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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 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
NASA NTRS1994

(abstract) Science-Project Interaction in the Low-Cost Mission

Large, complex, and highly optimized missions have performed most of the preliminary reconnaisance of the solar system. As a result we have now mapped significant fractions of its total surface (or surface-equivalent) area. Now, however, scientific exploration of the solar system is undergoing a major change in scale, and existing missions find it necessary to limit costs while fulfilling existing goals. In the future, NASA's Discovery program will continue the reconnaisance, exploration, and diagnostic phases of planetary research using lower cost missions, which will include lower cost mission operations systems (MOS). Historically, one of the more expensive functions of MOS has been its interaction with the science community. Traditional MOS elements that this interaction have embraced include mission planning, science (and engineering) event conflict resolution, sequence optimization and integration, data production (e.g., assembly, enhancement, quality assurance, documentation, archive), and other science support services. In the past, the payoff from these efforts has been that use of mission resources has been highly optimized, constraining resources have been generally completely consumed, and data products have been accurate and well documented. But because these functions are expensive we are now challenged to reduce their cost while preserving the benefits. In this paper, we will consider ways of revising the traditional MOS approach that might save project resources while retaining a high degree of service to the Projects' customers. Pre-launch, science interaction can be made simplier by limiting numbers of instruments and by providing greater redundancy in mission plans. Post launch, possibilities include prioritizing data collection into a few categories, easing requirements on real-time of quick-look data delivery, and closer integration of scientists into the mission operation.

mission operation science community interaction co↗
NASA NTRS2013

NASA Tech Briefs, March 2013

Topics covered include: Remote Data Access with IDL Data Compression Algorithm Architecture for Large Depth-of-Field Particle Image Velocimeters Vectorized Rebinning Algorithm for Fast Data Down-Sampling Display Provides Pilots with Real-Time Sonic-Boom Information Onboard Algorithms for Data Prioritization and Summarization of Aerial Imagery Monitoring and Acquisition Real-time System (MARS) Analog Signal Correlating Using an Analog-Based Signal Conditioning Front End Micro-Textured Black Silicon Wick for Silicon Heat Pipe Array Robust Multivariable Optimization and Performance Simulation for ASIC Design; Castable Amorphous Metal Mirrors and Mirror Assemblies; Sandwich Core Heat-Pipe Radiator for Power and Propulsion Systems; Apparatus for Pumping a Fluid; Cobra Fiber-Optic Positioner Upgrade; Improved Wide Operating Temperature Range of Li-Ion Cells; Non-Toxic, Non-Flammable, -80 C Phase Change Materials; Soft-Bake Purification of SWCNTs Produced by Pulsed Laser Vaporization; Improved Cell Culture Method for Growing Contracting Skeletal Muscle Models; Hand-Based Biometric Analysis; The Next Generation of Cold Immersion Dry Suit Design Evolution for Hypothermia Prevention; Integrated Lunar Information Architecture for Decision Support Version 3.0 (ILIADS 3.0); Relay Forward-Link File Management Services (MaROS Phase 2); Two Mechanisms to Avoid Control Conflicts Resulting from Uncoordinated Intent; XTCE GOVSAT Tool Suite 1.0; Determining Temperature Differential to Prevent Hardware Cross-Contamination in a Vacuum Chamber; SequenceL: Automated Parallel Algorithms Derived from CSP-NT Computational Laws; Remote Data Exploration with the Interactive Data Language (IDL); Mixture-Tuned, Clutter Matched Filter for Remote Detection of Subpixel Spectral Signals; Partitioned-Interval Quantum Optical Communications Receiver; and Practical UAV Optical Sensor Bench with Minimal Adjustability.

Source record↗
NASA NTRS1985

The handling of data in solar-terrestrial and in planetary physics

The evolution of data handling systems for solar-terrestrial and planetary physics applications is discussed. The main elements of data management systems for scientific satellites are described, including: command and control; data transmission; prioritization of data users; and data reduction and analysis. The design approaches incorporated into the data management systems of the Interplanetary Monitoring Platform (IMP), International Sun Earth Explorer (ISEE), and the Orbiting Geophysical Observatory, (OGO) spacecraft, are discussed. Emphasis is given to classical, centralized, and hybrid system designs. The report of the Committee on Data Management and Computation (CODMAC) concerning concepts for new data management systems is briefly summarized.

