Numerical Simulation and Erosion Prediction for an Electrical Submersible Pump

Electrical Submersible Pumps (ESP) are widely used in the oil industry to lift oil and gas at the same time with high efficiency. Many ESPs operate with multiphase flow – liquid, gas and a low concentration of sand, thus having problems of pressure degradation and erosion. To investigate these problems, a numerical method can be used, which provides details of the inner flow field. Using the commercial software ANSYS Fluent, 3D transient multiphase simulations are conducted for a Baker Hughes made ESP MVP-G400. The simulations focus on three parts: secondary flow path in multiphase flow, pressure degradation due to gas volume fraction and erosion prediction. Aside from the main flow path, the clearances and balancing holes inside the ESP create a secondary path which enables the flow to recirculate. Although the volume flow rate in this path is low compared with the main flow path, the erosion in the secondary path cannot be neglected and can result in pump failure. In this research, a water-air-sand three phase simulation is performed on dual stages of the ESP, with all secondary path included. Second part of this research focuses on the pressure degradation due to the presence of a gas phase, especially at the first stage near the pump inlet. The compressibility of gas and the bubble break-up and coalescence effects are considered using the Population Balancing Module in ANSYS Fluent. The last part is the erosion prediction. A low concentration of sand is often inevitable during the operation of an ESP, causing erosion and reducing the life span of the ESP. This erosion becomes more severe with the existence a of gas phase. The three-phase simulations with both Eulerian multiphase and particle tracking explain the erosion and the role of gas in this process, giving a reasonable qualitative prediction on the erosion of the ESP

Digitally reconstructed wall radiographs

Master'sMASTER OF SCIENC

General Dynamic Surface Reconstruction: Application to the 3D Segmentation of the Left Ventricle

Aquesta tesi descriu la nostra contribució a la reconstrucció tridimensional de les superfícies interna i externa del ventricle esquerre humà. La reconstrucció és un primer procés dins d'una aplicació global de Realitat Virtual dissenyada com una important eina de diagnòstic per a hospitals. L'aplicació parteix de la reconstrucció de les superfícies i proveeix a l'expert de manipulació interactiva del model en temps real, a més de càlculs de volums i de altres paràmetres d'interès. El procés de recuperació de les superfícies es caracteritza per la seva velocitat de convergència, la suavitat a les malles finals i la precisió respecte de les dades recuperades. Donat que el diagnòstic de patologies cardíaques requereix d'experiència, temps i molt coneixement professional, la simulació és un procés clau que millora la eficiència.Els nostres algorismes i implementacions han estat aplicats a dades sintètiques i reals amb diferències relatives a la quantitat de dades inexistents, casuístiques presents a casos patològics i anormals. Els conjunts de dades inclouen adquisicions d'instants concrets i de cicles cardíacs complets. La bondat del sistema de reconstrucció ha estat avaluada mitjançant paràmetres mèdics per a poder comparar els nostres resultats finals amb aquells derivats a partir de programari típic utilitzat pels professionals de la medicina.A més de l'aplicació directa al diagnòstic mèdic, la nostra metodologia permet reconstruccions de tipus genèric en el camp dels Gràfics 3D per ordinador. Les nostres reconstruccions permeten generar models tridimensionals amb un baix cost en quant a la interacció manual necessària i a la càrrega computacional associada. Altrament, el nostre mètode pot entendre's com un robust algorisme de triangularització que construeix superfícies partint de núvols de punts que poden obtenir-se d'escàners làser o sensors magnètics, per exemple.Esta tesis describe nuestra contribución a la reconstrucción tridimensional de las superficies interna y externa del ventrículo izquierdo humano. La reconstrucción es un primer proceso que forma parte de una aplicación global de Realidad Virtual diseñada como una importante herramienta de diagnóstico para hospitales. La aplicación parte de la reconstrucción de las superficies y provee al experto de manipulación interactiva del modelo en tiempo real, además de cálculos de volúmenes y de otros parámetros de interés. El proceso de recuperación de las superficies se caracteriza por su velocidad de convergencia, la suavidad en las mallas finales y la precisión respecto de los datos recuperados. Dado que el diagnóstico de patologías cardíacas requiere experiencia, tiempo y mucho conocimiento profesional, la simulación es un proceso clave que mejora la eficiencia.Nuestros algoritmos e implementaciones han sido aplicados a datos sintéticos y reales con diferencias en cuanto a la cantidad de datos inexistentes, casuística presente en casos patológicos y anormales. Los conjuntos de datos incluyen adquisiciones de instantes concretos y de ciclos cardíacos completos. La bondad del sistema de reconstrucción ha sido evaluada mediante parámetros médicos para poder comparar nuestros resultados finales con aquellos derivados a partir de programario típico utilizado por los profesionales de la medicina.Además de la aplicación directa al diagnóstico médico, nuestra metodología permite reconstrucciones de tipo genérico en el campo de los Gráficos 3D por ordenador. Nuestras reconstrucciones permiten generar modelos tridimensionales con un bajo coste en cuanto a la interacción manual necesaria y a la carga computacional asociada. Por otra parte, nuestro método puede entenderse como un robusto algoritmo de triangularización que construye superficies a partir de nubes de puntos que pueden obtenerse a partir de escáneres láser o sensores magnéticos, por ejemplo.This thesis describes a contribution to the three-dimensional reconstruction of the internal and external surfaces of the human's left ventricle. The reconstruction is a first process fitting in a complete VR application that will serve as an important diagnosis tool for hospitals. Beginning with the surfaces reconstruction, the application will provide volume and interactive real-time manipulation to the model. We focus on speed, precision and smoothness for the final surfaces. As long as heart diseases diagnosis requires experience, time and professional knowledge, simulation is a key-process that enlarges efficiency.The algorithms and implementations have been applied to both synthetic and real datasets with differences regarding missing data, present in cases where pathologies and abnormalities arise. The datasets include single acquisitions and complete cardiac cycles. The goodness of the reconstructions has been evaluated with medical parameters in order to compare our results with those retrieved by typical software used by physicians.Besides the direct application to medicine diagnosis, our methodology is suitable for generic reconstructions in the field of computer graphics. Our reconstructions can serve for getting 3D models at low cost, in terms of manual interaction and CPU computation overhead. Furthermore, our method is a robust tessellation algorithm that builds surfaces from clouds of points that can be retrieved from laser scanners or magnetic sensors, among other available hardware

