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Artificial neural networks for critical flutter speed prediction in semi-span flat-plate wings with geometric variations

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dc.contributor.advisor-co1Morais, Tobias Souza
dc.contributor.advisor1Almeida, Odenir de
dc.contributor.advisor1Latteshttp://lattes.cnpq.br/8197975215358295pt_BR
dc.contributor.referee1Venson, Giuliano Gardolinski
dc.contributor.referee1Latteshttp://lattes.cnpq.br/7831693644209178pt_BR
dc.contributor.referee2Morais, Tobias Souza
dc.contributor.referee2Latteshttp://lattes.cnpq.br/1662045974941011pt_BR
dc.creatorAmaral, Vinícius Nunes
dc.creator.Latteshttp://lattes.cnpq.br/5514706512103750pt_BR
dc.date.accessioned2026-07-24T12:06:10Z
dc.date.available2026-07-24T12:06:10Z
dc.date.issued2025-09-30
dc.description.degreenameTrabalho de Conclusão de Curso (Graduação)pt_BR
dc.description.resumoThis thesis presents a data‐driven method to predict modal damping and the critical flutter speed of semi‐span flat‐plate wings. A linear FE model (FEMAP/MSC Nastran SOL 145, PKNL with Doublet–Lattice aerodynamics) was calibrated with impact‐hammer modal tests and used to generate mode-by-mode g(V) and f(V) curves for spans 0.35- 0.50 m. From these runs we built a supervised dataset with inputs {S,V, f1,..., f4} and targets {g1,...,g4} and trained a regularized MLP that predicts damping across modes; flutter speed is inferred as the }rst zero‐crossing of the predicted g(V). On held-out simulation cases the network achieved low errors and reproduced the span-dependence of flutter speed; comparisons with wind-tunnel data (limited to ∼25 m/s) showed agreement within the experimental resolution. The approach provides near-instant estimates suitable for preliminary design, while remaining limited by the scope of the parametric sweep and the tunnel envelope. Future work includes broadening geometry variation (e.g., dihedral, sweep), expanding numerical and experimental coverage, and exploring physics-guided or unsupervised models that learn stability directly from raw time-series.pt_BR
dc.identifier.citationAMARAL, Vinícius Nunes. Artificial neural networks for critical flutter speed prediction in semi-span flat-plate wings with geometric variations. 2025. 56 f. Trabalho de Conclusão de Curso (Graduação em Engenharia Aeronáutica) – Universidade Federal de Uberlândia, Uberlândia, 2025.pt_BR
dc.identifier.urihttps://repositorio.ufu.br/handle/123456789/49039
dc.languageengpt_BR
dc.orcid.putcode221766410
dc.publisherUniversidade Federal de Uberlândiapt_BR
dc.publisher.countryBrasilpt_BR
dc.publisher.courseEngenharia Aeronáuticapt_BR
dc.rightsAcesso Abertopt_BR
dc.sizeorduration56pt_BR
dc.subjectAeroelasticitypt_BR
dc.subjectFlutterpt_BR
dc.subjectSemi-span wingpt_BR
dc.subjectModal dampingpt_BR
dc.subjectMulti-Layer Perceptron (MLP)pt_BR
dc.subjectFinite Element Method (FEM)pt_BR
dc.subjectMachine Learningpt_BR
dc.subjectMSC Nastran SOL 145pt_BR
dc.subjectPK/PKNL flutter methodspt_BR
dc.subjectDoublet–Lattice Method (DLM)pt_BR
dc.subject.cnpqCNPQ::ENGENHARIAS::ENGENHARIA AEROESPACIAL::ESTRUTURAS AEROESPACIAIS::AEROELASTICIDADEpt_BR
dc.titleArtificial neural networks for critical flutter speed prediction in semi-span flat-plate wings with geometric variationspt_BR
dc.typeTrabalho de Conclusão de Cursopt_BR
dspace.entity.typePublication

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TCC - Engenharia Aeronáutica