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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 649 records · Page 36
All sourcesNASA NTRSDOE OSTI
NASA NTRS2013

Domain-Specific Languages and Diagram Customization for a Concurrent Engineering Environment

A major open question for advocates of Model-Based Systems Engineering (MBSE) is the question of how system and subsystem engineers will work together. The Systems Modeling Language (SysML), like any language intended for a large audience, is in tension between the desires for simplicity and for expressiveness. In order to be more expressive, many specialized language elements may be introduced, which will unfortunately make a complete understanding of the language a more daunting task. While this may be acceptable for systems modelers, it will increase the challenge of including subsystem engineers in the modeling effort. One possible answer to this situation is the use of Domain-Specific Languages (DSL), which are fully supported by the Unified Modeling Language (UML). SysML is in fact a DSL for systems engineering. The expressive power of a DSL can be enhanced through the use of diagram customization. Various domains have already developed their own schematic vocabularies. Within the space engineering community, two excellent examples are the propulsion and telecommunication subsystems. A return to simple box-and-line diagrams (e.g., the SysML Internal Block Diagram) are in many ways a step backward. In order allow subsystem engineers to contribute directly to the model, it is necessary to make a system modeling tool at least approximate in accessibility to drawing tools like Microsoft PowerPoint and Visio. The challenge is made more extreme in a concurrent engineering environment, where designs must often be drafted in an hour or two. In the case of the Jet Propulsion Laboratory's Team X concurrent design team, a subsystem is specified using a combination of PowerPoint for drawing and Excel for calculation. A pilot has been undertaken in order to meld the drawing portion and the production of master equipment lists (MELs) via a SysML authoring tool, MagicDraw. Team X currently interacts with its customers in a process of sharing presentations. There are several inefficiencies that arise from this situation. The first is that a customer team must wait two weeks to a month (which is 2-4 times the duration of most Team X studies themselves) for a finalized, detailed design description. Another is that this information must be re-entered by hand into the set of engineering artifacts and design tools that the mission concept team uses after a study is complete. Further, there is no persistent connection to Team X or institutionally shared formulation design tools and data after a given study, again reducing the direct reuse of designs created in a Team X study. This paper presents the underpinnings of subsystem DSLs as they were developed for this pilot. This includes specialized semantics for different domains as well as the process by which major categories of objects were derived in support of defining the DSLs. The feedback given to us by the domain experts on usability, along with a pilot study with the partial inclusion of these tools is also discussed.

Cole, Bjorn↗
NASA NTRS2012

A Generalized Timeline Representation, Services, and Interface for Automating Space Mission Operations

Numerous automated and semi-automated planning & scheduling systems have been developed for space applications. Most of these systems are model-based in that they encode domain knowledge necessary to predict spacecraft state and resources based on initial conditions and a proposed activity plan. The spacecraft state and resources as often modeled as a series of timelines, with a timeline or set of timelines to represent a state or resource key in the operations of the spacecraft. In this paper, we first describe a basic timeline representation that can represent a set of state, resource, timing, and transition constraints. We describe a number of planning and scheduling systems designed for space applications (and in many cases deployed for use of ongoing missions) and describe how they do and do not map onto this timeline model.

Chien, Steve A.↗
NASA NTRS2012

Principles to Products: Toward Realizing MOS 2.0

This is a report on the Operations Revitalization Initiative, part of the ongoing NASA-funded Advanced Multi-Mission Operations Systems (AMMOS) program. We are implementing products that significantly improve efficiency and effectiveness of Mission Operations Systems (MOS) for deep-space missions. We take a multi-mission approach, in keeping with our organization's charter to "provide multi-mission tools and services that enable mission customers to operate at a lower total cost to NASA." Focusing first on architectural fundamentals of the MOS, we review the effort's progress. In particular, we note the use of stakeholder interactions and consideration of past lessons learned to motivate a set of Principles that guide the evolution of the AMMOS. Thus guided, we have created essential patterns and connections (detailed in companion papers) that are explicitly modeled and support elaboration at multiple levels of detail (system, sub-system, element...) throughout a MOS. This architecture is realized in design and implementation products that provide lifecycle support to a Mission at the system and subsystem level. The products include adaptable multi-mission engineering documentation that describes essentials such as operational concepts and scenarios, requirements, interfaces and agreements, information models, and mission operations processes. Because we have adopted a model-based system engineering method, these documents and their contents are meaningfully related to one another and to the system model. This means they are both more rigorous and reusable (from mission to mission) than standard system engineering products. The use of models also enables detailed, early (e.g., formulation phase) insight into the impact of changes (e.g., to interfaces or to software) that is rigorous and complete, allowing better decisions on cost or technical trades. Finally, our work provides clear and rigorous specification of operations needs to software developers, further enabling significant gains in productivity.

