Dynamic analysis can consider the complex behavior of mooring systems. However, the relatively long analysis time of the dynamic analysis makes it difficult to use in the design of mooring systems. To tackle this, we present a Bayesian optimization algorithm (BOA) which is well known as fast convergence using a small number of data points. The BOA evaluates design candidates using a probability-based objective function which is updated during the optimization process as more data points are achieved. In a case study, we applied the BOA to improve an initial mooring system that had been designed by human experts. The BOA was also compared with a genetic algorithm (GA) that used a pre-trained surrogate model for fast evaluation. The optimal designs that were determined by both the BOA and GA have a 50% lower maximum tension than the initial design. However, the computation time of the GA needed 20 times more than that of the BOA because of the training time of the surrogate model.

In the study of the motions of vessels floating side by side, it is necessary to employ accurate methods of analysis other than potential theory to overcome inaccurate solutions due to viscous effects. In this study, computational fluid dynamics (CFD) simulations for the analysis of single- and side-by-side vessels are performed. The CFD codes are formulated for solving the Navier–Stokes equations using the finite-volume method with OpenFOAM, an open-source CFD software program. Taking into consideration viscous damping and vortex shedding effects, advantage of viscous damping and vortex shedding, three-dimensional numerical simulations for six-degree-of-freedom motions of a single vessel and side-by-side vessels are conducted. The simulation results for a single vessel are validated by comparison with results obtained from ANSYS AQWA software and from model testing. Simulation results for side-by-side vessels are compared with the AQWA results in order to show that the CFD code can be used effectively in predicting the motions of side-by-side vessels in regular waves.

According to the National Oceanic and Atmospheric Administration (NOAA), the Earth's average temperature is increasing annually as global warming continues, and the temperature of the Arctic region is increasing twice as fast as those of other regions (NOAA, 2018). With Arctic glaciers melting owing to global warming, the importance of developing marine resources buried in the Arctic is emerging. According to the United States Geological Survey, undiscovered recoverable resources in the Arctic region include approximately 90 billion barrels (Bbbl) of oil, 1,669 trillion cubic feet (Tcf) of gas, and 44 Bbbl of natural gas liquid (Bird et al., 2008). It is estimated that 13% of the world's undiscovered oil volume and 30% of the world's undiscovered gas volume are buried in the Arctic region. In general, marine resources in the deep sea are developed by offshore structures, and it is important to collect and analyze the metocean data of sea areas where marine resources are buried for the operation and survival of offshore structures. Metocean data include wave, wind, current, water depth, tide, and soil conditions. To reflect metocean data in the design of offshore structures, extreme value analysis should be conducted (API, 2005). Metocean data can be collected from measurement data of actual sea areas that use buoys or ships, hindcast data that estimate the desired data of the past through numerical methods based on past metocean data, and satellite data that use satellites. According to DNVGL (2015), for the design of offshore structures, the significant wave height (Hs) and spectral peak wave period (Tp) of 100-year return values should be used for waves, whereas the 100-year return value of the one-hour average wind speed at 10 m above the mean sea level (MSL) should be used for wind speed. For ocean currents, the 10-year return value of the current speed should be reflected in the design. To derive return values through statistical methods, methods for selecting the collected data must be determined. Such methods can be primarily categorized into global and event models. Global models include the initial distribution and total sample methods that use the entire data statistically, and the event models include the peak over threshold method, which uses data above a certain threshold, and the annual maxim method, which uses only the annual maximum values (DNV, 2014). Extreme value distributions (EVDs) used for extreme value analyses include the Gumbel, Frechet, Weibull, and log-normal distributions. Methods for estimating the parameters of EVDs include