Vette, J. I.↗
NASA NTRS1994

A parallel strategy for implementing real-time expert systems using CLIPS

As evidenced by current literature, there appears to be a continued interest in the study of real-time expert systems. It is generally recognized that speed of execution is only one consideration when designing an effective real-time expert system. Some other features one must consider are the expert system's ability to perform temporal reasoning, handle interrupts, prioritize data, contend with data uncertainty, and perform context focusing as dictated by the incoming data to the expert system. This paper presents a strategy for implementing a real time expert system on the iPSC/860 hypercube parallel computer using CLIPS. The strategy takes into consideration not only the execution time of the software, but also those features which define a true real-time expert system. The methodology is then demonstrated using a practical implementation of an expert system which performs diagnostics on the Space Shuttle Main Engine (SSME). This particular implementation uses an eight node hypercube to process ten sensor measurements in order to simultaneously diagnose five different failure modes within the SSME. The main program is written in ANSI C and embeds CLIPS to better facilitate and debug the rule based expert system.

Ilyes, Laszlo A.↗
NASA NTRS2011

Telemedicine in Space Flight - Summary of a NASA Workshop

The Exploration Medical Capability Element of the Human Research Program at NASA Johnson Space Center hosted the Telemedicine Workshop in January 2011 to discuss the medical operational concept for a crewed mission to a near-Earth asteroid (NEA) and to identify areas for future work and collaboration. With the increased likelihood of a medical incident on a long duration exploration mission to a near-Earth asteroid, as well as the fact that there will likely be limited medical capabilities and resources available to diagnose and treat medical conditions, it is anticipated that a more structured use of telemedicine will become highly desirable. The workshop was convened to solicit expert opinion on current telemedicine practices and on medical care in remote environments. Workshop Objectives: The workshop brought together leaders in telemedicine and remote medicine from The University of Texas Medical Branch, Henry Ford Hospital, Ontario Telemedicine Network, U.S. Army Institute of Surgical Research, University of Miami, American Telemedicine Association, Doctors Without Borders, and the Pan American Health Organization. The primary objectives of the workshop were to document the medical operations concept for a crewed mission to a NEA, to determine gaps between current capabilities and the capabilities outlined in the operations concept, to identify research required to close these gaps, and to discuss potential collaborations with external-to-NASA organizations with similar challenges. Summary of Discussions and Conclusions: The discussions held during the workshop and the conclusions reached by the workshop participants were grouped into seven categories: Crew Medical Officers, Patient Area in Spacecraft, Training, Electronic Medical Records, Intelligent Care Systems, Consultation Protocols, Prophylactic Surgical Procedures, and Data Prioritization. The key points discussed under each category will be presented.

Barsten, K. N.↗
NASA NTRS2015

Magnetospheric Multiscale Instrument Suite Operations and Data System

The four Magnetospheric Multiscale (MMS) spacecraft will collect a combined volume of approximately 100 gigabits per day of particle and field data. On average, only 4 gigabits of that volume can be transmitted to the ground. To maximize the scientific value of each transmitted data segment, MMS has developed the Science Operations Center (SOC) to manage science operations, instrument operations, and selection, downlink, distribution, and archiving of MMS science data sets. The SOC is managed by the Laboratory for Atmospheric and Space Physics (LASP) in Boulder, Colorado and serves as the primary point of contact for community participation in the mission. MMS instrument teams conduct their operations through the SOC, and utilize the SOC's Science Data Center (SOC) for data management and distribution. The SOC provides a single mission data archive for the housekeeping and science data, calibration data, ephemerides, attitude and other ancillary data needed to support the scientific use and interpretation. All levels of data products will reside at and be publicly disseminated from the SDC. Documentation and metadata describing data products, algorithms, instrument calibrations, validation, and data quality will be provided. Arguably, the most important innovation developed by the SOC is the MMS burst data management and selection system. With nested automation and 'Scientist-in-the-Loop' (SITL) processes, these systems are designed to maximize the value of the burst data by prioritizing the data segments selected for transmission to the ground. This paper describes the MMS science operations approach, processes and data systems, including the burst system and the SITL concept.

