When designing mooring facilities for harbor and dock facilities, the port facility manager should consider the berthing energy of the approaching ship. However, it is difficult to directly estimate the ship’s berthing velocity, which has the greate...
When designing mooring facilities for harbor and dock facilities, the port facility manager should consider the berthing energy of the approaching ship. However, it is difficult to directly estimate the ship’s berthing velocity, which has the greatest effect on the berthing energy. Therefore, it is analyzed statistically based on the measured data. The ‘Brolsma curve’, which is widely referred to worldwide as the data on the berthing velocity, is the data collected in the 1970s. However, it does not correspond to the current situation because it is not reflected in the enlargement of the ship and the development of the mooring ability in the port facility. Therefore, it is necessary to determine the criteria of the new design berthing velocity by analyzing the actually measured ship berthing velocity recently.
In this study, I try to derive a new criterion by analyzing the berthing velocity data observed from a tanker terminal in Korea. The comparison and analysis of the domestic and international reference data related to the berthing velocity complements the ‘Port and Fishing Design Standards (2014)’and the improvement plan is prepared. The purpose of this study is to produce basic data that can be referenced in the design of mooring facilities by numerical analysis of the actual data analysis results.
The actual data of the berthing velocity used in this study is measured at a tanker terminal operated by three jetties located in Korea for about 17 months and the total number of data is 207. As a result of analyzing the actual data by jetty and ship size, it was confirmed that most of the data are within the design berthing velocity of each jetty.
In order to apply the actual data of berthing velocity to the probability distribution function, the frequency of berthing velocity was converted into histogram and compared with the three probability distributions of normal distribution, lognormal distribution and Weibull distribution. To find the most suitable probability distribution function, I conduct the goodness of fit test such as K-S test, A-D test and Q-Q plot. As a result, lognormal distribution was most suitable for the ship which was in laden condition and Weibull ditribution was most suitable in the case of ballast condition. Based on the results, I developed a method to calculate and use the predicted value of berthing velocity derived from the concept of probability of exceedance.
The relationship curve between the berthing velocity and the DWT was derived by using the relation between the ship’s specification and ship size. Using the linear regression analysis, the relational expressions corresponding to the confidence intervals of 50, 75, 90, 95, 98, and 99% were derived and rearranged into graphs.
This study suggests a method to estimate the proper berthing velocity according to ship size through analysis of actual data. It is necessary to revise old reference paper for berthing velocity that do not correspond to the reality such as the data of 'Harbor and Fishing Design Standards'. However, since the actual data used in this study is limited to domestic tanker terminal, further studies are needed to collect and analyze the data collected from various types of vessels and ports. The ultimate goal is to develop the new relationship curve between the vessel size and the berthing velocity that can replace Brolsma curve.