Search NASA⌕ Search

SEARCH · Search NASA

Results for “execution models”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
NASA NTRS2001

Data Reduction and Its Impact on Test-Analysis Correlation

A research project has been initiated to improve crash test and analysis correlation. The research has focused on two specimen types: simple metallic beams and plates; and a representative composite fuselage section. Impact tests were performed under carefully controlled conditions. In addition, the specimens were densely instrumented to enable not only correlation with finite element simulations, but to also assess the repeatability of the data. Simulations utilizing a detailed finite element model were executed in a nonlinear transient dynamic code. The results presented in this paper concentrate on the effect of several data reduction processes, to include filtering frequency and sampling rate, on the correlation accuracy.

Lyle, Karen H.↗
NASA NTRS2003

The Case for Durative Actions: A Commentary on PDDL2.1

The addition of durative actions to PDDL2.1 sparked some controversy. Fox and Long argued that actions should be considered as instantaneous, but can start and stop processes. Ultimately, a limited notion of durative actions was incorporated into the language. I argue that this notion is impoverished, and that the underlying philosophical position of regarding durative actions as being a shorthand for a start action, process, and stop action ignores the realities of modelling and execution for complex systems.

Smith, David E.↗
NASA NTRS2002

Autonomous planning and scheduling on the TechSat 21 mission

The Autonomous Sciencecraft Experiment (ASE) will fly onboard the Air Force TechSat 21 constellation of three spacecraft scheduled for launch in 2006. ASE uses onboard continuous planning, robust task and goal-based execution, model-based mode identification and reconfiguration, and onboard machine learning and pattern recognition to radically increase science return by enabling intelligent downlink selection and autonomous retargeting.

AI architectures applications intelligent agents m↗
NASA NTRS2002

Autonomous operations through onboard artificial intelligence

The Autonomous Sciencecraft Experiment (ASE) will fly onboard the Air Force TechSat 21 constellation of three spacecraft scheduled for launch in 2006. ASE uses onboard continuous planning, robust task and goal-based execution, model-based mode identification and reconfiguration, and onboard machine learning and pattern recognition to radically increase science return by enabling intelligent downlink selection and autonomous retargeting. Demonstration of these capabilities in a flight environment will open up tremendous new opportunities in planetary science, space physics, and earth science that would be unreachable without this technology.

Autonomous Sciencecraft Experiment ASE TechSat 21↗
NASA NTRS2002

Spacecraft autonomy using onboard processing for a SAR constellation mission

The Autonomous Sciencecraft Experiment (ASE) will fly onboard the Air Force TechSat 21 constellation of three spacecraft scheduled for launch in 2006. ASE uses onboard continuous planning, robust task and goal-based execution, model-based mode identification and reconfiguration, and onboard machine learning and pattem recognition to radically increase science retum by enabling intelligent downlink selection and autonomous retargeting. Demonstration of these capabilities in a flight environment will open up tremendous new opportunities in planetary science, space physics, and earth science that would be unreachable without this technology.

Autonomous Sciencecraft Experiment ASE spacecraft ↗
NASA NTRS2003

The autonomous sciencecraft constellations

The Autonomous Sciencecraft Experiment (ASE) will fly onboard the Air Force TechSat 21 constellation of three spacecraft scheduled for launch in 2006. ASE uses onboard continuous planning, robust task and goal-based execution, model-based mode identification and reconfiguration, and onboard machine learning and pattern recognition to radically increase science return by enabling intelligent downlink selection and autonomous retargeting. In this paper we discuss how these AI technologies are synergistically integrated in a hybrid multi-layer control architecture to enable a virtual spacecraft science agent. Demonstration of these capabilities in a flight environment will open up tremendous new opportunities in planetary science, space physics, and earth science that would be unreachable without this technology.

Autonomous Sciencecraft Experiment ASE AI technolo↗
NASA NTRS2002

Spacecraft autonomy using onboard processing for a SAR constellation mission

The Autonomous Sciencecraft Experiment (ASE) will fly onboard the Air Force TechSat 21 constellation of three spacecraft scheduled for launch in 2006. ASE uses onboard continuous planning, robust task and goal-based execution, model-based mode identification and reconfiguration, and onboard machine learning and pattern recognition to radically increase science return by enabling intelligent downlink selection and autonomous retargeting. In this paper we discuss how these AI technologies are synergistically integrated in a hybrid multi-layer control architecture to enable a virtual spacecruft science agent. Demonstration of these capabilities in a flight environment will open up tremendous new opportunities in planetary science, space physics, and earth science that would be unreachable without this technology.

