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At least 1,135 records · Page 63
All sourcesNASA NTRSDOE OSTI
NASA NTRS1992

Estimates of tropical analysis differences in daily values produced by two operational centers

To assess the uncertainty of daily synoptic analyses for the atmospheric state, the intercomparison of three First GARP Global Experiment level IIIb datasets is performed. Daily values of divergence, vorticity, temperature, static stability, vertical motion, mixing ratio, and diagnosed diabatic heating rate are compared for the period of 26 January-11 February 1979. The spatial variance and mean, temporal mean and variance, 2D wavenumber power spectrum, anomaly correlation, and normalized square difference are employed for comparison.

Kasahara, Akira↗
DOE OSTI2013

A Model-Free No-arbitrage Price Bound for Variance Options

We suggest a numerical approximation for an optimization problem, motivated by its applications in finance to find the model-free no-arbitrage bound of variance options given the marginal distributions of the underlying asset. A first approximation restricts the computation to a bounded domain. Then we propose a gradient projection algorithm together with the finite difference scheme to solve the optimization problem. We prove the general convergence, and derive some convergence rate estimates. Finally, we give some numerical examples to test the efficiency of the algorithm.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Detection of correlated fragments in a sequence of images by superimposed Fourier holograms

The problem of detecting correlated fragments in a sequence of images recorded by the superimposing holograms within the Fourier holography scheme with angular multiplication of a spatially modulated reference beam is considered. The approach to the solution of this problem is based on the properties of the variance of the image sum. It is shown that this problem can be solved by providing a constant distance between the signal and reference images when recording superimposed holograms and a partial mutual correlatedness of reference images. The detection efficiency is analysed from the point of view of estimated image data capacity, the degree of mutual correlation of reference images, and the hologram recording conditions. The results of a numerical experiment under the most complicated conditions (representation of images by realisations of homogeneous random fields) confirm the theoretical conclusions. (holographic memory)

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2014

In vitro dose measurements in a human cadaver with abdomen/pelvis CT scans

Purpose: To present a study of radiation dose measurements with a human cadaver scanned on a clinical CT scanner. Methods: Multiple point dose measurements were obtained with high-accuracy Thimble ionization chambers placed inside the stomach, liver, paravertebral gutter, ascending colon, left kidney, and urinary bladder of a human cadaver (183 cm in height and 67.5 kg in weight) whose abdomen/pelvis region was scanned repeatedly with a multidetector row CT. The flat energy response and precision of the dosimeters were verified, and the slight differences in each dosimeter's response were evaluated and corrected to attain high accuracy. In addition, skin doses were measured for radiosensitive organs outside the scanned region with OSL dosimeters: the right eye, thyroid, both nipples, and the right testicle. Three scan protocols were used, which shared most scan parameters but had different kVp and mA settings: 120-kVp automA, 120-kVp 300 mA, and 100-kVp 300 mA. For each protocol three repeated scans were performed. Results: The tube starting angle (TSA) was found to randomly vary around two major conditions, which caused large fluctuations in the repeated point dose measurements: for the 120-kVp 300 mA protocol this angle changed from approximately 110° to 290°, and caused 8% − 25% difference in the point dose measured at the stomach, liver, colon, and urinary bladder. When the fluctuations of the TSA were small (within 5°), the maximum coefficient of variance was approximately 3.3%. The soft tissue absorbed doses averaged from four locations near the center of the scanned region were 27.2 ± 3.3 and 16.5 ± 2.7 mGy for the 120 and 100-kVp fixed-mA scans, respectively. These values were consistent with the corresponding size specific dose estimates within 4%. The comparison of the per-100-mAs tissue doses from the three protocols revealed that: (1) dose levels at nonsuperficial locations in the TCM scans could not be accurately deduced by simply scaling the fix-mA doses with local mA values; (2) the general power law relationship between dose and kVp varied from location to location, with the power index ranged between 2.7 and 3.5. The averaged dose measurements at both nipples, which were about 0.6 cm outside the prescribed scan region, ranged from 23 to 27 mGy at the left nipple, and varied from 3 to 20 mGy at the right nipple over the three scan protocols. Large fluctuations over repeated scans were also observed, as a combined result of helical scans of large pitch (1.375) and small active areas of the skin dosimeters. In addition, the averaged skin dose fell off drastically with the distance to the nearest boundary of the scanned region. Conclusions: This study revealed the complexity of CT dose fluctuation and variation with a human cadaver.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

