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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 1,567 records · Page 87
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NASA NTRS1990

Volatile/mobile trace elements in Bholghati howardite

Ag, Au, Bi, Cd, Co, Cs, Ga, In, Rb, Sb, Se, Te, Tl, U, and Zn by RNAA are determined in two kinds of material from the Bholghati howardite: a carbonaceous chondrite clast (BH-4) that originated by primary nebular condensation/accretion processes; whole-rock samples and clasts resulting from parent-body processes, especially igneous differentiation. Trace element contents in Bholghati whole-rock (matrix) samples and/or white eucritic clasts in it are high for an achondrite, and variable. These characteristics are indicative of random condensation of volcanic emanations admixed with preexisting eucrite, diogenite, and howardite layers in the HED parent body. Other HED samples show similar patterns, and the conclusion that these reflect extensive volcanism in an asteroidal-sized object is supported by other Consortium noble gas and cosmochronologic measurements.

Wang, Ming-Sheng↗
NASA NTRS1992

The spatial distribution of coronae on Venus

Coronae on Venus are large, generally circular surface features that have distinctive tectonic, volcanic, and topographic expressions. They range in diameter from less than 200 km to at least 1000 km. Data from the Magellan spacecraft have now allowed complete global mapping of the spatial distribution of coronae on the planet. Unlike impact craters, which show a random (i.e., Poisson) spatial distribution, the distribution of coronae appears to be nonrandom. We investigate the distribution here in detail, and explore its implications in terms of mantle convection and surface modification processes.

Squyres, S. W.↗
NASA NTRS1994

Theoretical calculations of ion acceleration in the vicinity of comet Giacobini-Zinner

Ionization of cometary neutral molecules produces ions which are picked up by the solar wind. The cometary ion pickup process for comet Giacobini-Zinner is studied in two ways: (1) with a test particle method in which trajectories are numerically calculated for several thousand ions whose initial locations were chosen randomly with probability proportional to the neutral density and (2) with a quasi-linear diffusion model. The cometary ion distribution function was calculated with the test particle model at several locations upstream of the bow shock and for several types of magnetic fluctuations (or waves). These waves were allowed to propagate in both directions along the magnetic field at the Alfven speed. Both pitch angle scattering and energy diffusion are evident in the derived ion distributions. The monochromatic waves result in less ion acceleration than turbulent fluctuations with about the same amplitude. The calculated ion distribution functions are in reasonable agreement with the distributions measured in the vicinity of comet Giacobini-Zinner in 1985 by particle detectors on the ICE spacecraft when the ratio of power in sunward propagating Alfven waves to the power in antisunward propagating waves is assumed to lie between about 20% and 50%. However, the quasi-linear diffusion model results agree best with the measured distribution functions when the power ratio is only about 3 percent.

Mckenzie, M. L.↗
NASA NTRS1996

Force Measurements in Vibration and Acoustic Tests

The advent of triaxial, piezoelectric force gages and the associated signal processing is a precursor to several dynamics testing innovations. This new technology is applicable to spacecraft programs that JPL manages. An application of force measurement is force limiting (when testing spacecraft in random vibration tests). Base-drive and acoustic modal testing is a potential application.

