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Towards Data-Driven Models in the Prediction of Ship Performance (Speed—Power) in Actual Seas: A Comparative Study between Modern Approaches

Author

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  • Kiriakos Alexiou

    (Department of Industrial Design and Production Engineering, University of West Attica, 12243 Athens, Greece)

  • Efthimios G. Pariotis

    (Naval Architecture and Marine Engineering Section, Hellenic Naval Academy, 18539 Piraeus, Greece)

  • Helen C. Leligou

    (Department of Industrial Design and Production Engineering, University of West Attica, 12243 Athens, Greece)

  • Theodoros C. Zannis

    (Naval Architecture and Marine Engineering Section, Hellenic Naval Academy, 18539 Piraeus, Greece)

Abstract

In the extremely competitive environment of shipping, minimizing shipping cost is the key factor for the survival and growth of shipping companies. However, stricter rules and regulations that aim at the reduction of greenhouse gas emissions published by the International Maritime Organization, force shipping companies to increase the operational efficiency of their fleet. The prediction of a ship speed in actual seas with a given power by its engine is the most important performance indicator and thus makes it the “holy grail” in pursuing better efficiency. Traditionally, tank model tests and semi-empirical formulas were the preferred solution for the aforementioned prediction and are still widely applied. However, currently, with the increased computational power that is widely available, novel and more sophisticated methods taking into consideration computational fluid dynamics (CFD) and machine learning (ML) algorithms are emerging. In this paper, we briefly present the different approaches in the prediction of a ship’s speed but focus on ML methods comparing a representative number of the latest data-driven models used in papers, to provide guidelines, discover trends and identify the challenges to be faced by researchers. From this comparison, we can distinguish that artificial neural networks (ANN), being used in 73.3% of the reviewed papers, dominate as the algorithm of choice. Researchers mostly rely on physical laws governing the phenomena in the crucial part of data preprocessing tasks. Lastly, most researchers rely on data acquisition systems installed at ships in order to achieve usable results.

Suggested Citation

  • Kiriakos Alexiou & Efthimios G. Pariotis & Helen C. Leligou & Theodoros C. Zannis, 2022. "Towards Data-Driven Models in the Prediction of Ship Performance (Speed—Power) in Actual Seas: A Comparative Study between Modern Approaches," Energies, MDPI, vol. 15(16), pages 1-18, August.
  • Handle: RePEc:gam:jeners:v:15:y:2022:i:16:p:6094-:d:894818
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    References listed on IDEAS

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    1. Yan, Ran & Wang, Shuaian & Du, Yuquan, 2020. "Development of a two-stage ship fuel consumption prediction and reduction model for a dry bulk ship," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 138(C).
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