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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 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS1984

Effect of control logic modifications on airstart performance of F100 engine model derivative engines in an F-15 airplane

A series of airstarts were conducted in an F-15 airplane with two prototype Pratt and Whitney F100 Engine Model Derivative engines equipped with Digital Electronic Engine Control (DEEC) systems. The airstart envelope and the time required for airstarts were defined. Comparisons were made between the original airstart logic, and modified logic which was designed to improve the airstart capability. Spooldown airstarts with the modified logic were more successful at lower altitudes than were those with the original logic. Spooldown airstart times ranged from 33 seconds at 250 knots to 83 seconds at 175 knots. The modified logic improved the airstart time from 31% to 53%, with the most improved times at slower airspeeds. Jet fuel starter (JFS)-assisted airstarts were conducted at 7000 m and airstart times were significantly faster than unassisted airstarts. The effect of altitude on airstart times was small.

Crawford, D. B.↗
NASA NTRS1986

Augmentor performance of an F100 engine model derivative engine in an F-15 airplane

The transient performance of the F100 engine model derivative (EMD) augmentor was evaluated in an F-15 airplane. The augmentor was a newly designed 16-segment augmentor. It was tested with a segment-1 sprayring with 90 deg fuel injection, and later with a modified segment-1 sprayring with centerline fuel injection. With the 90 deg injection, no-lights occurred at high altitudes with airspeeds of 175 knots or less; however, the results were better than when using the standard F100-PW-100 engine. With the centerline fuel injection, all transients were successful to an altitude of 15,500 meters and an airspeed of 150 knots: no failures to light, blowouts, or stalls occurred. For a first flight evaluation, the augmentor transient performance was excellent.

Walton, James T.↗
NASA NTRS2017

Meteoroid Environment Modeling: the Meteoroid Engineering Model and Shower Forecasting

INTRODUCTION: The meteoroid environment is often divided conceptually into meteor showers and the sporadic meteor background. It is commonly but incorrectly assumed that meteoroid impacts primarily occur during meteor showers; instead, the vast majority of hazardous meteoroids belong to the sporadic complex. Unlike meteor showers, which persist for a few hours to a few weeks, sporadic meteoroids impact the Earth's atmosphere and spacecraft throughout the year. The Meteoroid Environment Office (MEO) has produced two environment models to handle these cases: the Meteoroid Engineering Model (MEM) and an annual meteor shower forecast. The sporadic complex, despite its year-round activity, is not isotropic in its directionality. Instead, their apparent points of origin, or radiants, are organized into groups called "sources". The speed, directionality, and size distribution of these sporadic sources are modeled by the Meteoroid Engineering Model (MEM), which is currently in its second major release version (MEMR2) [Moorhead et al., 2015]. MEM provides the meteoroid flux relative to a user-provided spacecraft trajectory; it provides the total flux as well as the flux per angular bin, speed interval, and on specific surfaces (ram, wake, etc.). Because the sporadic complex dominates the meteoroid flux, MEM is the most appropriate model to use in spacecraft design. Although showers make up a small fraction of the meteoroid environment, they can produce significant short-term enhancements of the meteoroid flux. Thus, it can be valuable to consider showers when assessing risks associated with vehicle operations that are brief in duration. To assist with such assessments, the MEO issues an annual forecast that reports meteor shower fluxes as a function of time and compares showers with the time-averaged total meteoroid flux. This permits missions to do quick assessments of the increase in risk posed by meteor showers.

