Classifying bubble cavitation with machine learning trained on physical models

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dc.catalogadorvdr
dc.contributor.advisorVan't Wout, Elwin
dc.contributor.authorGatica González, Trinidad
dc.contributor.otherPontificia Universidad Católica de Chile. Escuela de Ingeniería
dc.date.accessioned2024-10-02T12:19:32Z
dc.date.available2024-10-02T12:19:32Z
dc.date.issued2024
dc.descriptionTesis (Master of Science in Engineering)--Pontificia Universidad Católica de Chile, 2024
dc.description.abstractWhen an air filled bubble inside a liquid is excited by an acoustic signal the oscillations may cause cavitation where the bubble collapses and disintegrates into multiple smaller bubbles. This is a crucial phenomenon in various engineering applications, such as underwater noise and biomedical engineering. Accurately modeling cavitation is essential for optimal design of acoustical systems. In this study, our objective is to develop a machine learning model capable of classifying bubble oscillation as stable or transient cavitation. We implemented numerical solvers for the Rayleigh-Plesset, Keller-Miksis, and Gilmore differential equations to describe bubble oscillations with different mathematical models. These models takes the bubble radius, acoustic pressure, frequency, and temperature as input variables and provide a time series of the bubble radius. We used multiple thresholds to distinguish between stable and transient cavitation, based on variables such as the maximum radius, maximum velocity, acoustic emission, inertial and pressure functions for each model. We created a training dataset on a range of relevant physical scenarios. The machine learning algorithm combines different thresholds and models to predict the expected cavitation type. Our machine learning approach allows for fast predictions and uncertainty estimates of cavitation type on a wide range of scenarios. This approach offers greater robustness compared to numerical solution methods, leveraging four thresholds and three equations for enhanced accuracy.
dc.fechaingreso.objetodigital2024-10-02
dc.format.extentxxi, 184 páginas
dc.fuente.origenSRIA
dc.identifier.doi10.7764/tesisUC/ING/88054
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/88054
dc.identifier.urihttps:/doi.org/10.7764/tesisUC/ING/88054
dc.information.autorucEscuela de Ingeniería; Van't Wout, Elwin; 0000-0002-9096-5054; 1025361
dc.information.autorucEscuela de Ingeniería; Gatica González, Trinidad; S/I; 1069647
dc.language.isoen
dc.nota.accesocontenido completo
dc.rightsacceso abierto
dc.subjectCavitation
dc.subjectMachine-learning
dc.subjectDifferential equations
dc.subjectBubbles
dc.subjectClassification
dc.subject.ddc620
dc.subject.deweyIngenieríaes_ES
dc.subject.ods09 Industry, innovation and infrastructure
dc.subject.odspa09 Industria, innovación e infraestructura
dc.titleClassifying bubble cavitation with machine learning trained on physical models
dc.typetesis de maestría
sipa.codpersvinculados1025361
sipa.codpersvinculados1069647
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