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http://dx.doi.org/10.7837/kosomes.2022.28.7.1274

Prediction of Resistance Performance for Low-Speed Full Ship using Deep Neural Network  

TaeWon Park (Shipbuilding & Marine Simulation Center, Tongmyong University)
JangHoon Seo (Shipbuilding & Marine Simulation Center, Tongmyong University)
Dong-Woo Park (School of Naval Architecture & Ocean Engineering, Tongmyong University)
Publication Information
Journal of the Korean Society of Marine Environment & Safety / v.28, no.7, 2022 , pp. 1274-1280 More about this Journal
Abstract
The resistance performance evaluation of general ships using computational fluid dynamics requires a lot of time and cost, and various methods are being studied to reduce the time and cost. Existing methods using main particulars or cross sections of ships have limitations in estimating resistance performance that is greatly dependent on the shape of the ship. In this paper, we propose a deep neural network model that can quickly predict the resistance performance of the hull surface by inputting the geometric information of the hullform mesh. The proposed deep neural network model based on Perceiver IO can immediately predict resistance performance, unlike computational fluid dynamics techniques that require calculation in each time step. It shows the result of estimating the resistance performance with an average error of less than 1% in the data set for a 50 K tanker ship, a type of low-speed full ship.
Keywords
Resistance Performance; Deep Neural Network; Computational Fluid Dynamics; Low-Speed Full Ship; Hull Surface;
Citations & Related Records
Times Cited By KSCI : 3  (Citation Analysis)
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