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This monograph was prepared to give the practicing engineer a clear understanding of probability and statistics with special consideration to problems frequently encountered in aerospace engineering. It is conceived to be both a desktop reference and a refresher for aerospace engineers in government and industry. It could also be used as a supplement to standard texts for in-house training courses on the subject.
Computer simulation study to determine normality or nonnormality on any given population of sampled data - statistical probability distribution
The values of the Normalized Radar Backscattering Cross Section (NRCS), sigma (o), obtained by a scatterometer are random variables whose variance is a known function of the expected value. The probability density function can be obtained from the normal distribution. Models for the expected value obtain it as a function of the properties of the waves on the ocean and the winds that generated the waves. Point estimates of the expected value were found from various statistics given the parameters that define the probability density function for each value. Random intervals were derived with a preassigned probability of containing that value. A statistical test to determine whether or not successive values of sigma (o) are truly independent was derived. The maximum likelihood estimates for wind speed and direction were found, given a model for backscatter as a function of the properties of the waves on the ocean. These estimates are biased as a result of the terms in the equation that involve natural logarithms, and calculations of the point estimates of the maximum likelihood values are used to show that the contributions of the logarithmic terms are negligible and that the terms can be omitted.
Methods for the recovery of winds near the surface of the ocean from measurements of the normalized radar backscattering cross section must recognize and make use of the statistics (i.e., the sampling variability) of the backscatter measurements. Radar backscatter values from a scatterometer are random variables with expected values given by a model. A model relates backscatter to properties of the waves on the ocean, which are in turn generated by the winds in the atmospheric marine boundary layer. The effective wind speed and direction at a known height for a neutrally stratified atmosphere are the values to be recovered from the model. The probability density function for the backscatter values is a normal probability distribution with the notable feature that the variance is a known function of the expected value. The sources of signal variability, the effects of this variability on the wind speed estimation, and criteria for the acceptance or rejection of models are discussed. A modified maximum likelihood method for estimating wind vectors is described. Ways to make corrections for the kinds of errors found for the Seasat SASS model function are described, and applications to a new scatterometer are given.
Launching pad operations for missiles - statistical probability and prelaunch problems
Reliability estimation of redundant systems for determining statistical probability of mission success
Statistical-mechanical estimate of orbital collision probabilities
Statistical probability distribution of first occurrence and first recurrence times of given level crossing in continuous random process
Statistical probability and sampled data method for compressing data from deep space probes
Strong coupled rotational excitation problem in atom-diatomic molecule scattering system, discussing transition probability matrices statistical analysis
The identification of the parameters of the optimal control model from input-output data of the human operator is considered. Accepting the basic structure of the model as a cascade of a full-order observer and a feedback law, and suppressing the inherent optimality of the human controller, the parameters to be identified are the feedback matrix, the observer gain matrix, and the intensity matrices of the observation noise and the motor noise. The identification of the parameters is a statistical problem, because the system and output are corrupted by noise, and therefore the solution must be based on the statistics (probability density function) of the input and output data of the human operator. However, based on the statistics of the input-output data of the human operator, no distinction can be made between the observation and the motor noise, which shows that the model suffers from overparameterization.
