Influence of impingement edge geometry on cavity flow oscillations

AIAA Journal ◽  
1994 ◽  
Vol 32 (8) ◽  
pp. 1737-1740 ◽  
Author(s):  
J. C. F. Pereira ◽  
J. M. M. Sousa
2014 ◽  
Vol 51 (2) ◽  
pp. 424-433 ◽  
Author(s):  
V. Thangamani ◽  
K. Knowles ◽  
A. J. Saddington

Author(s):  
Varun Thangamani ◽  
Foo Ngai Kok

This study investigates the energy harvesting prospects of self-sustained flow oscillations emanating from grazing flow over a rectangular cavity by employing experimental and computational methods. Two cavity geometries with length-to-depth ratios of 2 and 3, exposed to an incoming flow of 30 m/s, were selected for the purpose. The power spectral density of the baseline cavity flows showed the presence of high-amplitude peaks whose frequencies agreed to those estimated from Rossiter’s feedback model. For energy harvesting, a piezoelectric beam was placed perpendicular to the aft wall and its natural frequency tuned to match closely with the dominant frequencies of the cavity flow oscillations. From the experiments, an average and maximum instantaneous power of 21.11 and 284.18 µW was recorded for the cavity with L/ D = 2 whereas for the cavity with L/ D = 3 the corresponding values were 32.16 and 403.46 µW respectively. Time-frequency analysis showed the forcing of the beam at the cavity oscillation frequency and the substantial increase in the amplitude of beam vibrations when this frequency was close to the natural frequency of the beam.


2021 ◽  
Vol 2021 ◽  
pp. 1-19
Author(s):  
Zhe Liu ◽  
Fangli Ning ◽  
Hui Ding ◽  
Qingbo Zhai ◽  
Juan Wei

The reduced-order model can accurately and efficiently predict unsteady problems in many aerospace engineering applications. The traditional reduced-order model based on proper orthogonal decomposition (POD) and Galerkin projection has poor robustness and large error in predicting complex problems. In this paper, a reduced-order model combining POD and deep learning is proposed to predict cavity flow oscillations under different flow conditions. Firstly, POD modes and corresponding coefficients are obtained by POD. Then, two deep learning frameworks, including multilayer perceptron (MLP) and long short-term memory (LSTM) neural networks, are used to predict the future POD coefficients, respectively. Finally, the cavity flow oscillations across multi-Mach numbers are predicted by the POD modes and the future coefficients. The results show that both of these frameworks can accurately predict cavity flow oscillations when the flow conditions change, and the time cost is reduced by order of magnitude. In addition, due to the performance of LSTM is better than that of MLP, its calculation speed is faster.


2004 ◽  
Vol 2004 (0) ◽  
pp. 214
Author(s):  
Takashi Yoshida ◽  
Toshihiko IKEDA ◽  
Shouichiro IIO

2006 ◽  
Vol 547 (-1) ◽  
pp. 317 ◽  
Author(s):  
CLARENCE W. ROWLEY ◽  
DAVID R. WILLIAMS ◽  
TIM COLONIUS ◽  
RICHARD M. MURRAY ◽  
DOUGLAS G. MACMYNOWSKI

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