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Communication Dans Un Congrès Année : 2019

Neural network system identification in noise-amplifier flows: an experimental study using optical-flow PIV data

Résumé

A neural-network system identification method along with a standard proper orthogonal decomposition was implemented to identify dynamical systems of typical noise-amplifier flows: the transitional flat plate boundary layer in the presence of the Tollmien-Schlichting instability and the backward-facing step flow. The influence of the sensor nature and the number of snapshots on the success of the network training and validation is discussed.
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Dates et versions

hal-02370719 , version 1 (19-11-2019)

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  • HAL Id : hal-02370719 , version 1

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Antonios Giannopoulos, Jean-Luc Aider. Neural network system identification in noise-amplifier flows: an experimental study using optical-flow PIV data. 15th International Conference on Fluid Control, Measurements and Visualization, May 2019, Naples, Italy. ⟨hal-02370719⟩
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