Prediction of added resistance using genetic programming

Prediction of added resistance using genetic programming In recent years, the increasing demand for a reduction of carbon emission has made hydrodynamic design and the optimization of hull design more important. For appropriate hydrodynamic design, the added resistance needs to be predicted. However, as existing methods including computer simulations or experiments require considerable amounts of time and money, it is difficult to consider the prediction result at the initial design stage. Therefore, in this paper, we propose a prediction method that can be used in the initial design stage for predicting the added resistance in waves, thereby contributing to the optimization of hull design and saving time and money. The proposed method is a nonlinear mathematical function and is based on genetic programming. For verification, the predicted results are compared with the experimental results and the strip theory results. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Ocean Engineering Elsevier

Prediction of added resistance using genetic programming

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Publisher
Elsevier
Copyright
Copyright © 2018 Elsevier Ltd
ISSN
0029-8018
eISSN
1873-5258
D.O.I.
10.1016/j.oceaneng.2018.01.089
Publisher site
See Article on Publisher Site

Abstract

In recent years, the increasing demand for a reduction of carbon emission has made hydrodynamic design and the optimization of hull design more important. For appropriate hydrodynamic design, the added resistance needs to be predicted. However, as existing methods including computer simulations or experiments require considerable amounts of time and money, it is difficult to consider the prediction result at the initial design stage. Therefore, in this paper, we propose a prediction method that can be used in the initial design stage for predicting the added resistance in waves, thereby contributing to the optimization of hull design and saving time and money. The proposed method is a nonlinear mathematical function and is based on genetic programming. For verification, the predicted results are compared with the experimental results and the strip theory results.

Journal

Ocean EngineeringElsevier

Published: Apr 1, 2018

References

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