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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 721 records · Page 40
All sourcesNASA NTRSDOE OSTI
NASA NTRS2007

Environmental Testing of the NEXT PM1R Ion Engine

The NEXT propulsion system is an advanced ion propulsion system presently under development that is oriented towards robotic exploration of the solar system using solar electric power. The subsystem includes an ion engine, power processing unit, feed system components, and thruster gimbal. The Prototype Model engine PM1 was subjected to qualification-level environmental testing in 2006 to demonstrate compatibility with environments representative of anticipated mission requirements. Although the testing was largely successful, several issues were identified including the fragmentation of potting cement on the discharge and neutralizer cathode heater terminations during vibration which led to abbreviated thermal testing, and generation of particulate contamination from manufacturing processes and engine materials. The engine was reworked to address most of these findings, renamed PM1R, and the environmental test sequence was repeated. Thruster functional testing was performed before and after the vibration and thermal-vacuum tests. Random vibration testing, conducted with the thruster mated to the breadboard gimbal, was executed at 10.0 Grms for 2 min in each of three axes. Thermal-vacuum testing included three thermal cycles from 120 to 215 C with hot engine re-starts. Thruster performance was nominal throughout the test program, with minor variations in a few engine operating parameters likely caused by facility effects. There were no significant changes in engine performance as characterized by engine operating parameters, ion optics performance measurements, and beam current density measurements, indicating no significant changes to the hardware as a result of the environmental testing. The NEXT PM1R engine and the breadboard gimbal were found to be well-designed against environmental requirements based on the results reported herein. The redesigned cathode heater terminations successfully survived the vibration environments. Based on the results of this test program and confidence in the engineering solutions available for the remaining findings of the first test program, specifically the particulate contamination, the hardware environmental qualification program can proceed with confidence

Snyder, John S.↗
NASA NTRS2003

An Overview of the NASA Aviation Safety Program (AVSP) Systemwide Accident Prevention (SWAP) Human Performance Modeling (HPM) Element

An overview is provided of the Human Performance Modeling (HPM) element within the NASA Aviation Safety Program (AvSP). Two separate model development tracks for performance modeling of real-world aviation environments are described: the first focuses on the advancement of cognitive modeling tools for system design, while the second centers on a prescriptive engineering model of activity tracking for error detection and analysis. A progressive implementation strategy for both tracks is discussed in which increasingly more complex, safety-relevant applications are undertaken to extend the state-of-the-art, as well as to reveal potential human-system vulnerabilities in the aviation domain. Of particular interest is the ability to predict the precursors to error and to assess potential mitigation strategies associated with the operational use of future flight deck technologies.

Foyle, David C.↗
NASA NTRS2010

The Fifteen-Year Attitude History of the Wide Field Planetary Camera 2 Radiator and Collection Efficiencies for Micrometeoroids and Orbital Debris

An examination of the Hubble Space Telescope (HST) Wide Field Planetary Camera 2 (WFPC-2) radiator assembly was conducted at NASA Goddard Space Flight Center (GSFC) during the summer of 2009. Immediately apparent was a distinct biasing of the largest 45 impact features towards one side of the radiator, in contrast to an approximately uniform distribution of smaller impacts. Such a distribution may be a consequence of the HST s attitude history and pointing requirements for the cold radiator, or of environmental effects, such as an anisotropic distribution of the responsible population in that size regime. Understanding the size-dependent spatial distribution of impact features is essential to the general analysis of these features. We have obtained from GSFC a 15 minute temporal resolution record of the state vector (Earth Centered Inertial position and velocity) and HST attitude, consisting of the orientation of the velocity and HST-sun vectors in HST body coordinates. This paper reviews the actual state vector and attitude history of the radiator in the context of the randomly tumbling plate assumption and assesses the statistical likelihood (or collection efficiency) of the radiator for the micrometeoroid and orbital debris environments. The NASA Marshall Space Flight Center s Meteoroid Environment Model is used to assess the micrometeoroid component. The NASA Orbital Debris Engineering Model (ORDEM) is used to model the orbital debris component. Modeling results are compared with observations of the impact feature spatial distribution, and the relative contribution of each environmental component are examined in detail.