Baker, D. N.↗
NASA NTRS2003

Rover traverse science for increased mission science return

In this paper, we will describe out methods for the prioritization of geologic data acquired by an in-situ rover. Our techniques are applicable to a wide range of data modalites, however out initial demonstration is focused on image analysis, as images consume a large volume of the downlink bandwidth for such missions.

data prioritization↗
NASA NTRS1985

Mission-oriented requirements for updating MIL-H-8501: Calspan proposed structure and rationale

This report documents the effort by Arvin/Calspan Corporation to formulate a revision of MIL-H-8501A in terms of Mission-Oriented Flying Qualities Requirements for Military Rotorcraft. Emphasis is placed on development of a specification structure which will permit addressing Operational Missions and Flight Phases, Flight Regions, Classification of Required Operational Capability, Categorization of Flight Phases, and Levels of Flying Qualities. A number of definitions is established to permit addressing the rotorcraft state, flight envelopes, environments, and the conditions under which degraded flying qualities are permitted. Tentative requirements are drafted for Required Operational Capability Class 1. Also included is a Background Information and Users Guide for the draft specification structure proposed for the MIL-H-8501A revision. The report also contains a discussion of critical data gaps and attempts to prioritize these data gaps and to suggest experiments that should be performed to generate data needed to support formulation of quantitative design criteria for the additional Operational Capability Classes 2, 3, and 4.

Chalk, C. R.↗
NASA NTRS2006

Spacecraft Data and Relay Management using Delay Tolerant Networking

NASA's demonstration of the successful transmission of relay data through the orbiting Mars Odyssey, Mars Global Surveyor, and Mars Express by the Mars Exploration Rovers has shown not only the benefit of using a relay satellite for multiple landed assets in a deep space environment but also the benefit of international standards for such architecture. As NASA begins the quest defined in the Vision for Exploration with robotic and manned missions to the Moon, continues its study of Mars, and is joined in these endeavors by countries world-wide, landed assets transmitting data through relay satellites will be crucial for completing mission objectives. However, this method of delivery of data will result in increased complexity in routing and prioritization of data transmission as the number of missions increases. Also, there is currently no standard method among organizations conducting such missions to return these data sets to Earth given a complex environment. One possibility for establishing such a standard is for mission designers to deploy protocols which fall under the umbrella of Delay Tolerant Networking (DTN). These developing standards include the Bundle Protocol (BP) which provides a standard, secure, store and forward mechanism designed for high latency and asymmetric communication links and the Licklider Transmission Protocol (LTP) which is used to provide a reliable deep space link transmission service.

network congestion control algorithm↗
NASA NTRS2003

An Automated Approach for Acquiring Onboard Rover Science

Rover traverse distances are increasing at a faster rate than downlink capacity is increasing. As this trend continues, the quantity of data that can be returned to Earth per meter traversed is reduced. The capacity of the rover to collect data, however, remains high. This circumstance leads to an opportunity to increase mission science return by carefully selecting the data with the highest science interest for downlink. We have developed an onboard science analysis technology for increasing science return from missions. Our technology evaluates the geologic data gathered by the rover. This analysis is used to prioritize the data for transmission, so that the data with the highest science value is transmitted to Earth. In addition, the onboard analysis results are used to identify additional science gathering opportunities. A planning and scheduling component of the system enables the rover to take advantage of the identified science opportunity.

Anderson, R. C.↗
NASA NTRS2006

Spacecraft data and relay management using delay tolerant networking

NASA’s demonstration of the successful transmission of relay data through the orbiting Mars Odyssey, Mars Global Surveyor, and Mars Express by the Mars Exploration Rovers has shown not only the benefit of using a relay satellite for multiple landed assets in a deep space environment but also the benefit of international standards for such an architecture. As NASA begins the quest defined in the Vision for Exploration with robotic and manned missions to the Moon, continues its study of Mars, and is joined in these endeavors by countries world-wide, landed assets transmitting data through relay satellites will be crucial for completing mission objectives. However, this method of data delivery will result in increased complexity in routing and prioritization of data transmission as the number of missions increases. Also, there is currently no standard method among organizations conducting such missions to return these data sets to Earth given a complex environment. One possibility for establishing such a standard is for mission designers to deploy protocols which fall under the umbrella of Delay Tolerant Networking (DTN). These developing standards include the Bundle Protocol (BP) which provides a standard, secure, store and forward mechanism designed for high latency and asymmetric communication links and the Licklider Transmission Protocol (LTP) which is used to provide a reliable deep space link transmission service.

