A prediction method for ship added resistance based on symbiosis of data-driven and physics-based models

Abstract

The prediction of ship added resistance in waves is an important part of ship design and operation. A prediction method that takes into account accuracy, efficiency, robustness and generalization ability is required. In this study, a prediction method for ship added resistance in head waves based on symbiosis of data-driven and physics-based models is proposed. The physics-based model is constructed based on the 2D strip method to provide physics-based information and constraints. The data-driven module is constructed based on the fully connected neural network structure and the radial basis function, providing a data-driven model parameter optimization framework. Results indicate that the Data-driven and Physics-based Symbiotic Model (DPSM) has an obvious adaptive correction effect on the results of its embedded physics-based model, and has a stronger generalization ability than the fully data-driven model. Prediction results of the DPSM are in good agreement with experimental results, whether for the training ships or the unfamiliar testing ships. Finally, a posteriori model parameter analysis shows the reason why the DPSM has advantages of accuracy and generalization ability.