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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 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
NASA NTRS2014

Magnetospheric Multiscale Science Mission Profile and Operations

The Magnetospheric Multiscale (MMS) mission and operations are designed to provide the maximum reconnection science. The mission phases are chosen to investigate reconnection at the dayside magnetopause and in the magnetotail. At the dayside, the MMS orbits are chosen to maximize encounters with the magnetopause in regions where the probability of encountering the reconnection diffusion region is high. In the magnetotail, the orbits are chosen to maximize encounters with the neutral sheet, where reconnection is known to occur episodically. Although this targeting is limited by engineering constraints such as total available fuel, high science return orbits exist for launch dates over most of the year. The tetrahedral spacecraft formation has variable spacing to determine the optimum separations for the reconnection regions at the magnetopause and in the magnetotail. In the specific science regions of interest, the spacecraft are operated in a fast survey mode with continuous acquisition of burst mode data. Later, burst mode triggers and a ground-based scientist in the loop are used to determine the highest quality data to downlink for analysis. This operations scheme maximizes the science return for the mission.

Mission Design↗
NASA NTRS2016

Communicating with Light: A New Dawn in the Information Age

You and I are living in a very special time; the age of Solar System exploration. Our Solar System is a complex masterpiece of which we knew so little from our ground-based observations. But within the span of a single lifetime, NASA has sent spacecraft to every planet and several moons, our first eyes to set upon undiscovered lands. Before we endeavored on this journey everything we knew of Pluto could have fit on a single file card, and now we downlink new data every day.

Optical Communications↗
NASA NTRS2016

Initial Satellite Formation Flight Results from the Magnetospheric Multiscale Mission

This paper will describe the results that have been obtained to date concerning MMS formation flying. The MMS spacecraft spin at a rate of 3.1 RPM, with spin axis roughly aligned with Ecliptic North. Several booms are used to deploy instruments: two 5 m magnetometer booms in the spin plane, two rigid booms of length 12.5 m along the positive and negative spin axes, and four flexible wire booms of length 60 m in the spin plane. Minimizing flexible motion of the wire booms requires that reorientation of the spacecraft spin axis be kept to a minimum: this is limited to attitude maneuvers to counteract the effects of gravity-gradient and apparent solar motion. Orbital maneuvers must therefore be carried out in essentially the nominal science attitude. These burns make use of a set of monopropellant hydrazine thrusters: two (of thrust 4.5 N) along the spin axis in each direction, and eight (of thrust 18 N) in the spin plane; the latter are pulsed at the spin rate to produce a net delta-v. An on-board accelerometer-based controller is used to accurately generate a commanded delta-v. Navigation makes use of a weak-signal GPS-based system: this allows signals to be received even when MMS is flying above the GPS orbits, producing a highly accurate determination of the four MMS orbits. This data is downlinked to the MMS Mission Operations Center (MOC) and used by the MOC Flight Dynamics Operations Area (FDOA) for maneuver design. These commands are then uplinked to the spacecraft and executed autonomously using the controller, with the ground monitoring the burns in real time.

satellite↗
NASA NTRS2018

Application of Machine-Learning Algorithms for On-Board Asteroid Shape Model Determination

The Application of Machine-learning Algorithms for On-board Asteroid Shape Model Determination project will develop an innovative system for spacecraft navigation to expand the capability of small spacecraft to meet the critical challenges associated with small-body exploration. Such challenges include accurate navigation in a microgravity environment and precision targeting of particular locations on an asteroid surface for sample collection. This on-board system will cut the computational "umbilical" back to Earth-currently necessary for the generation of a global shape model that requires thousands of images with sufficient resolution and adequate variation of incidence and emission angles, processed manually by a team of experts on Earth for several months. Small satellites have limited bandwidth and are unable to downlink the data volume required for this processing, restricting their ability to perform deep-space asteroid exploration.