Torgerson, J. Leigh↗
NASA NTRS2005

Science Alert Demonstration with a Rover Traverse Science Data Analysis System

The Onboard Autonomous Science Investigation System (OASIS) evaluates geologic data gathered by a planetary rover. This analysis is used to prioritize the data for transmission, so that the data with the highest science value is transmitted to Earth. In addition, the onboard analysis results are used to identify science opportunities. A planning and scheduling component of the system enables the rover to take advantage of the identified science opportunity. OASIS is a NASA-funded research project that is currently being tested on the FIDO rover at JPL for the use on future missions.

Castano, R.↗
NASA NTRS2019

Astrobiology Survey of a Lava Cave at Lava Beds National Monument by a Rover Carrying a Remote Sensing Instrument Payload

We report here on a survey of a lava tube cave by a rover that is instrumented for astrobiology missions. The NASA Ames testbed rover, CaveR, was deployed in Valentine Cave in Lava Beds National Monument (N. CA, USA) during August of 2018. The rover carried an instrument package consisting of Near Infrared and Visible Spectrometer System (NIRVSS) a point spectrometer operating in 1590-3400 nm range, sensitive to H2O and -OH bearing minerals, pyroxenes, and carbonates (Roush, et al 2018); the bore sighted Drill Operations Camera (DOC), a monochrome imager illuminated by LEDs at 410, 540, 640, 740, 905 and 940 nm; a Realsense ™ depth sensor system for 3D model generation; and a high resolution DSLR stereo camera. The payload was mounted on a tiltable instrument platform attached to the left side of the rover. The rover was driven manually in the cave by field operators, following instructions from a remote science operations team, and simulating a mission concept with science-guided autonomy. A simulated mission took place for 3 days with a team of 3 scientists selecting targets and interpreting data from the payload. To begin the mission, the rover drove along one wall of the cave imaging continuously with the Realsense in 20 m cave segments, three total. At the start of each day, the images from a 20m segment and a panorama stitched from them were provided to the science team to examine. The science team used these data to prioritize specific points along the cave wall for the collection of NIRVSS, DOC, and DSLR data. The objective of the data collection was to identify and study putative biological and mineralogical features in the cave. The data were delivered in xGDS, a customized mapping, planning, and data base management software developed at NASA Ames (Lee, et al 2013). Once the targets for further observations were selected, a plan for collecting the observations (positions in the cave and pointing for each requested observation) was constructed using xGDS and delivered to a rover team to execute the science data collection plan. Acquired data were delivered back to the science team for analysis. Preliminary results from the experiment illustrate the utility of the system (rover plus payload) to study the cave geology and mineralogy and its potential for identifying biomineral features.

Wong, Uland↗
NASA NTRSDate not supplied

Towards Automated Analytics of Research Publications

For readers of scientific publications it remains a big challenge to unambiguously relate the published research with the data used. To a substantial degree it is attributed to authors, journals, editors, and reviewers not prioritizing correct data citation, which impacts traceability, repeatability, and giving credits to published authors and their funding sources. Furthermore, uniform classification of the content of the published research is hampered by journals using journal specific topics and letting authors to assign free text keywords to their papers. We demonstrate automated analytics methods for extracting and relating datasets used and the research application areas by processing 1,300 research papers that referenced the NASA Giovanni service (but probably not the datasets in particular) as supporting their publication process. This presentation was given during the 2022 ESIP January meeting held virtually in January 2022.