Anz-Meador, Phillip D.↗
NASA NTRS2014

Overview of NASA GRCs Green Propellant Infusion Mission Thruster Testing and Plume Diagnostics

The Green Propellant Infusion Mission (GPIM) is sponsored by NASA's Space Technology Mission Directorate (STMD) Technology Demonstration Mission (TDM) office. The goal of GPIM is to advance the technology readiness level of a green propulsion system, specifically, one using the monopropellant, AF-M315E, by demonstrating ground handling, spacecraft processing, and on-orbit operations. One of the risks identified for GPIM is potential contamination of sensitive spacecraft surfaces from the effluents in the plumes of AF-M315E thrusters. NASA Glenn Research Center (GRC) is conducting activities to characterize the effects of AF-M315E plume impingement and deposition. GRC has established individual plume models of the 22-N and 1-N thrusters that will be used on the GPIM spacecraft. The models describe the pressure, temperature, density, Mach number, and species concentration of the AF-M315E thruster exhaust plumes. The models are being used to assess the impingement effects of the AF-M315E thrusters on the GPIM spacecraft. The model simulations will be correlated with plume measurement data from Laboratory and Engineering Model 22-N, AF-M315E thrusters. The thrusters will be tested in a small rocket, altitude facility at NASA GRC. The GRC thruster testing will be conducted at duty cycles representatives of the planned GPIM maneuvers. A suite of laser-based diagnostics, including Raman spectroscopy, Rayleigh spectroscopy, Schlieren imaging, and physical probes will be used to acquire plume measurements of AFM315E thrusters. Plume data will include temperature, velocity, relative density, and species concentration. The plume measurement data will be compared to the corresponding simulations of the plume model. The GRC effort will establish a data set of AF-M315E plume measurements and a plume model that can be used for future AF-M315E applications.

green Propellants↗
NASA NTRS2014

The Green Propellant Infusion Mission Thruster Performance Testing for Plume Diagnostics

The Green Propellant Infusion Mission (GPIM) is sponsored by NASA's Space Technology Mission Directorate (STMD) Technology Demonstration Mission (TDM) office. The goal of GPIM is to advance the technology readiness level of a green propulsion system, specifically, one using the monopropellant, AF-M315E, by demonstrating ground handling, spacecraft processing, and on-orbit operations. One of the risks identified for GPIM is potential contamination of sensitive spacecraft surfaces from the effluents in the plumes of AF-M315E thrusters. NASA Glenn Research Center (GRC) is conducting activities to characterize the effects of AF-M315E plume impingement and deposition. GRC has established individual plume models of the 22-N and 1-N thrusters that will be used on the GPIM spacecraft. The model simulations will be correlated with plume measurement data from Laboratory and Engineering Model 22-N, AF-M315E thrusters. The thrusters are currently being tested in a small rocket, altitude facility at NASA GRC. A suite of diagnostics, including Raman spectroscopy, Rayleigh spectroscopy, and Schlieren imaging are being used to acquire plume measurements of AF-M315E thrusters. Plume data will include temperature, velocity, relative density, and species concentration. The plume measurement data will be compared to the corresponding simulations of the plume model. The GRC effort will establish a data set of AF-M315E plume measurements and a plume model that can be used for future AF-M315E applications.

Green Propellants↗
NASA NTRS2021

An Assurance Case with a Model at its Core

We describe our pilot study development of an Assurance Case arguing the robustness of a spacecraft demonstration of optical communication. Our Assurance Case addresses the concern that optical communication may be interrupted by the presence of cloud cover, threatening the success of the demonstration. Central to our Assurance Case is its use of a model of atmospheric attenuation to support a key portion of the robustness argument. The conclusion for the demonstration is that its schedule slack plus ability to store data for transmission later accommodates occasional weather-caused atmospheric attenuation.We indicate how the overall structure of the Assurance Case derives from the Objectives Hierarchy set forth in the NASA Reliability and Maintainability standard. We then present the portions of the Assurance Case that argue (1) the atmospheric attenuation model is sufficiently accurate, (2) application of the model shows the desired robustness of the demonstration, and (3) all the model assumptions are satisfied in its application. Lastly, we suggest how an engineering model of the demonstration system and its use could inform the development of the Assurance Case encompassing additional plausible hazards.

