Thesis (Ph. D.)--Ohio University, 2001. / Title from PDF t.p.

Thesis (M.S.)--Ohio University, August, 2000. / Title from PDF t.p.

Thesis (Ph. D.)--Physics, Georgia Institute of Technology, 2008. / Committee Chair: Roman O. Grigoriev; Committee Member: Daniel Goldman; Committee Member: Michael Schatz; Committee Member: Minami Yoda; Committee Member: Predrag Cvitanovic.

Thesis (M.S.)--Ohio University, August, 1993. / Title from PDF t.p.

Thesis (Ph. D.)--Oregon State University, 2009. / Printout. Includes bibliographical references (leaves 119-124). Also available on the World Wide Web.

Thesis (M.S.)--University of Texas at El Paso, 2009. / Title from title screen. Vita. CD-ROM. Includes bibliographical references. Also available online.

The ingestion of sand-particles into turbomachinery decreases their longevity and perfor- mance and can even lead to failure. To address these problems, studying particle-laden flows is of high interest in the field. Due to their low computational cost, RANS simulations remain the preferred method in the design phase of engineering solutions. The reduction in computational cost stems from the fact that RANS simulations neglect velocity fluctuations and solve for the time averaged velocity field. This leads to non-physical results since these velocity fluctuations affect the particle trajectories. To improve the accuracy, the simula- tions of the particles are adapted with particle dispersion models that calculate the velocity fluctuations. This work investigates the dispersion of sand particles due to different types of particle dispersion models. A converging pipe with subsequent impact plate is used for this study. Spherical sand-particles ranging in size from 5 to 100 microns in diameter are injected against the main fluid flow into the pipe. The commercial CFD software Ansys Fluent is used and the RANS simulations are run with the k-omega SST turbulence model. Variants of both the Discrete Random Walk (DRW) and Continuous Random Walk (CRW) dispersion models are looked at. Tracer particles stayed close to the center axis inside the pipe and showed the largest dispersion through the models. The dispersion was higher for the CRW compared to the DRW model for all particle sizes. Inertial particles reached the inner walls of the pipe and showed a reduced effect by the dispersion models. The dispersion for larger particles mainly depends on the particle-wall interactions. / Master of Science / The movement of particles by a fluid is found in many natural and industrial processes. A prominent example from the aerospace sector is the ingestion of sand into gas turbine engines. Anybody who has seen videos or in person landings and takeoffs of helicopters and fixed-wing aircraft in dusty environments can see the large amounts of ingestion into the engines. These sand particles damage the engines and lead to a reduced lifetime, higher maintenance requirements and possible failure of the engine. To successfully predict the movement of those particles into and through the engine, simulations are an often used tool. Since the simulation of turbulent flows requires high computational effort, the most common approach is to only solve for the average of the fluid velocities over time in each location which greatly reduces the computational cost. As a result when including particles with the fluid flow, the effects that fluctuating velocities have on the movement of the particles are neglected. Models using random numbers can imitate the missing velocity fluctuations and include these effects in the simulations. There are different types of models with so-called Continuous Random Walk models calculating a continually changing fluctuating velocity as the particle travels through the domain. Discrete Random Walk models on the other hand have a constant fluctuating velocity for some time until it switches to another randomized fluctuating velocity as the particle travels through the domain. This work compares the effects of the different models on the motion of the particles. An increase in dispersion for larger and smaller particles is found for the Continuous Random Walk model.

No description available.

Thesis. 1976. M.S.--Massachusetts Institute of Technology. Dept. of Mechanical Engineering. / Microfiche copy available in Archives and Engineering. / Includes bibliographical references. / by Arthur R. Shean. / M.S.

Multiphase pipe flow is investigated using proper orthogonal decomposition for tomographic X-ray data, where holdup, cross-sectional phase distributions and phase interface characteristics within the pipe are obtained. Six cases of stratified and mixed flow with water content of 10%, 30% and 80% are investigated to gain insight into effects of velocity and proportion of water on the flow fields. Dispersed and slug flows are separately analyzed to consider the added interface complexity of the flow fields. These regimes are also highly applicable to industry operational flows. Instantaneous and fluctuating phase fractions of the four flow regime are analyzed and reduced order dynamical descriptions are generated. Stratified flow cases display coherent structures that highlight the liquid-liquid interface location while the mixed flow cases show minimal coherence of the eigenmodes. The dispersed flow displays coherent structures for the first few modes near the horizontal center of the pipe, representing the liquid-liquid interface location while the slug flow case shows coherent structures that correspond to the cyclical formation and break up of the slug in the first 5 modes. The low order descriptions of the high water content, stratified flow field indicates that main characteristics can be captured with minimal degrees of freedom. Reconstructions of the dispersed flow and slug flow cases indicate that dominant features are observed in the low order dynamical description utilizing less than 1% of the full order model. POD temporal coefficients a1, a2 and a3 show a high level of interdependence for the slug flow case. The coefficients also describe the phase fraction holdup as a function of time for both dispersed and slug flow. The second coefficient, a2, and the centerline holdup profile show a mean percent difference below 9% between the two curves. The mathematical description obtained from the decomposition will deepen the understanding of multiphase flow characteristics and is applicable to long distance multiphase transport pipelines, fluidized beds, hydroelectric power and nuclear processes to name a few.