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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 919 records · Page 51
All sourcesNASA NTRSDOE OSTI
NASA NTRS2014

Within-Tunnel Variations in Pressure Data for Three Transonic Wind Tunnels

This paper compares the results of pressure measurements made on the same test article with the same test matrix in three transonic wind tunnels. A comparison is presented of the unexplained variance associated with polar replicates acquired in each tunnel. The impact of a significance component of systematic (not random) unexplained variance is reviewed, and the results of analyses of variance are presented to assess the degree of significant systematic error in these representative wind tunnel tests. Total uncertainty estimates are reported for 140 samples of pressure data, quantifying the effects of within-polar random errors and between-polar systematic bias errors.

DeLoach, Richard↗
NASA NTRS2007

A Side by Side Comparison of Filter-Based PM(sub 2.5) Measurements at a Suburban Site: A Closure Study

Reliable determination of the effects of air quality on public health and the environment requires accurate measurement of PM(sub 2.5) mass and the individual chemical components of fine aerosols. This study seeks to evaluate PM(sub 2.5) measurements that are part of a newly established national network by comparing them with a more conventional sampling system. Experiments were carried out during 2002 at a suburban site in Maryland, United States, where two samplers from the U.S. Environmental Protection Agency (USEPA) Speciation Trends Network: Met One Speciation Air Sampling System STNS and Thermo Scientific Reference Ambient Air Sampler STNR, two Desert Research Institute Sequential Filter Samplers DRIF, and a continuous TEOM monitor (Thermo Scientific Tapered Element Oscillating Microbalance) were sampling air in parallel. These monitors differ not only in sampling configuration but also in protocol-specific sample analysis procedures. Measurements of PM(sub 2.5) mass and major contributing species were well correlated among the different methods with r-values > 0.8. Despite the good correlations, daily concentrations of PM(sub 2.5) mass and major contributing species were significantly different at the 95% confidence level from 5 to 100% of the time. Larger values of PM(sub 2.5) mass and individual species were generally reported from STNR and STNS. The January STNR average PM(sub 2.5) mass (8.8 (micro)g/per cubic meter) was 1.5 (micro)g/per cubic meter larger than the DRIF average mass. The July STNS average PM(sub 2.5) mass (27.8 (micro)g/per cubic meter) was 3.8 (micro)g/per cubic meter larger than the DRIF average mass. These differences can only be partially accounted for by known random errors. Variations in flow control, face velocity, and sampling artifacts likely influence the measurement of PM(sub 2.5) speciation and mass closure. Simple statistical tests indicate that the current uncertainty estimates used in the STN network may underestimate the actual uncertainty.

Haines, Jennifer C.↗
NASA NTRSDate not supplied

Nearest-Neighbor Machine Learning Feature Selection for Interpretation of Microbial Molecular Signatures from Isotope Ratio Mass Spectrometry Data

Mass spectrometry (MS) promises to be a powerful tool for potential biosignature detection during astrobiological missions on ocean worlds in our solar system. Accurate and generalizable machine learning methods could enhance science return on investment by predicting seawater chemistry and classifying isotopic biosignatures, either as a signature consistent with microbial life (biotic) or as a novelty (unclassified/unique). However, machine learning models are likely to be complex and involve interactions between MS features, making biosignatures difficult to interpret. Feature selection methods provide biological and chemical context that help interpret the mechanisms of machine learning models, but these methods also need the ability to detect complex interactions. Previously, we developed a machine learning feature selection algorithm called nearest-neighbor projected distance regression (NPDR) that has the ability to identify important model features that involve complex interactions and automatically reduce correlation and the dimensionality in a high-dimensional variable space. The standard distance metrics used in NPDR – Manhattan and Euclidean – assume the multivariate data are isotropic, which is often violated in real data due to differences in the covariance between variables. Thus, we extend NPDR to include a random forest distance, and other anisotropic distance metrics, for computing nearest neighbors. We also augment the isotope-ratio MS data with time-series features from the raw MS signal to improve biotic classification. We test NPDR on our novel experimental ocean world seawater analog MS data. We measure isotope fractionations of volatile CO 2 that could be measured in exospheres or plumes. Samples include baseline abiotic conditions using a range of possible seawater chemistry consistent with Europa and Enceladus, and biotic samples that include microbes in these seawaters. We use penalized NPDR with random forest proximity to identify interpretable microbial molecular signatures. We compare features with random forest importance, and we train a classifier that discriminates between biotic and abiotic samples with high accuracy. These ML-trained ocean-world analog MS data could be used to assist in identifying biosignatures during future missions.