Moorhead, Althea V.↗
NASA NTRS2017

Meteoroid Environment Modeling: The Meteoroid Engineering Model and Shower Forecasting

The meteoroid environment is often divided conceptually into meteor showers and the sporadic meteor background. It is commonly but incorrectly assumed that meteoroid impacts primarily occur during meteor showers; instead, the vast majority of hazardous meteoroids belong to the sporadic complex. Unlike meteor showers, which persist for a few hours to a few weeks, sporadic meteoroids impact the Earth's atmosphere and spacecraft throughout the year. The Meteoroid Environment Office (MEO) has produced two environment models to handle these cases: the Meteoroid Engineering Model (MEM) and an annual meteor shower forecast. The sporadic complex, despite its year-round activity, is not isotropic in its directionality. Instead, their apparent points of origin, or radiants, are organized into groups called "sources". The speed, directionality, and size distribution of these sporadic sources are modeled by the Meteoroid Engineering Model (MEM), which is currently in its second major release version (MEMR2) [Moorhead et al., 2015]. MEM provides the meteoroid flux relative to a user-provided spacecraft trajectory; it provides the total flux as well as the flux per angular bin, speed interval, and on specific surfaces (ram, wake, etc.). Because the sporadic complex dominates the meteoroid flux, MEM is the most appropriate model to use in spacecraft design. Although showers make up a small fraction of the meteoroid environment, they can produce significant short-term enhancements of the meteoroid flux. Thus, it can be valuable to consider showers when assessing risks associated with vehicle operations that are brief in duration. To assist with such assessments, the MEO issues an annual forecast that reports meteor shower fluxes as a function of time and compares showers with the time-averaged total meteoroid flux. This permits missions to do quick assessments of the increase in risk posed by meteor showers. Section II describes MEM in more detail and describes our current efforts to improve its characteristics for a future release. Section III describes the annual shower forecast and highlights recent improvements made to its algorithm and inputs.

Moorhead, Althea V.↗
NASA NTRS2017

Meteoroid Environment Modeling: the Meteoroid Engineering Model and Shower Forecasting

The meteoroid environment is often divided conceptually into meteor showers plus a sporadic background component. The sporadic complex poses the bulk of the risk to spacecraft, but showers can produce significant short-term enhancements of the meteoroid flux. The Meteoroid Environment Office (MEO) has produced two environment models to handle these cases: the Meteoroid Engineering Model (MEM) and an annual meteor shower forecast. Both MEM and the forecast are used by multiple manned spaceflight projects in their meteoroid risk evaluation, and both tools are being revised to incorporate recent meteor velocity, density, and timing measurements. MEM describes the sporadic meteoroid complex and calculates the flux, speed, and directionality of the meteoroid environment relative to a user-supplied spacecraft trajectory, taking the spacecraft's motion into account. MEM is valid in the inner solar system and offers near-Earth and cis-lunar environments. While the current version of MEM offers a nominal meteoroid environment corresponding to a single meteoroid bulk density, the next version of MEMR3 will offer both flux uncertainties and a density distribution in addition to a revised near-Earth environment. We have updated the near-Earth meteor speed distribution and have made the first determination of uncertainty in this distribution. We have also derived a meteor density distribution from the work of Kikwaya et al. (2011). The annual meteor shower forecast takes the form of a report and data tables that can be used in conjunction with an existing MEM assessment. Fluxes are typically quoted to a constant limiting kinetic energy in order to comport with commonly used ballistic limit equations. For the 2017 annual forecast, the MEO substantially revised the list of showers and their characteristics using 14 years of meteor flux measurements from the Canadian Meteor Orbit Radar (CMOR). Defunct or insignificant showers were removed and the temporal profiles of many showers were improved. In 2016 the MEO also adapted the forecast to the cislunar environment for the first time. We plan to make additional improvements to the model in the next two years using optical meteor flux measurements and mass indices.

Moorhead, Althea V.↗
NASA NTRS2017

Meteoroid Environment Modeling: the Meteoroid Engineering Model and Shower Forecasting

