Active Absorption of Random Waves in Wave Flume Using Artificial Neural Networks

Abstract

A wave flume is primarily intended to reproduce actual sea conditions in order to provide a reliable means of testing for small-scale models. The realization of scaled tests is extremely important for the validation of a project on real scale, since, through the laws of similitude, such tests make it possible to predict the behavior of structures in the ocean as well as their performance during operation. This research aims to develop, test, and validate an active control algorithm for wave absorption in a two-dimensional wave channel—that is, when the waves propagate in only one direction—based on artificial neural networks (ANN). The ANN control algorithm relies on the linear wave theory and the principle of time-reversal of wave propagation; i.e., the phenomenon of wave absorption corresponds to the wave generation when observed in the reverse direction of time. Through this principle, data from wave generation experiments, after proper manipulation, are used to train an ANN capable of generating the control signal used to move the wave-generator device, this time as a wave absorber