Machine learning algorithms↗
NASA NTRS2019

The Space Debris Sensor Experiment

The Space Debris Sensor (SDS) is a NASA Class 1E technology demonstration external payload aboard the International Space Station (ISS). With approximately one square meter of detection area, the SDS is attached to the European Space Agency Columbus module facing the ISS velocity vector with minimal obstruction from ISS hardware. The SDS is the first flight demonstration of the Debris Resistive/Acoustic Grid Orbital NASA-Navy Sensor (DRAGONS) technology developed and matured over 10 years by the NASA Orbital Debris Program Office (ODPO), in concert with the DRAGONS consortium, to provide information on the sub-millimeter scale orbital debris environment. The SDS demonstrated the capacity to read 4 resistive grids at 1 Hz, 40 acoustic sensors at 500 kHz, and record and downlink impact data to the ground. Observable and derived data from the SDS could provide information to models that are critical to understanding risks the small debris environment poses to spacecraft in low Earth orbit. The technology demonstrated by the SDS is a major step forward in monitoring and characterizing the space debris environment. This paper will address the technical performance of the SDS during its operational lifetime and its realization of technical and scientific goals. The SDS was intended to operate for 3 years; however, the payload incurred multiple anomalies during its operational life. Subsequently termed “Anomaly #1,” the first was the symptomatic loss of low data rate 1553 channel command and telemetry. The second, Anomaly #2, was loss of all low- and medium-data rate (Ethernet) telemetry. Anomaly #2 proved to be unrecoverable, leading to loss of the payload after approximately 26 days on-board the ISS. Therefore, this paper also addresses the anomalies that occurred during operation of the SDS, their attribution, and their resolution. Lessons learned are described when relevant to anomaly identification, attribution, and resolution.

Anz-Meador, P.↗
NASA NTRS1998

Principal Investigator Microgravity Services: USMP-3

Two accelerometers provided acceleration data during the STS-75 mission in support of the third United States Microgravity Payload (USMP-3) experiments. The Orbital Acceleration Research Experiment (OARE) and the Space Acceleration Measurement System (SAMS) provided a measure of the microgravity environment of the Space Shuttle Columbia. The OARE provided investigators with quasi-steady acceleration measurements after about a six hour time lag dictated by downlink constraints. SAMS data were downlinked in near-real-time and recorded on-board for post-mission analysis. These two accelerometer systems are briefly described. Using a combination of data analysis techniques, the microgravity environment related to several different Orbiter, crew, and experiment operations is presented and interpreted. The microgravity environment represented by SAMS and OARE data is comparable to the environments measured by these instruments on earlier microgravity science missions. Thruster activity on this mission seen in the SAMS data appear to be more frequent than on other microgravity missions with the combined firings of the F5L and F5R jets producing significant acceleration transients. Of the events studied, the crew activities performed in the middeck and flight deck, SPREE table rotations, a waste collection system compaction, and a fuel cell purge had negligible effects on the microgravity environment of the USMP-3 carriers. The Ku band antenna repositioning activity resulted in a brief interruption of the ubiquitous 17 Hz signal in the SAMS data. In addition, the auxiliary power unit operations during the Flight Control System checkout appeared to have a significant impact on the microgravity environment.

Rogers, Melissa J. B.↗
NASA NTRS1996

Summary Report of Mission Acceleration Measurements for STS-75, Launched February 22, 1996

Two accelerometers provided acceleration data during the STS-75 mission in support of the third United States Microgravity Payload (USMP-3) experiments. The Orbital Acceleration Research Experiment (OARE) and the Space Acceleration Measurement System (SAMS) provided a measure of the microgravity environment of the Space Shuttle Columbia. The OARE provided investigators with quasi-steady acceleration measurements after about a six hour time lag dictated by downlink constraints. SAMS data were downlinked in near-real-time and recorded on-board for post-mission analysis. An overview of the mission is provided as are brief discussions of these two accelerometer systems. Data analysis techniques used to process SAMS and OARE data are discussed Using a combination of these techniques, the microgravity environment related to several different Orbiter, crew, and experiment operations is presented and interpreted. The microgravity environment represented by SAMS and OARE data is comparable to the environments measured by the instruments on earlier microgravity science missions. The OARE data compared well with predictions of the quasi-steady environment. The SAMS data show the influence of thruster firings and crew motion (transient events) and of crew exercise, Orbiter systems, and experiment operations (oscillatory events). Thruster activity on this mission appears to be somewhat more frequent than on other microgravity missions with the combined firings of the F5L and F5R jets producing significant acceleration transients. The specific crew activities performed in the middeck and flight deck, the SPREE table rotations, the waste collection system compaction, and the fuel cell purge had negligible effects on the microgravity environment of the USMP-3 carriers. The Ku band antenna repositioning activity resulted in a brief interruption of the ubiquitous 17 Hz signal in the SAMS data. In addition, the auxiliary power unit operations during the Flight Control System checkout appeared to have a significant impact on the microgravity environment.

