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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 415 records · Page 23
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NASA NTRS2017

NASA Near Earth Network (NEN) Support for Lunar and L1/L2 CubeSats

The NASA Near Earth Network (NEN) consists of globally distributed tracking stations, including NASA, commercial, and partner ground stations, that are strategically located to maximize the coverage provided to a variety of orbital and suborbital missions, including those in LEO, GEO, HEO, lunar and L1/L2 orbits. The NENs future mission set includes and will continue to include CubeSat missions. The majority of the CubeSat missions destined to fly on EM-1, launching in late 2018, many in a lunar orbit, will communicate with ground based stations via X-band and will utilize the NASA Jet Propulsion Laboratory (JPL) developed IRIS radio. The NEN recognizes the important role CubeSats are beginning to play in carrying out NASAs mission and is therefore investigating the modifications needed to provide IRIS radio compatibility. With modification, the NEN could potentially expand support to the EM-1 lunar CubeSats.The NEN could begin providing significant coverage to lunar CubeSat missions utilizing three to four of the NENs mid-latitude sites. This coverage would supplement coverage provided by the JPL Deep Space Network (DSN). The NEN, with smaller apertures than DSN, provides the benefit of a larger beamwidth that could be beneficial in the event of uncertain ephemeris data. In order to realize these benefits the NEN would need to upgrade stations targeted based on coverage ability and current configuration/ease of upgrade, to ensure compatibility with the IRIS radio. In addition, the NEN is working with CubeSat radio developers to ensure NEN compatibility with alternative CubeSat radios for Lunar and L1/L2 CubeSats. The NEN has provided NEN compatibility requirements to several radio developers who are developing radios that offer lower cost and, in some cases, more capabilities with fewer constraints. The NEN is ready to begin supporting CubeSat missions. The NEN is considering network upgrades to broaden the types of CubeSat missions that can be supported and is supporting both the CubeSat community and radio developers to ensure future CubeSat missions have multiple options when choosing a network for their communications support.

Schaire, Scott↗
NASA NTRS2017

SO-QT: Collaborative Tool to Project the Future Space Object Population

Earth orbit gets increasingly congested, a challenge to space operators, both in governments and industry. We present a web tool that provides: 1) data on todays and the historic space object environments, by aggregating object-specific tracking data; and 2) future trends through a collaboration platform to collect information on planed launches. The collaborative platform enables experts to pool and compare their data in order to generate future launch scenarios. The tool is intended to support decision makers and mission designers while they investigate future missions and scholars as they develop strategies for space traffic management.

space debris↗
NASA NTRS2018

Post-Flight Data Analysis Tool

A software tool that facilitates the retrieval and analysis of post-flight data. This allows our team and other teams to effectively and efficiently analyze and evaluate post-flight data in order to certify commercial providers.

George, Marina↗
NASA NTRS2019

Evolved Gas Analyses of Mudstones from the Vera Rubin Ridge

The Sample Analysis at Mars (SAM) instrument suite on the Mars Science Laboratory (MSL) rover has been essential in understanding volatile-bearing phases in Gale Crater materials. SAM’s evolved gas analysis mass spectrometry (EGA-MS) has detected H2O, CO2, O2, H2, SO2, H2S, HCl, NO, and other trace gases, including organic fragments, in many samples. The identity and evolution temperature of evolved gases can support CheMin instrument mineral detection and place constraints on trace volatile-bearing phases or phases difficult to characterize with X-ray diffraction (e.g., amorphous phases). For the past ~500 sols, MSL has been exploring the Vera Rubin Ridge (VRR), which exhibits a striking hematite signature in orbital remote sensing data, in order to understand the depositional and diagenetic history recorded in the rocks and how it relates to the underlying Murray Formation. Four rock samples were drilled, one from the Blunts Point Member (Duluth, DU), one from the Pettegrrove Point Member (Stoer, ST), and two from the Jura Member. The Jura Member displays differences in color, summarized as grey and red, and a key goal was to constrain the cause of this color difference and the associated implications for depositional or post-depositional conditions. To investigate, a grey (Highfield, HF) and a red (Rock Hall, RH) Jura sample were drilled. Here we will give an overview of results from SAM EGA-MS analyses of VRR materials, with some comparisons to analyses of samples of the underlying Murray.

