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This note presents the results of flight pressure-distribution tests on the right upper wing panel of a Douglas M-3 airplane equipped with an elliptical tip of length equal to the wing chord. The results are given in such form that the load distribution for any normal-force coefficient within the usual range encountered in flight may easily be determined.
A second-order shock-expansion method applicable to bodies of revolution is developed by the use of the predictions of the generalized shock-expansion method in combination with characteristics theory. Equations defining the zero-lift pressure distributions and the normal-force and pitching-moment derivatives are derived. Comparisons with experimental results show that the method is applicable at values of the similarity parameter, the ratio of free-stream Mach number to nose fineness ratio, from about 0.4 to 2.
The general application of the proposed first-order reliability method was achieved through the universal normalization of engineering probability distribution data. The method superimposes prevailing deterministic techniques and practices on the first-order reliability method to surmount deficiencies of the deterministic method and provide benefits of reliability techniques and predictions. A reliability design factor is derived from the reliability criterion to satisfy a specified reliability and is analogous to the deterministic safety factor. Its application is numerically illustrated on several practical structural design and verification cases with interesting results and insights. Two concepts of reliability selection criteria are suggested. Though the method was developed to support affordable structures for access to space, the method should also be applicable for most high-performance air and surface transportation systems.
In the first part of this article, I describe a new approach to studying decision making in discrimination tasks that does not depend on the technical assumptions of signal detection theory (e.g., normality of the encoding distributions). Applying these new distribution-free tests to data from three experiments, I show that base rate and payoff manipulations had substantial effects on the participants' encoding distributions but no effect on their decision rules, which were uniformly unbiased in equal and unequal base rate conditions and in symmetric and asymmetric payoff conditions. In the second part of the article, I show that this seemingly paradoxical result is readily explained by the sequential sampling models of discrimination. I then propose a new, "model-free" test for response bias that seems to more properly identify both the nature and direction of the biases induced by the classical bias manipulations.
The capabilities of the Dense Media Radiative Transfer model using the Quasi Crystalline Approximation with Coherent Potential (QCA-CP) to reproduce measured radiometric data were tested using the University of Tokyo Ground Based Microwave Radiometer (GBMR-7) during the third Intensive Observation Period (IOP3) of the NASA Cold-land Processes Field Experiment (CLPX). The data were collected at the Local-Scale Observation Site (LSOS), a 0.8-ha study site consisting of two open meadows separated by trees. Intensive measurements were also made of snow depth and temperature, density, and grain size profiles. A DMRT model is needed to describe radiative transfer in a medium such as snow because the assumption of independent scattering used in classical radiative transfer theory (CRT) is not valid. Validation of the DMRT approach requires a relationship between measured snow grain size and the DMRT approximation of snow grain radius as spherical particles with a mean radius of the log-normal particle-size distribution. This relationship is very important for a better understanding of snow modelling and for practical applications. DMRT simulations were compared with observations of microwave brightnesses at 18.7, 36.5 and 89-GHz (V and H polarizations) collected on February-1 9-25, 2003. Observation angles ranged from 30\deg to 70\deg. Model inputs included measured snow parameters except mean grain size. The average snow temperature, fractional volume and depth were held constant, together with the ice and soil permittivities. The minimum and maximum measured mean grain sizes were used to test the capabilities of the DMRT to reproduce the brightnesses as upper and lower limits. The sensitivity to the largest and smallest measured grain size in the three classes of minimum, medium and maximum observed grain sizes was also investigated. DMRT particle sizes yielding a best-fit to the experimental data for each date were computed. Results show that the measured brightnesses fall within the range of simulated brightnesses using the smallest and largest measured grain size values. The DMRT best-fit radii are comparable to the average radii for the medium observed grain sizes.
We present observations of electromagnetic precursor waves, identified as whistler mode waves, at supercritical interplanetary shocks using the Wind search coil magnetometer. The precursors propagate obliquely with respect to the local magnetic field, shock normal vector, solar wind velocity, and they are not phase standing structures. All are right-hand polarized with respect to the magnetic field (spacecraft frame), and all but one are right-hand polarized with respect to the shock normal vector in the normal incidence frame. Particle distributions show signatures of specularly reflected gyrating ions, which may be a source of free energy for the observed modes. In one event, we simultaneously observe perpendicular ion heating and parallel electron acceleration, consistent with wave heating/acceleration due to these waves.
