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At least 253 records · Page 14

Collision probabilities in the presence of nebular gas drag

We are developing a model to determine what fraction of the planetesimals would have hit a protoplanet on their sunward journey as opposed to having a close approach and passing into an inferior orbit. The model involves direct numerical integration of restricted-three-body orbits using a predictor-corrector integrator. A simple gas drag law with a v(exp 2) dependence was also included in the equations of motion. Runs of 100 to 500 particles were already performed, while some future runs may require several times this number in order to get good impact statistics. All planetesimals start in superior orbits with semi-major axes 5 to 10 R(sub H) from the protoplanets, where R(sub H) is the protoplanet's Hill Sphere radius. The orbit is followed until the planetesimal passed into an inferior orbit at least 10 R(sub H) from the protoplanet. This process typically requires 10(exp 4) to 10(exp 5) orbits.

Kary, David M.↗

Future missions studies: Combining Schatten's solar activity prediction model with a chaotic prediction model

K. Schatten (1991) recently developed a method for combining his prediction model with our chaotic model. The philosophy behind this combined model and his method of combination is explained. Because the Schatten solar prediction model (KS) uses a dynamo to mimic solar dynamics, accurate prediction is limited to long-term solar behavior (10 to 20 years). The Chaotic prediction model (SA) uses the recently developed techniques of nonlinear dynamics to predict solar activity. It can be used to predict activity only up to the horizon. In theory, the chaotic prediction should be several orders of magnitude better than statistical predictions up to that horizon; beyond the horizon, chaotic predictions would theoretically be just as good as statistical predictions. Therefore, chaos theory puts a fundamental limit on predictability.

Ashrafi, S.↗

Prediction of Muscle Performance During Dynamic Repetitive Exercise

A method for predicting human muscle performance was developed. Eight test subjects performed a repetitive dynamic exercise to failure using a Lordex spinal machine. Electromyography (EMG) data was collected from the erector spinae. Evaluation of the EMG data using a 5th order Autoregressive (AR) model and statistical regression analysis revealed that an AR parameter, the mean average magnitude of AR poles, can predict performance to failure as early as the second repetition of the exercise. Potential applications to the space program include evaluating on-orbit countermeasure effectiveness, maximizing post-flight recovery, and future real-time monitoring capability during Extravehicular Activity.

Byerly, D. L.↗

POEMMA: Probe of Extreme Multi-Messenger Astrophysics

The Probe Of Extreme Multi-Messenger Astrophysics (POEMMA) mission is being designed to establish charged-particle astronomy with ultra-high energy cosmic rays (UHECRs) and to observe cosmogenic tau neutrinos (CTNs). The study of UHECRs and CTNs from space will yield orders-of-magnitude increase in statistics of observed UHECRs at the highest energies, and the observation of the cosmogenic flux of neutrinos for a range of UHECR models. These observations should solve the long-standing puzzle of the origin of the highest energy particles ever observed, providing a new window onto the most energetic environments and events in the Universe, while studying particle interactions well beyond accelerator energies. The discovery of CTNs will help solve the puzzle of the origin of UHECRs and begin a new field of Astroparticle Physics with the study of neutrino properties at ultra-high energies.

Olinto, Angela V.↗

Testing higher-order Lagrangian perturbation theory against numerical simulation. 1: Pancake models

We present results showing an improvement of the accuracy of perturbation theory as applied to cosmological structure formation for a useful range of quasi-linear scales. The Lagrangian theory of gravitational instability of an Einstein-de Sitter dust cosmogony investigated and solved up to the third order is compared with numerical simulations. In this paper we study the dynamics of pancake models as a first step. In previous work the accuracy of several analytical approximations for the modeling of large-scale structure in the mildly non-linear regime was analyzed in the same way, allowing for direct comparison of the accuracy of various approximations. In particular, the Zel'dovich approximation (hereafter ZA) as a subclass of the first-order Lagrangian perturbation solutions was found to provide an excellent approximation to the density field in the mildly non-linear regime (i.e. up to a linear r.m.s. density contrast of sigma is approximately 2). The performance of ZA in hierarchical clustering models can be greatly improved by truncating the initial power spectrum (smoothing the initial data). We here explore whether this approximation can be further improved with higher-order corrections in the displacement mapping from homogeneity. We study a single pancake model (truncated power-spectrum with power-spectrum with power-index n = -1) using cross-correlation statistics employed in previous work. We found that for all statistical methods used the higher-order corrections improve the results obtained for the first-order solution up to the stage when sigma (linear theory) is approximately 1. While this improvement can be seen for all spatial scales, later stages retain this feature only above a certain scale which is increasing with time. However, third-order is not much improvement over second-order at any stage. The total breakdown of the perturbation approach is observed at the stage, where sigma (linear theory) is approximately 2, which corresponds to the onset of hierarchical clustering. This success is found at a considerable higher non-linearity than is usual for perturbation theory. Whether a truncation of the initial power-spectrum in hierarchical models retains this improvement will be analyzed in a forthcoming work.

