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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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All sourcesNASA NTRSDOE OSTI
NASA NTRS1983

Packet telemetry and packet telecommand - The new generation of spacecraft data handling techniques

Because of rising costs and reduced reliability of spacecraft and ground network hardware and software customization, standardization Packet Telemetry and Packet Telecommand concepts are emerging as viable alternatives. Autonomous packets of data, within each concept, which are created within ground and space application processes through the use of formatting techniques, are switched end-to-end through the space data network to their destination application processes through the use of standard transfer protocols. This process may result in facilitating a high degree of automation and interoperability because of completely mission-independent-designed intermediate data networks. The adoption of an international guideline for future space telemetry formatting of the Packet Telemetry concept, and the advancement of the NASA-ESA Working Group's Packet Telecommand concept to a level of maturity parallel to the of Packet Telemetry are the goals of the Consultative Committee for Space Data Systems. Both the Packet Telemetry and Packet Telecommand concepts are reviewed.

Hooke, A. J.↗
NASA NTRS1992

Communications system evolutionary scenarios for Martian SEI support

In the Space Exploration Initiative (SEI) mission scenarios, expanding human presence is the primary driver for high data rate Mars-Earth communications. To support an expanding human presence, the data rate requirement will be gradual, following the phased implementation over time of the evolving SEI mission. Similarly, the growth and evolution of the space communications infrastructure to serve this requirement will also be gradual to efficiently exploit the useful life of the installed communications infrastructure and to ensure backward compatibility with long-term users. In work conducted over the past year, a number of alternatives for supporting high data rate Mars-Earth communications have been analyzed with respect to their compatibility with gradual evolution of the space communications infrastructure. The alternatives include RF, millimeter wave (MMW), and optical implementations, and incorporate both surface and space-based relay terminals in the Mars and Earth regions. Each alternative is evaluated with respect to its ability to efficiently meet a projected growth in data rate over time, its technology readiness, and its capability to satisfy the key conditions and constraints imposed by evolutionary transition. As a result of this analysis, a set of attractive alternative communications architectures have been identified and described, and a road map is developed that illustrates the most rational and beneficial evolutionary paths for the communications infrastructure.

Kwong, Paulman W.↗
NASA NTRS1989

Second catalog of interferometric measurements of binary stars (McAlister and Hartkopf 1988): Documentation for the machine-readable version

The machine-readable version of the catalog, as it is currently being distributed from the Astronomical Data Center, is described. The catalog is a compilation of measurements of binary- and multiple-star systems obtained by speckle interferometric techniques; this version supersedes a previous edition of the catalog published in 1985. Stars that have been examined for multiplicity with negative results are included, in which case upper limits for the separation are given. The second version is expanded from the first in that a file of newly resolved systems and six cross-index files of alternate designations are included. The data file contains alternate identifications for the observed systems, epochs of observation, reported errors in position angles and separation, and bibliographical references.

Warren, Wayne H., Jr.↗
NASA NTRS1983

Semiempirical Estimate of Aircraft Wing Weight

Computational method estimates weight of aircraft wings from theoretical relationships and empirical data. Permits comparison of alternative materials, methods of construction, and design philosophies. Method used to make tradeoffs in preliminary design phases on basis of simple input data and for more accurate calculations in later phases when more data are available.

York, P.↗
NASA NTRS2001

Did I Say Terabyte? I Meant Petabyte: Data Archiving in the Era of SDO

Two years ago (Gurman 1999, Bull, AAS, 31, 955), we discussed the treatment of archives of the order of 10 Tbyte per year from solar physics missions in the period 2004 - 2006 (e.g. Solar-B and STEREO). By early 2007, we expect that the Sun-Earth Connections community will have to deal with data sets from the Solar Dynamics Observatory (SDO) of order I Tbyte per day. As in the previous work, we examine several alternatives for dealing with data flow and service on a fire-hose scale, and show that off-the shelf, network-attached storage can provide an inexpensive and scaleable solution. We discuss some of the differences between an SDO data archive, as well as the range of requirements for data integrity, disaster recovery, &c. in various scenarios for archive concentration or distribution.

