Abstract
Resistance is one of the important performance indicators of ships. In this paper, a prediction method based on the Radial Basis Function neural network (RBFNN) is proposed to predict the resistance of a 13500 transmission extension unit (13500TEU) container ship at different drafts. The predicted draft state in the known range is called interpolation prediction; otherwise, it is extrapolation prediction. First, ship features are extracted to make the resistance Rt prediction. The resistance prediction results show that the performance of the RBFNN is significantly better than the other four machine learning models, backpropagation neural network (BPNN), support vector machine (SVM), random forest (RF), and extreme gradient boosting (XGBoost). Then, the ship data is processed in a dimensionless manner, and the models mentioned above are used to predict the total resistance coefficient Ct of the container ship. The prediction results show that the RBFNN prediction model still performs well. Good results can be obtained by RBFNN in interpolation prediction, even when using part of dimensionless features. Finally, the accuracy of the prediction method based on RBFNN is greatly improved compared with the modified admiralty coefficient.
Highlights
Ship resistance prediction has always been a hot area of ship research
The model ship resistance coefficient Ct is predicted by prediction models, backpropagation neural network (BPNN), Radial Basis Function neural network (RBFNN), support vector machine (SVM), random forest (RF), and XGBoost using these dimensionless features
The number of hidden layers will affect the performance of the BPNN
Summary
Ship resistance prediction has always been a hot area of ship research. Researchers usually use approximate methods such as model series data, empirical formula, and parent ship estimation method to predict ship resistance. Compared with the traditional resistance prediction methods, CFD technology has higher accuracy and is widely used. Both of these prediction methods have shortcomings. The work of AI algorithms relies on large-scale sample data These algorithms are increasingly used in shipping [9,10,11,12] and fluid mechanics [13,14,15,16]. The research aims to establish a prediction model of the model ship resistance at different draft states by using the ship’s features and resistance data. The second section introduces the ship features and the resistance of the 13500TEU model ship, as well as the data dimensionless process. The third section briefly defines different machine learning algorithms for resistance prediction (RBFNN, BPNN, SVM, RF, XGBoost).
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