Assessment of model estimates of land-atmosphere CO 2 exchange across Northern Eurasia

A warming climate is altering land-atmosphere exchanges of carbon, with a potential for increased vegetation productivity as well as the mobilization of permafrost soil carbon stores. Here we investigate land-atmosphere carbon dioxide (CO 2 ) cycling through analysis of net ecosystem productivity (NEP) and its component fluxes of gross primary productivity (GPP) and ecosystem respiration (ER) and soil carbon residence time, simulated by a set of land surface models (LSMs) over a region spanning the drainage basin of Northern Eurasia. The retrospective simulations cover the period 1960–2009 at 0.5° resolution, which is a scale common among many global carbon and climate model simulations. Model performance benchmarks were drawn from comparisons against both observed CO 2 fluxes derived from site-based eddy covariance measurements as well as regional-scale GPP estimates based on satellite remote-sensing data. The site-based comparisons depict a tendency for overestimates in GPP and ER for several of the models, particularly at the two sites to the south. For several models the spatial pattern in GPP explains less than half the variance in the MODIS MOD17 GPP product. Across the models NEP increases by as little as 0.01 to as much as 0.79 g C m⁻² yr⁻², equivalent to 3 to 340 % of the respective model means, over the analysis period. For the multimodel average the increase is 135 % of the mean from the first to last 10 years of record (1960–1969 vs. 2000–2009), with a weakening CO 2 sink over the latter decades. Vegetation net primary productivity increased by 8 to 30 % from the first to last 10 years, contributing to soil carbon storage gains. The range in regional mean NEP among the group is twice the multimodel mean, indicative of the uncertainty in CO 2 sink strength. The models simulate that inputs to the soil carbon pool exceeded losses, resulting in a net soil carbon gain amid a decrease in residence time. Our analysis points to improvements in model elements controlling vegetation productivity and soil respiration as being needed for reducing uncertainty in land-atmosphere CO 2 exchange. These advances will require collection of new field data on vegetation and soil dynamics, the development of benchmarking data sets from measurements and remote-sensing observations, and investments in future model development and intercomparison studies.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1994

An analysis of carrier phase jitter in an MPSK receiver utilizing map estimation

The use of 8 and 16 PSK TCM to support satellite communications in an effort to achieve more bandwidth efficiency in a power-limited channel has been proposed. This project addresses the problem of carrier phase jitter in an M-PSK receiver utilizing the high SNR approximation to the maximum aposteriori estimation of carrier phase. In particular, numerical solutions to the 8 and 16 PSK self-noise and phase detector gain in the carrier tracking loop are presented. The effect of changing SNR on the loop noise bandwidth is also discussed. These data are then used to compute variance of phase error as a function of SNR. Simulation and hardware data are used to verify these calculations. The results show that there is a threshold in the variance of phase error versus SNR curves that is a strong function of SNR and a weak function of loop bandwidth. The M-PSK variance thresholds occur at SNR's in the range of practical interest for the use of 8 and 16-PSK TCM. This suggests that phase error variance is an important consideration in the design of these systems.

