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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 1,189 records · Page 66
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Rigorous constraints on three-nucleon forces in chiral effective field theory from fast and accurate calculations of few-body observables

We explore the constraints on the three-nucleon force (3NF) of chiral effective field theory (χ EFT) that are provided by bound-state observables in the A = 3 and A = 4 sectors. Our statistically rigorous analysis incorporates experimental error, computational method uncertainty, and the uncertainty due to truncation of the χ EFT expansion at next-to-next-to-leading order. A consistent solution for the 3 H binding energy, the 4 He binding energy and radius, and the 3 H β-decay rate can only be obtained if χ EFT truncation errors are included in the analysis. Here, the β-decay rate is the only one of these that yields a nondegenerate constraint on the 3NF low-energy constants, which makes it crucial for the parameter estimation. We use eigenvector continuation for fast and accurate emulation of no-core shell model calculations of the few-nucleon observables. This facilitates sampling of the posterior probability distribution, allowing us to also determine the distributions of the parameters that quantify the truncation error. We find a χ EFT expansion parameter of Q = 0.33 ± 0.06 for these observables.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
NASA NTRS2020

An AeroCom–AeroSat study: intercomparison of satellite AOD datasets for aerosol model evaluation

To better understand and characterize current uncertainties in the important observational constraint of climate models of aerosol optical depth (AOD), we evaluate and intercompare 14 satellite products, representing nine different retrieval algorithm families using observations from five different sensors on six different platforms. The satellite products (super-observations consisting of 1°×1° daily aggregated retrievals drawn from the years 2006, 2008 and 2010) are evaluated with AErosol RObotic NETwork (AERONET) and Maritime Aerosol Network (MAN) data. Results show that different products exhibit different regionally varying biases (both under- and overestimates) that may reach ±50 %, although a typical bias would be 15 %–25 % (depending on the product). In addition to these biases, the products exhibit random errors that can be 1.6 to 3 times as large. Most products show similar performance, although there are a few exceptions with either larger biases or larger random errors. The intercomparison of satellite products extends this analysis and provides spatial context to it. In particular, we show that aggregated satellite AOD agrees much better than the spatial coverage (often driven by cloud masks) within the 1°×1° grid cells. Up to ∼50 % of the difference between satellite AOD is attributed to cloud contamination. The diversity in AOD products shows clear spatial patterns and varies from 10 % (parts of the ocean) to 100 % (central Asia and Australia). More importantly, we show that the diversity may be used as an indication of AOD uncertainty, at least for the better performing products. This provides modellers with a global map of expected AOD uncertainty in satellite products, allows assessment of products away from AERONET sites, can provide guidance for future AERONET locations and offers suggestions for product improvements. We account for statistical and sampling noise in our analyses. Sampling noise, variations due to the evaluation of different subsets of the data, causes important changes in error metrics. The consequences of this noise term for product evaluation are discussed.

Aerosol↗
NASA NTRS2012

Impact of Non-Gaussian Error Volumes on Conjunction Assessment Risk Analysis

An understanding of how an initially Gaussian error volume becomes non-Gaussian over time is an important consideration for space-vehicle conjunction assessment. Traditional assumptions applied to the error volume artificially suppress the true non-Gaussian nature of the space-vehicle position uncertainties. For typical conjunction assessment objects, representation of the error volume by a state error covariance matrix in a Cartesian reference frame is a more significant limitation than is the assumption of linearized dynamics for propagating the error volume. In this study, the impact of each assumption is examined and isolated for each point in the volume. Limitations arising from representing the error volume in a Cartesian reference frame is corrected by employing a Monte Carlo approach to probability of collision (Pc), using equinoctial samples from the Cartesian position covariance at the time of closest approach (TCA) between the pair of space objects. A set of actual, higher risk (Pc >= 10 (exp -4)+) conjunction events in various low-Earth orbits using Monte Carlo methods are analyzed. The impact of non-Gaussian error volumes on Pc for these cases is minimal, even when the deviation from a Gaussian distribution is significant.

