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005     20250515110928.0
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|a 10.34734/FZJ-2025-02462
037 _ _ |a FZJ-2025-02462
041 _ _ |a English
100 1 _ |0 P:(DE-HGF)0
|a Shao, Xiao
|b 0
|e Corresponding author
111 2 _ |a 35th Parallel CFD International Conference 2024
|c Bonn
|d 2024-09-02 - 2024-09-04
|g ParCFD 2024
|w Germany
245 _ _ |a Investigating the Effects of Spanwise Transversal Traveling Waves on a Turbulent Compressible Flat Plate Flow With the Aid of a Deep Autoencoder Network
260 _ _ |a Jülich
|b Forschungszentrum Jülich GmbH Zentralbibliothek, Verlag
|c 2025
295 1 0 |a Proceedings of the 35th Parallel CFD International Conference 2024
300 _ _ |a 89-93
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490 0 _ |a Schriften des Forschungszentrums Jülich IAS Series
|v 69
520 _ _ |a The impact of spanwise traveling transversal surface waves on drag reduction in turbulent compressible flat plate flow is explored. The findings indicate that when the traveling phase speed approaches the freestream velocity at 𝑀 = 0.7, a shock wave is induced in the spanwise direction. This shock wave effectively breaks down streamwise vortices into smaller scales, which significantly enhances drag reduction. The spanwise shock wave is a large-scale quasi-periodic phenomenon. To understand its impact on the multi-scale nature of turbulent flows, a nonlinear mode decomposing deep convolutional autoencoder is employed. The results show that the autoencoder reconstructs the flow field more accurately compared to Proper Orthogonal Decomposition (POD) and Dynamic Mode Decomposition(DMD). Additionally, it effectively separates the large-scale spanwise shock wave and small-scale turbulent structures, which achieves a clearer distinction than POD and DMD.
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700 1 _ |0 P:(DE-HGF)0
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770 _ _ |z 978-3-95806-819-3
773 _ _ |a 10.34734/FZJ-2025-02462
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