No description available.

No description available.

The human visual system has the ability o utilize motion information to infer the shapes of surfaces. More specifically, we are able to derive descriptions of rigidly rotating smooth surfaces entirely from the orthographic projection of the motions of their surface markings. A computational analysis of this ability is proposed based on "shape from motion" proposition. This proposition states that given the first spatial derivatives of the orthographically projected velocity and the acceleration fields of a rigidly rotating regular surface, then the angular velocity and the surface normal at each visible point on that surface are uniquely determined up to a reflection.

Development of improved turbulence closure models for compressible fluid flow simulations requires better understanding of the effects of compressibility on various underlying processes of turbulence. Fundamental studies of turbulent velocity gradients hold the key to understanding several non-linear processes like material element deformation, energy cascading, intermittency and mixing. Experiments, direct numerical simulation (DNS) and simple mathematical models are three approaches to study velocity gradients. With the goal of furthering our understanding of the effects of compressibility on turbulent velocity gradients, this dissertation (i) employs DNS results to characterize some of the effects of compressibility on turbulent velocity gradients, and (ii) develops simple mathematical models for velocity gradient dynamics in compressible turbulence. In the first part of the dissertation, effects of compressibility on velocity gradient invariants and the local topology of compressible turbulence are characterized employing DNS results of compressible decaying isotropic turbulence. Joint statistics of second and third invariants of velocity gradient tensor and the exact probability of occurrence of associated topologies conditioned upon dilatation (degree of compression/expansion of fluid) are computed. These statistics are found to be (i) highly dependent on dilatation and (ii) substantially different from the statistics observed in incompressible turbulence. These dilatation-conditioned statistics of compressible turbulence, however, are found to be fairly independent of Mach number and Reynolds number. In the second part of the dissertation, two mathematical models for compressible velocity gradient dynamics are developed. To take into account the significant aero-thermodynamic coupling that exists in compressible flows, the models are derived explicitly using the continuity, energy and state equations, along with the momentum equation. The modeling challenge involved in the development of these models lies in capturing the inherently non-local nature of pressure and viscous effects as a function of local terms to derive a closed set of ordinary differential equations. The models developed in this dissertation are evaluated in a variety of flow regimes - incompressible limit (low Mach number); pressure-released limit (extremely high Mach number); and intermediate (sub-sonic Mach numbers) - and are shown to recover a range of known compressibility effects.

Purpose: A time of flight approach to the analysis of 4D CT angiography is examined to calculate blood flow in arteries. Materials and Methods: Software was written to track contrast bolus TOF along a central vessel axis. Time density curves were analyzed to determine bolus time to peak at successive vessel cross-sections which were plotted against vessel path length. A line of best fit was plotted through the resulting data and 1/slope provided a measurement of velocity. Results: Validation was successful in simulation and in flow phantoms, though quality of results depended strongly on quality of curve fit. In phantoms and in vivo, accuracy and reproducibility of measurements improved with longer path lengths and, in vivo, depended on the avoidance of venous contamination. Conclusions: Quantitative functional intravascular information such as blood velocity and flow rate may be calculated from 4D CT angiography.

Purpose: A time of flight approach to the analysis of 4D CT angiography is examined to calculate blood flow in arteries. Materials and Methods: Software was written to track contrast bolus TOF along a central vessel axis. Time density curves were analyzed to determine bolus time to peak at successive vessel cross-sections which were plotted against vessel path length. A line of best fit was plotted through the resulting data and 1/slope provided a measurement of velocity. Results: Validation was successful in simulation and in flow phantoms, though quality of results depended strongly on quality of curve fit. In phantoms and in vivo, accuracy and reproducibility of measurements improved with longer path lengths and, in vivo, depended on the avoidance of venous contamination. Conclusions: Quantitative functional intravascular information such as blood velocity and flow rate may be calculated from 4D CT angiography.

No description available.

No description available.

