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At least 1,243 records · Page 69
All sourcesNASA NTRSDOE OSTI
NASA NTRS2004

MGS and Odyssey - Relay Satellites for the MER Mission

Both Mars Global Surveyor (MGS) and Mars Odyssey are currently in low altitude, nearly circular and highly inclined orbits about Mars. Thus, they are available and compatible to serve as relay satellites for the Mars Exploration Rover (MER) mission. Consequently, the MER project developed requirements for MGS to be overhead, at a specific time with a 30 second tolerance, during the atmospheric entry, descent and landing (EDL) phase of both MER vehicles. The result, after execution of a single orbit synchronization maneuver (OSM) on 10/03/03, 92.4 days or 1130 orbits before Spirit's EDL, was that MGS was over Spirit 8 seconds past the required time. This maneuver, with a delta-velocity of 0.534 m/s, caused the orbital period to change by 3.34 s and resulted in a time-phasing change of 62 min 19 s in order to achieve the EDL overflight. Based on the navigation and execution of an OSM on 01/04/04, MGS was overhead for the Opportunity EDL on 01/25/04,3.5 seconds after the required epoch. Requirements also existed for the Odyssey over-flight of the MER rovers after landing and various equipment deployments had been completed. Thus, these requirements were that Odyssey should rise no earlier than specified times with respect to each of the landing sites. The Odyssey over-flights of both Spirit and Opportunity on sol 1 were equally successful. This paper will present the navigation plan, trajectory propagation accuracy and maneuver execution for the successful MGS and Odyssey over-flights of both the MER rovers.

Mars Global Surveyor (MGS)↗
NASA NTRS2016

NEEMO 18-20: Analog Testing for Mitigation of Communication Latency During Human Space Exploration

NASA Extreme Environment Mission Operations (NEEMO) is an underwater spaceflight analog that allows a true mission-like operational environment and uses buoyancy effects and added weight to simulate different gravity levels. Three missions were undertaken from 2014-2015, NEEMO's 18-20. All missions were performed at the Aquarius undersea research habitat. During each mission, the effects of communication latencies on operations concepts, timelines, and tasks were studied. METHODS: Twelve subjects (4 per mission) were weighed out to simulate near-zero or partial gravity extravehicular activity (EVA) and evaluated different operations concepts for integration and management of a simulated Earth-based science team (ST) to provide input and direction during exploration activities. Exploration traverses were preplanned based on precursor data. Subjects completed science-related tasks including pre-sampling surveys, geologic-based sampling, and marine-based sampling as a portion of their tasks on saturation dives up to 4 hours in duration that were designed to simulate extravehicular activity (EVA) on Mars or the moons of Mars. One-way communication latencies, 5 and 10 minutes between space and mission control, were simulated throughout the missions. Objective data included task completion times, total EVA times, crew idle time, translation time, ST assimilation time (defined as time available for ST to discuss data/imagery after data acquisition). Subjective data included acceptability, simulation quality, capability assessment ratings, and comments. RESULTS: Precursor data can be used effectively to plan and execute exploration traverse EVAs (plans included detailed location of science sites, high-fidelity imagery of the sites, and directions to landmarks of interest within a site). Operations concepts that allow for pre-sampling surveys enable efficient traverse execution and meaningful Mission Control Center (MCC) interaction across communication latencies and can be done with minimal crew idle time. Imagery and contextual information from the EVA crew that is transmitted real-time to the intravehicular (IV) crewmember(s) can be used to verify that exploration traverse plans are being executed correctly. That same data can be effectively used by MCC (across comm latency) to provide meaningful feedback and instruction to the crew regarding sampling priorities, additional tasks, and changes to the EVA timeline. Text / data capabilities are preferred over voice capabilities between MCC and IV when executing exploration traverse plans over communication latency.