AMMOS↗
NASA NTRS2012

Model Based Systems Engineering on the Europa Mission Concept Study

At the start of 2011, the proposed Jupiter Europa Orbiter (JEO) mission was staffing up in expectation of becoming an official project later in the year for a launch in 2020. A unique aspect of the pre-project work was a strong emphasis and investment on the foundations of Model-Based Systems Engineering (MBSE). As so often happens in this business, plans changed: NASA's budget and science priorities were released and together fundamentally changed the course of JEO. As a result, it returned to being a study task whose objective is to propose more affordable ways to accomplish the science. As part of this transition, the question arose as to whether it could continue to afford the investment in MBSE. In short, the MBSE infusion has survived and is providing clear value to the study effort. By leveraging the existing infrastructure and a modest additional investment, striking advances in the capture and analysis of designs using MBSE were achieved. In the process, the need to remain relevant in the new environment has brought about a wave of innovation and progress. The effort has reaffirmed the importance of architecting. It has successfully harnessed the synergistic relationship of architecting to system modeling. We have found that MBSE can provide greater agility than traditional methods. We have also found that a diverse 'ecosystem' of modeling tools and languages (SysML, Mathematica, even Excel) is not only viable, but an important enabler of agility and adaptability. This paper will describe the successful application of MBSE in the dynamic environment of early mission formulation, the significant results produced and lessons learned in the process.

Bayer, Todd J.↗
NASA NTRS1997

On-Board Planning for New Millenium Deep Space One Autonomy

The Deep Space One (DS1) mission, scheduled to fly in 1998, will be the first NASA spacecraft to feature an on-board planner. The planner is part of an artificial intelligence based control architecture that comprises the planner/scheduler, a plan execution engine, and a model-based fault diagnosis and reconfiguration engine...This paper describes the on-board planning and scheduling component of the DS1 autonomy architecture.

Deep↗
NASA NTRSDate not supplied

ARCTRON: A Rapid Experimental Proving Ground for TPS Experiments and Arcjet Technology Development

Innovation in high-enthalpy facilities is fundamentally limited by the cost and risk of experimentation. New concepts for plasma control, diagnostics, facility components, and plasma-material interaction often require repeated iterations that are impractical to perform in production arcjets. As a result, promising ideas may remain unexplored or reach operational facilities only after significant development effort. ARCTRON is being developed as a rapid experimental proving ground where new ideas in plasma science, arcjet engineering, diagnostics, and material response can be conceived, tested, and quantitatively evaluated before transition to large-scale facilities. The platform combines radio-frequency (RF) and DC arc plasma generation, externally applied magnetic fields, configurable gas composition, reduced-pressure operation, laser heating, electrical biasing, and modular diagnostic access. These capabilities permit the plasma source, applied forcing, test article, and measurement configuration to be modified independently, allowing individual physical mechanisms to be isolated more readily than in a traditional test environment. One class of investigations addresses fundamental plasma-surface interaction physics. Conventional material tests often expose a specimen simultaneously to convective heating, reactive species, pressure, shear, radiation, and surface-current effects. The resulting material response may be measured accurately, while the contribution of each mechanism remains difficult to identify. ARCTRON is designed to vary these effects selectively. Plasma chemistry can be changed independently through configurable gas mixtures; magnetic fields and electrical biasing can modify charged-particle transport; laser heating can provide a non-plasma thermal input; and pressure, flow, and discharge mode can be varied over a broad operating space. This enables controlled tests of hypotheses involving surface catalycity, reactive-species transport, plasma-assisted oxidation, electromagnetic effects, shear, and the relative contributions of thermal and chemical loading. A second class of investigations enabled by this approach concerns the engineering of high-enthalpy facilities themselves. Arc-heated facilities are limited by electrode erosion, unstable arc attachment, localized heating, and damage to nozzles and other plasma-facing components. ARCTRON provides a lower-cost environment for testing concepts intended to mitigate these limitations. Candidate investigations include the use of applied magnetic fields to alter current paths and reduce plasma interaction with nozzle walls, ExB forcing to introduce controlled plasma rotation, magnetic or geometric approaches for distributing arc attachment, and alternative electrode or discharge configurations intended to reduce erosion and improve stability. Because the platform is reconfigurable, these concepts can be evaluated through repeated design--build--test cycles before they are considered for implementation in operational facilities. The platform also supports the development and validation of diagnostics that may be difficult to introduce initially into a large arcjet. Current and planned measurements include spatially resolved optical emission spectroscopy, electrostatic probes, fast imaging, pyrometry, calorimetry, laser-induced fluorescence, and absorption spectroscopy. These diagnostics are intended not merely to document a nominal operating condition, but to constrain the local plasma state and its relationship to component or material response. The modular facility geometry allows diagnostic concepts to be tested, calibrated, and compared under repeatable conditions before deployment in more demanding environments. ARCTRON is also supported by an integrated software suite. Automated control and data acquisition allow discharge parameters, gas composition, magnetic fields, diagnostic timing, and test configuration to be recorded as part of each experiment (STARDAC - Software for Testing, Analysis, Research Data, and Control). The Backend for Experiment Analysis, Storage, and Traceability (BEAST) is a database that provides the infrastructure needed to associate heterogeneous measurements with facility configuration, specimen identity, calibration state, geometry, and analysis provenance. This backend is particularly important for exploratory campaigns, in which many related configurations may be tested, and the value of an individual experiment depends on its connection to earlier and subsequent iterations. Complementary analysis capabilities, including computer-vision-based transient response measurements (arcjetCV), three-dimensional surface reconstruction (STARSCAN), and model-based Bayesian inference (SHIELD), and tomography data analysis (TOMATO, PuMA) can be incorporated when required by a specific hypothesis without becoming the focus of every campaign. The central objective of ARCTRON is therefore not to maximize heat flux or reproduce a complete flight environment. Its purpose is to reduce the cost and time required to ask consequential questions about plasma behavior, plasma-facing materials, diagnostics, and arcjet technology. By providing a controlled environment for rapid reconfiguration, mechanism isolation, quantitative measurement, and iterative engineering, ARCTRON can help mature concepts that would otherwise remain too speculative or too risky for evaluation in production facilities. The resulting knowledge can then guide the design of material models, focus test objectives in larger arcjets, reduce facility-development risk, and improve the physical basis of high-enthalpy ground testing. This work will present the ARCTRON architecture, operating modes, diagnostic suite, and digital experimental workflow. Initial experimental results from the first integrated operation of the facility will be presented, including flow characterization, power limitations, and deployment of the initial diagnostic suite. Ongoing development efforts aimed at catalycity characterization, magnetic plasma control, and advanced optical diagnostics will also be discussed, illustrating how the platform supports rapid iteration from concept to experiment.