geochemistry↗
NASA NTRS1984

Dynamic response and acoustic fatigue of stiffened composite structure

The results of acoustic fatigue and dynamic response tests performed on L-1011 graphite-epoxy (GrE) aileron and panel components are reported. The aileron featured glass microballoons between the GrE skins. Tests yielded random fatigue data from double and single cantilever coupons and modal data from impedance hammer and loudspeaker impulses. Numerical and sample test data were obtained on combined acoustic and shear loads, acoustic and thermal loads, random fatigue and damping of the integrally stiffened and secondary bonded panels. The fatigue data indicate a fatigue life beyond 10 million cycles. The acoustic data suggested that noise transmission could be enhanced in the integrally stiffened panels, which were more acoustic-fatigue resistant than were the secondary bonded panels.

Soovere, J.↗
NASA NTRS2018

A Deterministic Self-Organizing Map Approach and its Application on Satellite Data based Cloud Type Classification

A self-organizing map (SOM) is a type of competitive artificial neural network, which projects the high dimensional input space of the training samples into a low dimensional space with the topology relations preserved. This makes SOMs supportive of organizing and visualizing complex data sets and have been pervasively used among numerous disciplines with different applications. Notwithstanding its wide applications, the self-organizing map is perplexed by its inherent randomness, which produces dissimilar SOM patterns even when being trained on identical training samples with the same parameters every time, and thus causes usability concerns for other domain practitioners and precludes more potential users from exploring SOM based applications in a broader spectrum. Motivated by this practical concern, we propose a deterministic approach as a supplement to the standard self-organizing map. In accordance with the theoretical design, the experimental results with satellite cloud data demonstrate the effective and efficient organization as well as simplification capabilities of the proposed approach.

Initialization method↗
DOE OSTI2025

Inelastic Neutron Scattering Data for Ba₃Zn(Ru₀.₁Sb₁.₉)O₉

This dataset contains inelastic neutron scattering measurements on the magnetically dilute compound Ba₃Zn(Ru₀.₁Sb₁.₉)O₉, collected at the Spallation Neutron Source using the Cold Neutron Chopper Spectrometer (CNCS) and the SEQUOIA Fine-Resolution Fermi Chopper Spectrometer. The sample consists of nominally 5% magnetic Ru⁵⁺ ions (S = 3/2) randomly substituted into a diamagnetic Sb⁵⁺ matrix. Measurements were performed at base temperature (2–5 K) and 300 K using multiple incident energies (1.00, 3.32, 12.0, and 100 meV) to resolve magnetic excitations from isolated monomers and small Ru clusters (dimers, trimers, etc.). The dataset includes raw event-mode files, momentum- and energy-resolved spectra, and supporting metadata for instrument configuration, sample environment, and reduction parameters.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Sensitivity study of a full-scale industrial spray-injected fluidized bed reactor

The industrial fluidized bed reactor (FBR) described here is designed to convert an aqueous solid laden stream into a consistent solid product material. Additionally, the FBR is heated to 650 °C and the aqueous reactant is spray-atomized into the fluidized bed, converting it into a granular solid through a series of heterogeneous chemical reactions. The objective of this study is to use the MFiX two-fluid model as a screening tool to investigate the effects of off-nominal conditions for the purpose of establishing performance guidelines. This information will be helpful to assess whether the identified boundaries of the system are steep or gradual. Initially, 13 independent operating parameters were sampled in a one-at-a-time manner using high, low, and base values and ranked against a set of performance criteria. Five operating parameters were found to have the largest effect (bed particle size, bed particle density, coal particle size, spray feed flow rate, and fluidizing gas flow rate) on three quantities of interest—bed differential temperature, solids velocity, and bed voidage. Latin hypercube sampling was used to generate a minimal number of random values for various combinations of input parameters. The spray feed flow rate was the most significant parameter for the temperature differences, and the coal particle and bed particle size were significant contributors. The bed particle size had the largest effect on the low velocity of the bed particles, with the coal particle size as a contributing second effect. The high solid packing shows the coal particle size with the largest effect, and significant secondary contributions from the feed flow rate and fluidizing gas flow rate. Finally, the fits to the five-dimensional Gaussian Process models were 0.7797, 0.8664, and 0.9440 for the temperature, velocity, and solids packing respectively.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2023