DiVenti, Anthony↗
DOE OSTI2023

Tidal Turbine Benchmarking Project: Stage I - Steady Flow Blind Predictions: Preprint

This paper presents the first blind prediction stage of the Tidal Turbine Benchmarking Project being conducted and funded by the UK's EPSRC and Supergen ORE Hub. In this first stage, only steady flow conditions, at low and elevated turbulence (3.1%) levels, were considered. Prior to the blind prediction stage, a large laboratory scale experiment was conducted in which a highly instrumented 1.6m diameter tidal rotor was towed through a large towing tank in well-defined flow conditions with and without an upstream turbulence grid. Details of the test campaign and rotor design were released as part of this community blind prediction exercise. Participants were invited to use a range of engineering modelling approaches to simulate the performance and loads of the turbine. 26 submissions were received from 12 groups from across academia and industry using solution techniques ranging from blade resolved computational fluid dynamics through actuator line, boundary integral element methods, vortex methods to engineering Blade Element Momentum methods. The comparisons between experiments and blind predictions were extremely positive helping to provide validation and uncertainty estimates for the models, but also validating the experimental tests themselves. The exercise demonstrated that the experimental turbine data provides a robust data set against which researchers and design engineers can test their models and implementations to ensure robustness in their processes, helping to reduce uncertainty and provide increased confidence in engineering processes. Furthermore, the data set provides the basis by which modellers can evaluate and refine approaches.

benchmarking↗
NASA NTRS2006

JSC Orbital Debris Website Description

Purpose: The website provides information about the NASA Orbital Debris Program Office at JSC, which is the lead NASA center for orbital debris research. It is recognized world-wide for its leadership in addressing orbital debris issues. The NASA Orbital Debris Program Office has taken the international lead in conducting measurements of the environment and in developing the technical consensus for adopting mitigation measures to protect users of the orbital environment. Work at the center continues with developing an improved understanding of the orbital debris environment and measures that can be taken to control its growth. Major Contents: Orbital Debris research is divided into the following five broad efforts. Each area of research contains specific information as follows: 1) Modeling - NASA scientists continue to develop and upgrade orbital debris models to describe and characterize the current and future debris environment. Evolutionary and engineering models are described in detail. Downloadable items include a document in PDF format and executable software. 2) Measurements - Measurements of near-Earth orbital debris are accomplished by conducting ground-based and space-based observations of the orbital debris environment. The data from these sources provide validation of the environment models and identify the presence of new sources. Radar, optical and surface examinations are described. External links to related topics are provided. 3) Protection - Orbital debris protection involves conducting hypervelocity impact measurements to assess the risk presented by orbital debris to operating spacecraft and developing new materials and new designs to provide better protection from the environment with less weight penalty. The data from this work provides the link between the environment defined by the models and the risk presented by that environment to operating spacecraft and provides recommendations on design and operations procedures to reduce the risk as required. These data also help in the analysis and interpretation of impact features on returned spacecraft surfaces. 4) Mitigation - Controlling the growth of the orbital debris population is a high priority for NASA, the United States, and the major space-faring nations of the world to preserve near-Earth space for future generations. Mitigation measures can take the form of curtailing or preventing the creation of new debris, designing satellites to withstand impacts by small debris, and implementing operational procedures ranging from utilizing orbital regimes with less debris, adopting specific spacecraft attitudes, and even maneuvering to avoid collisions with debris. Downloadable items include several documents in PDF format and executable software.and 5) Reentry - Because of the increasing number of objects in space, NASA has adopted guidelines and assessment procedures to reduce the number of non-operational spacecraft and spent rocket upper stages orbiting the Earth. One method of postmission disposal is to allow reentry of these spacecraft, either from orbital decay (uncontrolled entry) or with a controlled entry. Orbital decay may be achieved by firing engines to lower the perigee altitude so that atmospheric drag will eventually cause the spacecraft to enter. However, the surviving debris impact footprint cannot be guaranteed to avoid inhabited landmasses. Controlled entry normally occurs by using a larger amount of propellant with a larger propulsion system to drive the spacecraft to enter the atmosphere at a steeper flight path angle. It will then enter at a more precise latitude, longitude, and footprint in a nearly uninhabited impact region, generally located in the ocean.