experimental diagnostics↗
NASA NTRSDate not supplied

ARCTRON: A Rapid Experimental Proving Ground for TPS Experiments and Arcjet Technology Development

Innovation in high-enthalpy facilities is fundamentally limited by the cost and risk of experimentation. New concepts for plasma control, diagnostics, facility components, and plasma-material interaction often require repeated iterations that are impractical to perform in production arcjets. As a result, promising ideas may remain unexplored or reach operational facilities only after significant development effort. ARCTRON is being developed as a rapid experimental proving ground where new ideas in plasma science, arcjet engineering, diagnostics, and material response can be conceived, tested, and quantitatively evaluated before transition to large-scale facilities. The platform combines radio-frequency (RF) and DC arc plasma generation, externally applied magnetic fields, configurable gas composition, reduced-pressure operation, laser heating, electrical biasing, and modular diagnostic access. These capabilities permit the plasma source, applied forcing, test article, and measurement configuration to be modified independently, allowing individual physical mechanisms to be isolated more readily than in a traditional test environment. One class of investigations addresses fundamental plasma-surface interaction physics. Conventional material tests often expose a specimen simultaneously to convective heating, reactive species, pressure, shear, radiation, and surface-current effects. The resulting material response may be measured accurately, while the contribution of each mechanism remains difficult to identify. ARCTRON is designed to vary these effects selectively. Plasma chemistry can be changed independently through configurable gas mixtures; magnetic fields and electrical biasing can modify charged-particle transport; laser heating can provide a non-plasma thermal input; and pressure, flow, and discharge mode can be varied over a broad operating space. This enables controlled tests of hypotheses involving surface catalycity, reactive-species transport, plasma-assisted oxidation, electromagnetic effects, shear, and the relative contributions of thermal and chemical loading. A second class of investigations enabled by this approach concerns the engineering of high-enthalpy facilities themselves. Arc-heated facilities are limited by electrode erosion, unstable arc attachment, localized heating, and damage to nozzles and other plasma-facing components. ARCTRON provides a lower-cost environment for testing concepts intended to mitigate these limitations. Candidate investigations include the use of applied magnetic fields to alter current paths and reduce plasma interaction with nozzle walls, ExB forcing to introduce controlled plasma rotation, magnetic or geometric approaches for distributing arc attachment, and alternative electrode or discharge configurations intended to reduce erosion and improve stability. Because the platform is reconfigurable, these concepts can be evaluated through repeated design--build--test cycles before they are considered for implementation in operational facilities. The platform also supports the development and validation of diagnostics that may be difficult to introduce initially into a large arcjet. Current and planned measurements include spatially resolved optical emission spectroscopy, electrostatic probes, fast imaging, pyrometry, calorimetry, laser-induced fluorescence, and absorption spectroscopy. These diagnostics are intended not merely to document a nominal operating condition, but to constrain the local plasma state and its relationship to component or material response. The modular facility geometry allows diagnostic concepts to be tested, calibrated, and compared under repeatable conditions before deployment in more demanding environments. ARCTRON is also supported by an integrated software suite. Automated control and data acquisition allow discharge parameters, gas composition, magnetic fields, diagnostic timing, and test configuration to be recorded as part of each experiment (STARDAC - Software for Testing, Analysis, Research Data, and Control). The Backend for Experiment Analysis, Storage, and Traceability (BEAST) is a database that provides the infrastructure needed to associate heterogeneous measurements with facility configuration, specimen identity, calibration state, geometry, and analysis provenance. This backend is particularly important for exploratory campaigns, in which many related configurations may be tested, and the value of an individual experiment depends on its connection to earlier and subsequent iterations. Complementary analysis capabilities, including computer-vision-based transient response measurements (arcjetCV), three-dimensional surface reconstruction (STARSCAN), and model-based Bayesian inference (SHIELD), and tomography data analysis (TOMATO, PuMA) can be incorporated when required by a specific hypothesis without becoming the focus of every campaign. The central objective of ARCTRON is therefore not to maximize heat flux or reproduce a complete flight environment. Its purpose is to reduce the cost and time required to ask consequential questions about plasma behavior, plasma-facing materials, diagnostics, and arcjet technology. By providing a controlled environment for rapid reconfiguration, mechanism isolation, quantitative measurement, and iterative engineering, ARCTRON can help mature concepts that would otherwise remain too speculative or too risky for evaluation in production facilities. The resulting knowledge can then guide the design of material models, focus test objectives in larger arcjets, reduce facility-development risk, and improve the physical basis of high-enthalpy ground testing. This work will present the ARCTRON architecture, operating modes, diagnostic suite, and digital experimental workflow. Initial experimental results from the first integrated operation of the facility will be presented, including flow characterization, power limitations, and deployment of the initial diagnostic suite. Ongoing development efforts aimed at catalycity characterization, magnetic plasma control, and advanced optical diagnostics will also be discussed, illustrating how the platform supports rapid iteration from concept to experiment.

