey data. We examined the number of samples needed to achieve a given level of SOC stock estimation accuracy. While stratified sampling using these variables enables an average sample size reduction of 17% (95% CI, 11% to 23%) compared to simple random sampling, doubly balanced sampling is consistently more efficient, reducing sample sizes by 32% (95% CI, 25% to 37%). The data most important to these efficiency gains are a remotely-sensed SOC index, SSURGO estimates of SOC stocks, and the topographic wetness index. We conclude that in order to meet the urgent challenge of climate change, SOC stocks in agricultural fields could be more efficiently estimated by taking advantage of this readily-available data, especially with doubly balanced sampling.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Machine learning framework for predicting uranium enrichments from M400 CZT gamma spectra

A machine learning framework was developed for predicting uranium enrichments from M400 CZT gamma spectra. This framework leverages the availability of a large amount of measured M400 gamma spectra and uses a recently updated version of Gamma Detector Response and Analysis Software (GADRAS) for gamma spectrum analysis and generation. It also leverages the existing machine learning modules in Python for gamma spectrum data processing, curation, model training, benchmarking, and optimization of the deep machine learning models. The framework is used to develop a deep learning model to analyze gamma spectra from a set of U 3 O 8 samples with enrichments ranging from 0.31 to 93.17% and UF 6 cylinders with enrichments ranging from 0.2 to 4.95%, and the model performance is tested using a set of measured spectra and the respective declared enrichment values. Results show that the model can correctly classify 99.35% of the U 3 O 8 sample enrichments, and can predict the samples’ enrichments within an average absolute error of 0.099% (in percentage points of enrichment). For the UF 6 cylinders, the average absolute error was approximately 0.03%, with an accuracy of 98% in classifying discrete enrichment values of UF 6 samples. Finally, the results also show that the model has performed significantly better in terms of predicting enrichments in UF 6 cylinders based on measured gamma spectra than the GEM code, with a standard deviation (of the relative errors) of 2.23% (compared with the 11.51% value for the GEM code) based on results from a set of test data.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