Fluid-structure interaction of a large ice sheet in waves

Ship resistance when operating in floating ice floes: A combined CFD&DEM approach

Analysis of the wake dynamics of a propeller operating before a rudder

The transient prediction of a pre-swirl stator pump-jet propulsor and a comparative study of hybrid RANS/LES simulations on the wake vortices

A review for numerical simulation methods of ship–ice interaction

A comparative DES study of wake vortex evolution for ducted and non-ducted propellers

In order to analyze the ice-going ship’s performance under the pack ice conditions, synthetic ice was introduced into a towing tank. A barrier using floating cylinder in the towing tank was designed to carry out the resistance experiment. The test results indicated that the encountering frequency between the ship model and the pack ice shifts towards a high-velocity point as the concentration of the pack ice increases, and this encountering frequency creates an unstable region of the resistance, and the unstable region shifts to the higher speed with the increasing concentration. The results also showed that for the same speed points, the ratio of the pack ice resistance to the open water resistance increases with the increasing concentration, and for the same concentrations, this ratio decreases as the speed increases. Motion characteristics showed that the mean value of the heave motion increases as the speed increases, and the pitch motion tends to increase with the increasing speed. In addition, the total resistance of the fullscale was predicted.

Experimental Investigation of the Resistance Performance and Heave and Pitch Motions of Ice-Going Container Ship Under Pack Ice Conditions

Influence of a pre-swirl stator and rudder bulb system on the propulsion performance of a large-scale ship model

In marine environments, ships are bound to be disturbed by several external factors, which can cause stochastic fluctuations and strong nonlinearity in the ship motion. Predicting ship motion is pivotal to ensuring ship safety and providing early warning of risks. This report proposes a real-time ship vertical acceleration prediction algorithm based on the long short-term memory (LSTM) and gated recurrent units (GRU) models of a recurrent neural network. The vertical acceleration time history data at the bow, middle, and stern of a large-scale ship model were obtained by performing a self-propulsion test at sea, and the original data were pre-processed by resampling and normalisation via Python. The prediction results revealed that the proposed algorithm could accurately predict the acceleration time history data of the large-scale ship model, and the root mean square error between the predicted and real values was no greater than 0.1. The optimised multivariate time series prediction program could reduce the calculation time by approximately 55% compared to that of a univariate time series prediction program, and the run time of the GRU model was better than that of the LSTM model.

Real-Time Prediction of Large-Scale Ship Model Vertical Acceleration Based on Recurrent Neural Network

Comprehensive test system for ship-model resistance and propulsion performance in actual seas

Zhao Dagang

Papers

A comparative DES study of wake vortex evolution for ducted and non-ducted propellers

Experimental Investigation of the Resistance Performance and Heave and Pitch Motions of Ice-Going Container Ship Under Pack Ice Conditions

Influence of a pre-swirl stator and rudder bulb system on the propulsion performance of a large-scale ship model

Real-Time Prediction of Large-Scale Ship Model Vertical Acceleration Based on Recurrent Neural Network

Comprehensive test system for ship-model resistance and propulsion performance in actual seas