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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 1,099 records · Page 61
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Investigation of the Far-IR Reflection Spectra of SmS Single Crystals and Polycrystals in the Homogeneity Range

The far- and mid-IR reflection spectra of Sm{sub 1+} {sub x}S (x = 0–0.17) samples are recorded and analyzed, as well as their electrical and structural parameters at a temperature of T = 300 K. The bond ionicity in SmS is shown to fall with a decrease in the area of the X-ray coherent scattering region and an increase in the concentration of donor impurities and, consequently, conduction electron concentration. The electrical conductivity of stoichiometric SmS single crystals and polycrystals can be determined with an error of 10% from the IR reflection spectra. Due to the low structural quality of the samples, the electrical conductivity cannot be determined in the case of deviation from stoichiometry.

36 MATERIALS SCIENCE↗
NASA NTRS2017

Detection of the Pairwise Kinematic Sunyaev-Zel'dovich Effect with BOSS DR11 and the Atacama Cosmology Telescope

We present a new measurement of the kinematic Sunyaev-Zel'dovich effect using data from the Atacama Cosmology Telescope (ACT) and the Baryon Oscillation Spectroscopic Survey (BOSS). Using 600 square degrees of overlapping sky area, we evaluate the mean pairwise baryon momentum associated with the positions of 50,000 bright galaxies in the BOSS DR11 Large Scale Structure catalog. A non-zero signal arises from the large-scale motions of halos containing the sample galaxies. The data fits an analytical signal model well, with the optical depth to microwave photon scattering as a free parameter determining the overall signal amplitude. We estimate the covariance matrix of the mean pairwise momentum as a function of galaxy separation, using microwave sky simulations, jackknife evaluation, and bootstrap estimates. The most conservative simulation-based errors give signal-to-noise estimates between 3.6 and 4.1 for varying galaxy luminosity cuts. We discuss how the other error determinations can lead to higher signal-to-noise values, and consider the impact of several possible systematic errors. Estimates of the optical depth from the average thermal Sunyaev-Zel'dovich signal at the sample galaxy positions are broadly consistent with those obtained from the mean pairwise momentum signal.

De Bernardis, F.↗
NASA NTRSDate not supplied

Planning Bias: Planning as a Source of Sampling Bias

Many data-driven planning methods are trained on data generated by planners. It is well known that many statistical learning methods are sensitive to sampling bias, and yet there has been little or no attention to planning as a sampling method and its role in introducing sampling bias into planner-generated training data. Recently, it has been demonstrated that A**,* in the presence of problems with variable heuristic error, prefers some solutions over other equally cost-optimal solutions. But, as we discuss in this paper, mitigation may not be as simple as resolving arbitrary tie-breaking by sampling from ties uniformly at random. In this paper, we formalize an intuition of planning bias. We focus on problems which output a single solution. Diverse planning only complicates the problem by generalizing it to bias in the set of sets; we show how it is subject to bias in the single solution. We make some useful observations about deterministic algorithms in contrast to non-deterministic algorithms. We explain how information entropy may be a good way to measure planning bias, and discuss some issues in evaluating practical approaches to measurement. We address the intuition that uniform random tiebreaking should mitigate bias; and sketch a novel approach to constructing an appropriate random distribution for duplicate detection during forward search for unbiased A*. Finally, we suggest directions for future work.

