Phase-resolved wave prediction for short crest wave fields using deep learning

The phase-resolved wave prediction of short crest waves is important to marine structures for both predicting deterministic motion and assisting decision-making. The present short crest wave phase-resolved prediction methods utilize Fast Fourier Transform (FFT) to process wave elevation fields in a large area, and these methods have shortcomings in calculation efficiency, calculation accuracy, and convenience. This paper proposes a long short-term memory wave prediction model (LSTM-WP model) based on deep learning to achieve a phase-resolved wave prediction of short crest waves. A tank experiment is conducted to verify and analyze the LSTM-WP model. From the results, it can be found that the LSTM-WP model provides high-precision predictions for the short crest wave surface under sea states of levels 4-7. Furthermore, the effects of the direction spectrum, predicted distance, and lead steps are discussed. It can be found that as the direction of the short crest wave becomes more concentrated, the prediction error rises more rapidly with increasing sea states. As the predicted distance increases, the prediction error of the LSTM-WP model increases linearly. As the number of lead steps increases, the prediction error of the LSTM-WP model shows a trend of first decreasing and then increasing.

Automated summary

This study proposes a long short-term memory wave prediction model (LSTM-WP model) based on deep learning, which can accurately predict the surface of short crest waves. The effects of predicted distance and lead steps on the prediction error are also discussed.