Because of EEDI regulations for ships and SDGs, it is of vital importance to develop ships with good propulsion performance, not only in still water, but also in waves. To predict the propulsion and seakeeping performance in waves, and in particular ship motions and added resistance, various theoretical calculation methods and CFD codes have been developed. To validate these estimation methods, experimental data on details of the flow induced by the ship disturbance are required. To meet this demand, the authors developed an innovative method for measuring and analyzing the spatial pressure distribution over the ship-hull surface using a large number of Fiber Bragg Grating (FBG) pressure sensors. However, pressure measurements with the conventional FBG pressure sensors are largely influenced by the difference in the temperature between water and air, referred to as the temperature interference. To resolve this issue, the authors developed a new FBG pressure sensor that incorporates three improvements and is significantly less influenced by temperature interference. Its performance was confirmed through comparisons with measured pressures obtained using existing strain-type and conventional FBG pressure sensors. Furthermore, the effects of different materials used for manufacturing ship models on the pressure measurement were investigated experimentally in towing tank tests. Finally, an experimental study was conducted to determine which of the three improvements in the latest FBG pressure sensor is essential for reducing the temperature interference.
It becomes increasingly important that the performance of a ship in actual seas is predicted accurately due to an increasing awareness about global warming and other environmental problems. The ship responses such as the ship motions, the stresses and the added resistances in actual seas vary with sea states and the ship speed. The onboard measurement data has this information, therefore the data is useful. However there is not an established analysis method of the onboard data because the data involves factors which are unexplained by the theoretical calculation. The ship responses which are caused by uncertainty sea states are stochastic variable. Therefore, the statistical analysis is useful in case of the prediction of ship responses with using the onboard data. The paper proposes a statistical method which estimates the correlation between sea states and ship responses for a short time as the conditional probability density function (PDF) by using the onboard measurement data. Two kinds of method are adopted to estimate the conditional PDF of ship speed loss for encounter sea states. On one hand it is parametric method, on the other it is nonparametric method. Using a time-series of sea states and ship speed loss during a one-year voyage obtained by an oceangoing simulation, the expectation and confidence interval of mean ship speed loss are estimated to validate these stochastic models based on each method. We conclude that the stochastic models are available. In particular, the stochastic model based on the nonparametric method offers an accurate prediction of the mean speed loss.
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