The near wake of a flat plate with circular and elliptic trailing edges is investigated with data from direct numerical simulations. The plate length and thickness are the same in both cases. The separating boundary layers are turbulent and statistically identical. Therefore the wake is symmetric in the two cases. The emphasis in this study is on a comparison of the wake-distributions of velocity components, normal intensity and fluctuating shear stress obtained in the two cases.
Probability of detection (POD) analysis is used in assessing reliably detectable flaw size in nondestructive evaluation (NDE). MIL-HDBK-18231and associated mh18232POD software gives most common methods of POD analysis. Real flaws such as cracks and crack-like flaws are desired to be detected using these NDE methods. A reliably detectable crack size is required for safe life analysis of fracture critical parts. The paper provides discussion on optimizing probability of detection (POD) demonstration experiments using Point Estimate Method. POD Point estimate method is used by NASA for qualifying special NDE procedures. The point estimate method uses binomial distribution for probability density. Normally, a set of 29 flaws of same size within some tolerance are used in the demonstration. The optimization is performed to provide acceptable value for probability of passing demonstration (PPD) and achieving acceptable value for probability of false (POF) calls while keeping the flaw sizes in the set as small as possible.
This work is reporting progress made for CERES GGEO LW improvement using Narrowband to Broadband radiance conversion algorithm and LW Angular Distribution Model (ADM) and normalization technique.
The spatial distribution of emission-line galaxies (ELGs) relative to normal galaxies in several areas of the sky is discussed. Current evidence supports the notion that ELGs trace a low-density population in all the surveyed areas with the possible exception of the CfA 'Slice of the Universe' survey. Based on this and other survey data in the north galactic cap, it is suggested that the ELGs inside the Bootes void may actually define the edge of a totally empty volume within an underdense distribution of normal galaxies.
Numerical solutions of the instability wave pattern behind a harmonic point source in a Blasius boundary layer are used to form line sources by superposition. For infinite-length spanwise line sources of constant amplitude and phase, the result is just the two-dimensional normal mode of the same frequency; for a sinusoidal amplitude or linear phase distribution, the result is an oblique normal mode of the same spanwise wavenumber. A finite-length spanwise source simulates a vibrating ribbon. In a study of the influence of the source length on the downstream amplitude, the effective field of view is found to have a half-angle of about 16 deg. If the source tips are within this field, the amplitude may be either greater than or less than the comparable normal-mode amplitude, depending on the distance from the source and the spanwise location. For an oblique line source, the downstream wave development at each spanwise location is found to be close to, but not identical with, that of an oblique normal mode which originates at the source with the initial wave angle of the source and satisfies the irrotationality condition on the wavenumber vector.
A major challenge for QCD theorists is to understand how to different categories of QCD physics contribute to a single observable. This is the goal of QCD factorization theory. Today, QCD physicists are tasked with describing increasingly detailed properties of transverse momentum dependent particle distributions, including their shapes and normalizations, their variation with energy and other kinematical variables, the identity of particles involved, correlations with spin, and the relationship between transverse momentum distributions in different processes. Embedded within that broad effort are specific fundamental questions at the heart of QCD theory. Namely, how does one give precise, quantitative meaning to quark and gluon concepts in hard scattering events, and what are the limits of those concepts? In the past two decades, there has been a renaissance in TMD QCD, with a broad spectrum of motivations driving the study of TMD observables. TMD QCD studies are in a transformative stage; subfields that traditionally progressed nearly independently of one other, such as hadronic structure studies and the study of perturbative QCD gluon radiation in high-energy collisions, are becoming intertwined. It is thus becoming increasingly clear that unified approaches -- those that incorporate all the relevant phenomena into a single formalism rooted in QCD fundamentals -- will be critical to the mission to expand the current understanding of QCD nuclear/hadronic dynamics. TMD QCD factorization theorems and their generalizations will be central to these efforts. The factorization theorems, in addition, provide the practical prescription for applying specific calculations to phenomenology. The goal of this project was to unify and push the boundaries of traditional TMD research and prepare it for use in future phenomenological applications.