Chappell, Steven P.↗
NASA NTRS1990

Discrete event simulation tool for analysis of qualitative models of continuous processing systems

An artificial intelligence design and qualitative modeling tool is disclosed for creating computer models and simulating continuous activities, functions, and/or behavior using developed discrete event techniques. Conveniently, the tool is organized in four modules: library design module, model construction module, simulation module, and experimentation and analysis. The library design module supports the building of library knowledge including component classes and elements pertinent to a particular domain of continuous activities, functions, and behavior being modeled. The continuous behavior is defined discretely with respect to invocation statements, effect statements, and time delays. The functionality of the components is defined in terms of variable cluster instances, independent processes, and modes, further defined in terms of mode transition processes and mode dependent processes. Model construction utilizes the hierarchy of libraries and connects them with appropriate relations. The simulation executes a specialized initialization routine and executes events in a manner that includes selective inherency of characteristics through a time and event schema until the event queue in the simulator is emptied. The experimentation and analysis module supports analysis through the generation of appropriate log files and graphics developments and includes the ability of log file comparisons.

Malin, Jane T.↗
NASA NTRS2016

Analog Testing of Operations Concepts for Mitigation of Communication Latency During Human Space Exploration

OBJECTIVES: NASA Extreme Environment Mission Operations (NEEMO) is an underwater spaceflight analog that allows a true mission‐like operational environment and uses buoyancy effects and added weight to simulate different gravity levels. Three missions were undertaken from 2014‐2015, NEEMO's 18‐20. All missions were performed at the Aquarius undersea research habitat. During each mission, the effects of varying operations concepts and tasks type and complexity on representative communication latencies associated with Mars missions were studied. METHODS: 12 subjects (4 per mission) were weighed out to simulate near‐zero or partial gravity extravehicular activity (EVA) and evaluated different operations concepts for integration and management of a simulated Earth‐based science backroom team (SBT) to provide input and direction during exploration activities. Exploration traverses were planned in advance based on precursor data collected. Subjects completed science‐related tasks including presampling surveys, geologic‐based sampling, and marine‐based sampling as a portion of their tasks on saturation dives up to 4 hours in duration that were to simulate extravehicular activity (EVA) on Mars or the moons of Mars. One‐way communication latencies, 5 and 10 minutes between space and mission control, were simulated throughout the missions. Objective data included task completion times, total EVA times, crew idle time, translation time, SBT assimilation time (defined as time available for SBT to discuss data/imagery after it has been collected, in addition to the time taken to watch imagery streaming over latency). Subjective data included acceptability, simulation quality, capability assessment ratings, and comments. RESULTS: Precursor data can be used effectively to plan and execute exploration traverse EVAs (plans included detailed location of science sites, high‐fidelity imagery of the sites, and directions to landmarks of interest within a site). Operations concepts that allow for presampling surveys enable efficient traverse execution and meaningful Mission Control Center (MCC) interaction across long communication latencies and can be done with minimal crew idle time. Imagery and information from the EVA crew that is transmitted real‐time to the intravehicular (IV) crewmember(s) can be used to verify that exploration traverse plans are being executed correctly. That same data can be effectively used by MCC (across comm latency) to provide further instructions to the crew from a SBT on sampling priorities, additional tasks, and changes to the plan. Text / data capabilities are preferred over voice capabilities between MCC and IV when executing exploration traverse plans over communication latency. Autonomous crew planning tools can be effective at modifying existing plans if the objectives and constraints are clearly defined.

Chappell, Steven P.↗
NASA NTRSDate not supplied

An Optimization Approach to Support Science Decision Making for Lunar Surface Exploration