Rogers, Melissa J. B.↗
NASA NTRS1994

Galileo spacecraft modeling for orbital operations

The Galileo Jupiter orbital mission using the Low Gain Antenna (LGA) requires a higher degree of spacecraft state knowledge than was originally anticipated. Key elements of the revised design include onboard buffering of science and engineering data and extensive processing of data prior to downlink. In order to prevent loss of data resulting from overflow of the buffers and to allow efficient use of the spacecraft resources, ground based models of the spacecraft processes will be implemented. These models will be integral tools in the development of satellite encounter sequences and the cruise/playback sequences where recorded data is retrieved.

Mclaughlin, Bruce A.↗
NASA NTRS1994

Galileo Spacecraft Modeling for Orbital Operations

The Galileo Jupiter orbital mission using the Low Gain Antenna (LGA) requires a higher degree of spacecraft state knowledge than was originally anticipated. Key elements of the revised design include onboard buffering of science and engineering data and extensive processing of data prior to downlink. In order to prevent loss of data resulting from overflow of the buffers and to allow efficient use of the spacecraft resources, ground based models of the spacecraft processes will be implemented. These models will be integral tools in the development of satellite encounter sequences and the cruise/playback sequences where recorded data is retrieved.

aerospace↗
NASA NTRS2006

Solving the Swath Segment Selection Problem

Several artificial-intelligence search techniques have been tested as means of solving the swath segment selection problem (SSSP) -- a real-world problem that is not only of interest in its own right, but is also useful as a test bed for search techniques in general. In simplest terms, the SSSP is the problem of scheduling the observation times of an airborne or spaceborne synthetic-aperture radar (SAR) system to effect the maximum coverage of a specified area (denoted the target), given a schedule of downlinks (opportunities for radio transmission of SAR scan data to a ground station), given the limit on the quantity of SAR scan data that can be stored in an onboard memory between downlink opportunities, and given the limit on the achievable downlink data rate. The SSSP is NP complete (short for "nondeterministic polynomial time complete" -- characteristic of a class of intractable problems that can be solved only by use of computers capable of making guesses and then checking the guesses in polynomial time).

Knight, Russell↗
NASA NTRS1999

STS-99 Crew Interviews: Janet L. Kavandi

This NASA JSC video release is one in a series of space shuttle astronaut interviews and was recorded Aug. 9, 1999. Mission Specialist, Janet L. Kavandi, Ph.D. provides answers to questions regarding her role in the Shuttle Radar Topography Mission (SRTM), mission objectives, which center on the three-dimensional mapping of the entire Earth's surface, shuttle imaging radar, payload mast deploy and retraction, data recording vs. downlinking, the fly cast maneuver, applications of recorded data, international participation (DLR), the National Imaging and Mapping Agency (NIMA), and EarthCam (educational middle school project). The interview is summed up by Dr. Kavandi explaining that the mission's objective, if successful, will result in the the most complete high-resolution digital topographic database of the Earth.

Source record↗
NASA NTRS2012

Fast and Adaptive Lossless Onboard Hyperspectral Data Compression System

Modern hyperspectral imaging systems are able to acquire far more data than can be downlinked from a spacecraft. Onboard data compression helps to alleviate this problem, but requires a system capable of power efficiency and high throughput. Software solutions have limited throughput performance and are power-hungry. Dedicated hardware solutions can provide both high throughput and power efficiency, while taking the load off of the main processor. Thus a hardware compression system was developed. The implementation uses a field-programmable gate array (FPGA). The implementation is based on the fast lossless (FL) compression algorithm reported in Fast Lossless Compression of Multispectral-Image Data (NPO-42517), NASA Tech Briefs, Vol. 30, No. 8 (August 2006), page 26, which achieves excellent compression performance and has low complexity. This algorithm performs predictive compression using an adaptive filtering method, and uses adaptive Golomb coding. The implementation also packetizes the coded data. The FL algorithm is well suited for implementation in hardware. In the FPGA implementation, one sample is compressed every clock cycle, which makes for a fast and practical realtime solution for space applications. Benefits of this implementation are: 1) The underlying algorithm achieves a combination of low complexity and compression effectiveness that exceeds that of techniques currently in use. 2) The algorithm requires no training data or other specific information about the nature of the spectral bands for a fixed instrument dynamic range. 3) Hardware acceleration provides a throughput improvement of 10 to 100 times vs. the software implementation. A prototype of the compressor is available in software, but it runs at a speed that does not meet spacecraft requirements. The hardware implementation targets the Xilinx Virtex IV FPGAs, and makes the use of this compressor practical for Earth satellites as well as beyond-Earth missions with hyperspectral instruments.

