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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 505 records · Page 28
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NASA NTRS2008

Technology Readiness of the NEXT Ion Propulsion System

The NASA's Evolutionary Xenon Thruster (NEXT) ion propulsion system has been in advanced technology development under the NASA In-Space Propulsion Technology project. The highest fidelity hardware planned has now been completed by the government/industry team, including: a flight prototype model (PM) thruster, an engineering model (EM) power processing unit, EM propellant management assemblies, a breadboard gimbal, and control unit simulators. Subsystem and system level technology validation testing is in progress. To achieve the objective Technology Readiness Level 6, environmental testing is being conducted to qualification levels in ground facilities simulating the space environment. Additional tests have been conducted to characterize the performance range and life capability of the NEXT thruster. This paper presents the status and results of technology validation testing accomplished to date, the validated subsystem and system capabilities, and the plans for completion of this phase of NEXT development. The next round of competed planetary science mission announcements of opportunity, and directed mission decisions, are anticipated to occur in 2008 and 2009. Progress to date, and the success of on-going technology validation, indicate that the NEXT ion propulsion system will be a primary candidate for mission consideration in these upcoming opportunities.

Benson, Scott W.↗
NASA NTRS2001

The Space Debris Environment for the ISS Orbit

With thirty-five planned missions over the next five years, the International Space Station (ISS) will be the focus for manned space activity. At least 6 different vehicles will transport crew and supplies to and from the nominally 400 km, 51.6 degree orbit. When completed, the ISS will be the largest space structure ever assembled and hence the largest target for space debris. Recent work at the Johnson Space Center has focused on updating the existing space debris models. The Orbital Debris Engineering Model, has been restructured to take advantage of state of the art desktop computing capability and revised with recent measurements from Haystack and Goldstone radars, additional analysis of LDEF and STS impacts, and the most recent SSN catalog. The new model also contains the capability to extrapolate the current environment in time to the year 2030. A revised meteoroid model based on the work of Divine has also been developed, and is called the JSC Meteoroid Model. The new model defines flux on the target per unit angle per unit speed, and for Earth orbit, includes the meteor showers. This paper quantifies the space debris environment for the ISS orbit from natural and anthropogenic sources. Particle flux and velocity distributions as functions of size and angle are be given for particles 10 microns and larger for altitudes from 350 to 450 km. The environment is projected forward in time until 2030.

Theall, Jeff↗
NASA NTRS2012

A Systems-Level Perspective on Engine Ice Accretion

Talk covers: (1) Problem of Engine Power Loss;(2) Modeling Engine Icing Effects; (3) Simulation of Engine Rollback; (4) Icing/Engine Control System Interaction; (5) Detection of Ice Accretion; (6) Potential Mitigation Strategies.

May, Ryan David↗
NASA NTRS2012

AeroPropulsoServoElasticity: Dynamic Modeling of the Variable Cycle Propulsion System

This presentation was made at the 2012 Fundamental Aeronautics Program Technical Conference and it covers research work for the Dynamic Modeling of the Variable cycle Propulsion System that was done under the Supersonics Project, in the area of AeroPropulsoServoElasticity. The presentation covers the objective for the propulsion system dynamic modeling work, followed by the work that has been done so far to model the variable Cycle Engine, modeling of the inlet, the nozzle, the modeling that has been done to model the affects of flow distortion, and finally presenting some concluding remarks and future plans.

Kopasakis, George↗
NASA NTRS2017

Marshall Engineering Thermosphere Model, Version MET-2007

The region of the Earth's atmosphere between about 90 and 500 km altitude is known as the thermosphere, while the region above about 500 km is known as the exosphere. For space vehicle operations, the neutral atmosphere in these regions is significant. Even at its low density, it produces torques and drags on vehicles and affects orbital lifetimes. The thermosphere density above 100 km altitude also modulates the flux of trapped radiation and orbital debris. Atomic oxygen at orbital altitudes is important because it can erode and chemically change exposed vehicle surfaces.

Suggs, R. J.↗
NASA NTRS2001

A Roadmap for Aircraft Engine Life Extending Control

The concept of Aircraft Engine Life Extending Control is introduced. A brief description of the tradeoffs between performance and engine life are first explained. The overall goal of the life extending controller is to reduce the engine operating cost by extending the on-wing engine life while improving operational safety. The research results for NASA's Rocket Engine life extending control program are also briefly described. Major building blocks of the Engine Life Extending Control architecture are examined. These blocks include: life prediction models, engine operation models, stress and thermal analysis tools, control schemes, and intelligent control systems. The technology areas that would likely impact the successful implementation of an aircraft engine life extending control are also briefly described. Near, intermediate, and long term goals of NASA's activities are also presented.

Guo, Ten-Huei↗
NASA NTRS1992

A probabilistic approach to the evaluation of fatigue damage in a space propulsion system injector element

The fatigue damage of a space propulsion system component is computed using probabilistic structural analysis methods. The analysis takes into account the variations in static and dynamic loads, the uncertainty in structural damping, and the scatter in material fatigue resistance. The key elements of the probabilistic approach include: (1) a numerical engine model for describing the global component interface loads consistent with engine balance, (2) models for computing the local mode boundary conditions on the component, (3) a static structural analysis model that captures the strains at a damage critical location as a function of engine performance variables, (4) a finite element model for assessment of the random amplitude stress response due to random base and pressure excitations with uncertain power and correlation length, and (5) advanced first order reliability methods for computing the probabilities associated with the fatigue damage.

Rajagopal, K. R.↗
NASA NTRS2009

Efficient Model-Based Diagnosis Engine

An efficient diagnosis engine - a combination of mathematical models and algorithms - has been developed for identifying faulty components in a possibly complex engineering system. This model-based diagnosis engine embodies a twofold approach to reducing, relative to prior model-based diagnosis engines, the amount of computation needed to perform a thorough, accurate diagnosis. The first part of the approach involves a reconstruction of the general diagnostic engine to reduce the complexity of the mathematical-model calculations and of the software needed to perform them. The second part of the approach involves algorithms for computing a minimal diagnosis (the term "minimal diagnosis" is defined below). A somewhat lengthy background discussion is prerequisite to a meaningful summary of the innovative aspects of the present efficient model-based diagnosis engine. In model-based diagnosis, the function of each component and the relationships among all the components of the engineering system to be diagnosed are represented as a logical system denoted the system description (SD). Hence, the expected normal behavior of the engineering system is the set of logical consequences of the SD. Faulty components lead to inconsistencies between the observed behaviors of the system and the SD (see figure). Diagnosis - the task of finding faulty components - is reduced to finding those components, the abnormalities of which could explain all the inconsistencies. The solution of the diagnosis problem should be a minimal diagnosis, which is a minimal set of faulty components. A minimal diagnosis stands in contradistinction to the trivial solution, in which all components are deemed to be faulty, and which, therefore, always explains all inconsistencies.

Fijany, Amir↗