Irina Gerasimov↗
NASA NTRSDate not supplied

Using Artificial Intelligence and Machine Learning to Enhance Mission Design and Operations of the Habitable Worlds Observatory (HWO)

One key aspect in the development of HWO is the early deployment of artificial intelligence (AI) and machine learning (ML) to enhance mission science and operations. Our subtask group is part of the HWO AI/ML working group and focuses on AI and ML for mission operations. Our task group seeks to educate other HWO working groups about AI and ML capabilities for mission operations, investigate how to bridge technology gaps, and enable new capabilities particularly in the areas of observational scheduling, instrument health monitoring, and downlink operations. We focus on mission tasking / scheduling both for mission analysis in development and operations. AI and ML for mission scheduling includes: tools to support proposal calls and review, ensuring fairness in calls for proposals, community peer reviews and ease workloads, as well as in-flight and ground software development (e.g., using natural language processing (NLP) to support process automation from requirements). AI and ML for the mission’s development and operations include 1) anomaly detection and prediction (from onboard and ground based tools) to monitor the spacecraft’s health, 2) ground-based automated scheduling for mission operations including long-term and short-term planning and maintenance, and 3) flight system flexible execution (as flight proven for Spitzer and JWST) to enable robust execution despite execution variations, and 4) data analysis for prioritization (e.g., real-time data evaluation leading to autonomous actions and adjustments, high-priority identification, onboard data compression, etc.). Incorporation of ML and AI will enable HWO to address the major science questions related to exoplanet characterization, general astrophysics, and solar system exploration and also extend the boundaries of space mission technologies.

Mark Moussa↗
NASA NTRS2024

Architecture of High-Altitude Operations (HAO) Discovery and Synchronization Service (DSS)

The aviation industry is evolving at an unprecedented pace, necessitating the development of efficient, secure, and interoperable systems to manage increasingly complex air traffic. Moreover, the demand for High-Altitude Operations (HAO) is increasing. Furthermore, air traffic control services are limited in HAO environments. HAO industry participants will need airspace access and flexibility to perform their missions in this airspace that provides provisions for scalability. The Discovery and Synchronization Service (DSS) will be a cornerstone of the HAO ecosystem, enabling the effective sharing of critical airspace data, including operational intent, aircraft trajectories, and airspace usage among various stakeholders and operators. The DSS architecture addresses these challenges with a distributed, decentralized, and interoperable system that facilitates seamless integration across diverse airspaces. It prioritizes secure data exchange while safeguarding data ownership. This white paper presents the vision, architecture, and benefits of the DSS for HAO, underscoring its potential to streamline operations, reduce redundancies, and establish a foundation for safe and efficient airspace management.

HAO↗
NASA NTRSDate not supplied

Using Coordinated, Multi-Agent Platforms for Dynamic Ocean Worlds Science

Planetary science missions have the opportunity to enhance science return through deployment of autonomous capabilities designed to dynamically respond to new information. Future outer solar system missions to ocean worlds in particular would benefit from this technology - intelligent science payloads (ISP) - because it would allow for a coordinated, near real-time response to ephemeral ‘events’ such as plumes, tectonism, surface implantation, volatile releases, thermal and magnetic anomalies, or radiation, as well as increasing the cadence and coverage of data collection. Prioritization and decision-making frameworks from ISP could be deployed at various scales - from analysis onboard a spacecraft with multiple instruments – to coordinated analyses among separate spacecraft in an e.g., distributed systems mission (DSM) composed of multiple SmallSats. Goddard’s Intelligent Science Payload team is developing an agile autonomous architecture for an icy ocean worlds DSM concept. Our goals are to coordinate data collection and onboard data analysis, and to make autonomous decisions for new data collection and analysis based on science priorities between multiple spacecraft with variable instrumentation and orbits. We use a range of data analysis tools to coordinate the DSM response, spanning from observations of data over a specified threshold to more computationally intensive machine learning algorithms (ML). ML algorithms here currently focus on determining the composition of an ocean world using mass spectrometry, and specifically methods for understanding ‘novelties’ and potential biosignatures. These algorithms could be used to quickly process and analyze onboard data that would be significantly delayed in downlink due to long communication delays for outer solar system missions in order to make dynamic science observations. Our ocean worlds case study ISP architecture is intended as an ‘agile’ and modular framework that could be used as a whole or as particular modules based on mission needs.

Distributed Systems↗