Yu-An Tzeng

The northern Taiwan Strait, characterized by deep waters and high wind energy density, presents significant potential for developing floating offshore wind turbines (FOWTs). However, the region is prone to typhoons, with substantial variations in wind speed and direction during typhoon eye passages, posing challenges to FOWT safety and performance. This study investigates the serviceability of a 10 MW FOWT installed offshore of Hsinchu under typical wind and wave conditions during the eye of Typhoon SOULIK. Wind and wave data were sourced from the ERA5 reanalysis database. Simulations were conducted using OrcaFlex 11.4c, which enables fully coupled dynamic analysis of the entire FOWT system, including the mooring system, platform, tower, turbine, and nacelle, facilitating accurate predictions of system behavior in complex offshore environments. This study evaluated scenarios of maximum wind speed, significant wave height, wind–wave misalignment, and minimum wind speed during typhoon eye passage, considering both idle and power production modes in accordance with IEC TS 61400-3-2 requirements. The results indicate that platform yaw motion exceeds IEC limits during typhoon events, particularly in power production mode. This highlights the need for reducing platform motion. It is recommended to further develop control strategies or implement an active control system for the platform to ensure operational reliability. This research provides critical insights into FOWT design and operational challenges in typhoon-prone regions.

This study presents an optimized system for ship route planning. Computational fluid dynamics simulations were used to modify Kwon’s semi-empirical speed loss estimation method, enabling efficient route planning under variable sea conditions. The study focused on improving the prediction of speed loss in irregular waves for container ships and further applying this to ship-optimized voyage planning. Dynamic programming was used for optimized voyage planning by modifying the ship course in response to meteorological data; this approach could balance both energy efficiency and safety. The modified speed loss predictions aligned closely with the simulation results, enhancing the reliability of weather routing decisions. Case studies for trans-Pacific and trans-Atlantic voyages demonstrated that the proposed system could significantly reduce the voyage time. These findings highlight the potential of real-time updates in voyage planning. The proposed system is a valuable tool for captains and fleet managers. The applicability of this system can be further broadened by validating it on different ship types.

The maritime industry faces the critical challenge of achieving net-zero greenhouse gas emissions by 2050, as mandated by the International Maritime Organization. This study introduces a novel speed optimization model, designed specifically for bulk carriers operating between two ports. Unlike conventional models that often assume static weather conditions, the proposed model incorporated variable weather conditions at different times of arrivals, as quantified by the Beaufort number (BN) and weather direction, for each leg of the voyage. Fuel consumption was estimated by applying regression to historical voyage data. This study employed a genetic algorithm (GA) to optimize vessel speed and thereby minimize fuel consumption. The model was tested by using different fuel consumption response curves relative to different BNs and weather directions. The results indicated that the proposed method could effectively reduce fuel consumption compared with the historical sailing mode by around 3%. The optimal speed recommendation indicated that the vessel should operate at a higher speed in circumstances associated with relatively low fuel consumption, such as lower BN and following sea conditions. Nonetheless, if it is possible to attain relatively low fuel consumption by adjusting the speed, the GA assesses the viability of this course of action. The study suggests that the predictive accuracy could be further enhanced by incorporating more granular, validated voyage data in future research.

In this study, nondominated sorting genetic algorithm II (NSGA-II) was used to minimize the cost and carbon emissions of a liquefied natural gas (LNG) dual-fuel ship for a given route. This study considered the regulations of emission control areas (ECA) and the European Union (EU) Emissions Trading System (ETS) to determine the optimal speed and LNG/oil ratio for the ship. NSGA-II used the arrival time at each port and the LNG usage ratio for each voyage leg as its genes. The time window for arrival, the fuel cost, and potential EU carbon emission regulations were used to estimate the cost of the considered voyage. Moreover, fuel consumption was determined using historical data that were divided by period, machinery, and voyage leg. The results indicated that the optimal speed and fuel ratio could be determined under any given fuel and carbon price profile by using NSGA-II. Finally, the effects of regulations and carbon price differences on the optimal speed and fuel ratio were investigated. The cost minimization solution was susceptible to being affected by the regulations of ECAs and the EU ETS. The speed profile of the cost minimization solution was found to have a tendency to travel at faster-than-average speeds outside ECAs and non-EU regions, and travel slower in ECAs and EU regions. Meanwhile, the selection of fuel type showed that 100% traditional fuel oil in all regions, but with sufficiently high EU carbon permit cost, tends to use 100% LNG in EU regions.