• Journal of the Society of Naval Architects of Korea
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• v.52 no.2
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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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• v.12 no.1
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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.

• Journal of the Korean Society of Marine Environment & Safety
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• v.20 no.6
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• pp.653-662
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• 2014

In this study, Mokpo national maritime university Training ship Centralizes Air Conditioning System was upgraded by installing onboard an Air-cooled Air conditioner. This resulted in the improvement of the performance and operation. This study compared refrigeration performance to former equipment and improving one. And through the actual measurement study about the cabin thermal environment, it will be used as basic data for marine air conditioning design and plan in the future. At same climate condition, when the Centralized Air Conditioning System and an improved air conditioning system operated, cabin temperature was at $24{\sim}28^{\circ}C$, humidity was 55~75 % as comfortable condition, Generator load measurement showed a saving of 48KW in the average load and 8 % in the full load factor. This also resulted in a saving of daily fuel oil consumption(FOC) at around 222 [${\ell}/day$] average. On the other hand, one cadet cabin(Cadet No.21) indicated a higher temperature due to heat transmission of engine room. It found us not to consider installing additional diffuser to reduce the heat transmission.

• Journal of the Society of Naval Architects of Korea
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• v.49 no.6
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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.

• Journal of Navigation and Port Research
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• v.40 no.4
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• pp.165-171
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• 2016

SEEMP (Ship Energy Efficiency Management Plan) has entered into force since 2013 for the reduction of GHG emission of operating ships. SEEMP guidelines include the hardware modification or installation of energy-saving device on ship. It also includes software based energy-saving technology such as optimum routing, speed optimization, etc. Hardware based technologies are not easy to apply to ongoing vessel due to the operational restriction. Therefore, IT based energy-saving technology was applied and its energy efficiency was evaluated using before and after energy-saving system applied voyage data. SEEMP advises a voluntary participation of EEOI (Ship Energy Efficiency Operation Indicator) use as an indicator of ship energy efficiency operation, and those results were also shown to evaluate the improvement efficiency of energy-saving system.

• International Journal of Naval Architecture and Ocean Engineering
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• v.13 no.1
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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.