Lee, M.-K.; Lee, I. Transfer Learning with Deep Neural Network toward the Prediction of Wake Flow Characteristics of Containerships. J. Mar. Sci. Eng.2023, 11, 1898.
Lee, M.-K.; Lee, I. Transfer Learning with Deep Neural Network toward the Prediction of Wake Flow Characteristics of Containerships. J. Mar. Sci. Eng. 2023, 11, 1898.
Lee, M.-K.; Lee, I. Transfer Learning with Deep Neural Network toward the Prediction of Wake Flow Characteristics of Containerships. J. Mar. Sci. Eng.2023, 11, 1898.
Lee, M.-K.; Lee, I. Transfer Learning with Deep Neural Network toward the Prediction of Wake Flow Characteristics of Containerships. J. Mar. Sci. Eng. 2023, 11, 1898.
Abstract
In this study, deep neural network (DNN) and transfer learning (TL) techniques were employed to predict the viscous resistance and wake distribution based on the positions of flow control fins (FCFs) applied to containerships of various sizes. Both methods utilized data collected through Computational Fluid Dynamics (CFD) analysis. The position of the flow control fin (FCF) and hull-form information were utilized as input data, and the output data included viscous resistance coefficients and components of propeller axial velocity. The base DNN model was trained and validated using a source dataset from a 1000 TEU containership. Grid search cross-validation technique was employed to optimize the hyperparameters of the base DNN model. Then, transfer learning was applied to predict the viscous resistance and wake distribution for containerships of varying sizes. To enhance the accuracy of feature prediction with a limited amount of dataset, learning rate optimization was conducted. Transfer learning involves retraining and reconfiguring the base DNN model, and the accuracy was verified based on the fine-tuning method of the learning model. The results of this study can provide hull designers for containerships with performance evaluation information by predicting wake distribution, without relying on CFD analysis.
Keywords
Flow Control Fin (FCF); Deep Neural Network (DNN); Transfer Learning (TL); containership; viscous resistance coefficients; wake flow distributions
Subject
Engineering, Marine Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