vibration tests acoustic tests spacecraft testing ↗
NASA NTRS2014

An Investigation of Low Earth Orbit Internal Charging

Low Earth orbit is usually considered a relatively benign environment for internal charging threats due to the low flux of penetrating electrons with energies of a few MeV that are encountered over an orbit. There are configurations, however, where insulators and ungrounded conductors used on the outside of a spacecraft hull may charge when exposed to much lower energy electrons of some 100's keV in a process that is better characterized as internal charging than surface charging. For example, the minimal radiation shielding afforded by thin thermal control materials such as metalized polymer sheets (e.g., aluminized Kapton or Mylar) and multilayer insulation may allow electrons of 100's of keV to charge underlying materials. Yet these same thermal control materials protect the underlying insulators and ungrounded conductors from surface charging currents due to electrons and ions at energies less than a few keV as well as suppress the photoemission, secondary electron, and backscattered electron processes associated with surface charging. We investigate the conditions required for this low Earth orbit "internal charging" to occur and evaluate the environments for which the process may be a threat to spacecraft. First, we describe a simple one-dimensional internal charging model that is used to compute the charge accumulation on materials under thin shielding. Only the electron flux that penetrates exposed surface shielding material is considered and we treat the charge balance in underlying insulation as a parallel plate capacitor accumulating charge from the penetrating electron flux and losing charge due to conduction to a ground plane. Charge dissipation due to conduction can be neglected to consider the effects of charging an ungrounded conductor. In both cases, the potential and electric field is computed as a function of time. An additional charge loss process is introduced due to an electrostatic discharge current when the electric field reaches a prescribed breakdown strength. For simplicity, the amount of charge lost in the discharge is treated as a random percentage of the total charge between a set maximum and minimum amount so a user can consider partial discharges of insulating materials (small loss of charge) or arcing from a conductor (large loss of charge). We apply the model to electron flux measurements from the NOAA-19 spacecraft to demonstrate that charging can reach levels where electrostatic discharges occur and estimate the magnitude of the discharge.

NeergaardParker, Linda↗
NASA NTRS2015

Development of a Climatology of Vertically Complete Wind Profiles from Doppler Radar Wind Profiler Systems

This paper describes in detail the QC and splicing methodology for KSC's 50- and 915-MHz DRWP measurements that generates an extensive archive of vertically complete profiles from 0.20-18.45 km. The concurrent POR from each archive extends from April 2000 to December 2009. MSFC NE applies separate but similar QC processes to each of the 50- and 915-MHz DRWP archives. DRWP literature and data examination provide the basis for developing and applying the automated and manual QC processes on both archives. Depending on the month, the QC'ed 50- and 915-MHz DRWP archives retain 52-65% and 16-30% of the possible data, respectively. The 50- and 915-MHz DRWP QC archives retain 84-91% and 85-95%, respectively, of all the available data provided that data exist in the non- QC'ed archives. Next, MSFC NE applies an algorithm to splice concurrent measurements from both DRWP sources. Last, MSFC NE generates a composite profile from the (up to) five available spliced profiles to effectively characterize boundary layer winds and to utilize all possible 915-MHz DRWP measurements at each timestamp. During a given month, roughly 23,000-32,000 complete profiles exist from 0.25-18.45 km from the composite profiles' archive, and approximately 5,000- 27,000 complete profiles exist from an archive utilizing an individual 915-MHz DRWP. One can extract a variety of profile combinations (pairs, triplets, etc.) from this sample for a given application. The sample of vertically complete DRWP wind measurements not only gives launch vehicle customers greater confidence in loads and trajectory assessments versus using balloon output, but also provides flexibility to simulate different DOL situations across applicable altitudes. In addition to increasing sample size and providing more flexibility for DOL simulations in the vehicle design phase, the spliced DRWP database provides any upcoming launch vehicle program with the capability to utilize DRWP profiles on DOL to compute vehicle steering commands, provided the program applies the procedures that this report describes to new DRWP data on DOL. Decker et al. (2015) details how SLS is proposing to use DRWP data and splicing techniques on DOL. Although automation could enhance the current DOL 50-MHz DRWP QC process and could streamline any future DOL 915-MHz DRWP QC and splicing process, the DOL community would still require manual intervention to ensure that the vehicle only uses valid profiles. If a program desires to use high spatial resolution profiles, then the algorithm could randomly add high-frequency components to the DRWP profiles. The spliced DRWP database provides lots of flexibility in how one performs DOL simulations, and the algorithms that this report provides will assist the aerospace and atmospheric communities that are interested in utilizing the DRWP.

