기계학습 모델을 활용한 선박 공칭반류장 예측
Estimation for Nominal Wake Field of Ships by Using Machine Learning Model
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초록
In this paper, we introduce the machine learning model to estimate the nominal wake field of a ship from the afterbody hullform using a 3 dimensional CNN (Convolutional Neural Network) model. The convolution layers extract the features of the hullform and they are connected to the nominal wake field. In this research, two different models were tested. The one learns the velocity field itself while the other learns the Fourier coefficients expressing the wake field. Both models showed about 4% volumetric mean velocity error for the test data not used in the learning process. In the case study of two sample ships included in the test data, the direct prediction model showed the better estimation results than the Fourier coefficient based model. Application cases for estimating cavitation performance using the developed model were also introduced.
Abstract
In this paper, we introduce the machine learning model to estimate the nominal wake field of a ship from the afterbody hullform using a 3 dimensional CNN (Convolutional Neural Network) model. The convolution layers extract the features of the hullform and they are connected to the nominal wake field. In this research, two different models were tested. The one learns the velocity field itself while the other learns the Fourier coefficients expressing the wake field. Both models showed about 4% volumetric mean velocity error for the test data not used in the learning process. In the case study of two sample ships included in the test data, the direct prediction model showed the better estimation results than the Fourier coefficient based model. Application cases for estimating cavitation performance using the developed model were also introduced.
참고문헌 (19)
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