Publicação: Artificial neural networks for critical flutter speed prediction in semi-span flat-plate wings with geometric variations
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CNPQ::ENGENHARIAS::ENGENHARIA AEROESPACIAL::ESTRUTURAS AEROESPACIAIS::AEROELASTICIDADE
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Morais, Tobias Souza
Resumo
This 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.
Abstract
Palavras-chave
Aeroelasticity, Flutter, Semi-span wing, Modal damping, Multi-Layer Perceptron (MLP), Finite Element Method (FEM), Machine Learning, MSC Nastran SOL 145, PK/PKNL flutter methods, Doublet–Lattice Method (DLM)
Citação
AMARAL, 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.