Osborne, William P.↗
NASA NTRS2003

First Year Wilkinson Microwave Anisotropy Probe(WMAP)Observations: The Angular Power Spectrum

We present the angular power spectrum derived from the first-year Wilkinson Microwave Anisotropy Probe (WMAP) sky maps. We study a variety of power spectrum estimation methods and data combinations and demonstrate that the results are robust. The data are modestly contaminated by diffuse Galactic foreground emission, but we show that a simple Galactic template model is sufficient to remove the signal. Point sources produce a modest contamination in the low frequency data. After masking approximately 700 known bright sources from the maps, we estimate residual sources contribute approximately 3500 mu sq Kappa at 41 GHz, and approximately 130 mu sq Kappa at 94 GHz, to the power spectrum [iota(iota + 1)C(sub iota)/2pi] at iota = 1000. Systematic errors are negligible compared to the (modest) level of foreground emission. Our best estimate of the power spectrum is derived from 28 cross-power spectra of statistically independent channels. The final spectrum is essentially independent of the noise properties of an individual radiometer. The resulting spectrum provides a definitive measurement of the CMB power spectrum, with uncertainties limited by cosmic variance, up to iota approximately 350. The spectrum clearly exhibits a first acoustic peak at iota = 220 and a second acoustic peak at iota approximately 540, and it provides strong support for adiabatic initial conditions. Researchers have analyzed the CT(sup Epsilon) power spectrum, and present evidence for a relatively high optical depth, and an early period of cosmic reionization. Among other things, this implies that the temperature power spectrum has been suppressed by approximately 30% on degree angular scales, due to secondary scattering.

Hinshaw, G.↗
NASA NTRS1980

Trophic classification of Tennessee Valley area reservoirs derived from LANDSAT multispectral scanner data

LANDSAT MSS data from four different dates were extracted from computer tapes using a semiautomated digital data handling and analysis system. Reservoirs were extracted from the surrounding land matrix by using a Band 7 density level slice of 3; and descriptive statistics to include mean, variance, and ratio between bands for each of the four bands were calculated. Significant correlations ( 0.80) were identified between the MSS statistics and many trophic indicators from ground truth water quality data collected at 35 reservoirs in the greater Tennessee Valley region. Regression models were developed which gave significant estimates of each reservoir's trophic state as defined by its trophic state index and explained in all four LANDSAT frames at least 85 percent of the variability in the data. To illustrate the spatial variations within reservoirs as well as the relative variations between reservoirs, a table look up elliptical classification was used in conjunction with each reservoir's trophic state index to classify each reservoir on a pixel by pixel basis and produce color coded thematic representations.

Meinert, D. L.↗
NASA NTRS2010

Characterizing Methods of Measuring Flow-Mediated Dilation in the Brachial Artery

Regulation of vascular tone is one of the many important functions of the vascular endothelium. Endothelial dysfunction is a critical early event in the pathogenesis of atherosclerosis and occurs in the absence of angiographic disease. Flow-Mediated Dilation (FMD) is a noninvasive technique commonly used to evaluate endothelium-dependent vasodilation in humans and gauge the health of the cardiovascular system. Reductions in brachial artery FMD have been strongly correlated with disease progression and are predictive of future cardiac events. The flow stimulus for brachial artery FMD occurs as a result of the increased shear stress following deflation of an occlusion cuff around the upper arm. Using 2-dimensional ultrasound, changes in arterial diameter up to 5-minutes following cuff deflation are calculated from baseline image measurements. Along with pulsed Doppler measures of flow velocity through the artery, flow-mediated, endothelium-dependent vasodilation can be assessed. There is debate among investigators, however, about the proper positioning of the occlusion cuff during FMD testing. It is thought that placement of the cuff around the upper arm may not accurately reflect the impact of nitric oxide, a critically important molecule released as a result of the increased shear stress created by the FMD technique. Data suggest that the production of other endogenous metabolites may also contribute to FMD-related changes when positioning the cuff around the upper arm. To overcome the potential influence of such molecules, researchers now suggest that the occlusion cuff be placed below the elbow allowing a more precise estimate of nitric oxide mediated dilation. The purpose of this study is to compare the differences in FMD between the two methodologies of occlusion cuff placement. In addition, this study will determine the method that is easier for ultrasound technicians to perform and will produce a low coefficient of variance between technicians. Ultimately, the results of this study will help in tracking any adverse cardiovascular effects of spaceflight.

