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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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Viewgraphs on a neurocomputing approach to model determination of large space structures are presented. Topics covered include: background on neural networks; math modeling with neural networks; computer architectures for real-time modeling; and hardware and software implementation considerations, OPTIMA/1 data acquisition, modeling, and control workstation.
Similitude requirements in scale model determination of shell buckling under impulsive pressures
The Application of Machine-learning Algorithms for On-board Asteroid Shape Model Determination project will develop an innovative system for spacecraft navigation to expand the capability of small spacecraft to meet the critical challenges associated with small-body exploration. Such challenges include accurate navigation in a microgravity environment and precision targeting of particular locations on an asteroid surface for sample collection. This on-board system will cut the computational "umbilical" back to Earth-currently necessary for the generation of a global shape model that requires thousands of images with sufficient resolution and adequate variation of incidence and emission angles, processed manually by a team of experts on Earth for several months. Small satellites have limited bandwidth and are unable to downlink the data volume required for this processing, restricting their ability to perform deep-space asteroid exploration.
The fundamental challenge in identification of nonlinear dynamic systems is determining the appropriate form of the model. A robust technique is presented which essentially eliminates this problem for many applications. The technique is based on the Minimum Model Error (MME) optimal estimation approach. A detailed literature review is included in which fundamental differences between the current approach and previous work is described. The most significant feature is the ability to identify nonlinear dynamic systems without prior assumption regarding the form of the nonlinearities, in contrast to existing nonlinear identification approaches which usually require detailed assumptions of the nonlinearities. Model form is determined via statistical correlation of the MME optimal state estimates with the MME optimal model error estimates. The example illustrations indicate that the method is robust with respect to prior ignorance of the model, and with respect to measurement noise, measurement frequency, and measurement record length.
Aircraft engine components invariably are subjected to mean stresses over and above the cyclic loads. In monolithic materials, it has been observed that tensile mean stresses are detrimental and compressive mean stresses are beneficial to fatigue life in comparison to a base of zero mean stress. Several mean stress models exist for monolithic metals, but each differ quantitatively in the extent to which detrimental or beneficial effects are ascribed. There have been limited attempts to apply these models to metal matrix composites. At the NASA Lewis Research Center, several mean stress models--the Smith-Watson- Topper, Walker, Normalized Goodman, and Soderberg models--were examined for applicability to this class of composite materials. The Soderberg approach, which normalizes the mean stress to a 0.02-percent yield strength, was shown to best represent the effect of mean stresses over the range covered. The other models varied significantly in their predictability and often failed to predict the composite behavior at very high tensile mean stresses. This work is the first to systematically demonstrate the influence of mean stresses on metal matrix composites and model their effects. Attention also was given to fatigue-cracking mechanisms in the Ti-15-3 matrix and to micromechanics analyses of mean stress effects.
Total aerodynamic forces and moments of free flying body in wind tunnel determined from accelerations measured and telemetered by onboard instruments
CISLUNAR is a stand-alone computer program designed to generate the trajectory of a low-thrust spacecraft travelling in Earth-Moon space. The program allows the creation of functional trajectories dependent on the supplied spacecraft characteristics. The trajectory generation is a user interactive process. The original intent was for the program user to modify the necessary control values until a staisfactory trajectory has been created.
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The possibility of determining O(5+) outflow velocities by using a Doppler dimming analysis of the resonantly scattered intensities of O VI lambda 1031.9 and lambda 1037.6 is addressed. The technique is sensitive to outflow velocities, W, in the range W greater than 30 and less than 250 km/s and can be used for probing regions of the inner solar corona, where significant coronal heating and solar wind acceleration may be occurring. These velocity measurements, when combined with measurements of other plasma parameters (temperatures and densities of ions and electrons) can be used to estimate the energy and mass flux of O(5+). In particular, it may be possible to locate where the flow changes from subsonic to supersonic and to identify source regions for the high and low speed solar wind. The velocity diagnostic technique is discussed with emphasis placed on the requirements needed for accurate outflow velocity determinations. Model determinations of outflow velocities based on simulated Doppler observations are presented.
A procedure for airplane model structure determination from flight data based on modified stepwise regression (MSR), several decision criteria and postulated aerodynamic model equations is presented. The MSR is constructed to force a linear model for the aerodynamic coefficient first, then select significant nonlinear terms and reject nonsignificant terms from the model. In addition to the statistical criteria in the stepwise regression, the prediction sum of squares (PRESS) criterion and analysis of residuals are examined for the selection of an adequate model. The procedure is used in examples with simulated and real flight data. It is shown that the MSR performs better than the ordinary stepwise regression and that the technique can be also applied to the large amplitude maneuvers.