Ghrist, Richard W.↗
DOE OSTI2016

Improved uncertainty quantification in nondestructive assay for nonproliferation

This paper illustrates methods to improve uncertainty quantification (UQ) for non-destructive assay (NDA) measurements used in nuclear nonproliferation. First, it is shown that current bottom-up UQ applied to calibration data is not always adequate, for three main reasons: (1) Because there are errors in both the predictors and the response, calibration involves a ratio of random quantities, and calibration data sets in NDA usually consist of only a modest number of samples (3–10); therefore, asymptotic approximations involving quantities needed for UQ such as means and variances are often not sufficiently accurate; (2) Common practice overlooks that calibration implies a partitioning of total error into random and systematic error, and (3) In many NDA applications, test items exhibit non-negligible departures in physical properties from calibration items, so model-based adjustments are used, but item-specific bias remains in some data. Therefore, improved bottom-up UQ using calibration data should predict the typical magnitude of item-specific bias, and the suggestion is to do so by including sources of item-specific bias in synthetic calibration data that is generated using a combination of modeling and real calibration data. Second, for measurements of the same nuclear material item by both the facility operator and international inspectors, current empirical (top-down) UQ is described for estimating operator and inspector systematic and random error variance components. A Bayesian alternative is introduced that easily accommodates constraints on variance components, and is more robust than current top-down methods to the underlying measurement error distributions.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2020

Revisiting OSIRIS-REx Touch-And-Go (TAG) Performance Given the Realities of Asteroid Bennu

The Origins, Spectral Interpretation, Resource Identification, and Security–Regolith Explorer (OSIRIS-REx) mission is a NASA New Frontiers mission that launched in 2016 and rendezvoused with the near-Earth asteroid (101955) Bennu in late 2018. Upon arrival, the surface of Bennu was found to be much rockier than expected. The original Touch-and-Go (TAG) requirement for sample collection was to deliver the spacecraft to a site with a 25-meter radius; however, the largest hazard-free sites are no larger than 8 meters in radius. To accommodate the dearth of safe sample collection sites, the project reevaluated all aspects of flight system performance pertaining to TAG in order to account for the demonstrated performance of the spacecraft and navigation prediction accuracies. More-over, the project has base lined on board natural feature tracking instead of lidar for providing the on board navigation state update during the TAG sequence. This paper summarizes the improvements in error source estimation, enhancements in on board trajectory correction, and results of recent Monte Carlo simulation to en-able sample collection with the given constraints. TAG delivery and on board navigation performance are presented for the final four candidate TAG sites.

Berry, Kevin↗
NASA NTRS1991

Error latency measurements in symbolic architectures

Error latency, the time that elapses between the occurrence of an error and its detection, has a significant effect on reliability. In computer systems, failure rates can be elevated during a burst of system activity due to increased detection of latent errors. A hybrid monitoring environment is developed to measure the error latency distribution of errors occurring in main memory. The objective of this study is to develop a methodology for gauging the dependability of individual data categories within a real-time application. The hybrid monitoring technique is novel in that it selects and categorizes a specific subset of the available blocks of memory to monitor. The precise times of reads and writes are collected, so no actual faults need be injected. Unlike previous monitoring studies that rely on a periodic sampling approach or on statistical approximation, this new approach permits continuous monitoring of referencing activity and precise measurement of error latency.

Young, L. T.↗
DOE OSTI2025

Covariance operator estimation via adaptive thresholding

This paper studies sparse covariance operator estimation for nonstationary processes with sharply varying marginal variance and small correlation lengthscale. We introduce a covariance operator estimator that adaptively thresholds the sample covariance function using an estimate of the variance component. Building on recent results from empirical process theory, we derive an operator norm bound on the estimation error in terms of the sparsity level of the covariance and the expected supremum of a normalized process. Furthermore, our theory and numerical simulations demonstrate the advantage of adaptive threshold estimators over universal threshold and sample covariance estimators in nonstationary settings.