Callender, Ariane R.↗
DOE OSTI2021

Reducing Ground-based Astrometric Errors with Gaia and Gaussian Processes

Stochastic field distortions caused by atmospheric turbulence are a fundamental limitation to the astrometric accuracy of ground-based imaging. This distortion field is measurable at the locations of stars with accurate positions provided by the Gaia DR2 catalog; we develop the use of Gaussian process regression (GPR) to interpolate the distortion field to arbitrary locations in each exposure. We introduce an extension to standard GPR techniques that exploits the knowledge that the 2D distortion field is curl-free. Applied to several hundred 90 s exposures from the Dark Energy Survey as a test bed, we find that the GPR correction reduces the variance of the turbulent astrometric distortions ≈12× , on average, with better performance in denser regions of the Gaia catalog. The rms per-coordinate distortion in the riz bands is typically ≈7 mas before any correction and ≈2 mas after application of the GPR model. The GPR astrometric corrections are validated by the observation that their use reduces, from 10 to 5 mas rms, the residuals to an orbit fit to riz-band observations over 5 yr of the r = 18.5 trans-Neptunian object Eris. We also propose a GPR method, not yet implemented, for simultaneously estimating the turbulence fields and the 5D stellar solutions in a stack of overlapping exposures, which should yield further turbulence reductions in future deep surveys.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Risk-Constrained Reinforcement Learning for Inverter-Dominated Power System Controls

Here, this paper develops a risk-aware controller for grid-forming inverters (GFMs) to minimize large frequency oscillations in GFM inverter-dominated power systems. To tackle the high variability from loads/renewables, we incorporate a mean-variance risk constraint into the classical linear quadratic regulator (LQR) formulation for this problem. The risk constraint aims to bound the time-averaged cost of state variability and thus can improve the worst-case performance for large disturbances. The resulting risk-constrained LQR problem is solved through the dual reformulation to a minimax problem, by using a reinforcement learning (RL) method termed as stochastic gradient-descent with max-oracle (SGDmax). In particular, the zero-order policy gradient (ZOPG) approach is used to simplify the gradient estimation using simulated system trajectories. Numerical tests conducted on the IEEE 68-bus system have validated the convergence of our proposed SGDmax for GFM model and corroborate the effectiveness of the risk constraint in improving the worst-case performance while reducing the variability of the overall control cost.

Frequency control↗
DOE OSTI2023

Evaluating Retention Index Score Assumptions to Refine GC–MS Metabolite Identification

As metabolomics grows into a high-throughput and high demand research field, current metrics for the identification of small molecules in gas chromatography–mass spectrometry (GC–MS) still require manual verification. Though steps have been taken to improve scoring metrics by combining spectral similarity (SS) and retention index (RI), the problem persists. A large body of literature has analyzed and refined SS scores, but few studies have explicitly studied improvements to RI scores. Here, in this study, we examined whether uninvestigated assumptions of the RI score are valid and propose ways to improve them. Query RIs were matched to library RI with a generous window of ±35 to avoid unintentional removal of valid compound identifications. Each match was manually verified as a true positive (TP), true negative, or unknown. Metabolites with at least 30 TP identifications were included in downstream analyses, resulting in a total of 87 metabolites from samples of varying complexity and type (e.g., amino acid mixtures, human urine, fungal species, and so on.). Our results showed that the RI score assumptions of normality, consistent variance across metabolites, and a mean error centered at 0 are often violated. We demonstrated through a cross-validation analysis that modifying these underlying assumptions according to empirical metabolite-specific distributions improved the TP and negative rankings. Further, we statistically determined the minimum number of samples required to estimate distributional parameters for scoring metrics. Overall, this work proposes a robust statistical pipeline to reduce the time bottleneck of metabolite identification by improving RI scores and thus minimize the effort to complete manual verification.