Gurman, Joseph B.↗
NASA NTRS2007

Advanced Systems for Monitoring Underwater Sounds

The term "Passive Acoustic Monitoring System" (PAMS) describes a developmental sensing-and-data-acquisition system for recording underwater sounds. The sounds (more precisely, digitized and preprocessed versions from acoustic transducers) are subsequently analyzed by a combination of data processing and interpretation to identify and/or, in some cases, to locate the sources of those sounds. PAMS was originally designed to locate the sources such as fish of species that one knows or seeks to identify. The PAMS unit could also be used to locate other sources, for example, marine life, human divers, and/or vessels. The underlying principles of passive acoustic sensing and analyzing acoustic-signal data in conjunction with temperature and salinity data are not new and not unique to PAMS. Part of the uniqueness of the PAMS design is that it is the first deep-sea instrumentation design to provide a capability for studying soniferous marine animals (especially fish) over the wide depth range described below. The uniqueness of PAMS also lies partly in a synergistic combination of advanced sensing, packaging, and data-processing design features with features adapted from proven marine instrumentation systems. This combination affords a versatility that enables adaptation to a variety of undersea missions using a variety of sensors. The interpretation of acoustic data can include visual inspection of power-spectrum plots for identification of spectral signatures of known biological species or artificial sources. Alternatively or in addition, data analysis could include determination of relative times of arrival of signals at different acoustic sensors arrayed at known locations. From these times of arrival, locations of acoustic sources (and errors in those locations) can be estimated. Estimates of relative locations of sources and sensors can be refined through analysis of the attenuation of sound in the intervening water in combination with water-temperature and salinity data acquired by instrumentation systems other than PAMS. A PAMS is packaged as a battery-powered unit, mated with external sensors, that can operate in the ocean at any depth from 2 m to 1 km. A PAMS includes a pressure housing, a deep-sea battery, a hydrophone (which is one of the mating external sensors), and an external monitor and keyboard box. In addition to acoustic transducers, external sensors can include temperature probes and, potentially, underwater cameras. The pressure housing contains a computer that includes a hard drive, DC-to- DC power converters, a post-amplifier board, a sound card, and a universal serial bus (USB) 4-port hub.

Lane, Michael↗
NASA NTRS1998

A low cost approach to simultaneous orbit, attitude, and rate estimation using an extended Kalman filter

An innovative approach to autonomous attitude and trajectory estimation is available using only magnetic field data and rate data. The estimation is performed simultaneously using an Extended Kalman Filter (EKF), a well known algorithm used extensively in onboard applications. The magnetic field is measured on a satellite by a magnetometer, an inexpensive and reliable sensor flown on virtually all satellites in low earth orbit. Rate data is provided by a gyro, which can be costly. This system has been developed and successfully tested in a post-processing mode using magnetometer and gyro data from 4 satellites supported by the Flight Dynamics Division at Goddard. In order for this system to be truly low cost, an alternative source for rate data must be utilized. An independent system which estimates spacecraft rate has been successfully developed and tested using only magnetometer data or a combination of magnetometer data and sun sensor data, which is less costly than a gyro. This system also uses an EKF. Merging the two systems will provide an extremely low cost, autonomous approach to attitude and trajectory estimation. In this work we provide the theoretical background of the combined system. The measurement matrix is developed by combining the measurement matrix of the orbit and attitude estimation EKF with the measurement matrix of the rate estimation EKF, which is composed of a pseudo-measurement which makes the effective measurement a function of the angular velocity. Associated with this is the development of the noise covariance matrix associated with the original measurement combined with the new pseudo-measurement. In addition, the combination of the dynamics from the two systems is presented along with preliminary test results.

Deutschmann, Julie↗
NASA NTRS1998

A Low Cost Approach to Simultaneous Orbit, Attitude, and Rate Estimation Using an Extended Kalman Filter

An innovative approach to autonomous attitude and trajectory estimation is available using only magnetic field data and rate data. The estimation is performed simultaneously using an Extended Kalman Filter, a well known algorithm used extensively in onboard applications. The magnetic field is measured on a satellite by a magnetometer, an inexpensive and reliable sensor flown on virtually all satellites in low earth orbit. Rate data is provided by a gyro, which can be costly. This system has been developed and successfully tested in a post-processing mode using magnetometer and gyro data from 4 satellites supported by the Flight Dynamics Division at Goddard. In order for this system to be truly low cost, an alternative source for rate data must be utilized. An independent system which estimate spacecraft rate has been successfully developed and tested using only magnetometer data or a combination of magnetometer data and sun sensor data, which is less costly than a gyro. This system also uses an Extended Kalman Filter. Merging the two systems will provide an extremely low cost, autonomous approach to attitude and trajectory estimation. In this work we provide the theoretical background of the combined system. The measurement matrix is developed by combining the measurement matrix of the orbit and attitude estimation EKF with the measurement matrix of the rate estimation EKF, which is composed of a pseudo-measurement which makes the effective measurement a function of the angular velocity. Associated with this is the development of the noise covariance matrix associated with the original measurement combined with the new pseudo-measurement. In addition, the combination of the dynamics from the two systems is presented along with preliminary test results.

