Abstract

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.

INTRODUCTION

The reliable prediction of the seakeeping behavior of ships in a seaway is a demanding task for naval architects and of great practical interest to ship owners/operators, as it affects both the design and operation of ships.

Early assessment methods were based on potential flow theory. The first widely accepted method was the so-called strip-theory-based S.T.F. method (1970), whose performance is well documented in the literature and in relevant benchmark activities at ITTC (1978). Later on, more advanced methods of various levels of complexity, in both time domain and frequency domain, were developed. Some early examples refer to the works of Sclavounos and Nakos (1988), King et al. (1988), Lin and Yue (1990), Iwashita and Ohkusu (1992), Duan and Dai (1999), etc. Beck & Reed (2000) gave an extensive review of these methods and the benchmark study during the ITTC Workshop on Seakeeping (Kim, 2010) reported the performance of many of these methods.

In parallel, sophisticated CFD tools based on free-surface RANSE methods were developed and underwent much progress with the fast development of hardware. Encouraging results have been presented by various researchers during recent international workshops (Larsson, et al., 2014; Shigunov et al., 2018) and CFD tools became a standard tool to assist practical ship design projects. As the computational resources are becoming inexpensive, full-scale viscous simulations are nowadays reported more often (e.g., Tezdogan et al., 2016).

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