Proceedings of the Thirty-second (2022) International Ocean and Polar Engineering ConferenceShanghai, China, June 5–10, 2022
Copyright © 2022 by the International Society of Offshore and Polar Engineers (ISOPE)
ISBN 978-1-880653-81-4; ISSN 1098-6189

www.isope.org

Research on Ship Resistance Forecast Based on Artificial Intelligence
Chen Li1, Yunfei Yang1, Jianglong Sun1,2,3 *1. School of Naval Architecture and Ocean Engineering (HUST), Wuhan, Hubei, China
2. Hubei Provincial Engineering Research Center of Data Techniques and Supporting Software for Ships (DTSSS), Wuhan, Hubei, China
3. Collaboration Innovation Center for Advanced Ship and Deep-Sea Exploration (CISSE), Shanghai, China

simulation method was used to calculate the optimized ship's resistance(Lv at all., 2013; Sun at all., 2016; Lyu at all., 2018). And the datarequired for the study was obtained by this method. In this paper, CFD
simulation and ship model test method are used to study the resistanceand flow field of the super cruise ship, and analyze the influence of theturbulence model on the CFD calculation results (Guo at all., 2013). Thispaper uses the CFD method to demonstrate the effect of scale on ship
resistance and investigate the uncertainty of full-scale resistancesimulations. (Terziev at all., 2019).

ABSTRACTBow lines optimization and resistance prediction are important contents
in ship design. In this paper, the bow lines optimization of the 47500DWT bulk carrier is carried out, the resistance of the optimized ship iscalculated by the CFD method, and the ship resistance prediction modelis established based on the BP neural network. This study verifies thefeasibility of the optimization method and the accuracy of using BP
neural network to predict ship resistance.

In recent years, artificial intelligence and machine learning have attracted
the attention of the academic community, and many scholars haveapplied machine learning to the field of ships. This paper optimizes thebow lines based on deep learning and evolution strategy, and the
optimized ship shape has better seakeeping (Yang at all., 2020). This
paper points out through research that the accuracy of forecasting ship
propulsion using artificial neural network is better than traditional
forecasting methods (Pederson at all., 2009). The research in this papershows that the accuracy of the artificial neural network model inpredicting ship resistance can meet the requirements (Couser at all.,2004). In this paper, multiple machine learning methods are used toestablish a ship resistance prediction model, and the prediction accuracyof the models established by different methods are compared (Yang atall., 2021).

KEY WORDS: Bow lines optimization; resistance prediction; 47500DWT bulk carrier; CFD; BP neural network.

INTRODUCTION
Ship resistance prediction is one of the important contents of shiphydrodynamic performance prediction, for which many scholars havedone a lot of work. Due to the complexity of ship resistance calculation,
there is no mature pure theoretical formula. At present, the commonlyused resistance prediction methods include model test, CFD numericalsimulation, and parameter estimation method.
Generally, after the hull lines are determined, the resistance of the shipmodel can be obtained by the method of model test. The ship model test
method mainly includes the process of model building, pool test, and test
result conversion (Tu et all., 2018). However, in the early stage of ship
design, the ship shape line has not yet been determined, and othermethods must be adopted if the model test is not ready. The parameter
estimation method is an empirical method developed on the basis ofmodel test data, among which the representative method is the standard
series atlas method. Standard series atlas methods include Taylor series,
SSPA series at all (Williams, 1969). It draws a large number of standard
series of ship resistance data into graphs or tables, so that the manual
calculation of the ship's resistance follows a relatively simple and directprocedure, so as to study the relationship between the hull parameters
and the hull's resistance, and guide the ship design (Shaher, 1971). With
the development of computer technology and computational fluid
dynamics theory, numerical simulation technology has been widely usedin the prediction of ship hydrodynamic performance (Xu at all., 2019).
In these papers, the ship's trim was optimized, and the CFD numerical

In ship design, the shape of the bow has a great influence on theresistance of the ship, so it is necessary to optimize the bow lines. The
purpose of this paper is to establish the ship resistance prediction modelof the optimized ship based on BP neural network, and to test theaccuracy of the model prediction. In this paper, the 47500DWT bulk
carrier is used as the original ship. First of all, the Latin hypercube andthe ship surface deformation method based on the RBF interpolation
principle are used as auxiliary tools to generate the optimized sample
ship. Then the CFD numerical simulation method is used to obtain theresistance of the sample ship. At last, the BP neural network is used toestablish a ship resistance prediction model, and the accuracy of themodel prediction is analyzed.

47500DWT BULK CARRIER3526