@article{ajcea2018643,
author={{Taghva, Hamid Reza and Ghassemi, Hassan and Nowruzi, Hashem},
title={Seakeeping Performance Estimation of the Container Ship under Irregular Wave Condition Using Artificial Neural Network},
journal={American Journal of Civil Engineering and Architecture},
volume={6},
number={4},
pages={147--153},
year={2018},
url={http://pubs.sciepub.com/ajcea/6/4/3},
issn={2328-3998},
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 (¦Ì), are presented to estimated of seakeeping performance by using ANN.},
doi={10.12691/ajcea-6-4-3}
publisher={Science and Education Publishing}
}