Trajectory sampling and finite-size effects in first-principles stopping power calculations

Abstract Real-time time-dependent density functional theory (TDDFT) is presently the most accurate available method for computing electronic stopping powers from first principles. However, obtaining application-relevant results often involves either costly averages over multiple calculations or ad hoc selection of a representative ion trajectory. We consider a broadly applicable, quantitative metric for evaluating and optimizing trajectories in this context. This methodology enables rigorous analysis of the failure modes of various common trajectory choices in crystalline materials. Although randomly selecting trajectories is common practice in stopping power calculations in solids, we show that nearly 30% of random trajectories in an FCC aluminum crystal will not representatively sample the material over the time and length scales feasibly simulated with TDDFT, and unrepresentative choices incur errors of up to 60%. We also show that finite-size effects depend on ion trajectory via “ouroboros” effects beyond the prevailing plasmon-based interpretation, and we propose a cost-reducing scheme to obtain converged results even when expensive core-electron contributions preclude large supercells. This work helps to mitigate poorly controlled approximations in first-principles stopping power calculations, allowing 1–2 order of magnitude cost reductions for obtaining representatively averaged and converged results.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Resolution of 100 photons and quantum generation of unbiased random numbers

Macroscopic quantum phenomena, such as observed in superfluids and superconductors, have led to promising technological advancements and some of the most important tests of fundamental physics. At present, quantum detection of light is mostly relegated to the microscale, where avalanche photodiodes are very sensitive to distinguishing single-photon events from vacuum but cannot differentiate between larger photon-number events. Beyond this, the ability to perform measurements to resolve photon numbers is highly desirable for a variety of quantum information applications including computation, sensing, and cryptography. True photon-number resolving detectors do exist, but they are currently limited to the ability to resolve on the order of 10 photons, which is too small for certain proposals. In this work, we extend photon measurement into the mesoscopic regime by implementing a detection scheme based on multiplexing highly quantum-efficient transition-edge sensors to accurately resolve photon numbers between zero and 100. Further, we then demonstrate the use of our system by implementing a quantum random number generator with no inherent bias. This method is based on sampling a coherent state in the photon-number basis and is robust against environmental noise, phase and amplitude fluctuations in the laser, loss and detector inefficiency as well as eavesdropping. Beyond true random number generation, our detection scheme serves as a means to implement quantum measurement and engineering techniques valuable for photonic quantum information processing.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2023

Measuring photometric redshifts for high-redshift radio source surveys

With the advent of deep, all-sky radio surveys, the need for ancillary data to make the most of the new, high-quality radio data from surveys like the Evolutionary Map of the Universe (EMU), GaLactic and Extragalactic All-sky Murchison Widefield Array survey eXtended, Very Large Array Sky Survey, and LOFAR Two-metre Sky Survey is growing rapidly. Radio surveys produce significant numbers of Active Galactic Nuclei (AGNs) and have a significantly higher average redshift when compared with optical and infrared all-sky surveys. Thus, traditional methods of estimating redshift are challenged, with spectroscopic surveys not reaching the redshift depth of radio surveys, and AGNs making it difficult for template fitting methods to accurately model the source. Machine Learning (ML) methods have been used, but efforts have typically been directed towards optically selected samples, or samples at significantly lower redshift than expected from upcoming radio surveys. This work compiles and homogenises a radio-selected dataset from both the northern hemisphere (making use of Sloan Digital Sky Survey optical photometry) and southern hemisphere (making use of Dark Energy Survey optical photometry). We then test commonly used ML algorithms such as k-Nearest Neighbours (kNN), Random Forest, ANNz, and GPz on this monolithic radio-selected sample. We show that kNN has the lowest percentage of catastrophic outliers, providing the best match for the majority of science cases in the EMU survey. We note that the wider redshift range of the combined dataset used allows for estimation of sources up to z = 3 before random scatter begins to dominate. When binning the data into redshift bins and treating the problem as a classification problem, we are able to correctly identify ≈ 76% of the highest redshift sources—sources at redshift z > 2.51 —as being in either the highest bin (z > 2.51) or second highest (z = 2.25).

