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dc.contributor.advisorRodríguez Pulecio, Sara Aida
dc.contributor.advisorLadino Ospina, Jair Alexander
dc.contributor.authorAponte Núñez, Rubén Darío
dc.date.accessioned2020-05-18T21:21:52Z
dc.date.available2020-05-18T21:21:52Z
dc.date.issued2018
dc.identifier.urihttp://hdl.handle.net/10893/15397
dc.description.abstractIn recent years, the application of numerical computational models based on computational fluid dynamics (CFD) to industrial problems has been increasing; Today CFD is used to optimize and develop equipment and processes in many types of industry including the energy industry. The main advantage of the solutions with CFD is in the obtaining of the operating conditions and the analysis of internal and external flows, which experimentally is very difficult and expensive to achieve. This document presents the results of the research project of a master's degree in engineering with an emphasis in mechanical engineering where the geometry that minimizes the erosive wear by hard particle and cavitation for the different operating regimes maintaining the efficiency of the 10MW Francis turbine of the Amaime hydroelectric plant was obtained. To achieve this, a Simplified Virtual Laboratory (SVL) methodology was implemented, consisting of the use of Computational Fluid Dynamics and an optimization technique. First, the simulation of the current geometry of the turbine was carried out to characterize and verify, with experimental data, that the model represents the current operating conditions; this required to generate 3D CAD geometries by means of planes and reverse engineering using three-dimensional scanning of complex elements of the turbine such as blades. Second, it was required to optimize the geometry of the runner blades, guide vanes, covers and labyrinths by the combined use of factorial design of experiments, artificial neural networks (ANN) and optimization techniques by genetic algorithms.spa
dc.format.extent1 recurso en línea (66 páginas)spa
dc.format.mimetypeapplication/pdfspa
dc.language.isoengspa
dc.publisherUniversidad del Vallespa
dc.subject.ddcTurbinas Francis
dc.subject.ddcDinámica de Fluidos Computacional
dc.subject.ddcOptimización
dc.subject.ddcDesgaste erosivo
dc.subject.ddcCavitacion
dc.subject.ddcRedes neurales
dc.titleGeometry optimization of a francis turbine for efficiency, cavitation and erosion using computational fluid dynamicsspa
dc.typeTrabajo de grado - Maestríaspa
dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
dc.publisher.placeColombiaspa
dc.type.coarhttp://purl.org/coar/resource_type/c_bdccspa
dc.type.contentTextspa
dc.type.driverinfo:eu-repo/semantics/masterThesisspa
dc.type.redcolhttps://purl.org/redcol/resource_type/TMspa
dc.type.versioninfo:eu-repo/semantics/publishedVersionspa
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85spa
dc.publisher.facultyFACULTAD DE INGENIERÍAspa
dc.description.degreelevelMaestríaspa
oaire.accessrightshttp://purl.org/coar/access_right/c_abf2spa
dc.description.degreenameMAGISTER EN INGENIERÍA ÉNFASIS EN INGENIERÍA MECÁNICAspa
dc.publisher.programMAESTRÍA EN INGENIERÍA-ÉNFASIS EN INGENIERÍA MECÁNICAspa


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