SEARCH · Search NASA
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.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
It is shown that the algorithms for determining the generating function and prediction error matrix of multivariate stationary stochastic processes developed by Wiener and Masani (1957), and later by Masani (1960) will work in some more general setting.
An efficient algorithm has been devised to compute the crossovers in satellite altimetry. The significance of the crossovers is twofold. First, they are needed to perform the crossover adjustment to remove the orbit error. Secondly, they yield important insight into oceanic variability. Nevertheless, there is no published algorithm to make this very time-consuming task easier, which is the goal of this report. The success of the algorithm is predicated on the ability to predict (by analytical means) the crossover coordinates to within 6 km and 1 sec of the true values. Hence, only one interpolation/extrapolation step on the data is needed to derive the crossover coordinates in contrast to the many interpolation/extrapolation operations usually needed to arrive at the same accuracy level if deprived of this information.
Fast Fourier transform (FFT) methods provide a fast and efficient means of processing large amounts of gravity or geoid data in local gravity field modelling. The FFT methods, however, has a number of theoretical and practical limitations, especially the use of flat-earth approximation, and the requirements for gridded data. In spite of this the method often yields excellent results in practice when compared to other more rigorous (and computationally expensive) methods, such as least-squares collocation. The good performance of the FFT methods illustrate that the theoretical approximations are offset by the capability of taking into account more data in larger areas, especially important for geoid predictions. For best results good data gridding algorithms are essential. In practice truncated collocation approaches may be used. For large areas at high latitudes the gridding must be done using suitable map projections such as UTM, to avoid trivial errors caused by the meridian convergence. The FFT methods are compared to ground truth data in New Mexico (xi, eta from delta g), Scandinavia (N from delta g, the geoid fits to 15 cm over 2000 km), and areas of the Atlantic (delta g from satellite altimetry using Wiener filtering). In all cases the FFT methods yields results comparable or superior to other methods.
On the Advanced Communications Technology Satellite (ACTS) being developed at NASA Lewis, low-burst-rate (LBR) traffic stations will access the ACTS multibeam package via two hopping beams that can be directed at certain areas in the continental U.S. An onboard baseband processor (BBP) demodulates uplink traffic, switches it between uplink and downlink beams at baseband, and then remodulates it for retransmission at 20 GHz. This study describes the demand-assigned operation of the ACTS LBR system, where the onboard switch is remote from both traffic stations and master control station (MCS). Network control uses inbound and outbound orderwire channels and a BBP control channel, allowing the MCS to coordinate assignment of individual 64-kb/s spacecraft channels. Models are developed to simulate the dynamics of the assignment process and verify the call blocking behavior, to predict control channel loads, and to evaluate algorithms for burst time plan rearrangement.
Significant advantages of the Variable Polarity Plasma Arc (VPPA) Welding Process include faster welding, fewer repairs, less joint preparation, reduced weldment distortion, and absence of porosity. The power distribution was analyzed for an argon plasma gas flow constituting the fluid in the VPPA Welding Process. The major heat loss at the torch nozzle is convective heat transfer; in the space between the outlet of the nozzle and the workpiece; radiative heat transfer; and in the keyhole in the workpiece, convective heat transfer. The power absorbed at the workpiece produces the molten puddle that solidifies into the weld bead. Crown and root widths, and crown and root heights of the weld bead are predicted. The basis is provided for an algorithm for automatic control of VPPA welding machine parameters to obtain desired weld bead dimensions.
SUPER PHACOMP developed after research into many versions of PHACOMP algorithm. Resulting program implements three of these versions, enabling comparison of results to give greater reliability to predictions. Inasmuch as certain calculations common to each, three versions woven together for efficiency. While prediction of formation of sigma phase is primary function, also performs other calculations of properties of alloys. Program benefits anyone working in design of alloys. Written in BASIC.
The success or failure of the ACTS experiment will depend on how accurately the rain-fade statistics and fade dynamics can be predicted in order to derive an appropriate algorithm that will combat weather vagaries, specifically for links with small terminals, such as very small aperture terminals (VSAT's) where the power margin is a premium. The planning process and hardware development program that will comply with the recommendations of the ACTS propagation study groups are described.