Johnson, Nicholas L.↗
NASA NTRS1987

Anatomy of a bearing torque problem

In the early 1970s, an antenna despin drive was developed for MBB solar science satellite HELIOS. A problem with high bearing drag torque that was encountered on the two flight models of this drive, after successful tests were completed on twelve bearings, an engineering model, and the qualification unit is discussed.

Phinney, Damon D.↗
NASA NTRSDate not supplied

High-Propellant Throughput Sub-kW Electric Propulsion System for Deep Space Science and Exploration

The National Aeronautics and Space Administration (NASA) is maturing high-propellant throughput sub-kilowatt electric propulsion technologies to enable small spacecraft deep space science and exploration missions with high delta-v requirements. The pathfinder model (PM) propulsion system consists of the H71M-PM Hall-effect thruster, a breadboard 1-kW power processing unit (PPU), and a propellant flow control system. The propulsion system requirements balance the needs of various high delta-v NASA and commercial industry mission concepts to achieve a design that both enables a variety of NASA small spacecraft deep space missions, while remaining viable for select commercial applications. The H71M-PM thruster has completed performance characterization and three 500-h short duration wear tests (SDWT). The propulsion system provides stable thrust generation over a wide range of operating conditions from 200 W to 1 kW, and 200 V to 400 V. The thruster has demonstrated a thrust as high as 68 mN at 300 V and 1 kW. The thruster has similarly demonstrated a specific impulse of 1850 s at 400 V and 1 kW. Key surfaces were machined between each SDWT to simulate accelerated discharge channel and pole cover erosion. Profilometry scans across masked pole cover surfaces were conducted to determine erosion rates. SDWT results support that a target thruster lifetime of 14 kh with 50% margin is achievable. Component testing has demonstrated propellant azimuthal flow uniformity better than ± 2 percent of the nominal value, azimuthal magnetic field uniformity better than ± 0.5 percent of the nominal value, and cathode heater cycle testing to greater than 30,000 cycles. Propulsion system integrated system testing is planned to use the H71M-PM and a breadboard 1-kW PPU. Pathfinder model test results are now supporting the design of the H71M-EM engineering model thruster. A second-generation breadboard PPU has been fabricated and is currently under test. NASA has made these technologies available to U.S. industry through a no cost, nonexclusive license agreement.

Hall↗
NASA NTRS2015

Evaluation of Finite-Rate Gas/Surface Interaction Models for a Carbon Based Ablator

Two sets of finite-rate gas-surface interaction model between air and the carbon surface are studied. The first set is an engineering model with one-way chemical reactions, and the second set is a more detailed model with two-way chemical reactions. These two proposed models intend to cover the carbon surface ablation conditions including the low temperature rate-controlled oxidation, the mid-temperature diffusion-controlled oxidation, and the high temperature sublimation. The prediction of carbon surface recession is achieved by coupling a material thermal response code and a Navier-Stokes flow code. The material thermal response code used in this study is the Two-dimensional Implicit Thermal-response and Ablation Program, which predicts charring material thermal response and shape change on hypersonic space vehicles. The flow code solves the reacting full Navier-Stokes equations using Data Parallel Line Relaxation method. Recession analyses of stagnation tests conducted in NASA Ames Research Center arc-jet facilities with heat fluxes ranging from 45 to 1100 wcm2 are performed and compared with data for model validation. The ablating material used in these arc-jet tests is Phenolic Impregnated Carbon Ablator. Additionally, computational predictions of surface recession and shape change are in good agreement with measurement for arc-jet conditions of Small Probe Reentry Investigation for Thermal Protection System Engineering.

