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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 1,153 records · Page 64
All sourcesNASA NTRSDOE OSTI
NASA NTRS1979

Rotary balance data for a typical single-engine general aviation design for an angle-of-attack range of 8 degrees to 35 degrees, 3. Effect of wing leading-edge modifications, model A

Aerodynamic characteristics obtained in a rotational flow environment utilizing a rotary balance located in the Langley spin tunnel are presented in plotted form for a 1/5 scale, single-engine, low-wing, general aviation airplane model. The configurations tested included the basic airplane, sixteen wing leading-edge modifications and lateral-directional control settings. Data are presented for all configurations without analysis for an angle of attack range of 8 deg to 35 deg and clockwise and counter-clockwise rotations covering an Omega b/2v range from 0 to 0.85. Also, data are presented above 35 deg of attack for some configurations.

Bihrle, W., Jr.↗
DOE OSTI2022

Active galactic nucleus feedback in NGC 3982

The energetic feedback from supermassive black holes can influence star formation at the centres of galaxies. Observational evidence for active galactic nucleus (AGN) impact on star formation can be searched for in galaxies by combining ultraviolet imaging and optical integral field unit data. The ultraviolet flux directly traces recent star formation, and the integral field unit data can reveal dust attenuation, gas ionisation mechanisms, and gas kinematics from the central regions of the galaxy disk. A pilot study on NGC 3982 shows star formation suppression in the central regions of the galaxy, likely due to negative AGN feedback, and enhanced star formation in the outer regions. The case of NGC 3982 could be observational evidence of AGN feedback operating in a Seyfert galaxy.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

A data integration framework of additive manufacturing based on FAIR principles

Abstract Laser-powder bed fusion (L-PBF) is a popular additive manufacturing (AM) process with rich data sets coming from both in situ and ex situ sources. Data derived from multiple measurement modalities in an AM process capture unique features but often have different encoding methods; the challenge of data registration is not directly intuitive. In this work, we address the challenge of data registration between multiple modalities. Large data spaces must be organized in a machine-compatible method to maximize scientific output. FAIR (findable, accessible, interoperable, and reusable) principles are required to overcome challenges associated with data at various scales. FAIRified data enables a standardized format allowing for opportunities to generate automated extraction methods and scalability. We establish a framework that captures and integrates data from a L-PBF study such as radiography and high-speed camera video, linking these data sets cohesively allowing for future exploration. Graphical abstract

36 MATERIALS SCIENCE↗
DOE OSTI2024

Development of High-Fidelity and Efficient Modeling Capabilities for Enabling Co-Optimization of Fuels and Multi-Mode Engines

The objective of this closely coordinated research project is to develop improved physical sub-models and innovative numerical algorithms to enable accurate and efficient predictions of multi-mode combustion to support EERE’s Co-Optima program. To this end, a research pro-gram is proposed that seeks • to develop accurate submodels for predicting multi-mode combustion regimes, wall-heat transfer, non-equilibrium plasma ignition, and combustion-mode transition, • to develop dynamic load-rebalancing algorithms and efficient semi-implicit time-integration schemes for enabling exascale computing, • to validate the computational submodels against experiments and direct numerical simulation (DNS) data in conjunction with sensitivity analysis, and • to support the direct transition of the resulting sub-models into other engine-simulation tools through the development of self-contained modules. All sub-models will be integrated into the open-source exascale engine-simulation platform Nek5000.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Synthetic Absorption Lines from Simulations of Multiphase Gas in Galactic Winds

Supernova-driven galactic winds are multiphase streams of gas that are often observed flowing at a range of velocities out of star-forming regions in galaxies. In this study, we use high-resolution 3D simulations of multiphase galactic winds modeled with the hydrodynamics code Cholla to investigate the connection between numerical studies and observations. Using a simulated interaction between a hot $T$ ~ 10 4 K supernova-driven wind and a cool $T$ ~ 10 7 K cloud of interstellar material, we create mock observables, including the optical depth ($\tau$) and covering fraction ($C$ $f$ ) of six commonly observed ions (Si II , C II , Si IV , C IV , N V , and O VI ) as a function of gas velocity. We compare our mock observables to surveys of galactic winds in the literature, finding good agreement with velocities and profiles of the low ions. We then compute "empirical" values for the optical depth and covering fraction following observational techniques and compare them to the values calculated directly from the simulation data. We find that the empirically computed values tend to underestimate the "true" value of $\tau$ for ions with high optical depth and overestimate the "true" value of $\tau$ for ions with low optical depth relative to the simulated data. In conclusion, the empirically estimated covering fractions match our direct calculations very well for the low-ionization ions; for the high-ionization ions, the empirical covering fractions underestimate the directly computed values by up to ~40%.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1994

Segmentation Of Polarimetric SAR Data

Report presents one in continuing series of studies of segmentation of polarimetric synthetic-aperture-radar, SAR, image data into regions. Studies directed toward refinement of method of automated analysis of SAR data.

