Data-driven order reduction and velocity field reconstruction using neural networks: The case of a turbulent boundary layer

Antonios Giannopoulos (1) , Jean-Luc Aider (1)

1 PMMH - Physique et mécanique des milieux hétérogenes (UMR 7636)

We present a data-driven methodology to achieve identification of coherent structures dynamics and system order reduction of an experimental turbulent boundary layer (TBL) flow. The flow is characterized using time-resolved Optical Flow Particle Image Velocimetry, leading to dense velocity fields that can be used both to monitor the overall dynamics of the flow and to define as many local visual sensors as needed. A Proper Orthogonal Decomposition (POD) is first applied to define a reduced-order system. A non-linear mapping between the local upstream sensors (inputssensors) and the full-field dynamics (POD coefficients) as outputs is sought using an optimal Focused Time-Delay (FTD) Artificial Neural Network (ANN). The choices of sensors, ANN architecture and training parameters are shown to play a critical role. It is verified that a shallow ANN, with the proper sensor memory size, can lead to a satisfying full-field dynamics identification, coherent structure reconstruction, and system order reduction of this turbulent flow.

Domaines: Mécanique des fluides [physics.class-ph]

Antonios Giannopoulos, Jean-Luc Aider. Data-driven order reduction and velocity field reconstruction using neural networks: The case of a turbulent boundary layer. Physics of Fluids, 2020, 32 (9), pp.095117. ⟨10.1063/5.0015870⟩. ⟨hal-03006985⟩