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http://dx.doi.org/10.3744/SNAK.2021.58.3.129

A Study on the Risk of Propeller Cavitation Erosion Using Convolutional Neural Network  

Kim, Ji-Hye (Department of Naval Architecture and Marine Engineering, Changwon National University)
Lee, Hyoungseok (Ship Performance Research Department, Hyundai Maritime Research Institute, Hyundai Heavy Industries)
Hur, Jea-Wook (Ship Performance Research Department, Hyundai Maritime Research Institute, Hyundai Heavy Industries)
Publication Information
Journal of the Society of Naval Architects of Korea / v.58, no.3, 2021 , pp. 129-136 More about this Journal
Abstract
Cavitation erosion is one of the major factors causing damage by lowering the structural strength of the marine propeller and the risk of it has been qualitatively evaluated by each institution with their own criteria based on the experiences. In this study, in order to quantitatively evaluate the risk of cavitation erosion on the propeller, we implement a deep learning algorithm based on a convolutional neural network. We train and verify it using the model tests results, including cavitation characteristics of various ship types. Here, we adopt the validated well-known networks such as VGG, GoogLeNet, and ResNet, and the results are compared with the expert's qualitative prediction results to confirm the feasibility of the prediction algorithm using a convolutional neural network.
Keywords
Convolutional Neural Network(CNN); Deep learning; Propeller; Cavitation; Erosion;
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