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Communication Dans Un Congrès Année : 2021

A NEURAL-NETWORK-BASED SYNTHETIC BOUNDARY CONDITION FOR THE SIMULATION OF CHANNEL TURBULENCE

Résumé

We describe a method combining Proper Orthogonal Decomposition (POD) and an artificial recurrent neural network to implement an inlet boundary condition for a turbulent channel flow. The boundary condition is reconstructed from POD modes, the amplitudes of which are predicted using a neural network (LSTM). We first assess the quality of the LSTM prediction in POD space. The reconstructed velocity is then used as an inlet boundary condition in a spatially developping channel. Statistics are compared with the reference and are found to be in relatively good agreement.
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Dates et versions

hal-04396658 , version 1 (16-01-2024)

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  • HAL Id : hal-04396658 , version 1

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Heesik Yoo, Berengere Podvin, Yann Fraigneau, Alexandre Allauzen. A NEURAL-NETWORK-BASED SYNTHETIC BOUNDARY CONDITION FOR THE SIMULATION OF CHANNEL TURBULENCE. 25th International Congress of Theoretical and Applied Mechanics, Aug 2020, Milano, Italy. ⟨hal-04396658⟩
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