Buchert, T.↗

Robust Strategy for Rocket Engine Health Monitoring

Monitoring the health of rocket engine systems is essentially a two-phase process. The acquisition phase involves sensing physical conditions at selected locations, converting physical inputs to electrical signals, conditioning the signals as appropriate to establish scale or filter interference, and recording results in a form that is easy to interpret. The inference phase involves analysis of results from the acquisition phase, comparison of analysis results to established health measures, and assessment of health indications. A variety of analytical tools may be employed in the inference phase of health monitoring. These tools can be separated into three broad categories: statistical, rule based, and model based. Statistical methods can provide excellent comparative measures of engine operating health. They require well-characterized data from an ensemble of "typical" engines, or "golden" data from a specific test assumed to define the operating norm in order to establish reliable comparative measures. Statistical methods are generally suitable for real-time health monitoring because they do not deal with the physical complexities of engine operation. The utility of statistical methods in rocket engine health monitoring is hindered by practical limits on the quantity and quality of available data. This is due to the difficulty and high cost of data acquisition, the limited number of available test engines, and the problem of simulating flight conditions in ground test facilities. In addition, statistical methods incur a penalty for disregarding flow complexity and are therefore limited in their ability to define performance shift causality. Rule based methods infer the health state of the engine system based on comparison of individual measurements or combinations of measurements with defined health norms or rules. This does not mean that rule based methods are necessarily simple. Although binary yes-no health assessment can sometimes be established by relatively simple rules, the causality assignment needed for refined health monitoring often requires an exceptionally complex rule base involving complicated logical maps. Structuring the rule system to be clear and unambiguous can be difficult, and the expert input required to maintain a large logic network and associated rule base can be prohibitive.

Santi, L. Michael↗

Comparison of asteroid and meteorite spectra - Classification by principal component analysis

The spectra of 103 meteorites and 411 asteroids have been statistically compared via principal component analysis, in order to arrive at an overview of spectral similarities between and among asteroids and meteorites; the result is a map of statistically defined relative spectral similarities measured by distance. There is much more spectral variance in the meteorites than in the asteroids. The analysis indicates no direct meteorite analogs from the outer-belt B, D, F, P, and T type asteroids, indicating that only a limited portion of the inner asteroid belt is sampled by meteorites.

Britt, Daniel T.↗

A STATISTICAL OPTIMIZING NAVIGATION PROCEDURE FOR SPACE FLIGHT

In a typical self-contained space navigation system, celestial observation data are gathered and processed to produce estimated velocity corrections. The results of this paper provide a basis for determining the best celestial measurements and the proper time s to implement velocity corrections. Fundamental to the navigation system is a procedure for processing celestial measurement data which permits incorporation of each individual measurement as it is made in order to provide an improved estimate of position and velocity. In order to "optimize" the navigation, a statistical evaluation of a number of alternative courses of action is made. The various alternatives, which form the basis of a decision process, concern the following: 1) which star and planet combination provides the "best" available observation; 2) whether the best observation gives a sufficient reduction in the predicted target error to warrant making the measurement; and 3) whether the uncertainty in the indicated velocity correction is a small enough percentage of the correction itself to justify an engine re-start and propellant expenditure. Numerical results are presented which illustrate the effectiveness of this approach to the space navigation problem.