Ablation↗
NASA NTRS2010

Modeling of LEO Orbital Debris Populations in Centimeter and Millimeter Size Regimes

The building of the NASA Orbital Debris Engineering Model, whether ORDEM2000 or its recently updated version ORDEM2010, uses as its foundation a number of model debris populations, each truncated at a minimum object-size ranging from 10 micron to 1 m. This paper discusses the development of the ORDEM2010 model debris populations in LEO (low Earth orbit), focusing on centimeter (smaller than 10 cm) and millimeter size regimes. Primary data sets used in the statistical derivation of the cm- and mm-size model populations are from the Haystack radar operated in a staring mode. Unlike cataloged objects of sizes greater than approximately 10 cm, ground-based radars monitor smaller-size debris only in a statistical manner instead of tracking every piece. The mono-static Haystack radar can detect debris as small as approximately 5 mm at moderate LEO altitudes. Estimation of millimeter debris populations (for objects smaller than approximately 6 mm) rests largely on Goldstone radar measurements. The bi-static Goldstone radar can detect 2- to 3-mm objects. The modeling of the cm- and mm-debris populations follows the general approach to developing other ORDEM2010-required model populations for various components and types of debris. It relies on appropriate reference populations to provide necessary prior information on the orbital structures and other important characteristics of the debris objects. NASA's LEO-to-GEO Environment Debris (LEGEND) model is capable of furnishing such reference populations in the desired size range. A Bayesian statistical inference process, commonly adopted in ORDEM2010 model-population derivations, changes a priori distribution into a posteriori distribution and thus refines the reference populations in terms of data. This paper describes key elements and major steps in the statistical derivations of the cm- and mm-size debris populations and presents results. Due to lack of data for near 1-mm sizes, the model populations of 1- to 3.16-mm objects are an empirical extension from larger debris. The extension takes into account the results of micro-debris (from 10 micron to 1 mm) population modeling that is based on shuttle impact data, in the hope of making a smooth transition between micron and millimeter size regimes. This paper also includes a brief discussion on issues and potential future work concerning the analysis and interpretation of Goldstone radar data.

Xu, Y.-L.↗
NASA NTRSDate not supplied

The Development and Deployment of Machine Learning Models for Aircraft Engine Concept Assessment

In today's competitive landscape, the effective development and utilization of machine-learning (ML) applications have become imperative across various sectors. This study presents an outline of the procedure involved in creating and implementing ML models for conceptualizing and evaluating aircraft engines. These models leverage supervised deep-learning algorithms to analyze patterns within an open-source repository containing data on both production and research conventional turbofan engines. The main areas of focus encompass crucial engine parameters like thrust-specific fuel consumption (TSFC), engine weight, engine diameter, and turbomachinery stage counts. While the creation of ML models is fundamental for their utilization, ensuring their seamless deployment holds equal significance. To address this aspect, a conversational AI chatbot is constructed, utilizing natural language processing (NLP) techniques, to facilitate the deployment of these ML models. The comprehensive workflow encompasses several key stages: gathering and enhancing engine data, training and cross validating the ML models, testing and evaluating their performance, and finally, deploying, monitoring, and updating the ML models. By following this systematic approach, the aim is to streamline the development and deployment process of ML models tailored for aircraft engine assessment.

Development↗
NASA NTRSDate not supplied

The Development and Deployment of Machine Learning Models for Aircraft Engine Concept Assessment

In today's competitive landscape, the effective development and utilization of machine-learning (ML) applications have become imperative across various sectors. This study presents an outline of the procedure involved in creating and implementing ML models for conceptualizing and evaluating aircraft engines. These models leverage supervised deep-learning algorithms to analyze patterns within an open-source repository containing data on both production and research conventional turbofan engines. The main areas of focus encompass crucial engine parameters like thrust-specific fuel consumption (TSFC), engine weight, engine diameter, and turbomachinery stage counts. While the creation of ML models is fundamental for their utilization, ensuring their seamless deployment holds equal significance. To address this aspect, a conversational AI chatbot is constructed, utilizing natural language processing (NLP) techniques, to facilitate the deployment of these ML models. The comprehensive workflow encompasses several key stages: gathering and enhancing engine data, training and cross validating the ML models, testing and evaluating their performance, and finally, deploying, monitoring, and updating the ML models. By following this systematic approach, the aim is to streamline the development and deployment process of ML models tailored for aircraft engine assessment.

