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No description available.

The motivation for this work is to study methods of estimating appropriate level-of-detail (LoD) object models by quantifying appearance errors prior to image synthesis. Visualization systems have been developed that employ LoD objects, however, the criteria are often based on heuristics that restrict the form of the object model and rendering method. Also, object illumination is not considered in the LoD selection. This dissertation proposes an image-based scene learning pre-process to determine appropriate LoD for each object in a scene. Scene learning employs sample images of an object, from many views and with a range of geometric representations, to produce a profile of the LoD image error as a function of viewing distance. Signal processing techniques are employed to quantify how images change with respect to object model resolution, viewing distance, and lighting direction. A frequency-space analysis is presented which includes use of the vision system?s contrast sensitivity to evaluate perceptible image differences with error metrics. The initial development of scene learning is directed to sampling the object?s appearance as a function of viewing distance and object geometry in scene space. A second phase allows local lighting to be incorporated in the scene learning pre-process. Two methods for re-lighting are presented that differ in accuracy and overhead; both allow properties of an object?s image to be computed without rendering. In summary, full-resolution objects pro-duce the best image since the 3D scene is as real as possible. A less realistic 3D scene with simpler objects produces a different appearance in an image, but by what amount? My the-sis is such can be had. Namely that object fidelity in the 3D scene can be loosened further than has previously been shown without introducing significant appearance change in an object and that the relationship between 3D object realism and appearance can be expressed quantitatively.

Large eddy simulation (LES) suffers from two primary sources of error: the numerical discretization scheme and the subgrid stress model (SGS). An attempt has been made to determine optimum combinations of SGS models and numerical schemes for use in performing practical LES for engineering-relevant problems. A formal quantification of numerical error present in finite-volume/finite-difference simulations was conducted. The effect of this error was explicitly added to a pseudospectral LES solver, and the modified pseudospectral solver was used to compute LES of decaying turbulence. In this way SGS modeling error and numerical error could be separately assessed. Verification of results was carried out using a commercially available finite-volume solver (FLUENT). Results showed that some combinations of SGS model and discretization scheme are more suitable for performing LES than others. Favorable combinations from the above findings were tested for an axisymmetric jet at Mach number 0.2. Results indicate good agreement with prior findings.

No description available.

No description available.

No description available.

No description available.

Let{(}φ_n(x)} be an orthonormal system in the set of Lebesgue square integrable functions L². Let f𝜖L². The generalized Fourier series of f with respect to {(}φ_n(x)} is the series ∑_n=0^∞ (f, φ_n) φ_n(x), where (f, φ_n) is the inner product of the functions f an φ_n. The e existence of a complete orthonormal system in L² is proven. Conditions for convergence of the generalized Fourier series are presented. A discussion of orthogonal polynomials with special emphasis on the Jacobi polynomial systems is presented. A least squares, differential correction, discrete observation procedure is employed to solve the potential equation with boundary conditions in tenns of three special Jacobi systems. / M.S.

The primary purpose of this study is to evaluate the use of Doppler Lidar sensors for assessing track weakening that would indicate early stages of track instability. Such track weakening could lead to gage widening or track buckling due to rail thermal expansion. A series of tests are performed at the Transportation Technology Center's High Tonnage Loop, where two sections of track are "doctored" to have weaker lateral strength, one on a tangent and another one in a curve. Multiple tests are performed at speeds ranging from 10 – 40 mph, during which the lateral and vertical deflections of the rail are measured under the weight of the passing wheels of a heavily-loaded gondola. The track weakness is created by removing the rail spikes from eight consecutive ties. The measurements from the soft sections are compared with a track section on a tangent that is determined to have nominally sufficient ("good") stiffness. The measurement system consists of four Doppler Lidar units, two oriented toward the rail gage face to measure lateral rail movement, and two directed to the top of the rail to measure vertical rail movement. The combination of the vertical and lateral measurements is used as an indicator of a lack of rail stability if larger-than-normal movement of the rail is detected in either direction. The data collected is analyzed through various methods designed to differentiate sections of track including Gaussian Mixture Model sorting algorithms, inspection via Short Time Fourier Transforms, Discrete Wavelet Transforms, and manual inspection. None of the methods can be done automatically; they each require a different amount of setup and pre-processing before the raw data can be made suitable for the analysis offered by each. The pre-processing can account for dropped data and can be used to identify some false positives such as switches or lubricators. The test results indicate that the system provides a distinctly different measurement in the sections that are doctored to have less track stability than the section with nominally sufficient stiffness. The detection of the loose track in the tangent sections, however, proves to be less reliable. For those, a mostly ad hoc approach is necessary to match the measured data with video images to pinpoint the exact location of the measurements. It is not clear to what extent such approaches would be feasible in practice. Further evaluations of the test data may be used to shed more light on practical analysis methods—possibly wavelets—that are more automated and less ad hoc. They can also provide alternative system setups or designs of experiments for future tests at TTC or on revenue service tracks. / Master of Science / The purpose of this study is to evaluate the effectiveness of a set of Doppler Lidar sensors for their ability to determine the locations of weaker sections of railroad track. These weaker sections could cause damage to the track or passing trains by deforming or buckling under load. A set of tests are performed at the Transportation Technology Center's High Tonnage Loop to evaluate these capabilities. The track had two sections, one of curved track the other of straight track, where the rail was purposefully weakened by removing retaining spikes from the railroad ties. The weakened sections were created by removing the vertical retaining spikes in eight consecutive ties. The tests were conducted at speeds of between 10 to 40 mph, and the sensors measured both the vertical and lateral movement of both rails. The results of these measurements were compared with the unaffected rail. The collected data is analyzed using various data processing techniques. These techniques included using a sorting algorithm to find sections of track with different characteristics as well as inspecting the time and frequency content of the data. None of these methods are automated, and each requires specific setup and adjustment to be effective. The data also needs to be prepared by correcting for any missing or incorrect data points. The tests indicate that the system is able to differentiate between the purposefully weakened track and the rest of the track, however the clearest results of this were for the weakened track in the curve. The straight track results were able to be found with the addition of aligning the video, Lidar, and GPS data sets. It is not clear whether the system could be improved to detect this type of weakness in straight track in practice. Additional testing and evaluation could serve to expand the range of data analysis methods used in differentiating the track conditions and could serve to automate the process. Additionally, alternative test setups could provide further information as to the capabilities of the sensors to detect different types of weakened track.