The Effect of Ship Coefficients on the Efficiency Gain f Propeller-Vane System

This paper discusses a correlation between non dimensional parameter of the ship (L/B,B/T, wake fraction, ratio between induced and advance velocity, thrust loading coefficient) and energy gain by applying vane-turbine in the propeller slipstream. The data based on the basis of statistical data (numerical) and model testing. The correlation data can be calculated by quantifying the contribution of causal variables to a targeted effect variable directly and indirectly through other variables and this would be examined by Path analysis. By using this coefficient, it is possible to demonstrate which variable has the main contribution on the efficiency gain. The Data analysis of Microsoft Office Excel software is used to approach the calculation. It is found that L/B and CT affected indirectly the efficiency gain of vane-turbin

An Investigation into the Operational Characteristics of High-Speed Crew Boat Based on Artificial Neural Network

Estimating shaft power of a crew boat is very important to be analysed because it has high-speed operational characteristics along with limited routes. To understand the phenomena, 3 sister crew-boats with operational distance about 40-60 nautical miles every day are investigated. The daily operational time is 8 hours and the configurations are: 4.04% full speed, 13.63% economical speed, 1.81% slow speed, 7.65% snatching, 1.25% manoeuvring, 5.29% idle, and the remaining time is in standby condition. The crew boats are fitted with a monitoring system namely SHIMOS®, in which data is sent to a server in the centre office every 2 minutes. The data consists of time capture, boat position (latitude and longitude), speed, left and right RPM engine, left and right flow-meter data engine, and average of fuel consumption data in everyday operation. Three years of data has been collected for the vessel. The present study proposed characteristics of crew-boat shaft power, which affected by external factors using Artificial Neural Network (ANN) back propagation method and optimisation in 4 hidden layers and 40 neurons with relative error 6.2%. The results demonstrates good agreement with previous popular method that using statistical models