Barbre, Robert E., Jr.↗
NASA NTRS2013

Time-Series Analysis of Supergranule Characterstics at Solar Minimum

Sixty days of Doppler images from the Solar and Heliospheric Observatory (SOHO) / Michelson Doppler Imager (MDI) investigation during the 1996 and 2008 solar minima have been analyzed to show that certain supergranule characteristics (size, size range, and horizontal velocity) exhibit fluctuations of three to five days. Cross-correlating parameters showed a good, positive correlation between supergranulation size and size range, and a moderate, negative correlation between size range and velocity. The size and velocity do exhibit a moderate, negative correlation, but with a small time lag (less than 12 hours). Supergranule sizes during five days of co-temporal data from MDI and the Solar Dynamics Observatory (SDO) / Helioseismic Magnetic Imager (HMI) exhibit similar fluctuations with a high level of correlation between them. This verifies the solar origin of the fluctuations, which cannot be caused by instrumental artifacts according to these observations. Similar fluctuations are also observed in data simulations that model the evolution of the MDI Doppler pattern over a 60-day period. Correlations between the supergranule size and size range time-series derived from the simulated data are similar to those seen in MDI data. A simple toy-model using cumulative, uncorrelated exponential growth and decay patterns at random emergence times produces a time-series similar to the data simulations. The qualitative similarities between the simulated and the observed time-series suggest that the fluctuations arise from stochastic processes occurring within the solar convection zone. This behavior, propagating to surface manifestations of supergranulation, may assist our understanding of magnetic-field-line advection, evolution, and interaction.

Dopplergrams↗
NASA NTRSDate not supplied

Recent Advancements in Electrical Capacitance Mass Gauging for Cryogenic PropellantTanks

The current lack of unsettled mass gauging is a key roadblock for many space activities, such as orbital refueling, missions to the Moon and Mars, and nuclear thermal and nuclear electric propulsion technologies. Liquid can form any one of an infinite number of configurations in microgravity, such as floating in globs or accumulating on tank surfaces in discontiguous volumes, or both. Capacitive sensing requires no moving parts and dissipates close to zero heat, making it an ideal candidate for cryogenic fluid mass gauging in settled and unsettled configurations. Capacitive sensing has a history of proven use in space propellant tanks, including tanks on the space shuttle, Saturn V, and the Apollo Lunar Excursion Module. Modern capacitive sensor technology allows using the entire tank as the capacitor volume by placing electrodes on the tank walls and propellant management surfaces. Capacitance is directly related to density, and therefore to mass for fixed volumes. In this presentation, we discuss the concepts behind whole-tank capacitance mass gauging and associated engineering challenges. We describe recent efforts to develop a micro-g unsettled cryogenic mass gauge using whole-tank capacitance sensing, including the development of test beds, electronics, and algorithms. We describe several mathematical processing techniques, including empirical-based averaging, electrical capacitance volume tomography, and spatial regularization. A modeling study, performed using settled configurations in gravity and no gravity, and with a set of 100 random fluid configurations, has indicated that spatial regularization, in which capacitance measurements are weighted to account for non-uniform electric fields, yields a mass fraction accuracy of 8% for any fluid configuration. Such a sensor is expected to operate in real time with a sampling frequency of at least 1 kHz.

cryogenic↗
NASA NTRS1980

An experimental distributed microprocessor implementation with a shared memory communications and control medium

The distributed processing concept is defined in terms of control primitives, variables, and structures and their use in performing a decomposed discrete Fourier transform (DET) application function. The design assumes interprocessor communications to be anonymous. In this scheme, all processors can access an entire common database by employing control primitives. Access to selected areas within the common database is random, enforced by a hardware lock, and determined by task and subtask pointers. This enables the number of processors to be varied in the configuration without any modifications to the control structure. Decompositional elements of the DFT application function in terms of tasks and subtasks are also described. The experimental hardware configuration consists of IMSAI 8080 chassis which are independent, 8 bit microcomputer units. These chassis are linked together to form a multiple processing system by means of a shared memory facility. This facility consists of hardware which provides a bus structure to enable up to six microcomputers to be interconnected. It provides polling and arbitration logic so that only one processor has access to shared memory at any one time.