Planning Scheduling Algorithms↗
DOE OSTI2017

An exploration of heterogeneity in supernova type Ia samples

We examine three SNe Type Ia datasets: Union2.1, JLA and Panstarrs to check their consistency using cosmology blind statistical analyses as well as cosmological parameter fitting. We find that the Panstarrs dataset is the most stable of the three to changes in the data, although it does not, at the moment, go to high enough redshifts to tightly constrain the equation of state of dark energy, w . The Union2.1, drawn from several different sources, appears to be somewhat susceptible to changes within the dataset. The JLA reconstructs well for a smaller number of cosmological parameters. At higher degrees of freedom, the dependence of its errors on redshift can lead to varying results between subsets. Panstarrs is inconsistent with the other two datasets at about 2σ confidence level, and JLA and Union2.1 are about 1σ away from each other. For the Ω{sub 0} {sub m} − w cosmological reconstruction, with no additional data, the 1σ range of values in w for selected subsets of each dataset is two times larger for JLA and Union2.1 as compared to Panstarrs. The range in Ω{sub 0} {sub m} for the same subsets remains approximately similar for all three datasets. We find that although there are differences in the fitting and correction techniques used in the different samples, the most important criterion is the selection of the SNe, a slightly different SNe selection can lead to noticeably different results both in the purely statistical analysis and in cosmological reconstruction. We note that a single, high quality low redshift sample could help decrease the uncertainties in the result. We also note that lack of homogeneity in the magnitude errors may bias the results and should either be modeled, or its effect neutralized by using other, complementary datasets. A supernova sample with high quality data at both high and low redshifts, constructed from a few surveys to avoid heterogeneity in the sample, and with homogeneous errors, would result in a more robust cosmological reconstruction.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS2006

Aerosol Indirect Effect Studies at Southern Great Plains During the May 2003 Intensive Operations Period

During May 2003 the Department of Energy's Atmospheric Radiation Measurement Program conducted an Intensive Operations Period (IOP) to measure the radiative effects of aerosol and clouds. A suite of both in situ and remote sensing measurements were available to measure aerosol and cloud parameters. This paper has three main goals: First, it focuses on comparison between in situ retrievals of the radiatively important drop effective radius r(sub e) and various satellite, airborne, and surface remote sensing retrievals of the same parameter. On 17 May 2003, there was a fortuitous, near-simultaneous sampling of a stratus cloud by five different methods. The retrievals of r(sub e) agree with one another to within approx.20%, which is approximately the error estimate for most methods. Second, a methodology for deriving a best estimate of r(sub e) from these different instruments, with their different physical properties and sampling volumes, is proposed and applied to the 17 May event. Third, the paper examines the response of r(sub e) to changes in aerosol on 3 days during the experiment and examines the consistency of remote sensing and in situ measurements of the effect of aerosol on r(sub e). It is shown that in spite of the generally good agreement in derived r(sub e), the magnitude of the response of r(sub e), to changes in aerosol is quite sensitive to the method of retrieving r(sub e) and to the aerosol proxy for cloud condensation nuclei. Nonphysical responses are sometimes noted, and it is suggested that further work needs to be done to refine these techniques.

Feingold, Graham↗
DOE OSTI2017

Deriving Physical Properties from Broadband Photometry with Prospector: Description of the Model and a Demonstration of its Accuracy Using 129 Galaxies in the Local Universe

Broadband photometry of galaxies measures an unresolved mix of complex stellar populations, gas, and dust. Interpreting these data is a challenge for models: many studies have shown that properties derived from modeling galaxy photometry are uncertain by a factor of two or more, and yet answering key questions in the field now requires higher accuracy than this. Here, we present a new model framework specifically designed for these complexities. Our model, Prospector- α , includes dust attenuation and re-radiation, a flexible attenuation curve, nebular emission, stellar metallicity, and a six-component nonparametric star formation history. The flexibility and range of the parameter space, coupled with Monte Carlo Markov chain sampling within the Prospector inference framework, is designed to provide unbiased parameters and realistic error bars. We assess the accuracy of the model with aperture-matched optical spectroscopy, which was excluded from the fits. We compare spectral features predicted solely from fits to the broadband photometry to the observed spectral features. Our model predicts H α luminosities with a scatter of ∼0.18 dex and an offset of ∼0.1 dex across a wide range of morphological types and stellar masses. This agreement is remarkable, as the H α luminosity is dependent on accurate star formation rates, dust attenuation, and stellar metallicities. The model also accurately predicts dust-sensitive Balmer decrements, spectroscopic stellar metallicities, polycyclic aromatic hydrocarbon mass fractions, and the age- and metallicity-sensitive features D{sub n}4000 and H δ . Although the model passes all these tests, we caution that we have not yet assessed its performance at higher redshift or the accuracy of recovered stellar masses.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2017