Al-Ghattas, Omar [University of Chicago, IL (Unite↗
NASA NTRS2015

Spatiotemporal Prediction of Fine Particulate Matter Using High-Resolution Satellite Images in the Southeastern US 2003-2011

Numerous studies have demonstrated that fine particulate matter (PM(sub 2.5), particles smaller than 2.5 micrometers in aerodynamic diameter) is associated with adverse health outcomes. The use of ground monitoring stations of PM(sub 2.5) to assess personal exposure, however, induces measurement error. Land-use regression provides spatially resolved predictions but land-use terms do not vary temporally. Meanwhile, the advent of satellite-retrieved aerosol optical depth (AOD) products have made possible to predict the spatial and temporal patterns of PM(sub 2.5) exposures. In this paper, we used AOD data with other PM(sub 2.5) variables, such as meteorological variables, land-use regression, and spatial smoothing to predict daily concentrations of PM(sub 2.5) at a 1 sq km resolution of the Southeastern United States including the seven states of Georgia, North Carolina, South Carolina, Alabama, Tennessee, Mississippi, and Florida for the years from 2003 to 2011. We divided the study area into three regions and applied separate mixed-effect models to calibrate AOD using ground PM(sub 2.5) measurements and other spatiotemporal predictors. Using 10-fold cross-validation, we obtained out of sample R2 values of 0.77, 0.81, and 0.70 with the square root of the mean squared prediction errors of 2.89, 2.51, and 2.82 cu micrograms for regions 1, 2, and 3, respectively. The slopes of the relationships between predicted PM2.5 and held out measurements were approximately 1 indicating no bias between the observed and modeled PM(sub 2.5) concentrations. Predictions can be used in epidemiological studies investigating the effects of both acute and chronic exposures to PM(sub 2.5). Our model results will also extend the existing studies on PM(sub 2.5) which have mostly focused on urban areas because of the paucity of monitors in rural areas.

PM2.5↗
DOE OSTI2022

Optimizing the accuracy of viscoelastic characterization with AFM force–distance experiments in the time and frequency domains

Atomic Force Microscopy (AFM) force-distance (FD) experiments have emerged as an attractive alternative to traditional micro-rheology measurement techniques owing to their versatility of use in materials of a wide range of mechanical properties. Here, we show that the range of time dependent behaviour which can reliably be resolved from the typical method of FD inversion (fitting constitutive FD relations to FD data) is inherently restricted by the experimental parameters: sampling frequency, experiment length, and strain rate. Specifically, we demonstrate that violating these restrictions can result in errors in the values of the parameters of the complex modulus. In the case of complex materials, such as cells, whose behaviour is not specifically understood a priori, the physical sensibility of these parameters cannot be assessed and may lead to falsely attributing a physical phenomenon to an artifact of the violation of these restrictions. We use arguments from information theory to understand the nature of these inconsistencies as well as devise limits on the range of mechanical parameters which can be reliably obtained from FD experiments. The results further demonstrate that the nature of these restrictions depends on the domain (time or frequency) used in the inversion process, with the time domain being far more restrictive than the frequency domain. Lastly, we demonstrate how to use these restrictions to better design FD experiments to target specific timescales of a material's behaviour through our analysis of a polydimethylsiloxane (PDMS) polymer sample.

information theory↗
DOE OSTI2022

A Multi-Branch Decoder Network Approach to Adaptive Temporal Data Selection and Reconstruction for Big Scientific Simulation Data

A key challenge in scientific simulation is that the simulation outputs often require intensive I/O and storage space to store the results for effective post hoc analysis. This article focuses on a quality-aware adaptive temporal data selection and reconstruction problem where the goal is to adaptively select simulation data samples at certain key timesteps in situ and reconstruct the discarded samples with quality assurance during post hoc analysis. This problem is motivated by the limitation of current solutions that a significant amount of simulation data samples are either discarded or aggregated during the sampling process, leading to inaccurate modeling of the simulated phenomena. Two unique challenges exist: 1) the sampling decisions have to be made in situ and adapted to the dynamics of the complex scientific simulation data; 2) the reconstruction error must be strictly bounded to meet the application requirement. To address the above challenges, we develop DeepSample , an error-controlled convolutional neural network framework, that jointly integrates a set of coherent multi-branch deep decoders to effectively reconstruct the simulation data with rigorous quality assurance. The results on two real-world scientific simulation applications show that DeepSample significantly outperforms other state-of-the-art methods on both sampling efficiency and reconstructed simulation data quality.