The multivariate adaptive regression spline (MARS) approach of Friedman and its Bayesian counterpart are effective approaches for the emulation of computer models. The traditional assumption of Gaussian errors limits the usefulness of MARS, and many popular alternatives, when dealing with stochastic computer models. Here, we propose a generalized Bayesian MARS (GBMARS) framework which admits the broad class of generalized hyperbolic distributions as the induced likelihood function. This allows us to develop tools for the emulation of stochastic simulators which are parsimonious, scalable, and interpretable and require minimal tuning, while providing powerful predictive and uncertainty quantification capabilities. GBMARS is capable of robust regression with t distributions, quantile regression with asymmetric Laplace distributions, and a general form of “Normal-Wald” regression in which the shape of the error distribution and the structure of the mean function are learned simultaneously. We demonstrate the effectiveness of GBMARS on various stochastic computer models, and we show that it compares favorably to several popular alternatives.
Random numbers were generated with the aid of a digital computer and transformed such that the probability density function of a discrete random load history composed of these random numbers had one of the following non-Gaussian distributions: Poisson, binomial, log-normal, Weibull, and exponential. The resulting random load histories were analyzed to determine their peak statistics and were compared with cumulative peak maneuver-load distributions for fighter and transport aircraft in flight.
Pressure measurements were made in flight on the right upper wing of an M-3 airplane. The effects of tip plan form, washout, and transverse camber were investigated with eight tip forms in unyawed conditions through the range of positive lift coefficients from zero lift to the stall. The conclusion is that the tip plan form does not influence the span distribution of the coefficients of normal force and moment. It is shown inferentially that temperature, humidity, and the aging of the wood and fabric wing structure used on the M-3 airplane have an appreciable influence on the load distribution.
This paper investigates the broadband noise reduction properties of a distributed (variable) facesheet liner. Candidate uniform samples are selected using the Wave Propagation Model and Two-Parameter Model developed at the NASA Langley Research Center. Three uniform and one distributed facesheet samples are tested in the Normal Incidence Tube (NIT) at NASA Langley. Initial results show that the distributed facesheet sample possesses broadband noise reduction properties. Acoustic impedances educed from these NIT tests are used as inputs into the Convected Helmholtz Equation (CHE) propagation code developed at NASA Langley to synthesize the acoustic pressure field in the Grazing Flow Impedance Tube (GFIT) with no flow. Three uniform, one distributed, as well as one three-zone liner, oriented in two different directions, are investigated. Preliminary results showed the distributed facesheet and three-zone liners exhibit broadband noise reduction properties.
The overall objective of this work was to characterize optical and fluorescence properties in the euphotic zone during two British Ocean Flux Study (BOFS) Arabian Sea cruises. This was later expanded in 1995 to include three U.S. JGOFS Arabian Sea Cruises. The region was to be divided into one or more "bio-optical provinces," within each of which a single set of regression models was to be developed to relate the vertical distribution of irradiance attenuation and normalized fluorescence (SF and NF) to remote sensing reflectance and diffuse attenuation coefficient. The working hypothesis was that over relatively large spatial and temporal scales, the vertical profiles of bio-optical properties were predictable. The specific technical objectives were: (1) To characterize the vertical distribution of the inherent and apparent optical properties by measuring downwelling and upwelling irradiances, upwelling radiances, scalar irradiance of PAR, and beam transmissions at each station - from these data, spectral diffuse attenuation coefficients, irradiance reflectances, remote sensing reflectances, surface-leaving radiances and beam attenuation coefficients were determined; (2) To characterize the spectral absorption of total particulate, detrital, and dissolved organic material at each station from discrete water samples; (3) To describe the vertical distribution of photoadaptive properties in the water column by measuring profiles of stimulated (SF) and natural (NF) fluorescence and examining relationships between SF and NF as a function of diffuse optical depth, pigment biomass and primary productivity; and (4) To establish locally derived, in-water algorithms relating remote sensing reflectance spectra to diffuse attenuation coefficients, phytoplankton pigment concentrations and primary productivity, through intercomparisons with in situ measurements, for application to SeaWiFS data.
Neural networks can be used to detect and identify abnormalities in real-time process data. Two basic approaches can be used, the first based on training networks using data representing both normal and abnormal modes of process behavior, and the second based on statistical characterization of the normal mode only. Given data representative of process faults, radial basis function networks can effectively identify failures. This approach is often limited by the lack of fault data, but can be facilitated by process simulation. The second approach employs elliptical and radial basis function neural networks and other models to learn the statistical distributions of process observables under normal conditions. Analytical models of failure modes can then be applied in combination with the neural network models to identify faults. Special methods can be applied to compensate for sensor failures, to produce real-time estimation of missing or failed sensors based on the correlations codified in the neural network.