Seakeeping Performance Estimation of the Container Ship under Irregular Wave Condition Using Artificial Neural Network

Hamid Reza Taghva,Hassan Ghassemi,Hashem Nowruzi

doi:10.12691/ajcea-6-4-3

Hamid Reza Taghva, Hassan Ghassemi + Show 1 more

Open Access

https://doi.org/10.12691/ajcea-6-4-3

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Abstract

In the current study, sea keeping performance of the S-175 container ship is estimated under irregular wave conditions by using numerical calculation and artificial neural networks (ANNs). For this purpose, strip theory is employed to calculate of the response amplitude operator (RAO) and wave resistance. Then, the RAO of heave, pitch, and roll motions and added resistance are used in the considered ANN. In our calculation, the ship dimensions are changed at the same displacement and body form. By comparing the RAO diagrams and according to survey seakeeping criteria, optimum hull is determined for seakeeping performance. In addition, predictive equations based on length of vessel (L), the breadth (B), draft (T) and wave encounter angle (μ), are presented to estimated of seakeeping performance by using ANN.

Highlights

Estimation of the ships seakeeping performance is one of the most important points in design procedure
Huang et al [7] confirmed the numerical estimation by seakeeping testing and analyzed the effects of nonlinear slamming and accidental sloshing on general Liquefied Natural Gas (LNG) carrier motions
Bagheri & Ghassemi presented the optimization of Wigley hull form in order to ensure the objective functions of the seakeeping performance [18]

Summary

Introduction

Estimation of the ships seakeeping performance is one of the most important points in design procedure. We calculate the RAO and analyze the body fluctuation in three roll, heave and pitch motions. Otumom and Sener [8] studied the effect of some parameters such as L, B, T, CCpp and LCB on the seakeeping By changing these parameters symmetrically, a new body form produced and presented the results. Shora et al [14] predicted the performance and cavitation volume of submerged propeller under different geometrical and physical characteristics by using CFD and ANNs. Effect of hull form coefficients on the vessel seakeeping performance and the parametric on the vessel body lines modeling to optimize seakeeping performance carried out by Khosravi & Ghassemi [15,16,17]. We used suitable ANNs to predict of the seakeeping behaviour

Problem Equations

Prediction of the seakeeping Behavior by ANN

Results and Discussions

Displacement

Conclusions