Optimal Design of Flow Control Fins for a Small Container Ship Based on Machine Learning

Author:

Lee Min-Kyung1,Lee Inwon12ORCID

Affiliation:

1. Department of Naval Architecture and Ocean Engineering, Pusan National University, Busan 46241, Republic of Korea

2. Global Core Research Center for Ships and Offshore Plants, Pusan National University, Busan 46241, Republic of Korea

Abstract

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.

Funder

National Research Foundation of Korea

Ministry of Science and ICT of Korea

Publisher

MDPI AG

Subject

Ocean Engineering,Water Science and Technology,Civil and Structural Engineering

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