Zhang, Yang↗
NASA NTRS1995

Calculation of surface and top of atmosphere radiative fluxes from physical quantities based on ISCCP data sets. 1: Method and sensitivity to input data uncertainties

The largest uncertainty in upwelling shortwave (SW) fluxes (approximately equal 10-15 W/m(exp 2), regional daily mean) is caused by uncertainties in land surface albedo, whereas the largest uncertainty in downwelling SW at the surface (approximately equal 5-10 W/m(exp 2), regional daily mean) is related to cloud detection errors. The uncertainty of upwelling longwave (LW) fluxes (approximately 10-20 W/m(exp 2), regional daily mean) depends on the accuracy of the surface temperature for the surface LW fluxes and the atmospheric temperature for the top of atmosphere LW fluxes. The dominant source of uncertainty is downwelling LW fluxes at the surface (approximately equal 10-15 W/m(exp 2)) is uncertainty in atmospheric temperature and, secondarily, atmospheric humidity; clouds play little role except in the polar regions. The uncertainties of the individual flux components and the total net fluxes are largest over land (15-20 W/m(exp 2)) because of uncertainties in surface albedo (especially its spectral dependence) and surface temperature and emissivity (including its spectral dependence). Clouds are the most important modulator of the SW fluxes, but over land areas, uncertainties in net SW at the surface depend almost as much on uncertainties in surface albedo. Although atmospheric and surface temperature variations cause larger LW flux variations, the most notable feature of the net LW fluxes is the changing relative importance of clouds and water vapor with latitude. Uncertainty in individual flux values is dominated by sampling effects because of large natrual variations, but uncertainty in monthly mean fluxes is dominated by bias errors in the input quantities.

Zhang, Y.-C.↗
NASA NTRS1999

Synoptic Mapping of Chemical Composition, Thermal Structure, and Air Motion from UARS Observations

An operational mapping algorithm was developed to process measurements of individual species observed by different satellite instruments on board UARS. Based on Fast Fourier Synoptic Mapping (FFSM), the algorithm accounts for the precessing orbit of UARS, the error of individual instruments, and gaps associated with instrument duty cycle and the satellite yaw maneuver. It provides synoptic structure and evolution on periods as short as 1 day, derived collectively from all observations of an individual species. The algorithm was applied to synoptically map temperature, thickness, and several chemical constituents observed by the instruments: MLS, CLAES, ISAMS, and HALOE. Each field variable observed by these instruments was mapped twice-daily in continuous global time series several months long. Mapped behavior produced via FFSM was compared against standard archived products generated via Kalman filtering. The standard map products reveal similar behavior, but are limited to features of larger scale. Tracer structure that develops through flow deformation and attending transport is therefore represented in those products more coarsely, eventually being sheared down to scales that are no longer represented properly. The synoptic time series also reveal a diurnal cycle for several of the constituents, one that emerges clearly in their space-time spectra. The zonal and meridional structure of diurnal variability was mapped by filtering the space-time spectrum, an intermediate product of FFSM, to those scales resolved by the UARS sampling. Geographical variations of the diurnal cycle have also been evaluated in its seasonal-mean structure by compositing distributions at individual local times. Both were compared to diurnal variations in chemical models and in dynamical models that account for transport by the diurnal tide. Diurnal variations were found to introduce spurious behavior into the archived products generated via Kalman filtering, behavior that is misrepresented in those synoptic time series. Aliasing by under-sampled diurnal variations introduces a bias into the time-mean structure of archived products. That systematic error is manifested as anomalous wave structure in the time-mean fields. By contrast, synoptic time series generated via FFSM faithfully represent such behavior as varying diurnally, so it does not corrupt time-mean structure. Tracer structure mapped via the above algorithm was being applied to determine the Brewer-Dobson circulation of the stratosphere through Lagrangian considerations. Preliminary results are encouraging: They reveal diabatic ascent in the tropics, balanced by diabatic descent over the winter hemisphere. This Lagrangian description of transport derived from UARS tracer measurements would be potentially valuable for diagnosing interannual changes of transport, specifically, in relation to interannual changes of total ozone. Regrettably, support was reduced to 50% of the approved funding level, leaving this component incomplete.