Autonomous Sciencecraft Experiment ASE virtual spa↗
NASA NTRS1994

An Advanced Simulation Framework for Parallel Discrete-Event Simulation

Discrete-event simulation (DEVS) users have long been faced with a three-way trade-off of balancing execution time, model fidelity, and number of objects simulated. Because of the limits of computer processing power the analyst is often forced to settle for less than desired performances in one or more of these areas.

parallel processing technologies DEVS (PDEVS) ssto↗
NASA NTRS2004

Controlling Distributed Planning

A system of software implements an extended version of an approach, denoted shared activity coordination (SHAC), to the interleaving of planning and the exchange of plan information among organizations devoted to different missions that normally communicate infrequently except that they need to collaborate on joint activities and/or the use of shared resources. SHAC enables the planning and scheduling systems of the organizations to coordinate by resolving conflicts while optimizing local planning solutions. The present software provides a framework for modeling and executing communication protocols for SHAC. Shared activities are represented in each interacting planning system to establish consensus on joint activities or to inform the other systems of consumption of a common resource or a change in a shared state. The representations of shared activities are extended to include information on (1) the role(s) of each participant, (2) permissions (defined as specifications of which participant controls what aspects of shared activities and scheduling thereof), and (3) constraints on the parameters of shared activities. Also defined in the software are protocols for changing roles, permissions, and constraints during the course of coordination and execution.

Clement, Bradley↗
NASA NTRS2012

Fault-Tolerant, Real-Time, Multi-Core Computer System

A document discusses a fault-tolerant, self-aware, low-power, multi-core computer for space missions with thousands of simple cores, achieving speed through concurrency. The proposed machine decides how to achieve concurrency in real time, rather than depending on programmers. The driving features of the system are simple hardware that is modular in the extreme, with no shared memory, and software with significant runtime reorganizing capability. The document describes a mechanism for moving ongoing computations and data that is based on a functional model of execution. Because there is no shared memory, the processor connects to its neighbors through a high-speed data link. Messages are sent to a neighbor switch, which in turn forwards that message on to its neighbor until reaching the intended destination. Except for the neighbor connections, processors are isolated and independent of each other. The processors on the periphery also connect chip-to-chip, thus building up a large processor net. There is no particular topology to the larger net, as a function at each processor allows it to forward a message in the correct direction. Some chip-to-chip connections are not necessarily nearest neighbors, providing short cuts for some of the longer physical distances. The peripheral processors also provide the connections to sensors, actuators, radios, science instruments, and other devices with which the computer system interacts.

Gostelow, Kim P.↗
NASA NTRS2013

Cassini Solstice Mission Maneuver Experience: Year Three

The Cassini spacecraft is now in its second Saturn tour extension, the Solstice Mission. By emphasizing propellant preservation over minimizing maneuver cycles, the Cassini Project is meeting the challenge of mission completion in 2017. Since June 2012, 18 of 21 maneuvers were performed to closely maintain the prescribed trajectory, saving downstream propellant. These and other maneuvers during the third year of the Solstice Mission (June 2012 to August 2013) are highlighted in this paper: 31 planned maneuvers targeted to 11 Titan flybys and the last planned Rhea encounter. An assessment of the updated maneuver execution-error models will also be presented.

Cassini↗
NASA NTRS2013

Cassini Solstice Mission Maneuver Experience: Year Three

The Solstice Mission is the final extension of the Cassini spacecraft s tour of Saturn and its moons. To accommodate an end-of-mission in 2017, the maneuver decision process has been refined. For example, the Cassini Project now prioritizes saving propellant over minimizing maneuver cycles. This paper highlights 30 maneuvers planned from June 2012 through July 2013, targeted to nine Titan flybys and the final Rhea encounter in the mission. Of these maneuvers, 90% were performed to maintain the prescribed trajectory and preserve downstream delta V. Recent operational changes to maneuver executions based on execution-error modeling and analysis are also discussed.

Equinox Mission↗
NASA NTRS2016

Orbit Determination Sensitivity Analysis for the Europa Multiple Flyby Mission Concept

This paper details the results of parametric variations on the notional Europa Mission Concept tour navigation strategy on orbit determination delivery and knowledge errors and the associated statistical ∆V consumption. Approach maneuver placement at encounter minus two and a half days and at encounter minus three and a half days is compared to the reference encounter minus three days location as well as variations to the data cutoff for approach maneuver design, which is baselined one day prior to maneuver execution. An execution error model with fixed and proportional components assigned to the magnitude and pointing directions is applied to each maneuver in a Monte Carlo simulation. Variations on the fixed and proportional contributions to both component errors are simulated and analyzed to show the effect of varying thruster characterization levels. The effects of varying levels of a priori satellite ephemeris errors are also characterized in this study.

