A review for numerical simulation methods of ship–ice interaction

Numerical analysis of the wake dynamics of a propeller

The development of human civilization over the last decade has reached a landmark as Industry 4.0 has been widely introduced. Several aspects of industry and manufacturing activities are changing due to the Internet of Things (IoT), location detection technologies, and advanced human–machine interfaces. To enact industrial affairs under those specifications, a sensor is required to transform physical events into numerical information. The use of sensors in marine applications also appears in research and studies, in which the sensor is used for both monitoring the phenomena of a designated subject and data acquisition. Achievements in quantifying complex phenomena in critical maritime designs are fascinating subjects to discuss regarding their development and current states, which may be reliable references for further research on developing sensors and related measurement analysis tools in marine, shipbuilding, and shipping fields. This comprehensive review covers several discussion topics, including the origins and development of sensor technology, applied sensor engineering in logistic and shipping activities, the hydrodynamic characterization of designed hulls, the monitoring of advanced machinery performance, Arctic-based field observations, the detection of vibration-based damage to offshore structures, corrosion control and monitoring, and the measurement of explosions on critical maritime infrastructures.

Advanced Development of Sensors’ Roles in Maritime-Based Industry and Research: From Field Monitoring to High-Risk Phenomenon Measurement

Propeller–duct interaction on the wake dynamics of a ducted propeller

Full-scale simulation of self-propulsion for a free-running submarine

Modified phase average algorithm for the wake of a propeller

To analyze the influence of duct geometrical parameters on hydrodynamic performance of pump jet propulsor, this paper presents a surface panel method for predicting performance of pump jet propulsor. And, moreover, a meshing method for blade, based on circular conical surface, is proposed. According to the actual situation of the internal flow of the pump jet propulsor, a tip leakage vortex model for the blades with flat top is also proposed. The hydrodynamic performance of pump jet propulsor under different conditions was calculated and compared with CFD results for the verification of the presented method. Then the influence of duct parameters on pump jet performance was analyzed from several aspects including performance index, surface pressure distribution, and flow velocity. The results show that variations of gap size, camber or attack angle of duct result in different hydrodynamic performance. Suitable selection of duct geometry can significantly increase the thrust efficiency of the pump jet propulsor, expand the range of effective work, and improve the loading characteristics of the blade.

Analysis of influence of duct geometrical parameters on pump jet propulsor hydrodynamic performance

Impact of skew on propeller tip vortex cavitation

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.

Chao Wang

Papers

Modified phase average algorithm for the wake of a propeller

Analysis of influence of duct geometrical parameters on pump jet propulsor hydrodynamic performance

Impact of skew on propeller tip vortex cavitation

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

Analysis of shadowing effect of propeller-ice milling conditions with peridynamics