Salby, Murry L.↗
DOE OSTI2019

Evaluation of Wind Speed Retrieval from Continuous-Wave Lidar Measurements of a Wind Turbine Wake Using Virtual Lidar Techniques

Wind lidar technology is being extensively used in utility-scale wind turbine wake measurements because of its capabilities to capture the dominant structures of interest in the near-wake region. However, wind lidar devices provide only line-of-sight (LOS) measurements, and retrieval of horizontal wind speed from the LOS measurements of a single lidar in the wind turbine wake could introduce errors due to the assumptions used in the retrieval process. With that in mind, the goals of this paper are to estimate the errors associated with the retrieval processes and provide guidelines for best practices. To achieve these goals, virtual lidar samples are obtained with large-eddy simulations (LES) by sampling the flow field with a model of the Technical University of Denmark (DTU) SpinnerLidar. LES is chosen to simulate the neutral and unstable atmospheric boundary layer (ABL), a benchmark case of the International Energy Agency Wind Technology Collaboration Programme (IEA Wind TCP) Task 31. The precursor ABL simulation is validated with the reference data provided by the IEA Wind Task 31 benchmark case. The data from the precursor simulation is used to drive a simulation of a wind turbine submerged in the ABL. A weighting function is applied to the LES data to mimic the real DTU SpinnerLidar measurements. In the end, the virtual lidar data are processed and compared with the original LES data to estimate the errors. Results show that lidar captures mean ABL profile without any noticeable error, whereas turbulence intensity is underpredicted ~15% at hub height for the neutral case. Volume averaging by the lidar technology has a significant effect in the shear layer of a wind turbine wake.

17 WIND ENERGY↗
DOE OSTI2018

Quantitative Analysis of Ca, Mg, and K in the Roots of Angelica pubescens f. biserrata by Laser-Induced Breakdown Spectroscopy Combined with Artificial Neural Networks

Laser-induced breakdown spectroscopy has been applied for the quantitative analysis of Ca, Mg, and K in the roots of Angelica pubescens Maxim. f. biserrata Shan et Yuan used in traditional Chinese medicine. Ca II 317.993 nm, Mg I 517.268 nm, and K I 769.896 nm spectral lines have been chosen to set up calibration models for the analysis using the external standard and artificial neural network methods. The linear correlation coefficients of the predicted concentrations versus the standard concentrations of six samples determined by the artificial neural network method are 0.9896, 0.9945, and 0.9911 for Ca, Mg, and K, respectively, which are better than for the external standard method. The artificial neural network method also gives better performance comparing with the external standard method for the average and maximum relative errors, average relative standard deviations, and most maximum relative standard deviations of the predicted concentrations of Ca, Mg, and K in the six samples. Finally, it is proved that the artificial neural network method gives better performance compared to the external standard method for the quantitative analysis of Ca, Mg, and K in the roots of Angelica pubescens.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1996

Effect of Correlated Precision Errors on Uncertainty of a Subsonic Venturi Calibration

An uncertainty analysis performed in conjunction with the calibration of a subsonic venturi for use in a turbine test facility produced some unanticipated results that may have a significant impact in a variety of test situations. Precision uncertainty estimates using the preferred propagation techniques in the applicable American National Standards Institute/American Society of Mechanical Engineers standards were an order of magnitude larger than precision uncertainty estimates calculated directly from a sample of results (discharge coefficient) obtained at the same experimental set point. The differences were attributable to the effect of correlated precision errors, which previously have been considered negligible. An analysis explaining this phenomenon is presented. The article is not meant to document the venturi calibration, but rather to give a real example of results where correlated precision terms are important. The significance of the correlated precision terms could apply to many test situations.