Clustering of quasars in SDSS-IV eBOSS: study of potential systematics and bias determination

Here, we study the first year of the eBOSS quasar sample in the redshift range 0.9 Q = 2.45 ± 0.05 at $\bar{z}$=1.55, together with its evolution with redshift. We use this information to determine the minimum mass of the halo hosting the quasars and the characteristic halo mass, which we find to be both independent of redshift within statistical error. Using a recently-measured quasar-luminosity-function we also determine the quasar duty cycle. Lastly, the size of this first year sample is insufficient to detect any luminosity dependence to quasar clustering and this issue should be further studied with the final ~500,000 eBOSS quasar sample.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Leveraging design of experiments to build chemometric models for the quantification of uranium (VI) and HNO3 by Raman spectroscopy

Partial least squares regression (PLSR) and support vector regression (SVR) models were optimized for the quantification of U(VI) (10–320 g L −1 ) and HNO 3 (0.6–6 M) by Raman spectroscopy with optimized calibration sets chosen by optimal design of experiments. The designed approach effectively minimized the number of samples in the calibration set for PLSR and SVR by selecting sample concentrations with a quadratic process model, despite complex confounding and covarying spectral features in the spectra. The top PLS2 model resulted in percent root mean square errors of prediction for U(VI), HNO 3 , and NO 3 − of 3.7%, 3.6%, and 2.9%, respectively. PLS1 models performed similarly despite modeling an analyte with a majority linear response (i.e., uranyl symmetric stretch) and another with more covarying vibrational modes (i.e., HNO 3 ). Partial least squares (PLS) model loadings and regression coefficients were evaluated to better understand the relationship between weaker Raman bands and covarying spectral features. Support vector machine models outperformed PLS1 models, resulting in percent root mean square error of prediction values for U(VI) and HNO 3 of 1.5% and 3.1%, respectively. The optimal nonlinear SVR model was trained using a similar number of samples (11) compared with the PLSR model, even though PLS is a linear modeling approach. The generic D-optimal design presented in this work provides a robust statistical framework for selecting training set samples in disparate two-factor systems. This approach reinforces Raman spectroscopy for the quantification of species relevant to the nuclear fuel cycle and provides a robust chemometric modeling approach to bolster online monitoring in challenging process environments.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Laser Confocal Microscopy Uncertainty Quantification Study

At Los Alamos National Laboratory (LANL), the Storage Safety and Engineering (SSE) team completes annual surveillance on a subset of in-use interim nuclear material storage containers in fulfilment of requirements outlined in DOE Manual M 441.1-1. The containers are selected through several methods, such as subject matter expert judgement, random selection, and trending items. Following these selections, the SSE team has the capacity to complete surveillance on 15-20 containers each fiscal year, composed of a combination of SAVY-4000 and Hagan storage containers. Through previous work, the stainless-steel components of the containers have been identified as life limiting components, with an emphasis on the thin-walled bodies. The team is focused on understanding the extent of general and pitting corrosion, due to observations of extensive corrosion from stored contents and bag-out-bag degradation. Quantifying corrosion effects on the thin-walled stainless steel container bodies, and understanding potential impacts to the respective design release rates and design qualification release rates is paramount to the team. To date, destructive examination (DE) has proven to be the most insightful method for developing an understanding on the extent of corrosion on used containers. To standardize this process, the SSE team developed a destructive examination guide for analyzing stainless steel components of the containers. Corroded containers of interest are identified during surveillance activities and set aside for sectioning and characterization. Following sectioning, a major step in the DE workflow is the utilization of laser confocal microscopy for scanning corroded samples of interest and extracting data on pits, such as count, depth, and equivalent diameter. Adhering to the techniques outlined in the DE guide, analysis has been completed on two Hagans and one SAVY-4000 container, with the maximum pit depth recorded as 139.1 ± 22.82 μm on a 17.5 year old Hagan. The findings from the completed destructive examinations will be utilized to support lifetime extension efforts of the SAVY-4000 as the team can better estimate corrosion rates and effects over time based on stored contents and age. Due to the implications of observing extreme pit depths that approach the nominal container body thickness of .0299 inches (0.759 mm) or minimum container thickness of 0.236” (0.6 mm), high confidence in the LCM measurements is desired. Through testing outlined in, it was concluded that the total error ascribed to the 20x objective when conducting large image mapping on the Keyence VK-X3050 laser confocal microscope (LCM) relative to a 50x objective (reference) is 16.4% (± 8.73%). For shallow features on the order of pristine SAVY surface defects (i.e. 5 μm), this uncertainty is appropriate. However, this conservative estimate of total error poses a fundamental concern for pit depths that approach the thickness of the measured samples. That is, with the measurement uncertainty currently employed on all measurements, the LCM would be unable to resolve if a pit with a depth of 515 μm is through wall. Standard step height samples were procured and used in the present study to assess the resolution and repeatability of height measurements. Understanding the resolution and repeatability of height measurements was the first focus of the team as it relates directly to pit depth, which is of primary concern. Calibration gratings were procured to evaluate the resolution and repeatability of measurements in the X and Y axes of the LCM stage. The results of the depth uncertainty study were conducted first and presented in the subsequent sections. The planar uncertainty study is appended to the depth study with conclusions from both summarized at the end of the report.