Aranki, Nazeeh I.↗
NASA NTRS2021

Automated Data Accountability for Missions in Mars Rover Data

As the Mars Curiosity Rover transmits data to the JPL Ground Data System (GDS), it frequently observes data loss and corruption, requiring re-transmits from the rover and Ground Data System Analysts (GDSA) to monitor the downlink process. As new missions are launched, the GDSA team redistributes analysts to these new missions, causing shortages in previous missions. The GDSA team can significantly benefit from the automation and optimization of the downlink process of telemetry data. In fact, there is a need for a better understanding of why the data is corrupted, so that the GDSA team can best determine the root cause of the issues in the GDS. This paper presents machine learning and deep learning based approaches to automate and optimize the detection of data loss. We first created a pipeline to automatically accumulate data from the telemetry databases (MAROS, Telemetry Data Storage, and GDS Elastic Search Database) in the downlink process. With our newly created datasets, we perform feature selection to supplement the GDSA understanding of the downlink process and provide supplemental analysis on the importance of different features. We implement various machine learning and deep learning based models, including support vector machines, ensemble methods, and deep neural networks and evaluate their accuracies in identifying whether a downlink process is complete or incomplete. We utilize fast hyperparameter optimization methods that allow our models to quickly be re-trained, allowing them to quickly be tuned and optimized on daily incoming data in real time. This hyperparameter optimization also allows our methods to be quickly integrated into other JPL missions. Our results show that our best-performing machine learning and deep learning based models outperform the existing GDSA detection software by 6 accuracy points and can aid analysts by providing insights into the data accountability problem. Since these various machine learning and deep learning approaches vary significantly in interpretability, we provide a discussion on the tradeoffs between their performance and trustworthiness in helping detect issues in data transmission.

Divsalar, Dariush↗
NASA NTRS2012

Onboard Science and Applications Algorithm for Hyperspectral Data Reduction

An onboard processing mission concept is under development for a possible Direct Broadcast capability for the HyspIRI mission, a Hyperspectral remote sensing mission under consideration for launch in the next decade. The concept would intelligently spectrally and spatially subsample the data as well as generate science products onboard to enable return of key rapid response science and applications information despite limited downlink bandwidth. This rapid data delivery concept focuses on wildfires and volcanoes as primary applications, but also has applications to vegetation, coastal flooding, dust, and snow/ice applications. Operationally, the HyspIRI team would define a set of spatial regions of interest where specific algorithms would be executed. For example, known coastal areas would have certain products or bands downlinked, ocean areas might have other bands downlinked, and during fire seasons other areas would be processed for active fire detections. Ground operations would automatically generate the mission plans specifying the highest priority tasks executable within onboard computation, setup, and data downlink constraints. The spectral bands of the TIR (thermal infrared) instrument can accurately detect the thermal signature of fires and send down alerts, as well as the thermal and VSWIR (visible to short-wave infrared) data corresponding to the active fires. Active volcanism also produces a distinctive thermal signature that can be detected onboard to enable spatial subsampling. Onboard algorithms and ground-based algorithms suitable for onboard deployment are mature. On HyspIRI, the algorithm would perform a table-driven temperature inversion from several spectral TIR bands, and then trigger downlink of the entire spectrum for each of the hot pixels identified. Ocean and coastal applications include sea surface temperature (using a small spectral subset of TIR data, but requiring considerable ancillary data), and ocean color applications to track biological activity such as harmful algal blooms. Measuring surface water extent to track flooding is another rapid response product leveraging VSWIR spectral information.

Chien, Steve A.↗
NASA NTRS2019

The Seismic Experiment for Interior Structure (SEIS): Experiment Data Distribution

The six sensors of SEIS (The Seismic Experiment for Interior Structure) [- one of three primary instruments on NASA's Mars Lander Insight] cover a broad range of the seismic bandwidth, from 0.01 hertz to 50 hertz, with possible extension to longer periods. Data are transmitted in the form of three continuous VBB (Very Broad-Band) components at 2 samples per second (sps), an estimation of the short period (SP) energy content from the SP at 1 sps, and a continuous compound VBB/SP vertical axis at 10 sps. The continuous streams are augmented by requested event data with sample rates from 20 to 100 sps. SEIS data products are downlinked from the spacecraft in raw CCSDS (Consultative Committee for Space Data Systems) packets and converted to both the Standard for the Exchange of Earthquake Data (SEED) format files and ASCII tables (GeoCSV) for analysis and archiving. Metadata are available in dataless SEED and StionXML. Time series data (waveforms) are available in miniseed and GeoCSV. Data are distributed according to FDSN (Federation of Digital Seismograph Networks - http://www.fdsn.org) formats and interfaces. Wind, pressure and temperature data from the Auxiliary Payload Sensor Suite (APSS) will also be available in SEED format, and can be used for decorrelation and diagnostic purposes on SEIS.

