합성곱 신경망을 이용한 프로펠러 캐비테이션 침식 위험도 연구

• Published in: 大韓造船學會 論文集 Vol. 58; no. 3; pp. 129 - 136
• Main Authors: 김지혜(Ji-Hye Kim), 이형석(Hyoungseok Lee), 허재욱(Jea-Wook Hur)
• Format: Journal Article
• Language: Korean
• Published: 대한조선학회 2021
• Subjects: 조선공학; Deep learning(딥러닝); Erosion(침식); Propeller(프로펠러); Convolutional Neural Network(CNN; 합성곱 신경망; Cavitation(캐비테이션)
• ISSN: 1225-1143; 2287-7355
• DOI: 10.3744/SNAK.2021.58.3.129

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.