36 MATERIALS SCIENCE↗
NASA NTRS1979

Spectrophotometry of Michigan-Tololo quasars

The Michigan-Tololo sample of quasars is discussed using observational data for 48% of this sample. Emission-line quasar characteristics are confirmed for 80% of the objects observed, including at least four new quasars with spectral features indicative of 'supernova-like' outflow. Approximately 73% of the redshifts predicted from the discovery plates are found to be accurate, with a mean error in z of 0.03; a large range of z (from about 0.1 to 3.16) is represented in the sample. The interpretation of the observed redshift distribution for quasars is strongly dependent upon the significance ascribed to wavelength-dependent selection effects. It appears that the behavior of the comoving quasar density above z = 2.0 may be represented by a constant or an exponential increase.

Lewis, D. W.↗
NASA NTRS1986

Coniferous forest classification and inventory using Landsat and digital terrain data

Machine-processing techniques were used in a Forest Classification and Inventory System (FOCIS) procedure to extract and process tonal, textural, and terrain information from registered Landsat multispectral and digital terrain data. Using FOCIS as a basis for stratified sampling, the softwood timber volumes of the Klamath National Forest and Eldorado National Forest were estimated within standard errors of 4.8 and 4.0 percent, respectively. The accuracy of these large-area inventories is comparable to the accuracy yielded by use of conventional timber inventory methods, but, because of automation, the FOCIS inventories are more rapid (9-12 months compared to 2-3 years for conventional manual photointerpretation, map compilation and drafting, field sampling, and data processing) and are less costly.

Franklin, J.↗
NASA NTRS2013

Comparison of Precision of Biomass Estimates in Regional Field Sample Surveys and Airborne LiDAR-Assisted Surveys in Hedmark County, Norway

