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- W4317568466 abstract "We propose and validate a data-driven experimental framework for studying flow-induced oscillations of elastically mounted pitching wings. We first train a Neural Network regression model for the nonlinear fluid torque obtained from purely prescribed oscillations. We then embed this model into an ordinary differential equation solver to solve for the governing equation of the passive system with desired structural parameters. The system dynamics predicted by the proposed data-driven method are characterized and compared with those obtained from cyber-physical experiments. The predicted and experimental amplitude, frequency and torque responses are found to be in good agreements. At the bifurcation point, the transient growth and saturation of the oscillation amplitude and fluid torque are also faithfully captured by the proposed method." @default.
- W4317568466 created "2023-01-21" @default.
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- W4317568466 date "2023-01-19" @default.
- W4317568466 modified "2023-09-27" @default.
- W4317568466 title "A Data-Driven Experimental Approach for Modeling Aeroelastic Instabilities of Pitching Wings" @default.
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- W4317568466 doi "https://doi.org/10.2514/6.2023-0278" @default.
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