Prediction of the motion of a ship in regular head waves using artificial neural networks

• Main Authors: Liu, Shukui, Xu, Rong, Papanikolaou, Apostolos
• Other Authors: School of Mechanical and Aerospace Engineering
• Format: Conference or Workshop Item
• Language: English
• Published: 2022
• Subjects: Engineering::Mechanical engineering; Artificial Neural Networks; Multi-Layer Perceptron; Seakeeping Assessment; Heave and Pitch; Head Waves; Ship Operation
• Online Access: https://hdl.handle.net/10356/160589; https://onepetro.org/ISOPEIOPEC/ISOPE21/conference/All-ISOPE21
• Institution: Nanyang Technological University

This paper presents a research on the application of artificial neural networks (ANNs) to predict the seakeeping behavior of ships in head waves. The decisive input parameters of the ANNs are identified by analyzing the general equations governing the ship motions. Then, a ship database that considers all major merchant ship types and hull forms is set up and an in-house frequency domain 3D panel method is used to predict the heave and pitch motions of these ships in head waves at typical operational speeds, thus, establishing a motion database, which provides data to train the ANN networks. Several types of neural networks are explored and systematically trained. The developed network is applied to the prediction of the motions of several ships, which are not in the database, to demonstrate their efficiency in quickly and accurately predicting the seakeeping performance of typical merchant ships.