Journal of Shanghai Jiao Tong University

Journal of Shanghai Jiaotong University >

2024 , Vol. 58 >Issue 2: 156 - 165

DOI: https://doi.org/10.16183/j.cnki.jsjtu.2022.316

Naval Architecture, Ocean and Civil Engineering

An Artificial Neural Network-Based Method for Prediction of Ice Resistance of Polar Ships

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  • 1. School of Naval Architecture and Ocean Engineering, Jiangsu University of Science and Technology, Zhenjiang 212100, Jiangsu, China
    2. School of Naval Architecture, Ocean and Civil Engineering, Shanghai Jiao Tong University, Shanghai 200240, China

Received date: 2022-08-19

  Revised date: 2023-02-17

  Accepted date: 2023-02-20

  Online published: 2024-03-04

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Abstract

Accurate prediction of ice resistance plays an important role in ensuring the safety of ship sailing in polar navigation in ice areas. In recent years, machine learning has been widely used in the field of ships, among which artificial neural network (ANN) is a common method. The focus of this paper is to design an ANN model for predicting the ice resistance of polar ships. According to the traditional empirical and semi-empirical formula, appropriate input characteristic parameters are selected. The radial basis function (RBF) neural network model is built based on a large number of ship model test data, and the genetic algorithm (GA) is used to optimize the model. The research shows that the radial basis function neural network model optimized by genetic algorithm (RBF-GA) based on seven characteristic parameters input has good generalization effect. Compared with the model test and full-scale test data, the average error is about 8%, which shows that the RBF-GA model has a high accuracy, and can be used as a tool for ice resistance prediction.

Key words： ice resistance; machine learning; radial basis function (RBF) neural network; genetic algorithm (GA); ship test

Cite this article

SUN Qianyang, ZHOU Li, DING Shifeng, LIU Renwei, DING Yi . An Artificial Neural Network-Based Method for Prediction of Ice Resistance of Polar Ships[J]. Journal of Shanghai Jiaotong University, 2024 , 58(2) : 156 -165 . DOI: 10.16183/j.cnki.jsjtu.2022.316

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