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Topologically assisted optimization for rotor design

hal.structure.identifierHarbin Institute of Technology [HIT]
dc.contributor.authorTIANYU, Wang
hal.structure.identifierSouth China University of Technology [Guangzhou] [SCUT]
dc.contributor.authorYANG, Yannian
hal.structure.identifierTsinghua University [Beijing] [THU]
dc.contributor.authorCHEN, Xuanwu
hal.structure.identifierSouthern University of Science and Technology [SUSTech]
dc.contributor.authorLI, Pengyu
hal.structure.identifierModeling Enablers for Multi-PHysics and InteractionS [MEMPHIS]
hal.structure.identifierInstitut de Mathématiques de Bordeaux [IMB]
dc.contributor.authorIOLLO, Angelo
hal.structure.identifierHarbin Institute of Technology [HIT]
dc.contributor.authorCORNEJO MACEDA, Guy
hal.structure.identifierHarbin Institute of Technology [HIT]
dc.contributor.authorNOACK, Bernd
dc.date.accessioned2024-04-04T02:31:35Z
dc.date.available2024-04-04T02:31:35Z
dc.date.issued2023-05-01
dc.identifier.issn1070-6631
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/190319
dc.description.abstractEnWe develop and apply a novel shape optimization exemplified for a two-blade rotor with respect to the figure of merit. This topologically assisted optimization contains two steps. First, a global evolutionary optimization is performed for the shape parameters, and then a topological analysis reveals the local and global extrema of the objective function directly from the data. This non-dimensional objective function compares the achieved thrust with the required torque. Rotor blades have a decisive contribution to the performance of quadcopters. A two-blade rotor with pre-defined chord length distribution is chosen as the baseline model. The simulation is performed in a moving reference frame with a k−ω turbulence model for the hovering condition. The rotor shape is parameterized by the twist angle distribution. The optimization of this distribution employs a genetic algorithm. The local maxima are distilled from the data using a novel topological analysis inspired by discrete scalar-field topology. We identify one global maximum to be located in the interior of the data and five further local maxima related to errors from non-converged simulations. The interior location of the global optimum suggests that small improvements can be gained from further optimization. The local maxima have a small persistence, i.e., disappear under a small ε perturbation of the figure of merit values. In other words, the data may be approximated by a smooth mono-modal surrogate model. Thus, the topological data analysis provides valuable insight for optimization and surrogate modeling.
dc.language.isoen
dc.publisherAmerican Institute of Physics
dc.title.enTopologically assisted optimization for rotor design
dc.typeArticle de revue
dc.identifier.doi10.1063/5.0145941
dc.subject.halMathématiques [math]
dc.subject.halPhysique [physics]
dc.identifier.arxiv2302.08728
dc.description.sponsorshipEuropeAccurate Roms for Industrial Applications
bordeaux.journalPhysics of Fluids
bordeaux.volume35
bordeaux.hal.laboratoriesInstitut de Mathématiques de Bordeaux (IMB) - UMR 5251*
bordeaux.issue5
bordeaux.institutionUniversité de Bordeaux
bordeaux.institutionBordeaux INP
bordeaux.institutionCNRS
bordeaux.peerReviewedoui
hal.identifierhal-04375654
hal.version1
hal.popularnon
hal.audienceInternationale
hal.origin.linkhttps://hal.archives-ouvertes.fr//hal-04375654v1
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