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

Evaluation of CloudSat Radiative Kernels Using ARM and CERES Observations and ERA5 Reanalysis

Despite the widespread use of the radiative kernel technique for studying radiative feedbacks and radiative forcings, there has not been any systematic, observation-based validation of the radiative kernel method. Here, we utilize observed and reanalyzed radiative fluxes and atmospheric profiles from the Atmospheric Radiation Measurement (ARM) program and ERA5 reanalysis to assess a set of observation-based radiative kernels from CloudSat for six ARM sites. The CloudSat radiative kernels, convoluted with the ERA5 state variables, can almost perfectly reconstruct the monthly anomalies of shortwave (SW) and longwave (LW) radiative fluxes in ERA5 at the surface (SFC) and top-of-atmosphere (TOA) with correlations significantly being greater than 0.95. The biases of kernel-estimated flux anomalies calculated using the ARM-observed state variables can be more than twice as large when compared with the ARM-observed surface flux anomalies and Clouds and Earth’s Radiant Energy System (CERES) observed anomalies at the TOA. Generally, clouds contribute to most (>60%) of the variance of flux anomalies at Southern Great Plain (SGP), Tropical Western Pacific (TWP), and Eastern North Atlantic (ENA), and surface albedo dominates (>69%) the variance of SW flux anomalies at North Slope of Alaska (NSA). Furthermore, the radiative kernels exhibit the lowest correlation (r ~ [0.55,0.85]) when reconstructing SFC LW flux anomalies at SGP, TWP, and ENA, whose biases are related to the possibility that the kernels may not fully capture the characteristics associated with MJO and ENSO at TWP and the presence of clouds at SGP and ENA.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Identifying structural changes with unsupervised machine learning methods

In this work, unsupervised machine learning methods are used to identify structural changes using the melting point transition in classical molecular dynamics simulations as an example application of the approach. Dimensionality reduction and clustering methods are applied to instantaneous radial distributions of atomic configurations from classical molecular dynamics simulations of metallic systems over a large temperature range. Principal component analysis is used to dramatically reduce the dimensionality of the feature space across the samples using an orthogonal linear transformation that preserves the statistical variance of the data under the condition that the new feature space is linearly independent. From there, k-means clustering is used to partition the samples into solid and liquid phases through a criterion motivated by the geometry of the reduced feature space of the samples, allowing for an estimation of the melting point transition. This pattern criterion is conceptually similar to how humans interpret the data but with far greater throughput, as the shapes of the radial distributions are different for each phase and easily distinguishable by humans. The transition temperature estimates derived from this machine learning approach produce comparable results to other methods on similarly small system sizes. These results show that machine learning approaches can be applied to structural changes in physical systems.

42 ENGINEERING↗
NASA NTRS1987

A further analysis for the minimum-variance deconvolution filter performance

Chi and Mendel (1984) analyzed the performance of minimum-variance deconvolution (MVD). In this correspondence, a further analysis of the performance of the MVD filter is presented. It is shown that the MVD filter performs like an inverse filter and a whitening filter as SNR goes to infinity, and like a matched filter as SNR goes to zero. The estimation error of the MVD filter is colored noise, but it becomes white when SNR goes to zero. This analysis also conects the error power-spectral density of the MVD filter with the spectrum of the causal-prediction error filter.

Chi, Chong-Yung↗
NASA NTRS2003

Gamma-ray Burst Energetics an the Gamma-ray Burst Hubble Diagram: Promises and Limitations

We present a complete sample of 29 gamma-ray bursts (GRBs) for which it has been possible to determine temporal breaks (or limits) from their afterglow light curves. We interpret these breaks within the framework of the uniform conical jet model, incorporating realistic estimates of the ambient density and propagating error estimates on the measured quantities. In agreement with our previous analysis of a smaller sample, the derived jet opening angles of those 16 bursts with redshifts result in a narrow clustering of geometrically corrected gamma-ray energies about 1.33 x 10(exp 51) ergs; the burst-to-burst variance about this value is 0.35 dex, a factor of 2.2. Despite this rather small scatter, we demonstrate in a series of GRB Hubble diagrams that the current sample cannot place meaningful constraints upon the fundamental parameters of the universe. Indeed, for GRBs to ever be useful in cosmographic measurements, we argue the necessity of two directions. First, GRB Hubble diagrams should be based upon fundamental physical quantities such as energy, rather than empirically derived and physically ill-understood distance indicators (such as those based upon prompt burst time-profiles and spectra). Second, a more homogeneous set should be constructed by culling subclasses from the larger sample. These subclasses, although now first recognizable by deviant energies, ultimately must be identifiable by properties other than those directly related to energy. We identify a new subclass of GRBs (" f-GRBs ") that appear both underluminous by factors of at least 10 and exhibit a rapid fading (f(sub nu is proportional to t(sup -2) at early times (t < or = 0.5 day). About 10%-20% of observed long-duration bursts appear to be f-GRBs.