The meteoroid environment is often divided conceptually into meteor showers plus a sporadic background component. The sporadic complex poses the bulk of the risk to spacecraft, but showers can produce significant short-term enhancements of the meteoroid flux. The Meteoroid Environment Office (MEO) has produced two environment models to handle these cases: the Meteoroid Engineering Model (MEM) and an annual meteor shower forecast. Both MEM and the forecast are used by multiple manned spaceflight projects in their meteoroid risk evaluation, and both tools are being revised to incorporate recent meteor velocity, density, and timing measurements. MEM describes the sporadic meteoroid complex and calculates the flux, speed, and directionality of the meteoroid environment relative to a user-supplied spacecraft trajectory, taking the spacecraft's motion into account. MEM is valid in the inner solar system and offers near-Earth and cis-lunar environments. While the current version of MEM offers a nominal meteoroid environment corresponding to a single meteoroid bulk density, the next version of MEMR3 will offer both flux uncertainties and a density distribution in addition to a revised near-Earth environment. We have updated the near-Earth meteor speed distribution and have made the first determination of uncertainty in this distribution. We have also derived a meteor density distribution from the work of Kikwaya et al. (2011). The annual meteor shower forecast takes the form of a report and data tables that can be used in conjunction with an existing MEM assessment. Fluxes are typically quoted to a constant limiting kinetic energy in order to comport with commonly used ballistic limit equations. For the 2017 annual forecast, the MEO substantially revised the list of showers and their characteristics using 14 years of meteor flux measurements from the Canadian Meteor Orbit Radar (CMOR). Defunct or insignificant showers were removed and the temporal profiles of many showers were improved. In 2016 the MEO also adapted the forecast to the cislunar environment for the first time. We plan to make additional improvements to the model in the next two years using optical meteor flux measurements and mass indices.

Moorhead, Althea V.↗
NASA NTRS1984

Comparison of free-piston Stirling engine model predictions with RE1000 engine test data

Predictions of a free-piston Stirling engine model are compared with RE1000 engine test data taken at NASA-Lewis Research Center. The model validation and the engine testing are being done under a joint interagency agreement between the Department of Energy's Oak Ridge National Laboratory and NASA-Lewis. A kinematic code developed at Lewis was upgraded to permit simulation of free-piston engine performance; it was further upgraded and modified at Lewis and is currently being validated. The model predicts engine performance by numerical integration of equations for each control volume in the working space. Piston motions are determined by numerical integration of the force balance on each piston or can be specified as Fourier series. In addition, the model Fourier analyzes the various piston forces to permit the construction of phasor force diagrams. The paper compares predicted and experimental values of power and efficiency and shows phasor force diagrams for the RE1000 engine displacer and piston. Further development plans for the model are also discussed.

Tew, R. C., Jr.↗
NASA NTRS1984

Comparison of free-piston Stirling engine model predictions with RE1000 engine test data

Predictions of a free-piston Stirling engine model are compared with RE1000 engine test data taken at NASA-Lewis Research Center. The model validation and the engine testing are being done under a joint interagency agreement between the Department of Energy's Oak Ridge National Laboratory and NASA-Lewis. A kinematic code developed at Lewis was upgraded to permit simulation of free-piston engine performance; it was further upgraded and modified at Lewis and is currently being validated. The model predicts engine performance by numerical integration of equations for each control volume in the working space. Piston motions are determined by numerical integration of the force balance on each piston or can be specified as Fourier series. In addition, the model Fourier analyzes the various piston forces to permit the construction of phasor force diagrams. The paper compares predicted and experimental values of power and efficiency and shows phasor force diagrams for the RE1000 engine displacer and piston. Further development plans for the model are also discussed.

Tew, R. C.↗
DOE OSTI2018

The fast linear accelerator modeling engine for FRIB online model service

Commissioning of a large accelerator facility like FRIB needs support from an online beam dynamics model. Considering the new physics challenges of FRIB such as modeling of non-axisymmetric superconducting RF cavities and multi-charge state acceleration, there is no readily available online beam tuning code. The design code of FRIB super-conducting linac, IMPACT-Z, is not suitable for online tuning because of its code design and running speed. Therefore, the Fast Linear Accelerator Modeling Engine (FLAME), specifically designed to fulfill FRIB’s online modeling challenges, is proposed. The physics model of FLAME, especially its novel way of modeling non-axisymmetric superconducting RF cavities using a multipole expansion based thin-lens kick model, is discussed in detail, and the benchmark results against FRIB design code is presented, after which the software design strategy of FLAME and its execution speed is presented.