Introduction: Scientific exploration is one of the three pillars of NASA’s Moon2Mars architecture, with crew surface extra vehicular activities (EVA) serving a critical enabling function. Development of surface EVA operational planning and execution, specifically integrating science and flight control teams (FCT), is currently being explored through analog scenarios. This integration, exercised, for example, through the Joint EVA and Hu-man Surface Mobility Test Team (JETT), allows for science input on EVA activities in near real-time through a Science Evaluation Room (SER), or Arte-mis science backroom, which integrates with the broader FCT through the Science Officer. The SER works within the FCT to support dynamic EVA planning in response to changes in operational constraints as well as science opportunities and re-prioritization, increasing the mission science return and accelerating the accomplishment of the Moon2Mars science objectives. The SER works within the FCT to provide recommendations to traverse execution in near real-time. One challenge is the requirement to deliver SER inputs to the FCT on operationally relevant timelines. Failure to do so may result in suboptimal execution of science exploration EVAs or even loss of key science objectives. To close this gap, we present a network optimization tool to allow the SER to provide rapid input to the FCT in response to changes in operational constraints or science opportunities. Inputs are predicated on approved science objectives, and clear rationale must be provided to the FCT for any requested change. Accordingly, this tool incorporates the Science Traceability Matrix (STM), SER prioritization scheme, and station characterization and action planning with operational constraints such as duration, traverse speed, and distance to maximize science objectives based on SER priorities, consistent with FCT operational requirements. Method: As a proof of concept, we used an existing linear programing software package used to simulate optimal routes through cellular metabolism. We built a Demonstrative Model with three STM objectives and four stations on a region of the Moon. The objectives were given an arbitrary prioritization and mapped to the stations through four possible crew actions. (Figs. 1 and 2). This station to STM mapping is consistent with the method used by the JETT5 Science Team to develop analog surface EVA science planning. We used a grid system with the landing site at the origin and the four stations placed across the positive x,y quadrant. Actions were assigned to each station and the accomplishment of those actions resulted in a numerical “reward” based on the ability of that action to achieve science objectives. The aggregate reward from each individual STM objective contributes to a global score (Science Yield), weighted by its priority. Operational constraints included a requirement to start and end at the landing site, 5 minutes each for initial station characterization and “clean up,” and variable total EVA time, traverse rate (fixed to 0.5 meters per second in our example), and time to perform each action (10, 5, 7, and 15 min for actions 1, 2, 3, and 4, respectively). Additional constraints and variables will be added in the future (e.g., sample mass, number of stations, traverse route constraints, illumination). Optimization. We converted the connections (arcs) between these stations (nodes) into a mixed integer linear programming optimization problem (arcs = constraints, nodes = variables) with the objective to maximize Science Yield. For any action, the Science Yield is equal to the relevance of that action to an STM objective [3, 2, and 1 point(s) for High, Med., and Low relevance, respectively], multiplied by the STM Objective Priority [3, 2, and 1 point(s) for High, Med., and Low priority, respectively]. This resulted in a model that computes the optimal station and action combination to maximize the Science Yield. These weightings can be adjusted by the SER as desired. Results: We explored three test cases for the Demonstrative Model. First, we set the maximum EVA duration to 120 minutes and computed the optimal route (Fig. 3A). The model suggested per-forming Actions 1 and 2 at Station P01, followed by Actions 1 and 2 at Station P02, and finally Actions 1 and 3 at Station P04 before returning to the Landing Site. Second, we adjusted the STM Objective Priori-ty order and computed the new optimal route (Fig. 3B). Under this situation, the model suggested per-forming all Actions at Station P02 followed by all Actions at Station P03. The previous test cases were relevant to SER planning activities. Next, we explored providing mid-EVA replanning input to the FCT. Scenario: While executing the Route in Fig. 3A the crew finishes at Station P01 and FCT decides that the EVA needs to finish in 45 minutes back at the Landing Site. FCT asks SER to recommend changes to the plan to accommodate this operation-al change. Using the model and incorporating these new constraints (start at Station P01, max. time of 45 min), the model suggested performing Actions 2 and 4 at Station P03 (Fig. 4), requiring 41 minutes to complete and return to the Landing Site. Interestingly, Station 3 was not part of the original route. Using the model, we determined the EVA would need 66 minutes, instead of 45, in order for the original Station P04 to yield a larger Science Yield than Station P03. The parametrization and simulation was per-formed in less than a minute, demonstrating the operational relevance of the approach. Future Efforts: The results from the Demonstrative Model suggest this tool can accelerate SER decision making on operationally relevant timelines. Use in analog activities, such as JETT5 or follow-ons, which have over a dozen stations for a crew to explore and over a dozen actions per station, will provide needed validation of the utility of this tool for planning EVAs, replanning mid-EVA, or planning follow-on EVAs based on previous results. Further integration with FCT execution monitoring tools may provide additional efficiency gains, al-lowing rapid and iterative exploration of operation-al and science decision space by the FCT and SER.