Mejzak, R. S.↗
DOE OSTI2020

Automatic Classification of Biological Targets in a Tidal Channel Using a Multibeam Sonar

Multibeam sonars are widely used for environmental monitoring of fauna at marine renewable energy sites. However, they can rapidly accrue vast volumes of data, which poses a challenge for data processing. Here, using data from a deployment in a tidal channel with peak currents of 1–2 m s –1 , we demonstrate the data-reduction benefits of real-time automatic classification of targets detected and tracked in multibeam sonar data. First, we evaluate classification capabilities for three machine learning algorithms: random forests, support vector machines, and k-nearest neighbors. For each algorithm, a hill-climbing search optimizes a set of hand-engineered attributes that describe tracked targets. Here, the random forest algorithm is found to be most effective—in postprocessing, discriminating between biological and nonbiological targets with a recall rate of 0.97 and a precision of 0.60. In addition, 89% of biological targets are correctly classified as either seals, diving birds, fish schools, or small targets. Model dependence on the volume of training data is evaluated. Second, a real-time implementation of the model is shown to distinguish between biological targets and nonbiological targets with nearly the same performance as in postprocessing. From this, we make general recommendations for implementing real-time classification of biological targets in multibeam sonar data and the transferability of trained models.

16 TIDAL AND WAVE POWER↗
NASA NTRS1994

An efficient system for reliably transmitting image and video data over low bit rate noisy channels

This research project is intended to develop an efficient system for reliably transmitting image and video data over low bit rate noisy channels. The basic ideas behind the proposed approach are the following: employ statistical-based image modeling to facilitate pre- and post-processing and error detection, use spare redundancy that the source compression did not remove to add robustness, and implement coded modulation to improve bandwidth efficiency and noise rejection. Over the last six months, progress has been made on various aspects of the project. Through our studies of the integrated system, a list-based iterative Trellis decoder has been developed. The decoder accepts feedback from a post-processor which can detect channel errors in the reconstructed image. The error detection is based on the Huber Markov random field image model for the compressed image. The compression scheme used here is that of JPEG (Joint Photographic Experts Group). Experiments were performed and the results are quite encouraging. The principal ideas here are extendable to other compression techniques. In addition, research was also performed on unequal error protection channel coding, subband vector quantization as a means of source coding, and post processing for reducing coding artifacts. Our studies on unequal error protection (UEP) coding for image transmission focused on examining the properties of the UEP capabilities of convolutional codes. The investigation of subband vector quantization employed a wavelet transform with special emphasis on exploiting interband redundancy. The outcome of this investigation included the development of three algorithms for subband vector quantization. The reduction of transform coding artifacts was studied with the aid of a non-Gaussian Markov random field model. This results in improved image decompression. These studies are summarized and the technical papers included in the appendices.

Costello, Daniel J., Jr.↗
NASA NTRS2011

The Relative Importance of Random Error and Observation Frequency in Detecting Trends in Upper Tropospheric Water Vapor

Recent published work assessed the amount of time to detect trends in atmospheric water vapor over the coming century. We address the same question and conclude that under the most optimistic scenarios and assuming perfect data (i.e., observations with no measurement uncertainty) the time to detect trends will be at least 12 years at approximately 200 hPa in the upper troposphere. Our times to detect trends are therefore shorter than those recently reported and this difference is affected by data sources used, method of processing the data, geographic location and pressure level in the atmosphere where the analyses were performed. We then consider the question of how instrumental uncertainty plays into the assessment of time to detect trends. We conclude that due to the high natural variability in atmospheric water vapor, the amount of time to detect trends in the upper troposphere is relatively insensitive to instrumental random uncertainty and that it is much more important to increase the frequency of measurement than to decrease the random error in the measurement. This is put in the context of international networks such as the Global Climate Observing System (GCOS) Reference Upper-Air Network (GRUAN) and the Network for the Detection of Atmospheric Composition Change (NDACC) that are tasked with developing time series of climate quality water vapor data.

