期刊
JOURNAL OF MARINE SCIENCE AND ENGINEERING
卷 11, 期 6, 页码 -出版社
MDPI
DOI: 10.3390/jmse11061149
关键词
flow control fin (FCF); artificial neural network (ANN); optimal design; container ship; wake flow distributions
The optimal design of flow control fins (FCFs) for a container ship was carried out using a machine learning approach. Instead of simulation-based performance evaluation, artificial neural network (ANN) was used for prediction. Systematic collection of wake distribution data via computational fluid dynamics (CFD) was done before the machine learning process. The results showed that both wake distributions and resistance performance were improved in a practically applicable timeframe.
In this study, the optimal design of flow control fins (FCFs) for a container ship was carried out via a machine learning approach. The conventional design practice for the FCF relied on simulation-based performance evaluation, which demands a large amount of analysis time. Instead of computational fluid dynamics (CFD)-based prediction, artificial neural network (ANN)-based prediction was attempted. Prior to the machine learning process, the wake distribution data were collected systematically via CFD. Based on the collected data, the wake distributions and resistance performance dependent on varying the fin positions were learned using the ANN, and the optimal fin position was selected with relevant optimization techniques. When multi-objective optimization was employed, it was found that both wake distributions and resistance performance were improved in a practically applicable timeframe. The current process is superior to conventional simulation-based optimization in terms of speed. From the viewpoint of prediction accuracy, in this study, ANN-based prediction was found to be equally accurate as CFD-based prediction. Thus, the results can provide a novel and reliable design methodology for the optimal design of ship appendages.
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