Science Operations↗
NASA NTRS1982

Interactive digital signal processor

The Interactive Digital Signal Processor (IDSP) is examined. It consists of a set of time series analysis Operators each of which operates on an input file to produce an output file. The operators can be executed in any order that makes sense and recursively, if desired. The operators are the various algorithms used in digital time series analysis work. User written operators can be easily interfaced to the sysatem. The system can be operated both interactively and in batch mode. In IDSP a file can consist of up to n (currently n=8) simultaneous time series. IDSP currently includes over thirty standard operators that range from Fourier transform operations, design and application of digital filters, eigenvalue analysis, to operators that provide graphical output, allow batch operation, editing and display information.

Mish, W. H.↗
NASA NTRS2002

Shuttle Abort Flight Management (SAFM) - Application Overview

One of the most demanding tasks that must be performed by the Space Shuttle flight crew is the process of determining whether, when and where to abort the vehicle should engine or system failures occur during ascent or entry. Current Shuttle abort procedures involve paging through complicated paper checklists to decide on the type of abort and where to abort. Additional checklists then lead the crew through a series of actions to execute the desired abort. This process is even more difficult and time consuming in the absence of ground communications since the ground flight controllers have the analysis tools and information that is currently not available in the Shuttle cockpit. Crew workload specifically abort procedures will be greatly simplified with the implementation of the Space Shuttle Cockpit Avionics Upgrade (CAU) project. The intent of CAU is to maximize crew situational awareness and reduce flight workload thru enhanced controls and displays, and onboard abort assessment and determination capability. SAFM was developed to help satisfy the CAU objectives by providing the crew with dynamic information about the capability of the vehicle to perform a variety of abort options during ascent and entry. This paper- presents an overview of the SAFM application. As shown in Figure 1, SAFM processes the vehicle navigation state and other guidance information to provide the CAU displays with evaluations of abort options, as well as landing site recommendations. This is accomplished by three main SAFM components: the Sequencer Executive, the Powered Flight Function, and the Glided Flight Function, The Sequencer Executive dispatches the Powered and Glided Flight Functions to evaluate the vehicle's capability to execute the current mission (or current abort), as well as more than IS hypothetical abort options or scenarios. Scenarios are sequenced and evaluated throughout powered and glided flight. Abort scenarios evaluated include Abort to Orbit (ATO), Transatlantic Abort Landing (TAL), East Coast Abort Landing (ECAL) and Return to Launch Site (RTLS). Sequential and simultaneous engine failures are assessed and landing footprint information is provided during actual entry scenarios as well as hypothetical "loss of thrust now" scenarios during ascent.

Hu, Howard↗
DOE OSTI2023

Time-Sensitive Quantum Key Distribution (TSQKD)

This is the final report for the Time-Sensitive Quantum Key Distribution (TSQKD) project and summarizes the demonstration and longer-term field testing at an electric utility, EPB of Chattanooga, in Chattanooga, TN. The demonstration was held virtually (due to the COVID pandemic) and successfully executed the tests outlined in a prior report, “Task 10: Develop a QKD Time-Sensitive Networking Test Suite” submitted 9/30/2020. The portable test equipment explained in Task 10 and earlier task reports was tested at GE Research remotely connected with Qubitekk’s QKD equipment in San Diego, CA. Then the equipment was shipped to Qubitekk for physical integration with Qubitekk’s Quantum DataLoc™ Key Server 1570nm (http://qubitekk.com/products/qkd-for-industrial-control-systems-ics/). The portable test equipment was shipped from Qubitekk to EPB for demonstration and extended field testing. A summary of the demonstration and test results are described in this report. The demo and extended field test results are enabled by the NETCONF/YANG IEEE P1913 data management model developed as part of this project (Task 5). The NETCONF/YANG IEEE P1913 data management model demonstrated significant practical utility in providing a common means of monitoring and configuring the QKD system. All data in this report comes from the IEEE P1913 data management model.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS1995

Are Event Traces Really That Necessary?

Many performance monitoring tools are currently available to the super-computing community. The performance data gathered and analyzed by these tools fall under two categories: statistics and event traces. Statistical data is much more compact but lack the probative power event traces offer. Event traces, on the other hand, can easily fill up the entire file system during execution such that the instrumented execution have to be terminated. In this paper, we propose an innovative methodology for monitoring and trace representation that offers a middle ground. The user can trace-off trace data size vs. quality incrementally. Specifically, the user will be able to limit the amount of trace collected and, at the same time, carry out some of the analysis event traces offer for the entire execution. With the help of a few CFD examples, we illustrate the use of our technique in performance tuning. We also compare quantitatively, the quality of the traces we collected vs. event traces.