Pardo, Constanza↗
NASA NTRS2006

Managing the On-Board Data Storage, Acknowledgement and Retransmission System for Spitzer

The Spitzer Space Telescope has a two-phase downlink system. Recorded data are transmitted during one telecom session. Then commands are sent during the next session to delete those data that were received on the ground and to retransmit those data that were missed. We must build science sequences that are as efficient as possible to make the best use of our supply of liquid helium. One way to improve efficiency is to use only the minimum time needed during telecom sessions to transmit the predicted volume of data. But, we must also not fill the on-board storage and must allow enough time margin to retransmit missed data. We describe tools and procedures that allow us to build science sequences that are single-fault tolerant in this regard and that allow us to recover quickly and safely from anomalies that affect the receipt or acknowledgment (i.e. deletion) of data.

on-board storage↗
NASA NTRS2005

Generation and Performance of Automated Jarosite Mineral Detectors for Vis/NIR Spectrometers at Mars

Sulfate salt discoveries at the Eagle and Endurance craters in Meridiani Planum by the Mars Exploration Rover Opportunity have proven mineralogically the existence and involvement of water in Mars past. Visible and near infrared spectrometers like the Mars Express OMEGA, the Mars Reconnaissance Orbiter CRISM and the 2009 Mars Science Laboratory Rover cameras are powerful tools for the identification of water-bearing salts and other high priority minerals at Mars. The increasing spectral resolution and rover mission lifetimes represented by these missions currently necessitate data compression in order to ease downlink restrictions. On board data processing techniques can be used to guide the selection, measurement and return of scientifically important data from relevant targets, thus easing bandwidth stress and increasing scientific return. We have developed an automated support vector machine (SVM) detector operating in the visible/near-infrared (VisNIR, 300-2500 nm) spectral range trained to recognize the mineral jarosite (typically KFe3(SO4)2(OH)6), positively identified by the Mossbauer spectrometer at Meridiani Planum. Additional information is included in the original extended abstract.

Gilmore, M. S.↗
NASA NTRS2016

Biomolecule Sequencer: Next-Generation DNA Sequencing Technology for In-Flight Environmental Monitoring, Research, and Beyond

On the International Space Station (ISS), technologies capable of rapid microbial identification and disease diagnostics are not currently available. NASA still relies upon sample return for comprehensive, molecular-based sample characterization. Next-generation DNA sequencing is a powerful approach for identifying microorganisms in air, water, and surfaces onboard spacecraft. The Biomolecule Sequencer payload, manifested to SpaceX-9 and scheduled on the Increment 4748 research plan (June 2016), will assess the functionality of a commercially-available next-generation DNA sequencer in the microgravity environment of ISS. The MinION device from Oxford Nanopore Technologies (Oxford, UK) measures picoamp changes in electrical current dependent on nucleotide sequences of the DNA strand migrating through nanopores in the system. The hardware is exceptionally small (9.5 x 3.2 x 1.6 cm), lightweight (120 grams), and powered only by a USB connection. For the ISS technology demonstration, the Biomolecule Sequencer will be powered by a Microsoft Surface Pro3. Ground-prepared samples containing lambda bacteriophage, Escherichia coli, and mouse genomic DNA, will be launched and stored frozen on the ISS until experiment initiation. Immediately prior to sequencing, a crew member will collect and thaw frozen DNA samples, connect the sequencer to the Surface Pro3, inject thawed samples into a MinION flow cell, and initiate sequencing. At the completion of the sequencing run, data will be downlinked for ground analysis. Identical, synchronous ground controls will be used for data comparisons to determine sequencer functionality, run-time sequence, current dynamics, and overall accuracy. We will present our latest results from the ISS flight experiment the first time DNA has ever been sequenced in space and discuss the many potential applications of the Biomolecule Sequencer for environmental monitoring, medical diagnostics, higher fidelity and more adaptable Space Biology Human Research Program investigations, and even life detection experiments for astrobiology missions.

Sequencer↗