43 PARTICLE ACCELERATORS↗
NASA NTRS1975

Thermal analytic model of 30 cm engineering model mercury ion thruster

A lumped parameter thermal nodal network was developed for a 30 cm Engineering Model Mercury Ion Thruster. The network consists of approximately 100 nodes coded in SINDA format for use on the Univac 1106/1108 computer. This model takes into account internal dissipation, radiation, and conduction as well as environmental heating. A series of tests were performed to simulate a wide range of thermal environments on an operating 30 cm thruster, instrumented to measure the temperature distribution within the thruster. The results of these tests were used to calibrate the analytical model. The analytical model along with comparisons between analytical and experimental results for the various operating conditions are presented.

Oglebay, J. C.↗
NASA NTRS1972

Multispectral scanner system for ERTS: Four band scanner system. Volume 2: Engineering model panoramic pictures and engineering tests

This document is Volume 2 of three volumes of the Final Report for the four band Multispectral Scanner System (MSS). The results are contained of an analysis of pictures of actual outdoor scenes imaged by the engineering model MSS for spectral response, resolution, noise, and video correction. Also included are the results of engineering tests on the MSS for reflectance and saturation from clouds. Finally, two panoramic pictures of Yosemite National Park are provided.

Source record↗
NASA NTRS1988

Magnetic emissions testing of the space station engineering model resistojet

The engineering model resistojet intended for altitude maintenance onboard the space station was tested for magnetic radiation emissions in the Radio Frequency Interference (RFI) facility at the Goddard Space Flight Center. The resistojet heater was supplied with power at 20 kHz and low voltage through a power controller. The resistojet was isolated from its power supply in the RFI enclosure, and the magnetic emission measured at three locations around the resistojet at various heater currents. At a heater current of 18.5 A the maximum magnetic emission was 61 dBpt at a distance of 1 m from the resistojet and at a location at the rear of the thruster. Calculations indicate that the case and heat shields provided a minimum of 4 dB of attenuation at a current of 18.5 A. Maximum radiation was measured at the rear of the resistojet along its major axis and was thought to be due to the magnetic radiation from the power leads. At a distance of 37 cm from the resistojet the maximum magnetic radiation measured was 73 dBpt at a current of 11.2 A. The power input leads were also a source of magnetic radiation. The engineering model rssistojet requires about 20 dB of additional shielding.

Briehl, Daniel↗
DOE OSTI2020

What Questions Would a Systems Engineer Ask to Assess Systems Engineering Models as Credible

Digital Systems Engineering strategies typically call for digital Systems Engineering models to be retained in repositories and certified as an authoritative source of truth (enabling model reuse, qualification, and collaboration). In order for digital Systems Engineering models to be certified as authoritative (credible), they need to be assessed - verified and validated - and with the amount of uncertainty in the model quantified (consider reusing someone else's model without knowing the author). Due to this increasing model complexity, the authors assert that traditional human-based methods for validating, verifying, and uncertainty quantification - such as human-based peer-review sessions - cannot sufficiently establish that a digital Systems Engineering model of a complex system is credible. Digital Systems Engineering models of complex systems can contain millions of nodes and edges. The authors assert that this level of detail is beyond the ability of any group of humans - even working for weeks at a time - to discern and catch every minor model infraction. In contrast, computers are highly effective at discerning infractions with massive amounts of information. The authors suggest that a better approach might be to focus the humans at what model patterns should be assessed and enable the computer to assess the massive details in accordance with those patterns - by running through perhaps 100,000 test loops. In anticipation of future projects to implement and automate the assessment of models at Sandia National Laboratories, a study was initiated to elicit input from a group of 25 Systems Engineering experts. The authors positioning query began with - 'What questions would a Systems Engineer ask to assess Systems Engineering models for credibility?" This report documents the results of that survey.

42 ENGINEERING↗