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dc.contributor.authorGranados-Ortiz, Francisco-Javier
dc.contributor.authorOrtega-Casanova, Joaquín
dc.date.accessioned2021-09-14T08:12:26Z
dc.date.available2021-09-14T08:12:26Z
dc.date.created2021
dc.date.issued2021
dc.identifier.urihttps://hdl.handle.net/10630/22832
dc.description.abstractComputational design is a key part in most engineering applications, thanks to the possibility to create new designs in a safer, quicker and reliable environment. The recent developments in engineering are also guiding the classical design life cycle to a more sophisticated frameworks, such as the implementation of Machine Learning methods to support the design process. This work shows the potential of using the namely Machine Learning-Aided Design Optimisation framework to optimise vortex-shedding based applications, and it is applied as example to a vortex shedding aerodynamic-based design extendable to other applications. This framework consisted of using a predictive model to discard useless computations and speed up the efficient construction of surrogate models. The method is applied to the optimisation of a mechanical vortex shedding-based passive mixer achieving a successful design in terms of minimisation of pressure drop and maximisation of mixing efficiency.es_ES
dc.language.isoenges_ES
dc.rightsinfo:eu-repo/semantics/openAccesses_ES
dc.subjectIngeniería mecánica - Congresoses_ES
dc.subject.othermachine learninges_ES
dc.subject.otherCFDes_ES
dc.subject.othervortex sheddinges_ES
dc.titleMachine Learning-Aided Design Optimisation(MLADO) in Vortex Shedding-Based Engineering Applicationses_ES
dc.typeinfo:eu-repo/semantics/conferenceObjectes_ES
dc.centroEscuela de Ingenierías Industrialeses_ES
dc.relation.eventtitle17th International Conference of Computational Methods in Sciences and Engineeringes_ES
dc.relation.eventplaceHeraclión (Grecia)es_ES
dc.relation.eventdate04/09/2021es_ES


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