An artificial-neural-network method, first developed for the measurement and control of atmospheric phase distortion, using stellar images, was used to estimate the optical aberration of the Hubble Space Telescope. A total of 26 estimates of distortion was obtained from 23 stellar images acquired at several secondary-mirror axial positions. The results were expressed as coefficients of eight orthogonal Zernike polynomials: focus through third-order spherical. For all modes other than spherical the measured aberration was small. The average spherical aberration of the estimates was -0.299 micron rms, which is in good agreement with predictions obtained when iterative phase-retrieval algorithms were used.
The purpose of the Advanced Communications Technology Satellite (ACTS) is to demonstrate the feasibility of the Ka-band (20 and 30 GHz) spectrum for satellite communications, as well as to help maintain U.S. leadership in satellite communications. ACTS incorporates such innovative schemes as time division multiple access (TDMA), microwave and baseband switching, onboard regeneration, and adaptive application of coding during rain-fade conditions. The success or failure of the ACTS experiment will depend on how accurately the rain-fade statistics and fade dynamics can be predicted in order to derive an appropriate algorithm that will combat weather vagaries, specifically for links with small terminals, such as very small aperture terminals (VSAT's) where the power margin is a premium. This article describes the planning process and hardware development program that will comply with the recommendations of the ACTS propagation study groups.
An updated gas dynamics model for gas interactions around the LDEF is presented that includes improved scattering algorithms. The primary improvement is more accurate predictions of surface fluxes in the wake region. The code used is the Integrated Spacecraft Environments Model (ISEM). Additionally, initial results of a detailed ISEM prediction model of the Solar Array Passive LDEF Experiment (SAMPLE), A0171, is presented. This model includes details of the A0171 geometry and outgassing characteristics of the many surfaces on the experiment. The detailed model includes the multiple scattering that exists between the ambient atmosphere, LDEF outgassing, and atomic oxygen erosion products. Predictions are made for gas densities, surface fluxes and deposition at three different time periods of the LDEF mission.
Radiative equilibrium surface temperatures, heating rates, streamlines, surface pressures, and flow-field features as predicted by the Langley Aerothermodynamic Upwind Relaxation Algorithm (Laura) are presented for the X-34 Technology Demonstrator. Results for two trajectory points corresponding to entry peak heating and two control surface deflections are discussed. This data is also discussed in context of Thermal Protection System (TPS) design issues. The work presented in this report is part of a larger effort to define the X-34 aerothermal environment, including the application of engineering codes and wind-tunnel studies.
Radiative equilibrium surface temperatures, heating rates, streamlines, surface pressures, and flow-field features as predicted by the Langley Aerothermodynamic Upwind Relaxation Algorithm (LAURA) are presented for the X-34 Technology Demonstrator. Results for two trajectory points corresponding to entry peak heating and two control surface deflections are discussed. This data is also discussed in the context of Thermal Protection System (TPS) design issues. The work presented in this report is part of a larger effort to define the X-34 aerothermal environment, including the application of engineering codes and wind-tunnel studies.
The present work addresses the development of an experimental and computational procedure for validating finite element models. A torus structure, part of an inflatable/rigidizable Hexapod, is used to demonstrate the approach. Because of fabrication, materials, and geometric uncertainties, a statistical approach combined with optimization is used to modify key model parameters. Static test results are used to update stiffness parameters and dynamic test results are used to update the mass distribution. Updated parameters are computed using gradient and non-gradient based optimization algorithms. Results show significant improvements in model predictions after parameters are updated. Lessons learned in the areas of test procedures, modeling approaches, and uncertainties quantification are presented.
