ニューラルネットワークによる船舶の推進性能推定とその船型最適化への応用

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Neural network process and genetic algorithm simulating function of nervous system in human brain and evolution process of lives respectively are important concerns in various fields of technology as provable tools for different kinds of design process taking into account excellent human ability on the basis of a great store of knowledge accumlated through his experiences. The author has studied the neural network and genetic algorithm, aiming at practical application of these biological process to ship's propulsive performance prediction and ship form optimization, in conjunction with much amount of the ship model experimental data accumlated in the Shipbuilding Research Centre of Japan through these 3 decades. This paper describes the outline of his study on application of the neural network and the genetic algorithm for propulsive performance prediction and hull form optimization on a medium sized container ship. This includes (1) examination on the reconstruction learning process, (2) study on the parametric expression of ship form and data-base construction, (3) examination on estimation process of propulsive performance factors and it's accuracy evaluation, (4) hull form optimization by use of genetic algorithm simulation process developed. High accuracy and short computational time in prediction are demonstrated together with much propulsive performance improvement by hull form optimization.
社団法人日本船舶海洋工学会の論文
2000-03-29

著者

金井健
(財)日本造船技術センター

ニューラルネットワークによる船舶の推進性能推定とその船型最適化への応用

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