Whiteman, David N.↗
DOE OSTI2022

Development of Short-Term Forecasting Models Using Plant Asset Data and Feature Selection

Nuclear power plants collect and store large volumes of heterogeneous data from various components and systems. With recent advances in machine learning (ML) techniques, these data can be leveraged to develop diagnostic and short-term forecasting models to better predict future equipment condition. Maintenance operations can then be planned in advance whenever degraded performance is predicted, thus resulting in fewer unplanned outages and the optimization of maintenance activities. This enables lower maintenance costs and improves the overall economics of nuclear power. This paper focuses on developing a short-term forecasting process that leverages a feature selection process to distill large volumes of heterogeneous data and predict specific equipment parameters. A variety of feature selection methods, including Shapley Additive Explanations (SHAP) and variance inflation factor (VIF), were used to select the optimal features as inputs for three ML methods: long short-term memory (LSTM) networks, support vector regression (SVR), and random forest (RF). Each combination of model and input features was used to predict a pump bearing temperature both 1 and 24 hours in advance, based on actual plant system data. The optimal inputs for the LSTM and SVR were selected using the SHAP values, while the optimal input for the RF consisted solely of the response variable itself. Each model produced similar 1-hour-ahead predictions, with root mean square errors (RMSEs) of roughly 0.006. For the 24-hour-ahead predictions, differences could be seen between LSTM, SVR, and RF, as reflected by model performances of 0.036 +- 0.014, 0.0026 +- 0, and 0.063 +- 0.004 RMSE, respectively. As big data and continuous online monitoring become more widely available, the proposed feature selection process can be used for many applications beyond the prediction of process parameters within nuclear infrastructure.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2016

Improved characteristics of amorphous indium-gallium-zinc-oxide-based resistive random access memory using hydrogen post-annealing

The authors report an improvement in resistive switching (RS) characteristics of amorphous indium-gallium-zinc-oxide (a-IGZO)-based resistive random access memory devices using hydrogen post-annealing. Because this a-IGZO thin film has oxygen off-stoichiometry in the form of deficient and excessive oxygen sites, the film properties can be improved by introducing hydrogen atoms through the annealing process. After hydrogen post-annealing, the device exhibited a stable bipolar RS, low-voltage set and reset operation, long retention (>10{sup 5 }s), good endurance (>10{sup 6} cycles), and a narrow distribution in each current state. The effect of hydrogen post-annealing is also investigated by analyzing the sample surface using X-ray photon spectroscopy and atomic force microscopy.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Permanently Magnetized Insulating Thin-Film Devices by Reduction

A reduction-based manufacturing process for creating technologically important multilayer structures from lattice-matched ferromagnetic insulators and ferromagnetic conductors is reported. The process is demonstrated by growing a permanently magnetized double-layer structure, consisting of lattice-matched conducting (Ni,Co) and insulating (Ni 0.4 Co 0.6 ) 3 O 3 layers, through a single deposition cycle. The orientation of the metal cation network is preserved after reduction. Close-packing displacements of Ni and Co take place in such a manner that the in-plane hexagonal arrangement is preserved. This is critical for ensuring high-quality interfaces joining the layers. At room temperature the hysteresis loop is centered. At low temperature the oxide layer becomes ferrimagnetically ordered, accompanied by a shift of the hysteresis loop along the magnetic field axis. The shift is assigned to exchange bias phenomenon. Biaxial compressive strain is responsible for the required ferrimagnetic ordering. Finally, spin valves and closely related magnetoresistance random access memory and spin-transfer-torque magnetic random access memory devices are addressed as potential applications.