Rignot, Eric J. M.↗
NASA NTRS1989

Digital first order hold circuit

There is provided a digitally controlled first order hold circuit and waveform synthesizer for digitally controlling the representation of a function over an approximation interval. In accordance with the operation of the invention, the first order hold circuit and waveform generator receives a digital data input signal which contains initial condition data, up/down data, and slope data for the approximation interval. The initial condition data is loaded into an up/down counter which is incremented using counting data at a rate depending on the value of the slope data and in a direction depending on the value of the up-down data. In order to minimize delays arising from data acquistion, two frequency synthesizer circuits are provided such that one frequency synthesizer provides counting data while the other frequency synthesizer receives slope data. During alternating intervals, the other frequency synthesizer circuit provides counting data while the other circuit receives slope data. In addition, long length data input signals covering a plurality of approximation intervals are provided to reduce the demands on a main system central processing unit.

Chan, Fred N.↗
DOE OSTI2024

Physics-Based Machine Learning Methods for U-235 Forensics Signatures

Signatures of low-intensity U-235 sources have been recently studied by utilizing a variety of machine learning (ML) classifiers using features derived from gamma spectral measurements collectedunder structured campaigns. Several ML classifiers, such as ensemble of tress and classification trees, revealed misleadingly-optimistic training error due to over-fitting, and furthermore,their performance is not directly relatable to the physical properties due to their data-driven, opaque designs. We present a regression-based ML method that first estimates the inverse distanceto the source and then utilizes a threshold to infer its presence, by representing the background as a source located at an infinite distance. For the inverse distance estimation, we study the ensembleof trees and Gaussian process regression methods, and a hyper parameter auto-tuning and selection method that employs five regression estimators. These methods avoid the over-fittingobserved in several ML classifiers, while providing the classification error nearly comparable to them based on independent test data. Their error is directly related to estimates of the inversephysical distance to source, and the precision of error determines the seperability property that determines the false alarm and missed detection rates. The property of monotonic decrease of thesource strength with increasing detector distance combined with Poisson distribution of measurements is utilized to analytically validate these methods by deriving the generalization equations ofunderlying regression methods.

Rao, Nageswara↗
DOE OSTI2024

Physics-Based Machine Learning Methods for U-235 Forensics Signatures

Signatures of low-intensity U-235 sources have been recently studied by utilizing a variety of machine learning (ML) classifiers using features derived from gamma spectral measurements collected under structured campaigns. Several ML classifiers, such as ensemble of tress and classification trees, revealed misleadingly-optimistic training error due to over-fitting, and furthermore, their performance is not directly relatable to the physical properties due to their data-driven, opaque designs. We present a regression-based ML method that first estimates the inverse distance to the source and then utilizes a threshold to infer its presence, by representing the background as a source located at an infinite distance. For the inverse distance estimation, we study the ensemble of trees and Gaussian process regression methods, and a hyper parameter auto-tuning and selection method that employs five regression estimators. These methods avoid the over-fitting observed in several ML classifiers, while providing the classification error nearly comparable to them based on independent test data. Their error is directly related to estimates of the inverse physical distance to source, and the precision of error determines the seperability property that determines the false alarm and missed detection rates. The property of monotonic decrease of the source strength with increasing detector distance combined with Poisson distribution of measurements is utilized to analytically validate these methods by deriving the generalization equations of underlying regression methods.

Rao, Nageswara↗
NASA NTRS1988

A relationship between radar backscatter and aerodynamic roughness - Preliminary results

The ability of the wind to move particles and the flux of windblown sand are both dependent on the topographic roughness of the surface, as measured by the aerodynamic roughness, z(0). For most surfaces, topographic roughness controls many of the characteristics of the radar return, and the magnitude of the radar backscatter can be regarded as a measure of the surface roughness at or near the wavelength scale. Radar backscatter data may therefore be useful in obtaining a value of aerodynamic roughness which can be used to assess aeolian sediment transport via remote sensing. In this study, calibrated L(HH), C(HH) and Ku(VV) radar data were used to derive characteristic backscatter coefficients sigma(0) for three lava flow units, an alluvial fan, and a playa surface. Preliminary analyses show that values of sigma(0) and z(0) both increase with topographic roughness and that there is a good correlation between the two coefficients. This correlation suggests that it may be possible to assess aerodynamic roughness directly from radar data.