Airborne scanning LiDAR (Light Detection and Ranging) has emerged as a promising tool to provide auxiliary data for sample surveys aiming at estimation of above-ground tree biomass (AGB), with potential applications in REDD forest monitoring. For larger geographical regions such as counties, states or nations, it is not feasible to collect airborne LiDAR data continuously ("wall-to-wall") over the entire area of interest. Two-stage cluster survey designs have therefore been demonstrated by which LiDAR data are collected along selected individual flight-lines treated as clusters and with ground plots sampled along these LiDAR swaths. Recently, analytical AGB estimators and associated variance estimators that quantify the sampling variability have been proposed. Empirical studies employing these estimators have shown a seemingly equal or even larger uncertainty of the AGB estimates obtained with extensive use of LiDAR data to support the estimation as compared to pure field-based estimates employing estimators appropriate under simple random sampling (SRS). However, comparison of uncertainty estimates under SRS and sophisticated two-stage designs is complicated by large differences in the designs and assumptions. In this study, probability-based principles to estimation and inference were followed. We assumed designs of a field sample and a LiDAR-assisted survey of Hedmark County (HC) (27,390 km2), Norway, considered to be more comparable than those assumed in previous studies. The field sample consisted of 659 systematically distributed National Forest Inventory (NFI) plots and the airborne scanning LiDAR data were collected along 53 parallel flight-lines flown over the NFI plots. We compared AGB estimates based on the field survey only assuming SRS against corresponding estimates assuming two-phase (double) sampling with LiDAR and employing model-assisted estimators. We also compared AGB estimates based on the field survey only assuming two-stage sampling (the NFI plots being grouped in clusters) against corresponding estimates assuming two-stage sampling with the LiDAR and employing model-assisted estimators. For each of the two comparisons, the standard errors of the AGB estimates were consistently lower for the LiDAR-assisted designs. The overall reduction of the standard errors in the LiDAR-assisted estimation was around 40-60% compared to the pure field survey. We conclude that the previously proposed two-stage model-assisted estimators are inappropriate for surveys with unequal lengths of the LiDAR flight-lines and new estimators are needed. Some options for design of LiDAR-assisted sample surveys under REDD are also discussed, which capitalize on the flexibility offered when the field survey is designed as an integrated part of the overall survey design as opposed to previous LiDAR-assisted sample surveys in the boreal and temperate zones which have been restricted by the current design of an existing NFI.

Comparison↗
NASA NTRS1980

Critique of a Hughes shuttle Ku-band data sampler/bit synchronizer

An alternative bit synchronizer proposed for shuttle was analyzed in a noise-free environment by considering the basic operation of the loop via timing diagrams and by linearizing the bit synchronizer as an equivalent, continuous, phased-lock loop (PLL). The loop is composed of a high-frequency phase-frequency detector which is capable of detecting both phase and frequency errors and is used to track the clock, and a bit transition detector which attempts to track the transitions of the data bits. It was determined that the basic approach was a good design which, with proper implementation of the accumulator, up/down counter and logic should provide accurate mid-bit sampling with symmetric bits. However, when bit asymmetry occurs, the bit synchronizer can lock up with a large timing error, yet be quasi-stable (timing will not change unless the clock and bit sequence drift). This will result in incorrectly detecting some bits.

Holmes, J. K.↗
NASA NTRSDate not supplied

The Spectral Information Based Angular Correction Methodology for Satellite Intercalibration Applications

Satellite inter-calibration often requires collocated observations with minimized discrepancies in sun-view angles, observation times, and sensor characteristics. The collocation criteria directly impact achievable inter-calibration accuracy. Addressing potential angular mismatches in inter-calibration samples is critical but not as fully recognized and addressed as spatial-temporal mismatches in many studies. To achieve high-accuracy corrections for errors due to mismatched sun-view geometry angles, an angular correction algorithm has been developed for the Climate Absolute Radiance and Refractivity Observatory Pathfinder (CPF) mission. This algorithm uses spectral correlation relationships to estimate differences in spectral radiances measured at different angles. This methodology can be extended for inter-calibrations between sensors measuring band radiances across a broad spectral region. We demonstrate its application in reducing angular mismatch errors between collocated measurements of multi-spectral imaging sensors, using the inter-calibration between the Moderate Resolution Imaging Spectrometer (MODIS) and the Visible Infrared Imaging Radiometer Suite (VIIRS) as an example. The angular correction allows for more relaxed collocation criteria so that more satellite-based inter-calibration samples can be utilized. Furthermore, implementing the angular correction algorithm improves inter-calibration accuracy in applications where angular mismatch errors have not been explicitly addressed previously.

Wan Wu↗
NASA NTRS1975

The use of Whatman-41 filters for high volume air sampling

The feasibility of using W41 filter media on a routine TSP high-volume monitoring network was determined by comparison with glass fiber (GF) filtering. Results indicate that suspended particulate samples from GF filters averaged slightly, but not significantly, higher than those from Whatman-41 filters. Some extra handling procedures were required to avoid errors due to the hygroscopic nature of W41 filters; these added procedures are not overly burdensome, however, and they allow the performance of analytical work, thus extending the capabilities of high-volume sampling. It was demonstrated that W41 filters are practical for air quality monitoring and elemental analysis in environments similar to Cleveland's.