42 ENGINEERING↗
DOE OSTI2021

Updates to the Regional Seismic Travel Time (RSTT) Model: 2. Path-dependent Travel-time Uncertainty

Abstract The regional seismic travel time (RSTT) model and software were developed to improve travel-time prediction accuracy by accounting for three-dimensional crust and upper mantle structure. Travel-time uncertainty estimates are used in the process of associating seismic phases to events and to accurately calculate location uncertainty bounds (i.e. event location error ellipses). We improve on the current distance-dependent uncertainty parameterization for RSTT using a random effects model to estimate slowness (inverse velocity) uncertainty as a mean squared error for each model parameter. The random effects model separates the error between observed slowness and model predicted slowness into bias and random components. The path-specific travel-time uncertainty is calculated by integrating these mean squared errors along a seismic-phase ray path. We demonstrate that event location error ellipses computed for a 90% coverage ellipse metric (used by the Comprehensive Nuclear-Test-Ban Treaty Organization International Data Centre (IDC)), and using the path-specific travel-time uncertainty approach, are more representative (median 82.5% ellipse percentage) of true location error than error ellipses computed using distance-dependent travel-time uncertainties (median 70.1%). We also demonstrate measurable improvement in location uncertainties using the RSTT method compared to the current station correction approach used at the IDC (median 74.3% coverage ellipse).

58 GEOSCIENCES↗
DOE OSTI2020

Toward particle-resolved accuracy in Euler–Lagrange simulations of multiphase flow using machine learning and pairwise interaction extended point-particle (PIEP) approximation

This study presents two different machine learning approaches for the modeling of hydrodynamic force on particles in a particle-laden multiphase flow. Results from particle-resolved direct numerical simulations (PR-DNS) of flow over a random array of stationary particles for eight combinations of particle Reynolds number (Re) and volume fraction (φ) are used in the development of the models. The first approach follows a two-step process. In the first flow prediction step, the perturbation flow due to a particle is obtained as an axisymmetric superposable wake using linear regression. In the second force prediction step, the force on a particle is evaluated in terms of the perturbation flow induced by all its neighbors using the generalized Faxén form of the force expression. In the second approach, the force data on all the particles from the PR-DNS simulations are used to develop an artificial neural network (ANN) model for direct prediction of force on a particle. Due to the unavoidable limitation on the number of fully resolved particles in the PR-DNS simulations, direct force prediction with the ANN model tends to over-fit the data and performs poorly in the prediction of test data. In contrast, due to the millions of grid points used in the PR-DNS simulations, accurate flow prediction is possible, which then allows accurate prediction of particle force. This hybridization of multiphase physics and machine learning is particularly important, since it blends the strength of each, and the resulting pairwise interaction extended point-particle model cannot be developed by either physics or machine learning alone.

42 ENGINEERING↗
DOE OSTI2025

Parallel sorting algorithm classification: is manual instrumentation necessary?

Understanding parallel algorithms is crucial for accelerating scientific simulations on complex, distributed memory, high-performance computers. Modern algorithm classification approaches learn semantics directly from source code to differentiate between algorithms, however, accessing source code is not always possible. We can learn about parallel algorithms from observing their performance, as programs running the same algorithms and using the same hardware should exhibit similar performance characteristics. We present an approach to learn algorithm classes from parallel performance data directly in order to classify algorithms without access to the source code. We extend previous work to enable classifying parallel sorting algorithms using automatic instrumentation instead of requiring manual region annotations in the source code. In this work, we design and demonstrate a study for classification of parallel sorting algorithms using parallel performance data collected from automatic instrumentation, and evaluate the performance of our new methodology on classification. We leverage Caliper to collect the performance data, Thicket for our exploratory data analysis (EDA), and PyTorch and Scikit-learn to evaluate the effectiveness of random forests, support vector machines (SVMs), decision trees, neural networks, and logistic regressions on parallel performance data. Additionally, we study noise in parallel performance data, whether the removal of noise and pre-processing of the data is necessary to accurately classify parallel sorting algorithms, and determine the effectiveness of features created from performance data. In conclusion, we demonstrate classification accuracy for these five different models of up to 97.7% across four different parallel algorithm classes.

Algorithm Classification↗