Schmidt, Melisa↗
DOE OSTI2016

Performance Models for Split-execution Computing Systems

Split-execution computing leverages the capabilities of multiple computational models to solve problems, but splitting program execution across different computational models incurs costs associated with the translation between domains. We analyze the performance of a split-execution computing system developed from conventional and quantum processing units (QPUs) by using behavioral models that track resource usage. We focus on asymmetric processing models built using conventional CPUs and a family of special-purpose QPUs that employ quantum computing principles. Our performance models account for the translation of a classical optimization problem into the physical representation required by the quantum processor while also accounting for hardware limitations and conventional processor speed and memory. We conclude that the bottleneck in this split-execution computing system lies at the quantum-classical interface and that the primary time cost is independent of quantum processor behavior.

multiprocessor↗
NASA NTRS1998

1997 NASA Academy in Aeronautics

The NASA Academy in Aeronautics at the Dryden Flight Research Center (DFRC) was a ten-week summer leadership training program conducted for the first time in the summer of 1997. Funding was provided by a contract between DFRC and Purdue University. Mr. Lee Duke of DFRC was the contract monitor, and Professor Dominick Andrisani was the principal investigator. Five student research associates participated in the program. Biographies of the research associates are given in Appendix 1. Dominick Andrisani served as Dean of the NASA Academy in Aeronautics. NASA Academy in Aeronautics is a unique summer institute of higher learning that endeavors to provide insight into all of the elements that make NASA aeronautical research possible. At the same time the Academy assigns the research associate to be mentored by one of NASA!s best researchers so that they can contribute towards an active flight research program. Aeronautical research and development are an investment in the future, and NASA Academy is an investment in aeronautical leaders of the future. The Academy was run by the Indiana Space Grant Consortium at Purdue in strategic partnership with the National Space Grant College and Fellowship Program. Research associates at the Academy were selected with help from the Space Grant Consortium that sponsored the research associate. Research associate stipend and travel to DFRC were paid by the students' Space Grant Consortium. All other student expenses were paid by the Academy. Since the Academy at DFRC had only five students the opportunity for individual growth and attention was unique in the country. About 30% of the working time and most of the social time of the students were be spent as a "group" or "team." This time was devoted to exchange of ideas, on forays into the highest levels of decision making, and in executing aeronautical research. This was done by interviewing leaders throughout the aerospace industry, seminars, working dinners, and informal discussions. The other 70% of the working time was spent working on the technical research project with the engineering mentors. Abstracts of those projects are given in Appendix 4.

Andrisani, Dominick, II↗
DOE OSTI2018

Checkpoint triggering in a computer system

According to an aspect, a method for triggering creation of a checkpoint in a computer system includes executing a task in a processing node and determining whether it is time to read a monitor associated with a metric of the task. The monitor is read to determine a value of the metric based on determining that it is time to read the monitor. A threshold for triggering creation of the checkpoint is determined based on the metric. A monitoring block size is determined for the checkpoint. A checkpoint interval is determined based on the monitoring block size, a checkpoint bandwidth, and a failure rate of the computer system. Based on determining that the value of the metric has crossed the threshold and the checkpoint interval has elapsed, the checkpoint including state data of the task is created to enable restarting execution of the task upon a restart operation.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

On the Path to SunShot - Executive Summary

The halfway mark of the SunShot Initiative’s 2020 target date is a good time to take stock: How much progress has been made? What have we learned? What barriers and opportunities must still be addressed to ensure that solar technologies achieve cost parity in 2020 and realize their full potential in the decades beyond? To answer these questions, the Solar Energy Technology Office launched the On the Path to SunShot series in early 2015 in collaboration with the National Renewable Energy Laboratory (NREL) and with contributions from Lawrence Berkeley National Laboratory (LBNL), Sandia National Laboratories (SNL), and Argonne National Laboratory (ANL). The reports focus on the areas of technology development, systems integration, and market enablers.