Deutschmann, Julie↗
DOE OSTI2020

DeepFreeze: Towards Scalable Asynchronous Checkpointing of Deep Learning Models

In the age of big data, deep learning has emerged as a powerful tool to extract insight and exploit its value, both in industry and scientific applications. One common pattern emerging in such applications is frequent checkpointing of the state of the learning model during training, needed in a variety of scenarios: analysis of intermediate states to explain features and correlations with training data, exploration strategies involving alternative models that share a common ancestor, knowledge transfer, resilience, etc. However, with increasing size of the learning models and popularity of distributed data-parallel training approaches, simple checkpointing techniques used so far face several limitations: low serialization performance, blocking I/O, stragglers due to the fact that only a single process is involved in checkpointing. This paper proposes a checkpointing technique specifically designed to address the aforementioned limitations, introducing efficient asynchronous techniques to hide the overhead of serialization and I/O, and distribute the load over all participating processes. Experiments with two deep learning applications (CANDLE and ResNet) on a pre-Exascale HPC platform (Theta) shows significant improvement over state-of-art, both in terms of checkpointing duration and runtime overhead.

checkpointing↗
DOE OSTI2022

Addressing delayed case reporting in infectious disease forecast modeling

Infectious disease forecasting is of great interest to the public health community and policymakers, since forecasts can provide insight into disease dynamics in the near future and inform interventions. Due to delays in case reporting, however, forecasting models may often underestimate the current and future disease burden. In this paper, we propose a general framework for addressing reporting delay in disease forecasting efforts with the goal of improving forecasts. We propose strategies for leveraging either historical data on case reporting or external internet-based data to estimate the amount of reporting error. We then describe several approaches for adapting general forecasting pipelines to account for under- or over-reporting of cases. We apply these methods to address reporting delay in data on dengue fever cases in Puerto Rico from 1990 to 2009 and to reports of influenza-like illness (ILI) in the United States between 2010 and 2019. Through a simulation study, we compare method performance and evaluate robustness to assumption violations. Our results show that forecasting accuracy and prediction coverage almost always increase when correction methods are implemented to address reporting delay. Some of these methods required knowledge about the reporting error or high quality external data, which may not always be available. Provided alternatives include excluding recently-reported data and performing sensitivity analysis. This work provides intuition and guidance for handling delay in disease case reporting and may serve as a useful resource to inform practical infectious disease forecasting efforts.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Domain Adaptation for Measurements of Strong Gravitational Lenses

Upcoming surveys are predicted to discover galaxy-scale strong lenses on the order of 10\textsuperscript{5}, making deep learning methods necessary in lensing data analysis. Currently, there is insufficient real lensing data to train deep learning algorithms, but the alternative of training only on simulated data results in poor performance on real data. Domain Adaptation may be able to bridge the gap between simulated and real datasets. We utilize domain adaptation for the estimation of Einstein radius ($\Theta_E$) in simulated galaxy-scale gravitational lensing images with different levels of observational realism. We evaluate two domain adaptation techniques - Domain Adversarial Neural Networks (DANN) and Maximum Mean Discrepancy (MMD). We train on a source domain of simulated lenses and apply it to a target domain of lenses simulated to emulate noise conditions in the Dark Energy Survey (DES). We show that both domain adaptation techniques can significantly improve the model performance on the more complex target domain dataset. This work is the first application of domain adaptation for a regression task in strong lensing imaging analysis. Our results show the potential of using domain adaptation to perform analysis of future survey data with a deep neural network trained on simulated data.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

Domain Adaptation for Measurements of Strong Gravitational Lenses

Upcoming surveys are predicted to discover galaxy-scale strong lenses on the order of $10^5$, making deep learning methods necessary in lensing data analysis. Currently, there is insufficient real lensing data to train deep learning algorithms, but the alternative of training only on simulated data results in poor performance on real data. Domain Adaptation may be able to bridge the gap between simulated and real datasets. We utilize domain adaptation for the estimation of Einstein radius ($\Theta_E$) in simulated galaxy-scale gravitational lensing images with different levels of observational realism. We evaluate two domain adaptation techniques - Domain Adversarial Neural Networks (DANN) and Maximum Mean Discrepancy (MMD). We train on a source domain of simulated lenses and apply it to a target domain of lenses simulated to emulate noise conditions in the Dark Energy Survey (DES). We show that both domain adaptation techniques can significantly improve the model performance on the more complex target domain dataset. This work is the first application of domain adaptation for a regression task in strong lensing imaging analysis. Our results show the potential of using domain adaptation to perform analysis of future survey data with a deep neural network trained on simulated data.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
NASA NTRS2001