NAVIGATION↗

Statistical fitting accuracy in photon correlation spectroscopy

Continuing our experimental investigation of the fitting accuracy associated with photon correlation spectroscopy, we collect 150 correlograms of light scattered at 90 deg from a thermostated sample of 91-nm-diameter, polystyrene latex spheres in water. The correlograms are taken with two correlators: one with linearly spaced channels and one with geometrically spaced channels. Decay rates are extracted from the single-exponential correlograms with both nonlinear least-squares fits and second-order cumulant fits. We make several statistical comparisons between the two fitting techniques and verify an earlier result that there is no sample-time dependence in the decay rate errors. We find, however, that the two fitting techniques give decay rates that differ by 1 percent.

Shaumeyer, J. N.↗

NASA Engineering and Safety Center Technical Bulletin No. 22-04: Uncertainty Quantification of Reduced Order Structural Dynamic Models

Uncertainty quantification (UQ) provides statistical bounds on prediction accuracy based on finite element model (FEM) uncertainty. An alternate method for UQ, called the Hybrid Parametric Variation (HPV) combines a parametric variation of the Hurty/Craig-Bampton (HCB) fixed-interface (FI) modal frequencies with a nonparametric variation (NPV) method. This provides a UQ method that can be traced to test data, which can be updated as additional data and improved correlated models become available.

Uncertainty Quantification↗

Predicting Satellite Close Approaches Using Statistical Parameters in the Context of Artificial Intelligence

In order to ensure a sustainable use of low earth orbit in particular and near Earth space in general, reliable and effective close approach prediction be-tween space objects is key. Only this allows for efficient and timely colli-sion avoidance. Space Situational Awareness (SSA) for commercial and government missions will be facing the rapidly growing amount of small and potentially less agile satellites as well as debris in the near earth realm, such as the increase in CubeSat launches and upcoming large constellations. At the same time, space object detection capabilities are expected to increase significantly, allowing for the reliable detection of smaller objects, e.g. when the Air Force Space Fence radar becomes operational. In combination, the space object catalog is expected to increase tremendously in size. In this paper, we introduce an investigative approach based on the latest capabili-ties in artificial intelligence in fostering the potential for fast and accurate close approach predictions. We consider the study of statistical and infor-mation theory parameters in contrast and complementary to the classical probability of collision computation alone, in order to determine the feasi-bility of reliably predicting close approaches.

Mashiku, A.↗

Predicting Satellite Close Approaches Using Statistical Parameters in the Context of Artificial Intelligence

In order to ensure a sustainable use of low earth orbit in particular and near Earth space in general, reliable and effective close approach prediction be-tween space objects is key. Only this allows for efficient and timely colli-sion avoidance. Space Situational Awareness (SSA) for commercial and government missions will be facing the rapidly growing amount of small and potentially less agile satellites as well as debris in the near earth realm, such as the increase in CubeSat launches and upcoming large constellations. At the same time, space object detection capabilities are expected to increase significantly, allowing for the reliable detection of smaller objects, e.g. when the Air Force Space Fence radar becomes operational. In combination, the space object catalog is expected to increase tremendously in size. In this paper, we introduce an investigative approach based on the latest capabili-ties in artificial intelligence in fostering the potential for fast and accurate close approach predictions. We consider the study of statistical and infor-mation theory parameters in contrast and complementary to the classical probability of collision computation alone, in order to determine the feasi-bility of reliably predicting close approaches.

Mashiku, Alinda↗

An optimized buffer controlled data compression system

The digital data compression system considered uses a buffer controlled aperture algorithm which minimizes the mean-squared error between the reconstructed receiver output and transmitter input. The data compression technique selected is based on the zero-order floating aperture prediction rule. It is assumed that the statistics of the input data are initially uniformly distributed, stationary, and first-order Markov. The problem is solved for stationary data. An approach is presented for extending the results to slowly varying uniformly distributed nonstationary Markov data.

