서울대학교 · Engineering
이 교수의 연구실은 해양 운송의 경제성과 환경적 지속 가능성을 동시에 향상시키기 위한 스마트 해양 기술을 핵심으로 연구를 진행하고 있습니다. 특히 선박의 연료 소비 최소화를 위한 경제적 항로 설계, 해양 환경 예측을 위한 딥러닝 기반 모델링, 선박 운영 효율성 평가를 위한 EEOI 추정 기법 등 실시간 데이터 기반의 지능형 해양 의사결정 시스템 개발에 집중하고 있습니다. 또한 선체 형상 최적화, 해상 구조물 설치 시뮬레이션, 선박 주변 위험도 평가 등 해양 안전과 설계 최적화 분야의 혁신적 기술 개발도 함께 수행하고 있습니다.
표시된 성과는 수집된 데이터 기준으로 산출되며, 일부 차이가 있을 수 있습니다.
With increases in international oil prices, the proportion of fuel cost to the operational costs of a ship is currently increasing. To reduce fuel cost, a method for determining an economical route for a ship based on the acquisition of the sea state and the estimation of fuel consumption is proposed. The proposed method consists of three items. The first item is to acquire the sea state information in real time. The second item is to estimate the fuel consumption of a ship according to the sea
To prevent pollution from ships, the Energy Efficiency Design Index (EEDI) is a mandatory guideline for all new ships. The Ship Energy Efficiency Management Plan (SEEMP) has also been applied by MARPOL to all existing ships. SEEMP provides the Energy Efficiency Operational Indicator (EEOI) for monitoring the operational efficiency of a ship. By monitoring the EEOI, the shipowner or operator can establish strategic plans, such as routing, hull cleaning, decommissioning, new building, etc. The key
The path planning of a ship requires much information, and one of the essential factors is predicting the ocean environment. Ocean weather can generally be gathered from forecasting information provided by weather centers. However, these data are difficult to obtain when satellite communication is unstable during voyages, or there are cases where forecast data for a more extended period of time are needed for the operation of the fleet. Therefore, shipping companies and classification societies
Fuel oil consumption (FOC) must be minimized to determine the economic route of a ship; hence, the ship power must be predicted prior to route planning. For this purpose, a numerical method using test results of a model has been widely used. However, predicting ship power using this method is challenging owing to the uncertainty of the model test. An onboard test should be conducted to solve this problem; however, it requires considerable resources and time. Therefore, in this study, a deep feed
