Study on Oscillation Phenomena in Morphing Cavities with Ramps of Leading Wall

Liu, Zhe; Ning, Fangli; Zhai, Qingbo; Ding, Hui; Wei, Juan; Li, Baoqing

doi:10.2514/1.c035993

Cited by 4 publications

(1 citation statement)

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“…In the above formulas, σ and tanh are the sigmoid and tanh activation functions, respectively, which are shown as [58], the simulation details and grid convergence have been described, and the accuracy of the simulation method has also been verified. e sonicFoam solver is applied, which is based on the PISO (pressure implicit with splitting of operator) algorithm.…”

Section: Long Short-term Memory Neural Networkmentioning

confidence: 99%

Reduced‐Order Modeling of Cavity Flow Oscillations across Multi‐Mach Numbers Using Deep Learning

Liu

Ning

Ding

et al. 2021

Shock and Vibration

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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.

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Section: Long Short-term Memory Neural Networkmentioning

confidence: 99%