This study introduces an artificial neural network system for ship motion prediction in seaways. To consider the physical characteristics of wave-induced ship motions, neural networks based on a Long Short-Term Memory (LSTM) encoder and decoder, and a convolutional neural network (CNN) are integrated. The LSTM encoder computes the state vector representing the memory effects of motion-induced radiated waves based on past motion records, that is, a sequence-to-one model. In the LSTM decoder, the motion time series is predicted using the encoded initial state vector and foreseen information on the ocean wave field around a vessel, that is, a sequence-to-sequence model. In addition, a CNN is adopted to compress the wave data into a vector sequence. Particularly, the present CNN uses spatiotemporal wave-field data, not a wave signal at single location. To validate the proposed system, a database for training the integrated system was constructed using a physics-based seakeeping program for various sea states. By applying the trained model, deterministic predictions were performed for a new ocean environment, and the accuracy and reliability of the testing results are investigated according to the input data and neural network structures. From the simulation results, it was confirmed that the present encoder–decoder system can conduct ship motion forecasting by effectively considering the motion memory effects and wave excitations as in the ship hydrodynamic model. In addition, excitations and resulting motion responses by short-crested waves can be considered through CNN-based wave-field data processing. Finally, the present machine-learning model also showed the capability of extracting ship operation information (maneuvering quantities) from the given wave-field data.

This paper considers scale effects on three-dimensional (3D) sloshing flows. A series of model tests were conducted for three differently scaled tanks. The model tanks considered in this study were 1:70, 1:50, and 1:30 scaled membrane type tanks based on a 138,000 m³ liquid natural gas carrier model. The tests were carried out for harmonic sway and roll motions for three different filling depths and with various excitation frequencies. The pressure measuring points in the tanks were the same, as if they were scaled up to actual size. The main parameters investigated in this study were the pressure peak and rise time of sampled sloshing pressures. These key sloshing load parameters were systematically analysed by a statistical approach, and the results from the tests at different scales were compared.

The effect of different bow shapes on the added resistance in waves was observed through a series of model tests. To this end, three different hull forms of KRISO Very Large Crude Carrier 2 were considered: an original hull form and two modified hulls with different bow shapes, called ax-bow and leadge-bow. The model tests were conducted for a wide range of wavelengths with two wave amplitudes in a regular head-sea condition at the design speed. Each test condition was imposed at least twice in order to check the repeatability of measurement, considering the uncertainties in model test and the nonlinear nature of the added resistance. This article introduces a preliminary study on the effects of surge motion, amplitude of incident wave, and green-water allowance around bow region. This article briefly includes the uncertainty analysis of recent study regarding the performance of the original hull. Based on the results of the experimental study for three different bow shapes, the parameters which influence the added resistance and motion responses are discussed.