Absorption in the wavemaker by Artificial neural networks (ANNs)

Abstract

Wave-making theories are becoming available, but their applicability is limited to specific ranges of waves and wavemaker types. Machine learning can also be used to discover nonlinear functional relationships. As a result, based on machine learning, this paper proposes a simple and universal framework for generating and absorbing waves.This framework trains neural networks to determine the transfer function between the wavemaker's free-surface elevation and velocity. To increase the generalization ability of neural networks, penalty term and data augmentation techniques based on wave-making mechanisms are introduced, rather than pure data-driven.As a result, once the wavemaker has the target wave profiles in front of it, it can generate waves while also absorbing reflected waves. Analytical solutions are used to validate the simulated wave profiles and wave orbital velocities, demonstrating that the proposed framework is effective at eliminating the re-reflection wave.The validation for generating the solitary wave and the New Year's wave is then performed, indicating that the generated waves agree very well with the desired wave elevation. The proposed framework can help with wavemaker design in the future, and it does not require any complex theoretical derivation.