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A novel attention enhanced deep neural network for hypersonic spatiotemporal turbulence prediction.
- Source :
- Physics of Fluids; May2024, Vol. 36 Issue 5, p1-20, 20p
- Publication Year :
- 2024
-
Abstract
- High Reynolds number turbulent flow of hypersonic vehicles exhibits multi-scale flow structures and non-equilibrium high-frequency characteristics, presenting a significant challenge for accurate prediction. A deep neural network integrated with attention mechanism as a reduced order model for hypersonic turbulent flow is proposed, which is capable of capturing spatiotemporal characteristics from high-dimensional numerical turbulent data directly. The network model leverages encoder–decoder architecture where the encoder captures high-level semantic information of input flow field, Convolutional Long Short-Term Memory network learns low-dimensional characteristic evolution, and the decoder generates pixel-level multi-channel flow field information. Additionally, skip connection structure is introduced at the decoding stage to enhance feature fusion while incorporating Dual-Attention-Block that automatically adjusts weights to capture spatial imbalances in turbulence distribution. Through evaluating the time generalization ability, the neural network effectively learns the evolution of multi-scale high-frequency turbulence characteristics. It enables rapid prediction of high Reynolds number turbulence evolution over time with reasonable accuracy while maintaining excellent computational efficiency. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 10706631
- Volume :
- 36
- Issue :
- 5
- Database :
- Complementary Index
- Journal :
- Physics of Fluids
- Publication Type :
- Academic Journal
- Accession number :
- 177609533
- Full Text :
- https://doi.org/10.1063/5.0210966