Prediction of the dynamics of a backward-facing step flow using focused time-delay neural networks and particle image velocimetry data-sets

Abstract

The objective of this experimental work was to evaluate the potential of an artificial Neural Network (NN) to predict the full-field dynamics of a standard separated, noise-amplifier flow: the Backward-Facing Step (BFS) flow at Reh=1385. Different upstream local visual sensors, based on the velocity fields measured by time-resolved Particle Image Velocimetry, were tested as inputs for the Neural Network. The dynamic coefficients of a Proper Orthogonal Decomposition (POD) were defined as goals-outputs for this non-linear mapping. The coefficients time-series were predicted and the instantaneous velocity fields were reconstructed with satisfying accuracy with a Focused Time-Delay Neural Network (FTDNN). Using a time-delay appears like a crucial choice to ensure an accurate prediction of the dynamics of the BFS flow. A shallow FDTNN is sufficient to obtain good accuracy with low computational time. The influence of the choices of inputs-sensors, the size of the training data-set, the number of neurons in the hidden layer as well as the sensor delay on the accuracy of the predicted flow are discussed for this experimental fluid system.