Data-driven modeling of pitching and plunging tandem wings in hovering flight

Troshin, Victor; Seifert, Avi

doi:10.1007/s00348-019-2688-3

Cited by 3 publications

(1 citation statement)

References 31 publications

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“…For these time-independent energetic modes, the reconstruction becomes a matter of approximating A using a limited number of sensors. In practice, approximating the coefficients can be achieved in several ways including the use of LSE (Lasagna et al, 2013) or through the use of a Kalman filter (Kalman, 1960;Troshin and Seifert, 2019). For the present study, we investigate three distinct methods of obtaining a reconstruction from the sparse probes.…”

Section: Proper Orthogonal Decompositionmentioning

confidence: 99%

Data-driven sparse reconstruction of flow over a stalled aerofoil using experimental data

Carter

Voogt

Soares

et al. 2021

DCE

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Recent work has demonstrated the use of sparse sensors in combination with the proper orthogonal decomposition (POD) to produce data-driven reconstructions of the full velocity fields in a variety of flows. The present work investigates the fidelity of such techniques applied to a stalled NACA 0012 aerofoil at $ {Re}_c=75,000 $ at an angle of attack $ \alpha ={12}^{\circ } $ as measured experimentally using planar time-resolved particle image velocimetry. In contrast to many previous studies, the flow is absent of any dominant shedding frequency and exhibits a broad range of singular values due to the turbulence in the separated region. Several reconstruction methodologies for linear state estimation based on classical compressed sensing and extended POD methodologies are presented as well as nonlinear refinement through the use of a shallow neural network (SNN). It is found that the linear reconstructions inspired by the extended POD are inferior to the compressed sensing approach provided that the sparse sensors avoid regions of the flow with small variance across the global POD basis. Regardless of the linear method used, the nonlinear SNN gives strikingly similar performance in its refinement of the reconstructions. The capability of sparse sensors to reconstruct separated turbulent flow measurements is further discussed and directions for future work suggested.

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Section: Proper Orthogonal Decompositionmentioning

confidence: 99%