Dosik, P. H.↗

Ten Years of Cloud Properties from MODIS: Global Statistics and Use in Climate Model Evaluation

The NASA Moderate Resolution Imaging Spectroradiometer (MODIS), launched onboard the Terra and Aqua spacecrafts, began Earth observations on February 24, 2000 and June 24,2002, respectively. Among the algorithms developed and applied to this sensor, a suite of cloud products includes cloud masking/detection, cloud-top properties (temperature, pressure), and optical properties (optical thickness, effective particle radius, water path, and thermodynamic phase). All cloud algorithms underwent numerous changes and enhancements between for the latest Collection 5 production version; this process continues with the current Collection 6 development. We will show example MODIS Collection 5 cloud climatologies derived from global spatial . and temporal aggregations provided in the archived gridded Level-3 MODIS atmosphere team product (product names MOD08 and MYD08 for MODIS Terra and Aqua, respectively). Data sets in this Level-3 product include scalar statistics as well as 1- and 2-D histograms of many cloud properties, allowing for higher order information and correlation studies. In addition to these statistics, we will show trends and statistical significance in annual and seasonal means for a variety of the MODIS cloud properties, as well as the time required for detection given assumed trends. To assist in climate model evaluation, we have developed a MODIS cloud simulator with an accompanying netCDF file containing subsetted monthly Level-3 statistical data sets that correspond to the simulator output. Correlations of cloud properties with ENSO offer the potential to evaluate model cloud sensitivity; initial results will be discussed.

Platnick, Steven E.↗

Single-Vector Calibration of Wind-Tunnel Force Balances

An improved method of calibrating a wind-tunnel force balance involves the use of a unique load application system integrated with formal experimental design methodology. The Single-Vector Force Balance Calibration System (SVS) overcomes the productivity and accuracy limitations of prior calibration methods. A force balance is a complex structural spring element instrumented with strain gauges for measuring three orthogonal components of aerodynamic force (normal, axial, and side force) and three orthogonal components of aerodynamic torque (rolling, pitching, and yawing moments). Force balances remain as the state-of-the-art instrument that provide these measurements on a scale model of an aircraft during wind tunnel testing. Ideally, each electrical channel of the balance would respond only to its respective component of load, and it would have no response to other components of load. This is not entirely possible even though balance designs are optimized to minimize these undesirable interaction effects. Ultimately, a calibration experiment is performed to obtain the necessary data to generate a mathematical model and determine the force measurement accuracy. In order to set the independent variables of applied load for the calibration 24 NASA Tech Briefs, October 2003 experiment, a high-precision mechanical system is required. Manual deadweight systems have been in use at Langley Research Center (LaRC) since the 1940s. These simple methodologies produce high confidence results, but the process is mechanically complex and labor-intensive, requiring three to four weeks to complete. Over the past decade, automated balance calibration systems have been developed. In general, these systems were designed to automate the tedious manual calibration process resulting in an even more complex system which deteriorates load application quality. The current calibration approach relies on a one-factor-at-a-time (OFAT) methodology, where each independent variable is incremented individually throughout its full-scale range, while all other variables are held at a constant magnitude. This OFAT approach has been widely accepted because of its inherent simplicity and intuitive appeal to the balance engineer. LaRC has been conducting research in a "modern design of experiments" (MDOE) approach to force balance calibration. Formal experimental design techniques provide an integrated view to the entire calibration process covering all three major aspects of an experiment; the design of the experiment, the execution of the experiment, and the statistical analyses of the data. In order to overcome the weaknesses in the available mechanical systems and to apply formal experimental techniques, a new mechanical system was required. The SVS enables the complete calibration of a six-component force balance with a series of single force vectors.

Parker, P. A.↗

The correlation of the output of a binary convolutional encoder

In this paper we discuss the correlation properties of the output of a binary convolutional encoder. We consider both random statistically independent inputs and first-order Markov inputs. It is shown that for random inputs, the ensemble average of the product of the outputs of a convolutional encoder taken two at a time is always either 0 or 1, and that for almost all good codes the ensemble average is always 0.

Braff, M.↗

Natural ELF noise evaluation for TSS emissions detection on the Earth's surface. The electric field component approach

The preliminary estimate of the local noise structure in the proximity of a receiver is essential to establishing the detectability of a given signal in presence of such noise. The possibility of detecting the Electric Field Component of the background noise by means of electric dipoles horizontally placed on the sea bed in shallow water is outlined, in order to find its spectral and statistical characteristics for the definition of the optimal receiving system.

Tacconi, G.↗