Statistical analysis of thrust vector characteristics for rocket engine clusters.
Rocket engine cluster thrust and moment vectors analyzed statistically for thrust misalignment
ROCKET ENGINE↗
SEARCH · Engineering Papers
Results for “statistical”
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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Statistics of impact crater accumulation on the lunar surface exposed to a distribution of impacting bodies.
Impact crater size vs incidence analyzed statistically in study of validity of time dependence on saturation for moon and Mars
LUNAR SURFACE↗
Statistical physics and physiology: monofractal and multifractal approaches
Even under healthy, basal conditions, physiologic systems show erratic fluctuations resembling those found in dynamical systems driven away from a single equilibrium state. Do such "nonequilibrium" fluctuations simply reflect the fact that physiologic systems are being constantly perturbed by external and intrinsic noise? Or, do these fluctuations actually, contain useful, "hidden" information about the underlying nonequilibrium control mechanisms? We report some recent attempts to understand the dynamics of complex physiologic fluctuations by adapting and extending concepts and methods developed very recently in statistical physics. Specifically, we focus on interbeat interval variability as an important quantity to help elucidate possibly non-homeostatic physiologic variability because (i) the heart rate is under direct neuroautonomic control, (ii) interbeat interval variability is readily measured by noninvasive means, and (iii) analysis of these heart rate dynamics may provide important practical diagnostic and prognostic information not obtainable with current approaches. The analytic tools we discuss may be used on a wider range of physiologic signals. We first review recent progress using two analysis methods--detrended fluctuation analysis and wavelets--sufficient for quantifying monofractual structures. We then describe recent work that quantifies multifractal features of interbeat interval series, and the discovery that the multifractal structure of healthy subjects is different than that of diseased subjects.
NASA Discipline Cardiopulmonary↗
Statistical mechanics in biology: how ubiquitous are long-range correlations?
The purpose of this opening talk is to describe examples of recent progress in applying statistical mechanics to biological systems. We first briefly review several biological systems, and then focus on the fractal features characterized by the long-range correlations found recently in DNA sequences containing non-coding material. We discuss the evidence supporting the finding that for sequences containing only coding regions, there are no long-range correlations. We also discuss the recent finding that the exponent alpha characterizing the long-range correlations increases with evolution, and we discuss two related models, the insertion model and the insertion-deletion model, that may account for the presence of long-range correlations. Finally, we summarize the analysis of long-term data on human heartbeats (up to 10(4) heart beats) that supports the possibility that the successive increments in the cardiac beat-to-beat intervals of healthy subjects display scale-invariant, long-range "anti-correlations" (a tendency to beat faster is balanced by a tendency to beat slower later on). In contrast, for a group of subjects with severe heart disease, long-range correlations vanish. This finding suggests that the classical theory of homeostasis, according to which stable physiological processes seek to maintain "constancy," should be extended to account for this type of dynamical, far from equilibrium, behavior.
Non-NASA Center↗
Statistical porcess control in Deep Space Network operation
This report describes how the Deep Space Mission System (DSMS) Operations Program Office at the Jet Propulsion Laboratory's (EL) uses Statistical Process Control (SPC) to monitor performance and evaluate initiatives for improving processes on the National Aeronautics and Space Administration's (NASA) Deep Space Network (DSN).
Deep Space Network operations statistical process ↗
Statistical Tolerance and Clearance Analysis for Assembly
Tolerance is inevitable because manufacturing exactly equal parts is known to be impossible. Furthermore, the specification of tolerances is an integral part of product design since tolerances directly affect the assemblability, functionality, manufacturability, and cost effectiveness of a product. In this paper, we present statistical tolerance and clearance analysis for the assembly. Our proposed work is expected to make the following contributions: (i) to help the designers to evaluate products for assemblability, (ii) to provide a new perspective to tolerance problems, and (iii) to provide a tolerance analysis tool which can be incorporated into a CAD or solid modeling system.
statistical tolerance clearance analysis Monte-Car↗
A Statistical Approach to Recognizing Source Classes for Unassociated Sources in the First FERMI-LAT Catalog
The Fermi Large Area Telescope (LAT) First Source Catalog (1FGL) provided spatial, spectral, and temporal properties for a large number of gamma -ray sources using a uniform analysis method. After correlating with the mostcomplete catalogs of source types known to emit gamma rays, 630 of these sources are "unassociated" (i.e., have no obvious counterparts at other wavelengths). Here, we employ two statistical analyses of the primary gamma-ray characteristics for these unassociated sources in an effort to correlate their gamma-ray properties with the active galactic nucleus (AGN) and pulsar populations in 1FGL. Based on the correlation results, we classify 221 AGN-like and 134 pulsar-like sources in the 1FGL unassociated sources. The results of these source "classifications" appear to match the expected source distributions, especially at high Galactic latitudes. While useful for planning future multiwavelength follow-up observations, these analyses use limited inputs, and their predictions should not be considered equivalent to "probable source classes" for these sources. We discuss multiwavelength results and catalog cross-correlations to date, and provide new source associations for 229 Fermi-LAT sources that had no association listed in the 1FGL catalog. By validating the source classifications against these new associations, we find that the new association matches the predicted source class in approximately 80% of the sources.