Joint EPA/NASA/USAF Interagency Depainting Study

Environmental regulations such as National Emission Standards for Hazardous Air Pollutants (NESHAPs) are drivers for the implementation of environmentally compliant methodologies in the manufacture of aerospace hardware. In 1995, the Environmental Protection Agency (EPA) promulgated the NESHAP for the Aerospace Manufacture and Rework (Aerospace NESHAP) industry. Affected facilities were to be in compliance by September 1998. Several aerospace manufacturing operations are regulated within the Aerospace NESHAP including Depainting operations. The National Aeronautics and Space Administration (NASA), EPA, and United States Air Force (USAF) combined resources to evaluate the performance of nine alternative depainting processes. The seven alternative depainting processes were: (1) Chemical stripping (non-methylene chloride); (2) Carbon Dioxide Blasting; (3) Xenon Flashlamp; (4) Carbon Dioxide Laser Stripping; (5) Plastic Media Blasting; (6) Sodium Bicarbonate Wet Stripping; and (7) Waterjet Blasting and Wheat Starch Blasting. All epoxy primer and polyurethane top coat system was applied to 2024-T3 clad and non-clad aluminum test specimens. Approximately 200 test specimens were evaluated in this study. Each coupon was subjected to three, four, or five complete depainting cycles. This paper discusses the conclusions from the study including the test protocol, test parameters, and achievable strip rates for the alternative depainting processes. Test data includes immersion corrosion testing, sandwich corrosion testing and hydrogen embrittlement testing for the non-methylene chloride chemical strippers. Additionally, the cumulative effect of the alternative depainting processes on the metallurgical integrity of the test substrate is addressed with the results from tensile and fatigue evaluations.

Clark-Ingram, M.↗
DOE OSTI2022

Fast Gaussian Process Estimation for Large-Scale In Situ Inference using Convolutional Neural Networks

Exascale computing will bring with it significant I/O limitations. One foreseeable consequence of such restrictions is that the user can save only a small fraction of complex simulation data to disk for subsequent analysis. An alternative is to fit statistical models to data in situ, that is, inside the simulation as it runs. This option requires extremely fast statistical estimation to avoid slowing down the simulation. Gaussian processes (GPs) have state-of-the-art predictive performance for modeling spatial data. However, standard estimation methods for GPs scale quite poorly to large data sets as parameter estimation requires inverting a covariance matrix to the size of the data set. In the presented work, we use a convolutional neural network (CNN) to predict the GP parameters for a spatial data set, from a simulation or otherwise, rather than optimize the parameters directly. Here, our presented case study models spatial data from E3SM, the Department of Energy’s Exascale climate model. The CNN is trained on synthetic data simulated from GP models with known parameters and then applied to data from the climate simulation. In the presented examples, the neural network scheme produces parameter estimates that compare well with standard methods such as maximum likelihood estimation in predictive performance but is obtained four orders of magnitude faster.

big data↗
NASA NTRS1981

Operational alternatives for LANDSAT in California

Data integration is defined and examined as the means of promoting data sharing among the various governmental and private geobased information systems in California. Elements of vertical integration considered included technical factors (such as resolution and classification) and institutional factors (such as organizational control, and legal and political barriers). Attempts are made to fit the theoretical elements of vertical integration into a meaningful structure for looking at the problem from a statewide focus. Both manual (mapped) and machine readable data systems are included. Special attention is given to LANDSAT imagery because of its strong potential for integrated use and its primary in the California Integrated Remote Sensing System program.

Wilson, P.↗
NASA NTRS1983

Organizing Space Shuttle parametric data for maintainability

A model of organization and management of Space Shuttle data is proposed. Shuttle avionics software is parametrically altered by a reconfiguration process for each flight. As the flight rate approaches an operational level, current methods of data management would become increasingly complex. An alternative method is introduced, using modularized standard data, and its implications for data collection, integration, validation, and reconfiguration processes are explored. Information modules are cataloged for later use, and may be combined in several levels for maintenance. For each flight, information modules can then be selected from the catalog at a high level. These concepts take advantage of the reusability of Space Shuttle information to reduce the cost of reconfiguration as flight experience increases.

Angier, R. C.↗
NASA NTRS1992

Shock-induced transformations in the system NaAlSiO4-SiO2 - A new interpretation

New internally consistent interpretations of the phases represented by the high pressure phase shock wave data for an albite-rich rock, jadeite, and nepheline in the system NaAlSiO4-SiO2, are obtained using the results of static high pressure investigations, and the recent discovery of the hollandite phase in a shocked meteorite. We conclude that nepheline transforms directly to the calcium ferrite structure, whereas albite transforms possibly to the hollandite structure. Shock Hugoniots for the other plagioclase and alkali feldspars also indicate that these transform to hollandite structures. The pressure-volume data at high pressure could alternatively represent the compression of an amorphous phase. Moreover, the shock Hugoniot data are expected to reflect the properties of the melt above shock stresses of 60-80 GPa. The third order Birch-Murnaghan equation of state parameters are given for the calcium ferrite type NaAlSiO4 and for albite-rich, orthoclase-rich, and anorthite-rich hollandites.

Sekine, Toshimori↗