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2019

Automatic Analyte-Ion Recognition and Background Removal for Ambient Mass-Spectrometric Data Based on Cross-Correlation

Ambient mass spectrometry is a powerful approach for rapid, high-throughput, and direct sample analysis. Due to the open-air desorption and ionization processes, random fluctuations of ambient conditions can lead to large variances in mass-spectral signals over time. The mass-spectral data also can be further complicated due to multiple analytes present in the sample, background-ion signals stemming from the desorption/ionization source itself, and other laboratory-specific conditions (e.g., ambient laboratory air, nearby hardware). Thus, background removal and analyte-ion recognition can be quite difficult, particularly in non-targeted analyses. Here, we demonstrate the use of a cross-correlation-based approach to exploit chemical information encoded in the time domain to group/categorize mass-spectral peaks from a single analysis dataset. Ions that originate from ambient (or other) background species were readily flagged and removed from spectra; the result was a decrease in mass-spectral complexity by over 70% due to the removal of these background ions. Meanwhile, analyte ions were differentiated and categorized based on their time-domain profiles. With sufficient mass resolving-power and mass-spectral acquisition rate, isolated mass spectra containing ions from the same species in a sample could be extracted, leading to a reduction in mass-spectral complexity by more than 98% in some cases. The cross-correlation approach was tested with different ionization sources as well as reproducible and irreproducible sample introduction. Software built in-house enabled fully automated data processing, which can be performed within a few seconds. Ultimately, this approach provides an additional dimension of analyte separation in ambient mass-spectrometric analyses with information that is already recorded throughout the analysis. .

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2019

Emittance growth due to random force error

In Refs. Qiang (2019) and Kesting (2015), the authors showed that the emittance growth rate due to the force error of macroparticle sampling will be proportional to the step size of simulation. In this note, we show that for the random force error, the emittance growth rate will be independent of the step size, which is consistent with the simulation observation.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
NASA NTRS2003

Analysis of the Effect of Surface Modification on Polyimide Composites Coated with Erosion Resistant Materials

The aim of this research is to enhance performance of composite coatings through modification of graphite-reinforced polyimide composite surfaces prior to metal bond coat/ hard topcoat application for use in the erosive and/or oxidative environments of advanced engines. Graphite reinforced polyimide composites, PMR-15 and PMR-II-50, formed by sheet molding and pre-pregging will be surface treated, overlaid with a bond coat and then coated with WC-Co. The surface treatment will include cleaning, RF plasma or ultraviolet light- ozone etching, and deposition of SiO(x) groups. These surface treatments will be studied in order to investigate and improve adhesion and oxidation resistance. The following panels were provided by NASA-Glenn Research Center(NASA-GRC): Eight compression molded PMR-II-50; 6 x 6 x 0.125 in. Two vacuum-bagged PMR-II-50; 12 x 12 x 0.125 in. Eight compression molded PMR-15; 6 x 6 x 0.125 in. One vacuum-bagged PMR-15; 12 x 12 x 0.125 in. All panels were made using a 12 x 12 in. T650-35 8HS (3K-tow) graphite fabric. A diamond-wafering blade, with deionized water as a cutting fluid, was used to cut PMR-II-50 and PMR-15 panels into 1 x 1 in. pieces for surface tests. The panel edges exhibiting delamination were used for the preliminary surface preparation tests as these would be unsuitable for strength and erosion testing. PMR-15 neat resin samples were also provided by NASA GRC. Surface profiles of the as-received samples were determined using a Dektak III Surface profile measuring system. Two samples of compression molded PMR-II-50 and PMR-15, vacuum-bagged PMR-II-50 and PMR-15 were randomly chosen for surface profile measurement according to ANSI/ASME B46.1. Prior to each measurement, the samples were blasted with compressed air to remove any artifacts. Five 10 mm-long scans were made on each sample. The short and long wavelength cutoff filter values were set at 100 and 1000 m, diamond stylus radius was 12.5 microns. Table 1 is a summary of the arithmetic average roughness (Ra) and waviness (Wa) for the composite surfaces.

Ndalama, Tchinga↗
NASA NTRS1993

Marked point process models of raindrop-size distributions

The principal process considered in this paper is the flux of raindrops through a volume of the atmosphere. This process is of fundamental importance for a wide variety of engineering and environmental problems, notably remote sensing of precipitation, infiltration of rainfall, soil erosion, atmospheric deposition of pollutants, and design of microwave communication systems. A marked point process model is developed in which the point process represents the arrival times of drops at the upper surface of a sample volume and the mark associated with a drop is its diameter. In the model, both the rate of occurrence of raindrops and the distribution of drop diameters vary randomly over time. Results that relate the drop-size distribution within the sample volume to the probability law of the drop-arrival process are presented. These results allow straightforward comparisons between temporal characterizations of drop-size distributions and spatial characterizations. Representations for derived processes such as rainfall rate and reflectivity are shown to be quite accurate using raindrop data from North Carolina.