SOURCES↗
Monte Carlo Bayesian Inference on a Statistical Model of Sub-gridcolumn Moisture Variability Using High-resolution Cloud Observations : Sensitivity Tests and Results - Part II
Part I presented a Monte Carlo Bayesian method for constraining a complex statistical model of GCM sub-gridcolumn moisture variability using high-resolution MODIS cloud data, thereby permitting large-scale model parameter estimation and cloud data assimilation. This part performs some basic testing of this new approach, verifying that it does indeed significantly reduce mean and standard deviation biases with respect to the assimilated MODIS cloud optical depth, brightness temperature and cloud top pressure, and that it also improves the simulated rotational-Ramman scattering cloud optical centroid pressure (OCP) against independent (non-assimilated) retrievals from the OMI instrument. Of particular interest, the Monte Carlo method does show skill in the especially difficult case where the background state is clear but cloudy observations exist. In traditional linearized data assimilation methods, a subsaturated background cannot produce clouds via any infinitesimal equilibrium perturbation, but the Monte Carlo approach allows finite jumps into regions of non-zero cloud probability. In the example provided, the method is able to restore marine stratocumulus near the Californian coast where the background state has a clear swath. This paper also examines a number of algorithmic and physical sensitivities of the new method and provides guidance for its cost-effective implementation. One obvious difficulty for the method, and other cloud data assimilation methods as well, is the lack of information content in the cloud observables on cloud vertical structure, beyond cloud top pressure and optical thickness, thus necessitating strong dependence on the background vertical moisture structure. It is found that a simple flow-dependent correlation modification due to Riishojgaard (1998) provides some help in this respect, by better honoring inversion structures in the background state.
Monte Carlo Bayesian inference↗
Metrology Optical Power Budgeting in SIM Using Statistical Analysis Techniques
The Space Interferometry Mission (SIM) is a space-based stellar interferometry instrument, consisting of up to three interferometers, which will be capable of micro-arc second resolution. Alignment knowledge of the three interferometer baselines requires a three-dimensional, 14-leg truss with each leg being monitored by an external metrology gauge. In addition, each of the three interferometers requires an internal metrology gauge to monitor the optical path length differences between the two sides. Both external and internal metrology gauges are interferometry based, operating at a wavelength of 1319 nanometers. Each gauge has fiber inputs delivering measurement and local oscillator (LO) power, split into probe-LO and reference-LO beam pairs. These beams experience power loss due to a variety of mechanisms including, but not restricted to, design efficiency, material attenuation, element misalignment, diffraction, and coupling efficiency. Since the attenuation due to these sources may degrade over time, an accounting of the range of expected attenuation is needed so an optical power margin can be book kept. A method of statistical optical power analysis and budgeting, based on a technique developed for deep space RF telecommunications, is described in this paper and provides a numerical confidence level for having sufficient optical power relative to mission metrology performance requirements.
stellar interferometry↗
Preliminary Statistics from the NASA Alphasat Beacon Receiver in Milan, Italy
NASA Glenn Research Center (GRC) and the Politecnico di Milano (POLIMI) have initiated a joint propagation campaign within the framework of the Alphasat propagation experiment to characterize rain attenuation, scintillation, and gaseous absorption effects of the atmosphere in the 40 GHz band. NASA GRC has developed and installed a KQ-band (20-40 GHz) beacon receiver at the POLIMI campus in Milan, Italy, which receives the 20-40 GHz signals broadcast from the Alphasat Aldo Paraboni TDP5 beacon payload. The primary goal of these measurements is to develop a physical model to improve predictions of communications systems performance within the Q-band. Herein, we provide an overview of the design and data calibration procedure, and present 6 months of preliminary statistics of the NASA propagation terminal, which has been installed and operating in Milan since May 2014. The Q-band receiver has demonstrated a dynamic range of 40 dB at an 8-Hz sampling rate. A weather station with an optical disdrometer is also installed to characterize rain drop size distribution for correlation with physical based models
Radio Transmission↗
Online Dectection and Modeling of Safety Boundaries for Aerospace Application Using Bayesian Statistics
The behavior of complex aerospace systems is governed by numerous parameters. For safety analysis it is important to understand how the system behaves with respect to these parameter values. In particular, understanding the boundaries between safe and unsafe regions is of major importance. In this paper, we describe a hierarchical Bayesian statistical modeling approach for the online detection and characterization of such boundaries. Our method for classification with active learning uses a particle filter-based model and a boundary-aware metric for best performance. From a library of candidate shapes incorporated with domain expert knowledge, the location and parameters of the boundaries are estimated using advanced Bayesian modeling techniques. The results of our boundary analysis are then provided in a form understandable by the domain expert. We illustrate our approach using a simulation model of a NASA neuro-adaptive flight control system, as well as a system for the detection of separation violations in the terminal airspace.