Greeley, Ronald↗
NASA NTRS2021

Data Assimilation of Terrestrial Water Storage Observations to Estimate Precipitation Fluxes: A Synthetic Experiment

The Gravity Recovery and Climate Experiment (GRACE) mission and its Follow-On (GRACE-FO) mission provide unprecedented observations of terrestrial water storage (TWS) dynamics at basin to continental scales. Established GRACE data assimilation techniques directly adjust the simulated water storage components to improve the estimation of groundwater, streamflow, and snow water equivalent. Such techniques artificially add/subtract water to/from prognostic variables, thus upsetting the simulated water balance. To overcome this limitation, we propose and test an alternative assimilation scheme in which precipitation fluxes are adjusted to achieve the desired changes in simulated TWS. Using a synthetic data assimilation experiment, we show that the scheme improves performance skill in precipitation estimates in general, but that it is more robust for snowfall than for rainfall, and it fails in certain regions with strong horizontal gradients in precipitation. The results demonstrate that assimilation of TWS observations can help correct (adjust) the model’s precipitation forcing and, in turn, enhance model estimates of TWS, snow mass, soil moisture, runoff, and evaporation. A key limitation of the approach is the assumption that all errors in TWS originate from errors in precipitation. Nevertheless, the proposed approach produces more consistent improvements in simulated runoff than the established GRACE data assimilation techniques.

Data Assimilation↗
NASA NTRS2010

IVS Working Group 4: VLBI Data Structures

In 2007 the IVS Directing Board established IVS Working Group 4 on VLBI Data Structures. This note discusses the current VLBI data format, goals for a new format, the history and formation of the Working Group, and a timeline for the development of a new VLBI data format.

Gipson, John↗
NASA NTRS1988

Man-systems requirements for the control of teleoperators in space

The microgravity of the space environment has profound effects on humans and, consequently, on the design requirements for subsystems and components with which humans interact. There are changes in the anthropometry, vision, the perception of orientation, posture, and the ways in which we exert energy. The design requirements for proper human engineering must reflect each of the changes that results, and this is especially true in the exercise of control over remote and teleoperated systems where the operator is removed from any direct sense of control. The National Aeronautics and Space Administration has recently completed the first NASA-wide human factors standard for microgravity. The Man-Systems Integration Standard, NASA-STD-3000, contains considerable information on the appropriate design criteria for microgravity, and there is information that is useful in the design for teleoperated systems. There is not, however, a dedicated collection of data which pertains directly to the special cases of remote and robotic operations. The design considerations for human-system interaction in the control of remote systems in space are discussed, with brief details on the information to be found in the NASA-STD-3000, and arguments for a dedicated section within the Standard which deals with robotic, teleoperated and remote systems and the design requirements for effective human control of these systems in the space environment, and from the space environment.

Shields, Nicholas L., Jr.↗
NASA NTRS1997

Combination of TOPEX/POSEIDON Data with a Hydrographic Inversion for Determination of the Oceanic General Circulation and its Relation to Geoid Accuracy

A global estimate of the absolute oceanic general circulation from a geostrophic inversion of in situ hydrographic data is tested against and then combined with an estimate obtained from TOPEX/POSEIDON altimetric data and a geoid model computed using the JGM-3 gravity-field solution. Within the quantitative uncertainties of both the hydrographic inversion and the geoid estimate, the two estimates derived by very different methods are consistent. When the in situ inversion is combined with the altimetry/geoid scheme using a recursive inverse procedure, a new solution, fully consistent with both hydrography and altimetry, is found. There is, however, little reduction in the uncertainties of the calculated ocean circulation and its mass and heat fluxes because the best available geoid estimate remains noisy relative to the purely oceanographic inferences. The conclusion drawn from this is that the comparatively large errors present in the existing geoid models now limit the ability of satellite altimeter data to improve directly the general ocean circulation models derived from in situ measurements. Because improvements in the geoid could be realized through a dedicated spaceborne gravity recovery mission, the impact of hypothetical much better, future geoid estimates on the circulation uncertainty is also quantified, showing significant hypothetical reductions in the uncertainties of oceanic transport calculations. Full ocean general circulation models could better exploit both existing oceanographic data and future gravity-mission data, but their present use is severely limited by the inability to quantify their error budgets.