McAdam, A. C.↗
NASA NTRS2019

The Search for the Gamma Lyrid Progenitor

On 5 February 2015, a previously unknown meteor shower, the γ Lyrids were observed for the first time. Because of the Sun-Earth-Moon viewing geometry, however, stream members were observed almost exclusively by the Canadian Meteor Orbit Radar (CMOR). The γ Lyrids did not appear again until 2018, and that outburst was stronger than in 2015. This study analyzed the 2015 and 2018 CMOR data in order to determine the orbital parameters of the stream in an attempt to determine the γ Lyrid parent body. Of primary importance is to determine if the γ Lyrids will recur in a predictable manner. Two bodies, with dramatically different orbital parameters and evolutionary behaviors, emerged as the leading candidates: 2003 EH1 and 1854 R1.

Grazier, Kevin R.↗
NASA NTRS2019

Utilizing Convolutional Neural Networks for Global Seagrass Habitat Mapping

Convolutional neural networks (CNNs) are becoming an increasingly prevalent machine learning algorithm due to their high accuracy and lack of reliance on heuristic processes. One of the major drawbacks of convolutional neural networks is their reliance on large amounts of training data in order to generate sensible results. This talk will cover how our team has utilized the strengths and overcome the weaknesses of convolutional neural networks as they apply to seagrass habitat mapping. We will share our technical CNN results over time, detail the requirements and challenges that our team overcame and explore how other teams can better incorporate a stronger seagrass component into their machine learning projects.

Convolutional↗
NASA NTRS2020

GPU Supported Simulation of Transition-edge Sensor Arrays

We present numerical simulations of full transition-edge sensor (TES) arrays utilizing graphical processing units (GPUs). With the support of GPUs, it is possible to perform simulations of large pixel arrays to assist detector development. Comparisons with TES small-signal and noise theory confirm the representativity of the simulated data. In order to demonstrate the capabilities of this approach, we present its implementation in xifusim, a simulator for the X-ray Integral Field Unit, a cryogenic X-ray spectrometer on board the future Athena X-ray observatory.

M Lorenz↗
NASA NTRSDate not supplied

Regional Variations in the Diurnal Cycle of Tropical Precipitation as Represented by IMERG, ERA5, and Spaceborne Ku Radar

The diurnal cycle of precipitation is highly regional and is typically a product of multiple competing effects that can be highly localized. The diurnal cycle in high precipitation regions such as the Amazon and the Maritime Continent are of particular interest, especially due to the complex coastal effects which take place over the Maritime Continent. The high spatial and temporal resolution provided by the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (GPM) mission (IMERG) dataset, is used in this study to examine the fine-scale features of the diurnal cycle in these regions. Using an 18-year (2000 – 2018) record of IMERG precipitation observations, diurnal and semidiurnal phase and amplitude are calculated using a fast Fourier transform (FFT) method on precipitation averaged for each half-hour of the day at 0.1°x0.1° spatial resolution. We first introduce an objective method of identifying locations where the diurnal signals are robust and strong. Clear patterns of precipitation phase propagation with distance from shore are shown over both regions, with the diurnal phase and amplitude exhibiting a strong dependence on the distance from the coastline. Semidiurnal cycles are generally weaker than the diurnal cycle except in some isolated locations. Similar analysis is also conducted on the ERA5 reanalysis data in order to evaluate the model’s representation of the precipitation diurnal cycle. The model captures the broad scale patterns of diurnal variability but does not capture all the fine scale patterns nor the exact timing that is observed by IMERG. Comparisons are also made to a long record Ku radar dataset created by combining Tropical Rainfall Measuring Mission (TRMM) and GPM observations, thus providing an additional point of comparison for the timing of the ERA5 precipitation peak, since the timing precipitation can be different, even in between observational datasets.

