Resumen
This study proposes machine learning-based prediction models to estimate hull form performance. The developed models can predict the residuary resistance coefficient (CR" role="presentation">????CR
C
R
), wake fraction (wTM" role="presentation">??????wTM
w
T
M
), and thrust deduction fraction (t). The multi-layer perceptron and convolutional neural network models, wherein the hull shape was considered as images, were evaluated. A prediction model for the open-water characteristics of the propeller was also generated. The experimental data used in the learning process were obtained from model test results conducted in the Korea Research Institute of Ships and Ocean Engineering towing tank. The prediction results of the proposed models showed good agreement with the model test values. According to the ITTC procedures, the service speed and shaft revolution speed of a ship can be extrapolated from the values obtained from the predictive models. The proposed models demonstrated sufficient accuracy when applied to the sample hull forms based on data not used for training. Thus, they can be implemented in the preliminary design phase of hull forms.