experimental diagnostics↗
NASA NTRS2013

Automated Generation of Fault Management Artifacts from a Simple System Model

Our understanding of off-nominal behavior - failure modes and fault propagation - in complex systems is often based purely on engineering intuition; specific cases are assessed in an ad hoc fashion as a (fallible) fault management engineer sees fit. This work is an attempt to provide a more rigorous approach to this understanding and assessment by automating the creation of a fault management artifact, the Failure Modes and Effects Analysis (FMEA) through querying a representation of the system in a SysML model. This work builds off the previous development of an off-nominal behavior model for the upcoming Soil Moisture Active-Passive (SMAP) mission at the Jet Propulsion Laboratory. We further developed the previous system model to more fully incorporate the ideas of State Analysis, and it was restructured in an organizational hierarchy that models the system as layers of control systems while also incorporating the concept of "design authority". We present software that was developed to traverse the elements and relationships in this model to automatically construct an FMEA spreadsheet. We further discuss extending this model to automatically generate other typical fault management artifacts, such as Fault Trees, to efficiently portray system behavior, and depend less on the intuition of fault management engineers to ensure complete examination of off-nominal behavior.

Spinup and Orient↗
NASA NTRS2013

State Machine Modeling of the Space Launch System Solid Rocket Boosters

The Space Launch System is a Shuttle-derived heavy-lift vehicle currently in development to serve as NASA's premiere launch vehicle for space exploration. The Space Launch System is a multistage rocket with two Solid Rocket Boosters and multiple payloads, including the Multi-Purpose Crew Vehicle. Planned Space Launch System destinations include near-Earth asteroids, the Moon, Mars, and Lagrange points. The Space Launch System is a complex system with many subsystems, requiring considerable systems engineering and integration. To this end, state machine analysis offers a method to support engineering and operational e orts, identify and avert undesirable or potentially hazardous system states, and evaluate system requirements. Finite State Machines model a system as a finite number of states, with transitions between states controlled by state-based and event-based logic. State machines are a useful tool for understanding complex system behaviors and evaluating "what-if" scenarios. This work contributes to a state machine model of the Space Launch System developed at NASA Ames Research Center. The Space Launch System Solid Rocket Booster avionics and ignition subsystems are modeled using MATLAB/Stateflow software. This model is integrated into a larger model of Space Launch System avionics used for verification and validation of Space Launch System operating procedures and design requirements. This includes testing both nominal and o -nominal system states and command sequences.

model-based systems engineering↗