L J Hayden↗
NASA NTRSDate not supplied

Enabling Space Biological Knowledge Discovery Through Image and Video Data Sharing

Increased biomedical risks and challenges associated with deep space missions and experiments (cis-Lunar, Mars transit/surface) require new knowledge discovery and development of novel ecosystems. Supporting distant and long-duration missions and experiments requires biological data (from yeast, microbes, fruit flies, C. elegans, plants, crops, rodents, humans) be findable, accessible, interoperable, reusable (FAIR), and maximally open-access. As data-intensive, bioinformatic, meta-analytical, and computer-assisted approaches continue to be a centerpiece of modern research, the NASA Biological and Physical Sciences division is expanding its Open Science capabilities beyond NASA GeneLab. The NASA Ames Life Sciences Data Archive (ALSDA) is a repository which is responsible for collecting and access to space biological imagery and video, alongside tabular and environmental data. In this presentation, we will discuss strategies dealing with archiving, curating, and accessibility of images from very distinct imaging modalities (e.g., micro-computed tomography, magnetic resonance imaging, photographic images of plants, fluorescence microscopy, behavioral videos, etc.). There are two main challenges: 1. Open-source data storage and 2. Metadata related to the imagery-video. Both have been solved by leveraging two existing open-source systems. For data storage, ALSDA is utilizing components through the Open Microscopy Environment (OME), which can read most imaging proprietary formats and display on a web interface complex multidimensional images (Z stack, multi-channel, temporal, spectral). Most technical metadata from imaging modalities are captured seamlessly. For metadata capturing experimental details, ALSDA (like GeneLab) uses the ISA-Tab specification which relies on the ISA data model to order and classify metadata. The ISA data model uses a tree structure with three files to capture the metadata: The top layer is the Investigations file, the second layer is the Study file(s), and the last layer is the Assay file(s). We believe such an approach may be useful for other types of image research data from other investigators in the AGU community.

imaging↗
NASA NTRSDate not supplied

Using Optical Lightning Detection Data from the 2017 GOES-R Post Launch Test Field Campaign Flights as an Evaluation of Convective Processes

The primary mechanisms for thunderstorm electrification and lightning production are widely understood and allow connections between lightning intensity and occurrence to be used to derive storm strength and forecast the probability of related weather phenomena. However, these assumed relationships between lightning and convective properties were discovered primarily using ground-based lightning sensors and measurements, meaning the true depth of insight from space or air based detections is widely unexplored. In 2017, a field campaign was launched to validate the instrumentation aboard the GOES-16 satellite. It involved flying a NASA-ER2 aircraft over multiple storms across the continental US and it included the Fly’s Eye GLM Simulator (FEGS) as a proxy for the Geostationary Lightning Mapper (GLM). Utilizing the data from this field campaign, one of the main goals of this project is to investigate how attributes of convection impact optical lightning detection and the subsequent assumptions that can be made in relation to convective dynamics and precipitation microphysics. Hydrometeor identification, 3D winds retrieval, and additional radar and LiDAR data analysis will be utilized alongside FEGS and interferometer data in order to observe these relationships. This presentation will be to lay out the initial conclusions from our analysis and to explore any new insights or contradictions within our findings.

Kaitlyn Wheeler↗
NASA NTRS1987

Edge detection - Image-plane versus digital processing

To optimize edge detection with the familiar Laplacian-of-Gaussian operator, it has become common to implement this operator with a large digital convolution mask followed by some interpolation of the processed data to determine the zero crossings that locate edges. It is generally recognized that this large mask causes substantial blurring of fine detail. It is shown that the spatial detail can be improved by a factor of about four with either the Wiener-Laplacian-of-Gaussian filter or an image-plane processor. The Wiener-Laplacian-of-Gaussian filter minimizes the image-gathering degradations if the scene statistics are at least approximately known and also serves as an interpolator to determine the desired zero crossings directly. The image-plane processor forms the Laplacian-of-Gaussian response by properly combining the optical design of the image-gathering system with a minimal three-by-three lateral-inhibitory processing mask. This approach, which is suggested by Marr's model of early processing in human vision, also reduces data processing by about two orders of magnitude and data transmission by up to an order of magnitude.

