Dynamic Data-Driven Aeroelastic Response Prediction with Discrete Sensor Observations

Zhao, Xiangxue; Kania, Randall; Kebbie-Anthony, Abu; Azarm, Shapour; Balachandran, Balakumar

doi:10.2514/6.2018-2173

Cited by 8 publications

(1 citation statement)

References 25 publications

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“…Most of the researches mentioned above focused on the flow field around static objects. The associated and available methods typically combine proper orthogonal decomposition (POD) with other algorithms, such as linear stochastic estimation [26] and particle filter [27]. However, it is difficult to predict the evolution of the aerodynamics of dynamic airfoil over time.…”

Section: Introductionmentioning

confidence: 99%

Experimental Study of Dynamical Airfoil and Aerodynamic Prediction

et al. 2022

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Dynamic stall is a critical limiting factor for airfoil aerodynamics and a challenging problem for active flow control. In this experimental study, dynamic stall was measured by high-frequency surface pressure tapes and pressure-sensitive paint (PSP). The influence of the oscillation frequency was examined. Dynamic mode decomposition (DMD) with time-delay embedding was proposed to predict the pressure field on the oscillating airfoil based on scattered pressure measurements. DMD with time-delay embedding was able to reconstruct and predict the dynamic stall based on scattered measurements with much higher accuracy than standard DMD. The reconstruction accuracy of this method increased with the number of delay steps, but this also prolonged the computation time. In summary, using the Koopman operator obtained by DMD with time-delay embedding, the future dynamic pressure on an oscillating airfoil can be accurately predicted. This method provides powerful support for active flow control of dynamic stall.

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Section: Introductionmentioning

confidence: 99%