Statistics↗
Statistics Examples from Kennedy Space Center and Cape Canaveral Air Force Station
A few examples of projects in meteorology and engineering that used statistics at Kennedy Space Center and Cape Canaveral Air Force Station.
Statistics↗
Local starburst galaxies and their descendants. Statistics from the Sloan Digital Sky Survey
Despite strong interest in the starburst phenomenon in extragalactic astronomy, the concept remains ill-defined. Here we use a strict definition of starburst to examine the statistical properties of starburst galaxies in the local universe. We also seek to establish links between starburst galaxies, post-starburst (hereafter postburst) galaxies, and active galaxies. Data were selected from the Sloan Digital Sky Survey DR7. We applied a novel method of treating dust attenuation and derive star formation rates, ages, and stellar masses assuming a two-component stellar population model. Dynamical masses are calculated from the width of the H-alpha line. These masses agree excellently with the photometric masses. The mass (gas+stars) range is approximately 10( exp 9) - 10(exp 11.5) solar mass. As a selection criterion for starburst galaxies, we use, the birthrate parameter, b = SFR/SFR, requiring that b is greater than 3. For postburst galaxies, we use, the equivalent width of Hdelta in absorption with the criterion EW (sub Hdelta_abs) is greater than 6 A. Results. We find that only 1% of star-forming galaxies are starburst galaxies. They contribute 3-6% to the stellar production and are therefore unimportant for the local star formation activity. The median starburst age is 70 Myr roughly independent of mass, indicating that star formation is mainly regulated by local feedback processes. The b-parameter strongly depends on burst age. Values close to b = 60 are found at ages approximately 10 Myr, while almost no starbursts are found at ages greater than 1 Gyr. The median baryonic burst mass fraction of sub-L galaxies is 5% and decreases slowly towards high masses. The median mass fraction of the recent burst in the postburst sample is 5-10%. A smaller fraction of the postburst galaxies, however, originates in non-bursting galaxies. The age-mass distribution of the postburst progenitors (with mass fractions is greater than 3%) is bimodal with a break at logM(solar mass ) 10.6, above which the ages are doubled. The starburst and postburst luminosity functions (LFs) follow each other closely until M(sub r ) (is) approximately -21, when active galactic nuclei (AGNs) begin to dominate. The postburst LF continues to follow the AGN LF, while starbursts become less significant. This suggests that the number of luminous starbursts is underestimated by about one dex at high luminosities, because of having large amounts of dust and/or being outshone by an AGN. It also indicates that the starburst phase preceded the AGN phase. Finally, we look at the conditions for global gas outflow caused by stellar feedback and find that massive starburst galaxies are susceptible to such outflows.
Statistical distributions↗
A Framework for the Analysis of Deep Neural Networks in Autonomous Aerospace Applications using Bayesian Statistics
Deep Neural Networks (DNNs) are considered to be key components in many autonomous systems. Applications range from vision-based obstacle avoidance to intelligent/learning control and planning. Safety-critical applications as found in the aerospace domain require that the behavior of the DNN is validated and tested rigorously for safety of the autonomous system (AUS). In this paper, we present a framework to support testing of DNNs and the analysis of the network structure. Our framework employs techniques from statistical modeling and active learning to effectively generate test cases for DNN safety testing and performance analysis. We will present results of a case study on a physics-based Deep recurrent residual neural network (DR-RNN), which has been trained to emulate the aerodynamics behavior of a fixed-wing aircraft.
Deep Neural networks↗
A Framework for Software Health Management Using Bayesian Statistics
Software Health Management (SWHM) must make sure that the software always remains in safe and healthy regions of the state space. Boundaries between healthy and unhealthy regions are important for the detection of violations and health management.In this position paper, we present a framework, which employs techniques from Bayesian statistical modeling and active learning to efficiently characterize health boundaries in high-dimensional spaces. We will discuss, how this framework supports SWHM during design time and during operation of learning/adapting software systems
software health management↗
Development of TID Hardness Assurance Methodologies to Capitalize on Statistical Radiation Environment Models
We develop methods for bounding part-to-part variation in TID data. When used in conjunction with statistical radiation environment models, these methods allow development of RHA that deliver estimated piece part reliability for any desired confidence level, moving beyond risk avoidance methodologies based on radiation design margin. Two methods are developed, and their results compared for realistic data.
Total Ionizing Dose↗
Development of TID Hardness Assurance Methodologies to Capitalize on Statistical Radiation Environment Models
We develop methods for bounding part-to-part variation in TID data. When used in conjunction with statistical radiation environment models, these methods allow development of RHA that deliver estimated piece part reliability for any desired confidence level, moving beyond risk avoidance methodologies based on radiation design margin. Two methods are developed, and their results compared for realistic data.
Total ionizing dose↗
Earth-Space Links and Fade-Duration Statistics
The available data on fade-duration statistics are reviewed, with emphasis on mobile and fixed satellite links. Fades in mobile links are due to roadside-tree shadowing, and fades in fixed links are due to rain attenuation.
fade-duration↗