Huck, Friedrich O.↗
NASA NTRS2014

Climate Informatics: Accelerating Discovering in Climate Science with Machine Learning

The goal of climate informatics, an emerging discipline, is to inspire collaboration between climate scientists and data scientists, in order to develop tools to analyze complex and ever-growing amounts of observed and simulated climate data, and thereby bridge the gap between data and understanding. Here, recent climate informatics work is presented, along with details of some of the field's remaining challenges. Given the impact of climate change, understanding the climate system is an international priority. The goal of climate informatics is to inspire collaboration between climate scientists and data scientists, in order to develop tools to analyze complex and ever-growing amounts of observed and simulated climate data, and thereby bridge the gap between data and understanding. Here, recent climate informatics work is presented, along with details of some of the remaining challenges.

climate change↗
NASA NTRS1999

Design Description of the X-33 Avionics Architecture

In this paper, we provide a design description of the X-33 avionics architecture. The X-33 is an autonomous Single Stage to Orbit (SSTO) launch vehicle currently being developed by Lockheed Martin for NASA as a technology demonstrator for the VentureStar Reusable Launch Vehicle (RLV). The X-33 avionics provides autonomous control of die vehicle throughout takeoff, ascent, descent, approach, landing, rollout, and vehicle safing. During flight the avionics provides communication to the range through uplinked commands and downlinked telemetry. During pre-launch and post-safing activities, the avionics provides interfaces to ground support consoles that perform vehicle flight preparations and maintenance. The X-33 Avionics is a hybrid of centralized and distributed processing elements connected by three dual redundant Mil-Std 1553 data buses. These data buses are controlled by a central processing suite located in the avionics bay and composed of triplex redundant Vehicle Mission Computers (VMCs). The VMCs integrate mission management, guidance, navigation, flight control, subsystem control and redundancy management functions. The vehicle sensors, effectors and subsystems are interfaced directly to the centralized VMCs as remote terminals or through dual redundant Data Interface Units (DIUs). The DIUs are located forward and aft of the avionics bay and provide signal conditioning, health monitoring, low level subsystem control and data interface functions. Each VMC is connected to all three redundant 1553 data buses for monitoring and provides a complete identical data set to the processing algorithms. This enables bus faults to be detected and reconfigured through a voted bus control configuration. Data is also shared between VMCs though a cross channel data link that is implemented in hardware and controlled by AlliedSignal's Fault Tolerant Executive (FTE). The FTE synchronizes processors within the VMC and synchronizes redundant VMCs to each other. The FTE provides an output-voting plane to detect, isolate and contain faults due to internal hardware or software faults and reconfigures the VMCs to accommodate these faults. Critical data in the 1553 messages are scheduled and synchronized to specific processing frames in order to minimize data latency. In order to achieve an open architecture, military and commercial off-the-shelf equipment is incorporated using common processors, standard VME backplanes and chassis, the VxWorks operating system, and MartixX for automatic code generation. The use of off-the-shelf tools and equipment helps reduce development time and enables software reuse. The open architecture allows for technology insertion, while the distributed modular elements allow for expansion to increased redundancy levels to meet the higher reliability goals of future RLVs.

Reichenfeld, Curtis J.↗
NASA NTRS1996

Analysis of the access patterns at GSFC distributed active archive center

The Goddard Space Flight Center (GSFC) Distributed Active Archive Center (DAAC) has been operational for more than two years. Its mission is to support existing and pre Earth Observing System (EOS) Earth science datasets, facilitate the scientific research, and test Earth Observing System Data and Information System (EOSDIS) concepts. Over 550,000 files and documents have been archived, and more than six Terabytes have been distributed to the scientific community. Information about user request and file access patterns, and their impact on system loading, is needed to optimize current operations and to plan for future archives. To facilitate the management of daily activities, the GSFC DAAC has developed a data base system to track correspondence, requests, ingestion and distribution. In addition, several log files which record transactions on Unitree are maintained and periodically examined. This study identifies some of the users' requests and file access patterns at the GSFC DAAC during 1995. The analysis is limited to the subset of orders for which the data files are under the control of the Hierarchical Storage Management (HSM) Unitree. The results show that most of the data volume ordered was for two data products. The volume was also mostly made up of level 3 and 4 data and most of the volume was distributed on 8 mm and 4 mm tapes. In addition, most of the volume ordered was for deliveries in North America although there was a significant world-wide use. There was a wide range of request sizes in terms of volume and number of files ordered. On an average 78.6 files were ordered per request. Using the data managed by Unitree, several caching algorithms have been evaluated for both hit rate and the overhead ('cost') associated with the movement of data from near-line devices to disks. The algorithm called LRU/2 bin was found to be the best for this workload, but the STbin algorithm also worked well.