Development↗
NASA NTRS2024

The Development and Deployment of Machine Learning Models for Aircraft Engine Concept Assessment

In today's competitive landscape, the effective development and utilization of machine-learning (ML) applications have become crucial across diverse economic sectors. This study presents an outline of the procedure involved in creating and implementing ML models for conceptualizing and evaluating aircraft engines. These models leverage supervised deep-learning algorithms to analyze patterns within an open-source repository containing data on both production and research conventional turbofan engines. The main areas of focus encompass crucial engine parameters like thrust-specific fuel consumption (TSFC), engine weight, engine diameter, and turbomachinery stage counts. While the creation of ML models is fundamental for their utilization, ensuring their seamless deployment holds equal significance. To address this aspect, a conversational AI chatbot that specifically focuses on propulsion has been developed. Leveraging natural language processing (NLP) techniques, this chatbot simplifies the deployment of machine learning (ML) models. The comprehensive workflow encompasses several key stages: gathering and enhancing engine data, training and cross validating the ML models, testing and evaluating their performance, and finally, deploying, monitoring, and updating the ML models. By following this systematic approach, the aim is to streamline the development and deployment process of ML models tailored for aircraft engine assessment.

AI Chatbot↗
NASA NTRSDate not supplied

A Model-Based Systems Engineering Journey to Developing a Concept of Operations

Starting in 2017, NASA’s Human Research Program (HRP) Exploration Medical Capability (ExMC) element began a systems engineering transition from traditional, document-centric development to model-centric development when defining its foundation medical systems. These foundation medical systems define a Concept of Operations (ConOps) and identify the generic requirements for a medical system based on assumptions about a generic crew and mission environments and guidance from NASA standards (e.g., Medical “Levels of Care”). By making the transition, ExMC intends to improve communication among stakeholders about foundation medical system requirements and content. In addition, this transition will enable ExMC to lower both development and crew treatment risks for future, mission-specific medical systems. ExMC followed a Model Based Systems Engineering (MBSE) paradigm when developing the foundation medical systems. A model-based approach provides several advantages over a traditional, document-centric approach. First, when Systems Engineers (SE) develop diagrams in a model using a standard modeling language, they produce information dense pictures that facilitate understanding much more efficiently with less room for misinterpretation than text. Second, due to the evolving nature of projects, documentation becomes out of date the minute it is published. This can result in people making decisions based on information that is no longer current, especially if they are referencing a locally-stored copy of a document. A model, on the other hand, is always up to date with the latest approved changes and information. It serves as a single point of truth. Third, a model-centric approach centralizes all important information in one place. Rather than having to flip through separate ConOps documents, design specifications, requirements specifications, and the like to coordinate information, a model captures the content in one, integrated spot. This integration makes tracing information from end-to-end easier with greater reliability. The ExMC Systems Engineering Lifecycle follows a well-defined process. ExMC Systems Engineers perform all major steps of the process, regardless of the development methodology. One of the first steps in the process is developing the ConOps that describes the operation of the system from the point of view of the users. It includes a list of the users and their needs, the goals of the medical system, key assumptions about the system, and definitions of the medical system’s operational environments. For this development effort, ExMC chose to replace the traditional text-based ConOps document with a model. While the decision to change the development workflow was not difficult, implementing the structural and organizational workflows were. It required showing ExMC’s users, most of whom are not Systems Engineers, how the information they require would be presented in the model and to gain their acceptance of this approach. This paper documents key lessons learned during the ConOps transformation by focusing on how the model represents information, the agile workflow used by SEs when developing the model and how it integrates into a project plan, how leadership influenced key users to accept the transformation, and how the users interact with the model information.

Jeffrey Robert Cohen↗