Hudson, S. T.↗
NASA NTRS2003

Karhunen-Loeve Estimation of the Power Spectrum Parameters from the Angular Distribution of Galaxies in Early Sloan Digital Sky Survey Data

We present measurements of parameters of the three-dimensional power spectrum of galaxy clustering from 222 square degrees of early imaging data in the Sloan Digital Sky Survey (SDSS). The projected galaxy distribution on the sky is expanded over a set of Karhunen-Loeve (KL) eigenfunctions, which optimize the signal-to-noise ratio in our analysis. A maximum likelihood analysis is used to estimate parameters that set the shape and amplitude of the three-dimensional power spectrum of galaxies in the SDSS magnitude-limited sample with r* less than 21. Our best estimates are gamma = 0.188 +/- 0.04 and sigma(sub 8L) = 0.915 +/- 0.06 (statistical errors only), for a flat universe with a cosmological constant. We demonstrate that our measurements contain signal from scales at or beyond the peak of the three-dimensional power spectrum. We discuss how the results scale with systematic uncertainties, like the radial selection function. We find that the central values satisfy the analytically estimated scaling relation. We have also explored the effects of evolutionary corrections, various truncations of the KL basis, seeing, sample size, and limiting magnitude. We find that the impact of most of these uncertainties stay within the 2 sigma uncertainties of our fiducial result.

Szalay, Alexander S.↗
DOE OSTI2023

Sample-efficient verification of continuously-parameterized quantum gates for small quantum processors

Most near-term quantum information processing devices will not be capable of implementing quantum error correction and the associated logical quantum gate set. Instead, quantum circuits will be implemented directly using the physical native gate set of the device. These native gates often have a parameterization (e.g., rotation angles) which provide the ability to perform a continuous range of operations. Verification of the correct operation of these gates across the allowable range of parameters is important for gaining confidence in the reliability of these devices. In this work, we demonstrate a procedure for sample-efficient verification of continuously-parameterized quantum gates for small quantum processors of up to approximately 10 qubits. This procedure involves generating random sequences of randomly-parameterized layers of gates chosen from the native gate set of the device, and then stochastically compiling an approximate inverse to this sequence such that executing the full sequence on the device should leave the system near its initial state. We show that fidelity estimates made via this technique have a lower variance than fidelity estimates made via cross-entropy benchmarking. This provides an experimentally-relevant advantage in sample efficiency when estimating the fidelity loss to some desired precision. We describe the experimental realization of this technique using continuously-parameterized quantum gate sets on a trapped-ion quantum processor from Sandia QSCOUT and a superconducting quantum processor from IBM Q, and we demonstrate the sample efficiency advantage of this technique both numerically and experimentally.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
NASA NTRS2010

MISSE 2 PEACE Polymers Experiment Atomic Oxygen Erosion Yield Error Analysis

Atomic oxygen erosion of polymers in low Earth orbit (LEO) poses a serious threat to spacecraft performance and durability. To address this, 40 different polymer samples and a sample of pyrolytic graphite, collectively called the PEACE (Polymer Erosion and Contamination Experiment) Polymers, were exposed to the LEO space environment on the exterior of the International Space Station (ISS) for nearly 4 years as part of the Materials International Space Station Experiment 1 & 2 (MISSE 1 & 2). The purpose of the PEACE Polymers experiment was to obtain accurate mass loss measurements in space to combine with ground measurements in order to accurately calculate the atomic oxygen erosion yields of a wide variety of polymeric materials exposed to the LEO space environment for a long period of time. Error calculations were performed in order to determine the accuracy of the mass measurements and therefore of the erosion yield values. The standard deviation, or error, of each factor was incorporated into the fractional uncertainty of the erosion yield for each of three different situations, depending on the post-flight weighing procedure. The resulting error calculations showed the erosion yield values to be very accurate, with an average error of 3.30 percent.

McCarthy, Catherine E.↗