14 SOLAR ENERGY↗
NASA NTRS2007

A Multibody Slosh Analysis for the Lunar Reconnaissance Orbiter

The Lunar Reconnaissance Orbiter (LRO) undergoes a series of thruster maneuvers to attain lunar orbit. The first of the series of lunar orbit insertion (LOI) maneuvers is crucial to the success of the mission. Therefore, it is important to characterize the disturbances acting on the spacecraft during this phase of the mission. This paper focuses on the internal disturbance force caused by fuel slosh and its impact on attitude control. During the first LOI maneuver (LOI-1), approximately 50% of the total fuel mass is used or roughly 25% of the spacecraft s wet mass, during the 38-minute burn. The forces imparted on the spacecraft from the fuel are dependent on the fill level of the two fuel tanks. During LOI-1, the fill level in both tanks varies greatly and thus so does the disturbance level caused by the fuel. It is therefore necessary to account for the time-varying mass properties of the spacecraft and the effects of the varying fuel levels during the entire 38-minute maneuver. Two simulations are developed in Mathworks s Simulink to analyze the fuel slosh effect. The first model, a baseline model, is a rigid body dynamics model where the fuel slosh is not modeled. The second is a multibody model, developed using a multibody dynamics toolbox, where each of the two fuel tanks and the remaining spacecraft body are treated as separate rigid bodies. The simulations are executed in a piece-wise fashion to account for the time-varying mass properties, and to accommodate the multibody toolbox. Disturbances caused by fuel slosh during both lunar and mission orbit insertions will be analyzed through simulation of different dynamics models. Results of the analysis will show the effects of the slosh disturbance on the spacecraft s attitude.

Shah, Neerav↗
NASA NTRS1984

Parallel processor engine model program

The Parallel Processor Engine Model Program is a generalized engineering tool intended to aid in the design of parallel processing real-time simulations of turbofan engines. It is written in the FORTRAN programming language and executes as a subset of the SOAPP simulation system. Input/output and execution control are provided by SOAPP; however, the analysis, emulation and simulation functions are completely self-contained. A framework in which a wide variety of parallel processing architectures could be evaluated and tools with which the parallel implementation of a real-time simulation technique could be assessed are provided.

Mclaughlin, P.↗
NASA NTRS1981

Methodology for measurement of fault latency in a digital avionic miniprocessor

Using a gate level emulation of a typical avionics miniprocessor, fault injection experiments were performed to (1) determine the time to detect a fault by comparison monitoring, (2) forecast a program's ability to detect faults and (3) validate the fault detection coverage of a typical self test program. To estimate time to detect, six programs ranging in complexity from 6 to 147 instructions, were emulated. Each program was executed repetitively in the presence of a single stuck at fault at a gate node or device pin. Detection was assumed to occur whenever the computed outputs differed from the corresponding outputs of the same program executed in a nonfaulted processor. Histograms of faults detected versus number of repetitions to detection were tabulated. Using a simple model of fault detection, which was based in an analog with the selection of balls in an urn, distributions of time to detect were computed and compared with those obtained empirically. A self test program of 2,000 executable instructions was designed expressly for the study. The only requirement imposed on the design was that it should achieve 95% coverage. The program was executed in the presence of a single stuck-at fault at a gate node on device pin. The proportion of detected faults are tabulated. In all experiments faults were selected at random over gate nodes or device pins.

Mcgough, J. C.↗
NASA NTRS1994

Predictive sufficiency and the use of stored internal state

In all embedded computing systems, some delay exists between sensing and acting. By choosing an action based on sensed data, a system is essentially predicting that there will be no significant changes in the world during this delay. However, the dynamic and uncertain nature of the real world can make these predictions incorrect, and thus, a system may execute inappropriate actions. Making systems more reactive by decreasing the gap between sensing and action leaves less time for predictions to err, but still provides no principled assurance that they will be correct. Using the concept of predictive sufficiency described in this paper, a system can prove that its predictions are valid, and that it will never execute inappropriate actions. In the context of our CIRCA system, we also show how predictive sufficiency allows a system to guarantee worst-case response times to changes in its environment. Using predictive sufficiency, CIRCA is able to build real-time reactive control plans which provide a sound basis for performance guarantees that are unavailable with other reactive systems.

Musliner, David J.↗