Smith, James A.↗
DOE OSTI2018

Basic properties of eutectic chloride salts NaCl-KCl-ZnCl 2 and NaCl-KCl-MgCl 2 as HTFs and thermal storage media measured using simultaneous DSC-TGA

Concentrated solar power (CSP) technologies tend to work at high temperatures for better energy efficiency, which need high temperature heat transfer fluids (HTF) and thermal storage. A good HTF should have low melting point and be thermally stable at high temperatures. A thermal storage medium needs a high heat capacity in order to store more energy in per unit of mass or volume. The objective of this work is to study the basic thermophysical properties of several eutectic molten salts for HTF and thermal storage media, which can work at upper limit temperatures of 850 °C. The eutectic salts are mixtures by NaCl-KCl-ZnCl 2 , and by NaCl-KCl-MgCl 2 , respectively. The heat capacity, melting point, and heat of fusion of the eutectic salts at different temperatures were measured using Differential Scanning Calorimetry (DSC) and Thermal Gravimetric Analysis (TGA) simultaneously. Details of the measurements and obtained experience of operating the equipment are provided in the work. For each of the eutectic salts multiple samples were tested to check repeatability and to minimize random errors. Finally, in theoretical analysis, mixing rules from literature was used to estimate the heat capacity of the new HTFs, which showed good agreement with experimental data.

14 SOLAR ENERGY↗
DOE OSTI2019

Challenges and opportunities in attosecond and XFEL science

In 2009, the world’s first hard X-FEL brought a ten-billion-fold gain in X-ray peak brightness. This fuelled the avidly pursued, but still elusive, dream of single-shot imaging of non-crystalline samples with near-atomic resolution. The overarching concept of ‘diffract-before-destroy’ imaging is that a powerful burst of ultrafast X-rays can produce a coherent diffraction pattern of a nanoscale object — before the object explodes due to Coulomb repulsion caused by a massively parallel ionization of the atoms in the sample. A collection of snapshots of identical objects in random orientations then provide information from which a 3D structure can be retrieved.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Hot Stars with Kepler Planets Have High Obliquities

It has been known for a decade that hot stars with hot Jupiters tend to have high obliquities. Less is known about the degree of spin–orbit alignment for hot stars with other kinds of planets. Here, we reassess the obliquities of hot Kepler stars with transiting planets smaller than Neptune, based on spectroscopic measurements of their projected rotation velocities (vsin i). The basis of the method is that a lower obliquity—all other things being equal—causes sin i to be closer to unity and increases the value of vsin i. We sought evidence for this effect using a sample of 150 Kepler stars with effective temperatures between 5950 and 6550 K and a control sample of 101 stars with matching spectroscopic properties and random orientations. The planet hosts have systematically higher values of vsin i than the control stars, but not by enough to be compatible with perfect spin–orbit alignment. The mean value of sin i is 0.856 ± 0.036, which is 4σ away from unity (perfect alignment), and 2σ away from π/4 (random orientations). There is also evidence that the hottest stars have a broader obliquity distribution: when modeled separately, the stars cooler than 6250 K have 〈sin i〉=0.928±0.042 while the hotter stars are consistent with random orientations. This is similar to the pattern previously noted for stars with hot Jupiters. Based on these results, obliquity excitation for early-G and late-F stars appears to be a general outcome of star and planet formation, rather than being exclusively linked to hot Jupiter formation.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1982

Topics in global convergence of density estimates

The problem of estimating a density f on R sup d from a sample Xz(1),...,X(n) of independent identically distributed random vectors is critically examined, and some recent results in the field are reviewed. The following statements are qualified: (1) For any sequence of density estimates f(n), any arbitrary slow rate of convergence to 0 is possible for E(integral/f(n)-fl); (2) In theoretical comparisons of density estimates, integral/f(n)-f/ should be used and not integral/f(n)-f/sup p, p 1; and (3) For most reasonable nonparametric density estimates, either there is convergence of integral/f(n)-f/ (and then the convergence is in the strongest possible sense for all f), or there is no convergence (even in the weakest possible sense for a single f). There is no intermediate situation.

Devroye, L.↗