Invasive perennial pepperweed (Lepidium latifolium) has spread rapidly throughout the western United States in the past fifteen years. Pepperweed outcompetes many native species for water and nutrients, disturbing sensitive ecosystems. The purpose of this study is to map the contemporary distribution of pepperweed throughout the wetland ecosystem of the restored South San Francisco Bay Salt Ponds and create a habitat suitability model to predict future spread. Pepperweed reflectance data were collected in-situ with the GER 1500 spectroradiometer along with presence and absence pepperweed GPS data points. A Spectral Angle Mapper (SAM) classification algorithm (Method A) was used to distinguish pepperweed spectra and map its distribution on an EO-1 Hyperion image of the study area. Similarly, a supervised classification was run on an ASTER image of the study area. A GIS multivariate habitat suitability model was created to predict areas most susceptible to pepperweed colonization. Variables incorporated into the model were tidal extent, propensity for disturbance, pond salinity, proximity to levees and channels, and terrain curvature. A Generalized Additive Model (GAM) was also used to generate a suitability map (Method B) and investigate the statistical probability that each variable contributed to predict pepperweed spread. Results from the GAM revealed distance to channels, distance to disturbance factors, extreme pond salinity and curvature as statistically significant in determining the locations of suitable pepperweed habitats
Accounting for the statistical geometric and material variability of structures in analysis has been a topic of considerable research for the last 30 years. The determination of quantifiable measures of statistical probability of a desired response variable, such as natural frequency, maximum displacement, or stress, to replace experience-based "safety factors" has been a primary goal of these studies. There are, however, several problems associated with their satisfactory application to realistic structures, such as bladed disks in turbomachinery. These include the accurate definition of the input random variables (rv's), the large size of the finite element models frequently used to simulate these structures, which makes even a single deterministic analysis expensive, and accurate generation of the cumulative distribution function (CDF) necessary to obtain the probability of the desired response variables. The research presented here applies a methodology called probabilistic dynamic synthesis (PDS) to solve these problems. The PDS method uses dynamic characteristics of substructures measured from modal test as the input rv's, rather than "primitive" rv's such as material or geometric uncertainties. These dynamic characteristics, which are the free-free eigenvalues, eigenvectors, and residual flexibility (RF), are readily measured and for many substructures, a reasonable sample set of these measurements can be obtained. The statistics for these rv's accurately account for the entire random character of the substructure. Using the RF method of component mode synthesis, these dynamic characteristics are used to generate reduced-size sample models of the substructures, which are then coupled to form system models. These sample models are used to obtain the CDF of the response variable by either applying Monte Carlo simulation or by generating data points for use in the response surface reliability method, which can perform the probabilistic analysis with an order of magnitude less computational effort. Both free- and forced-response analyses have been performed, and the results indicate that, while there is considerable room for improvement, the method produces usable and more representative solutions for the design of realistic structures with a substantial savings in computer time.
The historical record of large volcanic eruptions from 1500 to 1980 is subjected to detailed time series analysis. In two weak but probably statistically significant periodicities of about 11 and 80 yr, the frequency of volcanic eruptions increases (decreases) slightly around the times of solar minimum (maximum). Time series analysis of the volcanogenic acidities in a deep ice core from Greenland reveals several very long periods ranging from about 80 to about 350 yr which are similar to the very slow solar cycles previously detected in auroral and C-14 records. Solar flares may cause changes in atmospheric circulation patterns that abruptly alter the earth's spin. The resulting jolt probably triggers small earthquakes which affect volcanism.
Over the past several years, a number of candidate scenarios for the human exploration of Mars have been advanced. These have had a range of mission objectives, scope, scale, complexity and probable cost. The Exploration Programs Office (ExPO) has developed a reference Mars exploration program and a means of comparing it to other proposed Mars programs. The reference program was initiated in a workshop of Mars exploration advocates which defined two objectives of equal importance for early Mars exploration - understanding Mars and understanding the potential of Mars to support humans. These goals were used to define a set of transportation and surface elements that could carry out a robust exploration program. The approach to comparing alternate architectures has three principal parts: (1) Bringing the architectures into rough commonality in terms of surface mission objectives and hardware capabilities; (2) Providing a common level of human support for flights to and from Mars; and (3) Comparing the complexity of the elements needed to carry out the program and using partial redundancy to approximate the same statistical probability of mission success. This top-level approach has been applied to the ExPO reference program, the 'Mars Directs strategy (Zubrin, 1991) and the Stanford International Mars Mission (Lusignan, 1992).
Mathematical model for statistical probability of internal microbial spacecraft contamination
Evacuation areas are defined for those transportation accidents where volatile chemical propellant tanks are exposed to fire in the wreckage and eventually explode with consequent risks from fragments in surrounding populated areas. An evacuation area with a minimum radius of 600 m (2000 ft) is recommended to limit the statistical probability of fatality to one in 100 such accidents. The result was made possible by the derivation of a distribution function of distances reached by fragments from bursting chemical car tanks. Data concerning fragments was obtained from reports or tank car pressure bursts between 1958 and 1971.
Evacuation areas are defined for those transportation accidents where volatile chemical propellant tanks are exposed to fire in the wreckage and eventually explode with consequent risks from fragments in surrounding populated areas. An evacuation area with a minimum radius of 600 m is recommended to limit the statistical probability of fatality to one in 100 such accidents. The result of this study was made possible by the derivation of a distribution function of distances reached by fragments from bursting chemical car tanks. Data concerning fragments were obtained from reports of tank car pressure bursts between 1958 and 1971.