• 대한조선학회논문집
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• 제52권2호
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• pp.110-118
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• 2015

Weather routing method is one of the best practices of SEEMP (Ship Energy Efficiency Management Plan) for fuel-efficient operation of ship. KR is carrying out a basic research for development of the weather routing algorithm and making a monitoring system by FOC (Fuel Oil Consumption) analysis compared to the reference, which is the great circle route. The added resistances applied global sea/weather data can be calculated using ship data, and the results can be corrected to ship motions. The global sea/weather data such as significant wave height, ocean current and wind data can be used to calculate the added resistances. The reference route in a usual navigation is the great circle route, which is the shortest distance route. The global sea/weather data can be divided into grids, and the nearest grid data from a ship's position can be used to apply a ocean going vessel's sea conditions. Powell method is used as an optimized routing technique to minimize FOC considered sea/weather conditions, and FOC result can be compared with the great circle route result.

• International Journal of Naval Architecture and Ocean Engineering
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• 제12권1호
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• pp.440-454
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• 2020

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 parameter in calculating EEOI is Fuel Oil Consumption (FOC). It can be measured on board while a ship is operating. This means that only the shipowner or operator can calculate the EEOI of their own ships. If the EEOI can be calculated without the actual FOC, however, then the other stakeholders, such as the shipbuilding company and Class, or others who don't have the measured FOC, can check how efficiently their ships are operating compared to other ships. In this study, we propose a method to estimate the EEOI without requiring the actual FOC. The Automatic Identification System (AIS) data, ship static data, and environment data that can be publicly obtained are used to calculate the EEOI. Since the public data are of large capacity, big data technologies, specifically Hadoop and Spark, are used. We verify the proposed method using actual data, and the result shows that the proposed method can estimate EEOI from public data without actual FOC.

• 해양환경안전학회지
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• 제20권6호
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• pp.653-662
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• 2014

본 연구에서는 목포해양대학교의 실습선 새누리호를 대상으로 선박의 중앙집중 공조시스템에 공랭식 에어컨을 직접 설치하여 성능을 개선시킨 공기조화시스템으로 운전하였을 경우의 냉방 성능을 비교하고, 선실의 온열환경에 대한 실측조사를 통해서 향후 선박용 공기조화 설계 및 계획에 경험적 기초참고자료로 활용하고자 하는 것이다. 연구결과 동일한 외기조건에서 기존의 중앙집중방식 공조시스템과 개선된 공조시스템으로 운전하였을 경우, 모든 선실의 온도는 $24{\sim}28^{\circ}C$, 습도는 55~75 %로 쾌적한 조건임을 알 수 있었고, 발전기 부하를 측정결과 공기조화시스템의 성능개선에 따라 평균 부하 48 KW, 전부하시 부하율 약 8 %정도 감소하여 1일 연료소모량 FOC는 하루 평균 222[L/day]의 기름이 절약됨을 알 수 있었다. 또한 학생 선실(Cadet No. 21)은 기관실의 전열로 인해서 온도가 높게 나타났는데, 이것은 공기조화 설계 시 취출구 개수 및 전열부하를 고려하지 못한 결과로 판단된다.

• 대한조선학회논문집
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• 제49권6호
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• pp.491-497
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• 2012

A hull-form for a 32,000G/T class Ro-Pax ferry has developed in accordance with a need of ferry operators to reduce fuel oil consumption(FOC) due to the drastic increase in oil prices recently and strengthening of environmental rules and regulations such as CO2 emission. A twin-skeg type is applied as the hull-form in lieu of an open-shaft type in order to improve propulsion performance. In order to achieve this object, flow control devices are installed to reduce a propeller induced vibration which is a main reason to obstruct the application of twin-skeg type passenger vessels owing to an uncomfortable vibration level. Numerical simulation by using an in-house code and a commercial code (Fluent) has performed to find out an optimum design of the flow control devices and to check an improvement in cavity volume. Model tests in Samsung Ship Model Basin are carried out to evaluate propulsion performance with the developed twin-skeg type hull and a reference hull of open-shaft type. In conclusion, it is shown that the twin-skeg type hull is better than the open-shaft in FOC by around 7% and in cavity volume by 20% as well.

• 한국항해항만학회지
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• 제40권4호
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• pp.165-171
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• 2016

IMO에서는 선박온실가스 규제를 위해 2013년부터 현존선의 선박에너지효율관리계획인 SEEMP (Ship Energy Efficiency Management Plan)의 시행을 강제화하고 있다. SEEMP에서 권고하는 에너지절감기술 가이드라인은 크게 하드웨어적인 장비의 탑재 및 개조 또는 소프트웨어적인 기술을 통한 연료유 절감효과로 구분된다. 신조선의 경우 하드웨어적인 기술구현이 용이하지만 현존선의 경우 운항상 제약으로 인해 소프트웨어적인 에너지 절감기술 구현이 적용되고 있다. IT기반의 선박에너지절감 시스템 성능평가를 위해 해상시험을 수행하였고, 시스템 적용 전후의 항차데이터를 이용하여 연료유 절감효과를 비교 분석 하였다. 또한, SEEMP에서 자발적인 사용을 권고하고 있는 선박 경제운항 지표 (EEOI, Ship Energy Efficiency Operation Indicator) 분석을 통한 성능평가 결과를 제시하였다.

• International Journal of Naval Architecture and Ocean Engineering
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• 제13권1호
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• pp.641-649
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• 2021

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-forward neural network (DFN) is used to predict ship power using deep learning methods that involve data pattern recognition. To use data in the DFN, the input data and a label (output of prediction) should be configured. In this study, the input data are configured using ocean environmental data (wave height, wave period, wave direction, wind speed, wind direction, and sea surface temperature) and the ship's operational data (draft, speed, and heading). The ship power is selected as the label. In addition, various treatments have been used to improve the prediction accuracy. First, ocean environmental data related to wind and waves are preprocessed using values relative to the ship's velocity. Second, the structure of the DFN is changed based on the characteristics of the input data. Third, the prediction accuracy is analyzed using a combination comprising five hyperparameters (number of hidden layers, number of hidden nodes, learning rate, dropout, and gradient optimizer). Finally, k-means clustering is performed to analyze the effect of the sea state and ship operational status by categorizing it into several models. The performances of various prediction models are compared and analyzed using the DFN in this study.