Even though interest in developing the Arctic region is increasing continuously, the standard procedure to be used to analyze the station-keeping performance of a floater considering ice loads has not been established yet. In this paper, the effectiveness of heading control with a dynamic positioning system is analyzed to evaluate the improvement of the performance of the station-keeping system in the ice conditions. Complex environmental loads with ice-induced forces were generated and applied to a ship type floater with dynamic positioning and mooring systems. Three-hour time-domain simulations were conducted for the two different station-keeping systems with mooring only and mooring with a dynamic positioning system. Position offsets and mooring line tensions for the two scenarios were compared with maximum values and most probable maxima (MPM) values. The results of the simulation showed that the heading control can reduce 8.2% of MPM values for the mooring lines and improve the station-keeping performance by about 16.3%. The validity of the station-keeping system that was designed was confirmed, and it is expected that the specification of mooring lines can be relaxed with the heading control.

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A new asymmetric synthetic route to (+)-decursivine and (+)-serotobenine is formulated. The key developments are the de novo construction of the crucial eight-membered 3,4-fused tricyclic indole ring engaged by the intramolecular Fischer indole synthesis and the stereocontrolled assembly of the dihydrobenzofuran subunit mediated by the asymmetric intramolecular Rh-carbenoid C−H insertion. BF 3 -mediated selective C15 epimerization followed by removal of the amine masking groups completed the target natural compounds' asymmetric and divergent total syntheses. Letter pubs.acs.org/OrgLett

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.

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