Stokes’ law that is traditionally used for modeling the sedimentation of flocs, incorrectly assumes that the floc is solid and spherical. Consequently the settling rates of flocs cannot be estimated using the Stokes law. The application of fractal dimensions to study the internal structure and settling of flocs formed in lime softening process was investigated. An optical microscope with motorized stage was used to measure the fractal dimensions of lime softening flocs directly on their images in 2 and 3D space. The fractal dimensions of the lime softening flocs were 1.15-1.27 for floc boundary, 1.49-1.90 for cross-sectional area and 2.55-2.99 for floc volume. Free settling tests were used for indirect determination of 3D fractal dimension. The measured settling velocity of flocs ranged from 0.1 to 7.1 mm/s (average: 2.37 mm/s) for the flocs with equivalent diameters from 10µm to 260µm (average: 124 µm). Floc settling model incorporating variable floc fractal dimensions as well as variable primary particle size was found to describe the settling velocity of large (>60 µm) lime softening flocs better than Stokes’ law. Settling velocities of smaller flocs (<60 µm) could still be quite well predicted by the Stokes’ law. The variation of fractal dimensions with lime floc size in this study indicated that two mechanisms are involved in the formation of these flocs: cluster-cluster aggregation for small flocs (>60 µm) and diffusion-limited aggregation for large flocs (<60 µm). Therefore, the relationship between the floc fractal dimension and floc size appears to be determined by floc aggregation mechanisms. The settling velocity of lime softening flocs was also modeled by a general model that assumes multiple normally distributed fractal dimensions for each floc size. The settling velocities were in the range of 0-10mm/s and in good agreement with measured settling velocities (0.1-7.1mm/s). The Stokes’ law overestimates the settling velocity of lime flocs. It seems that the settling velocity of flocs is mainly controlled by aggregation mechanisms and forming large floc does not guarantee improved sedimentation. The multifractal analysis of lime softening flocs showed that these aggregates are multifractal and a spectrum of fractal dimensions is required to describe the structure of an individual floc.

Stokes’ law that is traditionally used for modeling the sedimentation of flocs, incorrectly assumes that the floc is solid and spherical. Consequently the settling rates of flocs cannot be estimated using the Stokes law. The application of fractal dimensions to study the internal structure and settling of flocs formed in lime softening process was investigated. An optical microscope with motorized stage was used to measure the fractal dimensions of lime softening flocs directly on their images in 2 and 3D space. The fractal dimensions of the lime softening flocs were 1.15-1.27 for floc boundary, 1.49-1.90 for cross-sectional area and 2.55-2.99 for floc volume. Free settling tests were used for indirect determination of 3D fractal dimension. The measured settling velocity of flocs ranged from 0.1 to 7.1 mm/s (average: 2.37 mm/s) for the flocs with equivalent diameters from 10µm to 260µm (average: 124 µm). Floc settling model incorporating variable floc fractal dimensions as well as variable primary particle size was found to describe the settling velocity of large (>60 µm) lime softening flocs better than Stokes’ law. Settling velocities of smaller flocs (<60 µm) could still be quite well predicted by the Stokes’ law. The variation of fractal dimensions with lime floc size in this study indicated that two mechanisms are involved in the formation of these flocs: cluster-cluster aggregation for small flocs (>60 µm) and diffusion-limited aggregation for large flocs (<60 µm). Therefore, the relationship between the floc fractal dimension and floc size appears to be determined by floc aggregation mechanisms. The settling velocity of lime softening flocs was also modeled by a general model that assumes multiple normally distributed fractal dimensions for each floc size. The settling velocities were in the range of 0-10mm/s and in good agreement with measured settling velocities (0.1-7.1mm/s). The Stokes’ law overestimates the settling velocity of lime flocs. It seems that the settling velocity of flocs is mainly controlled by aggregation mechanisms and forming large floc does not guarantee improved sedimentation. The multifractal analysis of lime softening flocs showed that these aggregates are multifractal and a spectrum of fractal dimensions is required to describe the structure of an individual floc.