Modeling and visualization of medical anesthesiology acts

Dissertação para obtenção do Grau de Mestre em Engenharia InformáticaIn recent years, medical visualization has evolved from simple 2D images on a light board to 3D computarized images. This move enabled doctors to find better ways of planning surgery and to diagnose patients. Although there is a great variety of 3D medical imaging software, it falls short when dealing with anesthesiology acts. Very little anaesthesia related work has been done. As a consequence, doctors and medical students have had little support to study the subject of anesthesia in the human body. We all are aware of how costly can be setting medical experiments, covering not just medical aspects but ethical and financial ones as well. With this work we hope to contribute for having better medical visualization tools in the area of anesthesiology. Doctors and in particular medical students should study anesthesiology acts more efficiently. They should be able to identify better locations to administrate the anesthesia, to study how long does it take for the anesthesia to affect patients, to relate the effect on patients with quantity of anaesthesia provided, etc. In this work, we present a medical visualization prototype with three main functionalities: image pre-processing, segmentation and rendering. The image pre-processing is mainly used to remove noise from images, which were obtained via imaging scanners. In the segmentation stage it is possible to identify relevant anatomical structures using proper segmentation algorithms. As a proof of concept, we focus our attention in the lumbosacral region of the human body, with data acquired via MRI scanners. The segmentation we provide relies mostly in two algorithms: region growing and level sets. The outcome of the segmentation implies the creation of a 3D model of the anatomical structure under analysis. As for the rendering, the 3D models are visualized using the marching cubes algorithm. The software we have developed also supports time-dependent data. Hence, we could represent the anesthesia flowing in the human body. Unfortunately, we were not able to obtain such type of data for testing. But we have used human lung data to validate this functionality

A discrete graph Laplacian for signal processing

In this thesis we exploit diffusion processes on graphs to effect two fundamental problems of image processing: denoising and segmentation. We treat these two low-level vision problems on the pixel-wise level under a unified framework: a graph embedding. Using this framework opens us up to the possibilities of exploiting recently introduced algorithms from the semi-supervised machine learning literature. We contribute two novel edge-preserving smoothing algorithms to the literature. Furthermore we apply these edge-preserving smoothing algorithms to some computational photography tasks. Many recent computational photography tasks require the decomposition of an image into a smooth base layer containing large scale intensity variations and a residual layer capturing fine details. Edge-preserving smoothing is the main computational mechanism in producing these multi-scale image representations. We, in effect, introduce a new approach to edge-preserving multi-scale image decompositions. Where as prior approaches such as the Bilateral filter and weighted-least squares methods require multiple parameters to tune the response of the filters our method only requires one. This parameter can be interpreted as a scale parameter. We demonstrate the utility of our approach by applying the method to computational photography tasks that utilise multi-scale image decompositions. With minimal modification to these edge-preserving smoothing algorithms we show that we can extend them to produce interactive image segmentation. As a result the operations of segmentation and denoising are conducted under a unified framework. Moreover we discuss how our method is related to region based active contours. We benchmark our proposed interactive segmentation algorithms against those based upon energy-minimisation, specifically graph-cut methods. We demonstrate that we achieve competitive performance

A Data Fusion and Visualisation Platform for Multi-Phase Flow by Electrical Tomography

Electrical tomography, e.g. electrical resistance tomography (ERT) and electrical capacitance tomography (ECT), has been successfully applied to many industries for measuring and visualising multiphase flow. This research aims to investigate the data fusion and visualisation technologies with electrical tomography as the key data processing tools of a platform for multiphase flow characterisation. Gas-oil-water flow is a common flow in the gas and oil industries but still presents challenges in understanding its complex dynamics. This research systematically studied the data fusion and visualisation technologies using dual-modality electrical tomography (ERT-ECT). Based on a general framework, two data fusion methods, namely threshold and fuzzy logic with decision tree, were developed to quantify and qualify the flow. The experimental results illustrated the feasibility of the methods integrated with the framework to visualise and measure flows in six typical common flow regimes, including stratified, wavy stratified, slug, plug, annular, and bubble flow. In addition, the performance of ERT-ECT was also evaluated. A 3D visualisation approach, namely Bubble Mapping, was proposed to transform concentration distribution to individual bubbles. With a bubble-based lookup table and enhanced isosurface algorithms, the approach overcomes the limits of the conventional concentration tomograms in visualisation of bubbles with sharp boundaries between gas and liquid, providing sophisticated flow dynamic information. The experiments proved that Bubble Mapping is able to visualise typical flow regimes in different pipeline orientations. Two sensing methods were proposed, namely asymmetrical sensing and imaging (ASI) and regional imaging with limited measurement (RILM), to improve the precision of the velocity profile derived from the cross-correlation method by enhancing ERT sensing speed, which is particularly helpful for industrial flows that their disperse phase velocity is very high, e.g. 20 m/s of the gas phase. It is expected that the outcome of this study will significantly move electrical tomography for multiphase flow applications beyond its current challenges in both quantification and qualification