Ganachaud, Alexandre↗
NASA NTRS2002

Flexible Wing Base Micro Aerial Vehicles: Assessment of Controllability of Micro Air Vehicles

In the last several years, we have developed unique types of micro air vehicles that utilize flexible structures and extensible covering materials. These MAVs can be operated with maximum dimensions as small as 6 inches and carry reasonable payloads, such as video cameras and transmitters. We recently demonstrated the potential of these vehicles by winning the Fourth International Micro Air Vehicle Competition, held at Ft. Huachucha, Arizona in May 2000. The pilots report that these vehicles have unusually smooth flying characteristics and are relatively easy to fly, both in the standard RC mode and "through the camera" when at greater distances. In comparison, they find that similar sized vehicles with more conventional rigid construction require much more input from the pilot just to maintain control. To make these subjective observations more quantitative, we have devised a system that can conveniently record a complete history of all the RC transmitter stick movements during a flight. Post-flight processing of the stick movement data allows for direct comparisons between different types of MAVs when flown by the same pilot, and also comparisons between pilots. Eventually, practical micro air vehicles will be autonomously controlled, but we feel that the smoothest flying and easiest to fly embodiments will also be the most successful in the long run. Comparisons between several types of micro air vehicles will be presented, along with interpretations of the data.

Jenkins, David A.↗
DOE OSTI2025

Meteorological Conditions in Urban Sites in Knoxville, USA. 2023

This 2023 dataset, which contains four csv files, reports hourly air temperature, wind speed, solar radiation, incident short wave, reflected short wave, incident long wave, emitted long wave, net radiation, and photosynthetically active radiation (PAR) data measured in urban parks in Knoxville, Tennessee, USA. The sites include Cumberland Estates Park (CEP), Socially Equal Energy Efficient Development (SEEED), West Hills (WHP), and Victor Ashe Park (VAP). Air temperature, wind speed and direction, and solar radiation data were obtained from a METER ATMOS 41 All-in-One Weather Station (Pullman, Washington, USA). Incident and emitted radiation (shortwave and longwave) measurements were made using an Apogee (Logan, Utah, USA) net radiometer (Model SN-500-SS). The SQ-521 Full-Spectrum Quantum sensor (Apogee Instruments, Inc) recorded the PAR. The measurement data for CEP, SEEED, and VAP started on July 1, 2023, while WHP started on August 26, 2023. All measurement ended on December 31, 2023. This work is a part of a larger study which investigates the impact of soil moisture and plant evapotranspiration on ambient temperature and relative humidity in several city parks in Knoxville, Tennessee.

Salvador, Christian [ORNL] (ORCID:0000000283287777↗
NASA NTRS1992

Guide star catalogue data retrieval software 2

The Guide Star Catalog (GSC), being the largest astronomical catalog to date, is widely used by the astronomical community for all sorts of applications, such as statistical studies of certain sky regions, searches for counterparts to observational phenomena, and generation of finder charts. It's format (2 CD-ROM's) requires minimum hardware and is ideally suited for all sorts of conditions, especially observations. Unfortunately, the actual GSC data is not easily accessible. It takes the form of FITS tables, and the coordinates of the objects are given in one coordinate system (equinox 2000). The included reading software is rudimentary at best. Thus, even generation of a simple finder chart is not a trivial undertaking. To solve this problem, at least for PC users, GUIDARES was created. GUIDARES is a user-friendly program that lets you look directly at the data in the GSC, either as a graphical sky map or as a text table. GUIDARES can read a sampling of GSC data from a given sky region, store this sampling in a text file, and display a graphical map of the sampled region in projected celestial coordinates (perfect for finder charts). GUIDARES supports rectangular and circular regions defined by coordinates in the equatorial, ecliptic (any equinox) or galactic systems.

Smirnov, O. M.↗
DOE OSTI2021

Correlator convolutional neural networks as an interpretable architecture for image-like quantum matter data

Image-like data from quantum systems promises to offer greater insight into the physics of correlated quantum matter. However, the traditional framework of condensed matter physics lacks principled approaches for analyzing such data. Machine learning models are a powerful theoretical tool for analyzing image-like data including many-body snapshots from quantum simulators. Recently, they have successfully distinguished between simulated snapshots that are indistinguishable from one and two point correlation functions. Thus far, the complexity of these models has inhibited new physical insights from such approaches. Here, we develop a set of nonlinearities for use in a neural network architecture that discovers features in the data which are directly interpretable in terms of physical observables. Applied to simulated snapshots produced by two candidate theories approximating the doped Fermi-Hubbard model, we uncover that the key distinguishing features are fourth-order spin-charge correlators. Our approach lends itself well to the construction of simple, versatile, end-to-end interpretable architectures, thus paving the way for new physical insights from machine learning studies of experimental and numerical data.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