Models of the TOPEX/Poseidon spacecraft are developed by means of finite-element analyses for use in generating acceleration histories for various orbit orientations which account for nonconservative radiation forces. The acceleration profiles are developed with an analysis based on the use of the 'box-wing' model in which the satellite is modeled as a combination of flat plates. The models account for the effects of solar, earth-albedo, earth-IR, and spacecraft-thermal radiation. The finite-element analysis gives the total force and induced accelerations acting on the satellite. The plate types used in the analysis have parameters that can be adjusted to optimize model performance according to the micromodel analysis and tracking observations. Acceleration related to solar radiation pressure is modeled effectively, and the techniques are shown to be useful for the precise orbit determinations required for spacecraft such as the TOPEX/Poseidon.
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The successful parametric modeling of the aerodynamics for an airplane operating at high angles of attack or sideslip is performed in two phases. First the aerodynamic model structure must be determined and second the associated aerodynamic parameters (stability and control derivatives) must be estimated for that model. The purpose of this paper is to document two versions of a stepwise regression computer program which were developed for the determination of airplane aerodynamic model structure and to provide two examples of their use on computer generated data. References are provided for the application of the programs to real flight data. The two computer programs that are the subject of this report, STEP and STEPSPL, are written in FORTRAN IV (ANSI l966) compatible with a CDC FTN4 compiler. Both programs are adaptations of a standard forward stepwise regression algorithm. The purpose of the adaptation is to facilitate the selection of a adequate mathematical model of the aerodynamic force and moment coefficients of an airplane from flight test data. The major difference between STEP and STEPSPL is in the basis for the model. The basis for the model in STEP is the standard polynomial Taylor's series expansion of the aerodynamic function about some steady-state trim condition. Program STEPSPL utilizes a set of spline basis functions.
Analytical model of turbine-type flowmeter guides in the selection of valid extrapolation of available calibration data. An expression for flowmeter performance is developed to include the effects of fluid friction, bearing drag, and magnetic drag upon helical rotor design.
Cycle life regression model, cycle life prediction model, and acceleration factors are discussed. A method was presented to: (1) select a mathematical model; (2) determine model coefficients using accelerated test data; (3) test model fit of the accelerated test data; and (4) predict normal packs.
Seen by the human eye, precipitation particles are commonly drops of rain, flakes of snow, or lumps of hail that reach the ground. Remote sensors and numerical models usually deal with information about large collections of rain, snow, and hail (or graupel --also called soft hail ) in a volume of air. Therefore, the size and number of the precipitation particles and how particles interact, evolve, and fall within the volume of air need to be represented using physical laws and mathematical tools, which are often implemented as cloud and precipitation microphysical parameterizations in numerical models. To account for the complexity of the precipitation physical processes, scientists have developed various types of such schemes in models. The accuracy of numerical weather forecasting may vary dramatically when different types of these schemes are employed. Therefore, systematic evaluations of cloud and precipitation schemes are of great importance for improvement of weather forecasts. This study is one such endeavor; it pursues quantitative assessment of all the available cloud and precipitation microphysical schemes in a weather model (MM5) through comparison with the observations obtained by National Aeronautics and Space Administration (NASA) s and Japan Aerospace Exploration Agency (JAXA) s Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) and microwave imager (TMI). When satellite sensors (like PR or TMI) detect information from precipitation particles, they cannot directly observe the microphysical quantities (e.g., water species phase, density, size, and amount etc.). Instead, they tell how much radiation is absorbed by rain, reflected away from the sensor by snow or graupel, or reflected back to the satellite. On the other hand, the microphysical quantities in the model are usually well represented in microphysical schemes and can be converted to radiative properties that can be directly compared to the corresponding PR and TMI observations. This study employs this method to evaluate the accuracy of the simulated radiative properties by the MM5 model with different microphysical schemes. It is found that the representations of particle density, size, and mass in the different schemes in the MM5 model determine the model s performance when predicting a winter storm over the eastern Pacific Ocean. Schemes lacking moderate density particles (i.e. graupel), with snow flakes that are too large, or with excessive mass of snow or graupel lead to degraded prediction of the radiative properties as observed by the TRMM satellite. This study demonstrates the uniqueness of the combination of both an active microwave sensor (PR) and passive microwave sensor (TMI) onboard TRMM on assessing the accuracy of numerical weather forecasting. It improves our understanding of the physical and radiative properties of different types of precipitation particles and provides suggestions for better representation of cloud and precipitation processes in numerical models. It would, ultimately, contribute to answering questions like "Why did it not rain when the forecast says it would?"
Descriptive model for determining optimal human performance in aerospace systems