Boone, Dylan R.↗
NASA NTRS1993

Toward an Embedded Training Tool for Deep Space Network Operations

There are three issues to consider when building an embedded training system for a task domain involving the operation of complex equipment: (1) how skill is acquired in the task domain; (2) how the training system should be designed to assist in the acquisition of the skill, and more specifically, how an intelligent tutor could aid in learning; and (3) whether it is feasible to incorporate the resulting training system into the operational environment. This paper describes how these issues have been addressed in a prototype training system that was developed for operations in NASA's Deep Space Network (DSN). The first two issues were addressed by building an executable cognitive model of problem solving and skill acquisition of the task domain and then using the model to design an intelligent tutor.

Johnson, W. Lewis↗
NASA NTRS2021

Maneuver Design Implementation and Verification for the Mars 2020 Mission

On February 18, 2021, the Perseverance rover landed in Jezero Crater on Mars, transported by the Mars 2020 spacecraft on a nearly seven-month journey to the Red Planet. The execution of three propulsive maneuvers during the interplanetary cruise phase was required to remove the launch-injection bias and deliver the spacecraft to the Mars atmospheric entry point. This paper focuses on the maneuver implementation and verification process between the navigation and spacecraft teams. Additionally, this paper discusses the execution error models that were used to determine maneuver performance and delivery accuracy at the atmospheric entry interface point.

Kruizinga, Gerhard↗
DOE OSTI2018

SISTec: Mathematical Calibration of Large Clearance Monitors - 18265

One major path for clearance of large amount of waste arising from the decommission of nuclear power plants is based on measurement with large clearance monitors (also known as free release measurement facilities). These devices utilize multi-detector plastic scintillator arrays (large clearance monitors can consist of up to 24 plastic scintillators) to offer both, extremely low minimum detectable activities (MDA) and short measurement times for waste packages weighing up to one metric ton. However, a major problem with these devices is to obtain nuclide specific efficiencies, especially for geometrically complex large waste packages. Normal procedure for estimating the efficiency is to device dummy waste packages and attach point sources with well-known activities to unfavourable positions in these packages. In accordance with this procedure, calibration factors can be derived in a radiological conservative way. Devising such test dummies is not always possible and the procedure is prone to generating large measurement uncertainties. In this publication, we propose a new method to derive calibration factors by means of the Los Alamos National Laboratory (LANL) simulation tool Monte Carlo N-Particle (MCNP) and a statistical-phenomenological model for the detector characterization. In a multi-step approach, first the detector response for a distribution of activity in a waste package is calculated and subsequently the detectors are mathematically characterized. Applying state of the art statistical and optimizing procedures, parameters for the detector response are derived in a self-consistent way. This also allows to rigorously propagate the uncertainties to the final calculated calibration factors according to the Guide to the Expression of Uncertainty in Measurement (GUM supplement 1). To validate the derived parameters for the detector characterization a minimally invasive experimental setup was devised and installed into the large clearance monitor. It consisted of an aluminum swivel arm which could position point sources to the relevant coordinates. The so collected measurement data not only allowed to investigate the precision of the calculated calibration factors, but also to probe the location of concentrated activity sources in the waste package. Furthermore, data on measurements with dummy waste packages were collected to test the reproducibility of well-known calibration factors. We present the software tool SIStec, which will provide an intuitive user-interface to generate and simulate waste package models and execute the above-mentioned method. Using SIStec, different scenarios for the distribution of activity in the waste package can be probed. This will also allow to optimize the clearance process on various levels. For example, measurement time for pre-measurements can be reduced in exchange for unfavourable calibration factors, which would still allow to meet the regulator limits for clearance. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2016

U.S. Department of Energy, Office of Nuclear Energy, Nuclear Fuels Storage and Transportation Planning Project Modeling Tools

The U.S. Department Energy, Office of Nuclear Energy's (DOE-NE) Nuclear Fuels Storage and Transportation (NFST) is developing foundational capabilities and tools to support and inform the future deployment of consolidated interim storage and large-scale transportation of SNF. These tools include Used Nuclear Fuel Storage, Transportation, and Disposal Analysis Resource and Data System (UNF-Standards), Stakeholder Tool for Assessing Radioactive Transportation (START), Transportation and Storage Logistics (TSL), Next- Generation System Analysis Model (NGSAM), Execution Strategy Analysis (ESA), Multi-Objective Evaluation Framework (MOEF), and the Centralized Used Fuel Resource for Information Exchange (CURIE). (authors)

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