Johnson, Theodore↗
NASA NTRS1978

Rotorcraft system identification techniques for handling qualities and stability and control evaluation

An integrated approach to rotorcraft system identification is described. This approach consists of sequential application of (1) data filtering to estimate states of the system and sensor errors, (2) model structure estimation to isolate significant model effects, and (3) parameter identification to quantify the coefficient of the model. An input design algorithm is described which can be used to design control inputs which maximize parameter estimation accuracy. Details of each aspect of the rotorcraft identification approach are given. Examples of both simulated and actual flight data processing are given to illustrate each phase of processing. The procedure is shown to provide means of calibrating sensor errors in flight data, quantifying high order state variable models from the flight data, and consequently computing related stability and control design models.

Hall, W. E., Jr.↗
NASA NTRS1982

Mollier-I, S-Diagrams for Combustion Gases in Data Processing

In order to have all the thermal and caloric states of combustion gases accessible in a computer, closed mathematical approximation equations were established for the real factors, the enthalpy and the entropy of a real combustion gas. The equations approximate the various effects of molecular forces real gas influence and dissociation - at temperatures of 200 K to 6,000 K, pressures of 0.001 to 1,000 bar, and in the range from stoichiometric composition to air. A system of subprograms is listed in FORTRAN, by means of which thermodynamic calculations can be carried out in the same manner as with Mollier I,S diagrams.

Zacharias, F.↗
NASA NTRS1986

Constructing a coherent long-term global total ozone climatology from the BUV, MFR, and SBUV/TOMS data sets

Multichannel Filter Radiometer (MFR) data are intercompared with Solar Backscattered Ultraviolet (SBUV) and Total Ozone Mapping Spectrometer (TOMS) data during a five-month period of their data overlap in order to determine the usefulness of MFR data to represent the SBUV/TOMS and backscatter ultraviolet spectrometer data during their data gap period. Total ozone differences found were latitudinally and monthly dependent. Average differences between the TOMS and the MFR ozone data vary from -12.4 to +13.3 percent, with differences in the tropical to middle latitudes generally between + or - 4 percent. Biases between the data sets can be removed for certain latitude zones by applying an appropriate algorithm to the MFR ozone data.

Ellis, J. S.↗
NASA NTRS2004

RXTE, Chandra, and XMM Spectroscopy of the Fe-K Lines and Compton Reflection in Type 1 AGN

This award pertains to an RXTE observation of the Seyfert 1 galaxy Akn 120. The purpose of the observation was to measure the Fe-K emission line and the Compton reflection continuum with RXTE, simultaneously with Chandra and XMM. Such measurements can severely constrain accretion disk models of the central engine since the Fe-K line emission and Compton reflection are intimately related in terms of the physics of X-ray reprocessing in optically-thick matter. Akn 120 was selected for this study because it is amongst the brightest AGN in its class and has a particularly strong and apparently broad Fe-K emission line. The results could then also be used to lay the ground work for even higher resolution studies with Astro-E2. Unfortunately, the Chandra observation was not performed but a contemporaneous XMM observation was performed by another group of researchers. Those data recently became public and can be compared with the RXTE data. In addition, non-contemporaneous observations with other missions do still provide additional important constraints (for example any non-varying line or continuum emission components can be established and used to reject or preserve various model scenarios). We analyzed the RXTE data and found a strong Fe-K emission line (resolved even with the poor resolution of RXTE), and a strong Compton-reflection continuum (see Fig. l(a)). We found that the results of archival ASCA data on Akn 120 had not been published in the literature so we analyzed the ASCA data too, in order to compare with the new RXTE data. Fig. l(b) shows that the ASCA data also reveal a strong, broad FeK emission line (but the data are not sensitive to the Compton-reflection continuum). We compared our spectral fitting results for the RXTE and ASCA data with the results from XMM and from previous RXTE observations.

Yaqoob, Tahir↗