Neustadter, H. E.↗
NASA NTRS1995

Thermal infrared spectra of the Moon

Contrary to recent assertions in the literature, lunar emission spectra obtained at an altitude of 32 km with a balloon-borne telescope are virtually undistorted by atmospheric absorption, except in the ozone region. These spectra have been found to correlate closely with laboratory emission spectra of returned lunar samples measured in a simulated lunar environment. Thus, lunar spectra obtained with the balloon-borne telescope system are the standards against which groundbased spectral measurements of the Moon should be compared. Comparison of balloon-borne measurements with recent groundbased spectral measurements suggests that the latter suffer from some source of systematic error. When high-quality lunar spectra are available, they will be best interpreted by comparison with returned lunar samples, rather than terrestrial minerals or rocks, because of subtle spectral differences between lunar and terrestrial minerals perhaps associated with incipient alteration of the latter.

Salisbury, John W.↗
DOE OSTI2019

An anomaly detection framework for dynamic systems using a Bayesian hierarchical framework

Complex systems are susceptible to many types of anomalies, faults, and abnormal behavior caused by a variety of off-nominal conditions that may ultimately result in major failures or catastrophic events. Early and accurate detection of these anomalies using system inputs and outputs collected from sensors and smart devices has become a challenging problem and an active area of research in many application domains. In this article, we present a new Bayesian hierarchical framework that is able to model the relationship between system inputs (sensor measurements) and outputs (response variables) without imposing strong distributional/parametric assumptions while using only a subset of training samples and sensor attributes. Then, an optimal cost-sensitive anomaly detection framework is proposed to determine whether a sample is an anomalous one taking into consideration the trade-off between misclassification errors and detection rates. The model can be used for both supervised and unsupervised settings depending on the availability of data regarding the behavior of the system under anomaly conditions. The unsupervised model is particularly useful when it is prohibitive to identify in advance the anomalies that a system may present and where no data are available regarding the behavior of the system under anomaly conditions. A Bayesian hierarchical setting is used to structure the proposed framework and help with accommodating uncertainty, imposing interpretability, and controlling the sparsity and complexity of the proposed anomaly detection framework. A Markov chain Monte Carlo algorithm is also developed for model training using past data. Furthermore, the numerical experiments conducted using a simulated data set and a wind turbine data set demonstrate the successful application of the proposed work for system response modeling and anomaly detection.

17 WIND ENERGY↗
DOE OSTI2019

Disconnected covariance of 2-point functions in large-scale structure

Optimal analyses using the 2-point functions of large-scale structure probes require accurate covariance matrices. A covariance matrix of the 2-point function comprises the disconnected part and the connected part. While the connected covariance only becomes important on small scales, the disconnected covariance is dominant on large scales, where the survey window has a significant impact. In this work, we develop an analytical method to compute the disconnected covariance, accounting for the window effect. Derived under the flat-sky approximation, our formalism is applicable to wide surveys by swapping in the curved-sky window functions. Our method works for both the power spectrum and the correlation function, and applies to the covariances of various probes including the multipoles and the wedges of 3D clustering, the angular and the projected statistics of clustering and shear, as well as the cross covariances between different probes. We verify the analytic covariance against the sample covariance from the galaxy mock simulations in two test cases: (1) the power spectrum multipole covariance, and (2) the joint covariance of the projected correlation function and the correlation function multipoles. Our method achieve good agreement with the mocks, while at a negligible computational cost. Unlike mocks, our analytic covariance is free of sampling noise, which often leads to numerical problems and the need to inflate the errors. In addition, our method can use the best-fit power spectrum as input, in contrast to the standard procedure of using a fiducial model that may deviate significantly from the truth. We also show that a naive diagonal power spectrum covariance underestimates the signal-to-noise ratio compared to our analytic covariance. The code that accompanies this paper is available at https://github.com/eelregit/covdisc.

79 ASTRONOMY AND ASTROPHYSICS↗