Abstract
Energy efficiency measures for cargo ships can be categorized into design and operation based measures. The majority of design measures are based on hull form optimization, propulsion system selection and speed optimization. Operational measures are mainly based on selecting the optimized speed, trim, propeller pitch and rpm for energy efficiency. These measures should be applied onboard according to the loading conditions and weather and sea state conditions, such as wave height and direction, and wind speed and direction. The variety of alternatives requires an online decision support system, as the alternatives cannot be placed in a simple guideline or booklet. Such decision support systems are mainly based on the system identification approach to data obtained during voyages in various conditions. Although these systems can be mounted into any ship connected to several online monitoring sensors, they do not generally contain sufficient ship specific knowledge; hence their performance is based on the ship conditions generated during the learning phase of the decision support system.
A new decision support system is proposed based on the calculation of viscous resistance by RANS CFD, wave resistance by BEM/RANS CFD, propulsion efficiency by RANS CFD, resistance increase in waves by Strip Theory, wind resistance by RANS CFD, and resistance increase due to rudder actions by RANS CFD. All these parameters are calculated before the installation of the decision support system and correlated during the sea trial voyages with actual voyage conditions. Ship trim, draft, propeller pitch and rpm are optimized for energy efficiency in different weather conditions. A pilot application is applied into a Ro-Ro ship and sea trials are conducted for validation.
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Insel, M., Gokcay, S., Saydam, A.Z. (2018). A Decision Support System for Energy Efficient Ship Propulsion. In: Ölçer, A., Kitada, M., Dalaklis, D., Ballini, F. (eds) Trends and Challenges in Maritime Energy Management. WMU Studies in Maritime Affairs, vol 6. Springer, Cham. https://doi.org/10.1007/978-3-319-74576-3_11
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DOI: https://doi.org/10.1007/978-3-319-74576-3_11
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