Bloom, J. S.↗
NASA NTRS2021

Evaluation of CloudSat Radiative Kernels Using ARM and CERES Observations and ERA5 Reanalysis

Despite the widespread use of the radiative kernel technique for studying radiative feedbacks and radiative forcings, there has not been any systematic, observation-based validation of the radiative kernel method. Here, we utilize observed and reanalyzed radiative fluxes and atmospheric profiles from the Atmospheric Radiation Measurement (ARM) program and ERA5 reanalysis to assess a set of observation-based radiative kernels from CloudSat for six ARM sites. The CloudSat radiative kernels, convoluted with the ERA5 state variables, can almost perfectly reconstruct the monthly anomalies of shortwave (SW) and longwave (LW) radiative fluxes in ERA5 at the surface (SFC) and top-of atmosphere (TOA) with correlations significantly being greater than 0.95. The biases of kernel-estimated flux anomalies calculated using the ARM-observed state variables can be more than twice as large when compared with the ARM-observed surface flux anomalies and Clouds and Earth’s Radiant Energy System (CERES) observed anomalies at the TOA. Generally, clouds contribute to most (>60%) of the variance of flux anomalies at Southern Great Plain (SGP), Tropical Western Pacific (TWP), and Eastern North Atlantic (ENA), and surface albedo dominates (>69%) the variance of SW flux anomalies at North Slope of Alaska (NSA). The radiative kernels exhibit the lowest correlation (r~[0.55,0.85]) when reconstructing SFC LW flux anomalies at SGP, TWP, and ENA, whose biases are related to the possibility that the kernels may not fully capture the characteristics 47 associated with MJO and ENSO at TWP and the presence of clouds at SGP and ENA.

Radiative kernels↗
DOE OSTI2024

Empirical Validation of UBEM: An Assessment of Bias in Urban Building Energy Modeling for Chicago

Residential and commercial buildings currently account for 30% of total global final energy consumption. Urban-scale building energy modeling (UBEM) can enable scalable investments and unlock building improvements by quantifying energy, demand, emissions, and cost reductions of specific measures or packages for building-specific technologies in large geographic regions. While the sophistication of UBEM data sources and technologies have increased dramatically in the past decade, there remains a knowledge gap for empirical validation and sources of bias between building-specific energy models and measured data at varying geographic scales.As UBEM continues to develop, systemic analysis of accuracy, bias, and limitations of the resulting models is necessary to inform best practices and move toward standardization. These are characterized for the Automatic Building Energy Modeling (AutoBEM) software suite with an initial case study involving metered electricity consumption data from 247,188 buildings in Chicago, Illinois, USA - averaged across years 2019-2021 - compared to the following datasets: (1) the AutoBEM-generated nation-scale Model America version 2 (MAv2) data for 596,064 buildings, (2) tax assessor data for 579,829 buildings, (3) tax assessor data filled with MAv2, and (4) 102 representative dynamic archetypes. The accuracy is reported for every building type and vintage combination, along with multiple sources of bias for unique building descriptors. The AutoBEM simulation workflow produced energy consumption estimates that closely match aggregated metered electricity consumption data for different types of buildings constructed during various time periods at the city scale - with initial normalized mean bias error of 10.9%, and 1.1% after removing outliers. Contribution of statistically significant factors including building type, land use, age, and size to variance in UBEM bias is quantified.

Garg, Ankur↗