By leveraging a new uncertainty markup technique, two risk analysis methods are used to compute the uncertainty of lunar-return shock layer radiation predicted by the High temperature Aerothermodynamic Radiation Algorithm (HARA). The effects of epistemic uncertainty, or uncertainty due to a lack of knowledge, is considered for the following modeling parameters: atomic line oscillator strengths, atomic line Stark broadening widths, atomic photoionization cross sections, negative ion photodetachment cross sections, molecular bands oscillator strengths, and electron impact excitation rates. First, a simplified shock layer problem consisting of two constant-property equilibrium layers is considered. The results of this simplified problem show that the atomic nitrogen oscillator strengths and Stark broadening widths in both the vacuum ultraviolet and infrared spectral regions, along with the negative ion continuum, are the dominant uncertainty contributors. Next, three variable property stagnation-line shock layer cases are analyzed: a typical lunar return case and two Fire II cases. For the near-equilibrium lunar return and Fire 1643-second cases, the resulting uncertainties are very similar to the simplified case. Conversely, the relatively nonequilibrium 1636-second case shows significantly larger influence from electron impact excitation rates of both atoms and molecules. For all cases, the total uncertainty in radiative heat flux to the wall due to epistemic uncertainty in modeling parameters is 30% as opposed to the erroneously-small uncertainty levels (plus or minus 6%) found when treating model parameter uncertainties as aleatory (due to chance) instead of epistemic (due to lack of knowledge).
Consistency-based diagnosis relies heavily on the assumption that discrepancies between model predictions and sensor observations can be detected accurately. When sources of uncertainty like sensor noise and model abstraction exist robust schemes have to be designed to make a binary decision on whether predictions are consistent with observations. This risks the occurrence of false alarms and missed alarms when an erroneous decision is made. Moreover when multiple sensors (with differing sensing properties) are available the degree of match between predictions and observations can be used to guide the search for fault candidates. In this paper we propose a novel approach to handle this problem using Bayesian networks. In the consistency- based diagnosis formulation, automatically generated Bayesian networks are used to encode a probabilistic measure of fit between predictions and observations. A Bayesian network inference algorithm is used to compute most probable fault candidates.
A paper describes algorithms for guidance and control (G&C) of a spacecraft maneuvering near a planet, moon, asteroid, comet, or other small astronomical body. The algorithms were developed following a model-predictive-control approach along with a convexification of the governing dynamical equations, control constraints, and trajectory and state constraints.
Confidence-based Feature Acquisition (CFA) is a novel, supervised learning method for acquiring missing feature values when there is missing data at both training (learning) and test (deployment) time. To train a machine learning classifier, data is encoded with a series of input features describing each item. In some applications, the training data may have missing values for some of the features, which can be acquired at a given cost. A relevant JPL example is that of the Mars rover exploration in which the features are obtained from a variety of different instruments, with different power consumption and integration time costs. The challenge is to decide which features will lead to increased classification performance and are therefore worth acquiring (paying the cost). To solve this problem, CFA, which is made up of two algorithms (CFA-train and CFA-predict), has been designed to greedily minimize total acquisition cost (during training and testing) while aiming for a specific accuracy level (specified as a confidence threshold). With this method, it is assumed that there is a nonempty subset of features that are free; that is, every instance in the data set includes these features initially for zero cost. It is also assumed that the feature acquisition (FA) cost associated with each feature is known in advance, and that the FA cost for a given feature is the same for all instances. Finally, CFA requires that the base-level classifiers produce not only a classification, but also a confidence (or posterior probability).
The Ares I-X trajectory reconstruction produced best estimated trajectories of the flight test vehicle ascent through stage separation, and of the first and upper stage entries after separation. The trajectory reconstruction process combines on-board, ground-based, and atmospheric measurements to produce the trajectory estimates. The Ares I-X vehicle had a number of on-board and ground based sensors that were available, including inertial measurement units, radar, air-data, and weather balloons. However, due to problems with calibrations and/or data, not all of the sensor data were used. The trajectory estimate was generated using an Iterative Extended Kalman Filter algorithm, which is an industry standard processing algorithm for filtering and estimation applications. This paper describes the methodology and results of the trajectory reconstruction process, including flight data preprocessing and input uncertainties, trajectory estimation algorithms, output transformations, and comparisons with preflight predictions.