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There are many options available when selecting a computational model for two-phase flows. It is important to understand all the features of the model selected, including when the model is appropriate and how using it may affect your results. This work examines how volume displacement effects in two-phase Eulerian-Lagrangian models manifest themselves. Some test cases are examined to determine what input these effects have on the flow, and if we can predict when they will become important. Bubble injection into a traveling vortex ring is studied in-depth, as it provides significant insight into the physics of these volume displacement effects. When a few bubbles are entrained into a traveling vortex ring, it has been shown that even at extremely low volume loadings, their presence can significantly affect the structure of the vortex core (Sridhar & Katz 1999). A typical Eulerian-Lagrangian point-particle model with two-way coupling for this dilute system, wherein the bubbles are assumed subgrid and momentum point-sources are used to model their effect on the flow, is shown to be unable to accurately capture the experimental trends of bubble settling location, bubble escape, and vortex distortion for a range of bubble parameters and vortex strengths. Accounting for fluid volume displacement due to bubble motion, using a model termed as volumetric coupling, experimental trends on vortex distortion and bubble settling location are well captured. The fluid displacement effects are studied by introducing the notion of a volume displacement force, the net force on the fluid due to volumetric coupling, which is found to be dominant even at the low volume loadings investigated here. A method of quantifying of these forces is derived and used to study the effects for a wide range of particle to fluid density ratios in Taylor-Green vortices. A simple modification to the standard point-particle Lagrangian approach is developed, wherein the interphase reaction source terms are consistently altered to account for the fluid displacement effects and reactions due to bubble accelerations. / Graduation date: 2013 / Access restricted to the OSU Community at author's request from Sept. 24, 2012 - Sept. 24, 2013

Orientador: Janito Vaqueiro Ferreira / Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Mecânica / Made available in DSpace on 2018-08-24T20:08:14Z (GMT). No. of bitstreams: 1 MartinezRicardo_DianaMarcela_M.pdf: 3485991 bytes, checksum: 9bd64ce80b2e1c25ed98644d4f52fd64 (MD5) Previous issue date: 2014 / Resumo: Este trabalho tem como objetivo desenvolver um processo de identificação da vazão de gás em um dos equipamentos usados na indústria petrolífera, às bombas centrífugas submersas (BCS) em regime de escoamento multifásico. Estas bombas apresentam falhas frequentes prematuras quando a vazão de gás é alta, as quais ocorrem por falta de informação do tipo de escoamento bifásico presente na bomba no tempo de operação. Por isto estudos de identificação experimental são requeridos nesta área. Neste contexto a presente pesquisa tem seu foco na obtenção de modelos mediante dados experimentais, recompilados diretamente da resposta do sistema que descrevem o comportamento da vazão de gás na planta de interesse, como: vibração, vazão, elevação entre outros. Estes modelos estão baseados na identificação não paramétrica e no algoritmo de aprendizagem de Máquina de Vetores de Suporte (SVM), onde os parâmetros ocultos da máquina de aprendizagem serão obtidos mediante algoritmos genéticos, visando obter modelos mais representativos / Abstract: This work develops a process to identify the flow of gas in one of the equipment used in the oil industry, the electric submersible pump (EPS) under multiphase flow. These pumps feature frequent premature failures when the gas flow is high. That occurs due to lack of information on the type of two ¿ phase flow in the pump in operation time. Experimental studies for this identification are required in this area. In this context, the present research focuses on obtaining models by experimental data collected directly from the system response which describes the behavior of the gas flow on the system of interest such as: vibration, fluid, elevation etc. These models are based on nonparametric identification and in learning algorithm support vector machine (SVM), where the hidden parameters of the learning machine will be obtained by genetic algorithms in order to obtain more representative models / Mestrado / Mecanica dos Sólidos e Projeto Mecanico / Mestra em Engenharia Mecânica