Performance Assessment of Ship Energy Efficiency

A special issue of Journal of Marine Science and Engineering (ISSN 2077-1312). This special issue belongs to the section "Ocean Engineering".

Deadline for manuscript submissions: closed (15 October 2021) | Viewed by 13930

Special Issue Editor


E-Mail Website
Guest Editor
School of Naval Architecture and Marine Engineering, National Technical University of Athens, Athens, Greece
Interests: naval engineering; ship performance assessment; data analysis; energy efficiency
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue focuses on innovative tools and methods for the assessment and improvement of ship energy efficiency. There is an increasing interest in this field, which has been amplified by the available technologies forcost effectively obtaining large amounts of operational data, the advances in big data analytics, and specifically those related with the predictive and prescriptive analytics based on data-driven knowledge of the underlying physical processes. 

On the other hand, there is a need in the maritime industry for both standardized frameworks for the evaluation of technological interventions (such as the ISO 19030 for the monitoring of changes in hull and propeller performance) and for efficient tools for real-time situation awareness. Furthermore, the IMO’s framework for the reduction of GHG emissions encourages designs that enhance ship efficiency, while operational measures can be adopted as possible short-term measures.

Researchers from both academia and the industry are invited to submit original papers that advance the state of the art of this field. The studies shall cover either design and/or operational aspects related with ship energy performance monitoring, assessment, and optimization. 

Prof. Dr. Nikos Themelis
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Marine Science and Engineering is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • ship operational efficiency
  • performance monitoring, assessment and optimization
  • design solutions for energy efficient ships (hull design, power systems, and propulsion efficiency devices)
  • big data analytics
  • machine learning models
  • statistical inference
  • operational measures (e.g., weather routing, speed and trim optimization)

Published Papers (3 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

16 pages, 4406 KiB  
Article
Effects of a Bulbous Bow Shape on Added Resistance Acting on the Hull of a Ship in Regular Head Wave
by Trung-Kien Le, Ngo Van He, Ngo Van Hien and Ngoc-Tam Bui
J. Mar. Sci. Eng. 2021, 9(6), 559; https://doi.org/10.3390/jmse9060559 - 21 May 2021
Cited by 7 | Viewed by 3299
Abstract
In this study, the effect of bow shape on resistance acting on a hull in regular head waves was investigated by applying a commercial Computational Fluid Dynamics (CFD) code. For this purpose, the hydrodynamic performance as well as the resistance of ships with [...] Read more.
In this study, the effect of bow shape on resistance acting on a hull in regular head waves was investigated by applying a commercial Computational Fluid Dynamics (CFD) code. For this purpose, the hydrodynamic performance as well as the resistance of ships with blunt and bulbous bows were simulated. By analyzing the obtained CFD simulation results, the effects of the bow shape on the hydrodynamic performance and resistance of the ships were found. A new bulbous bow shape with drastically reduced added resistance acting on the hull in waves is proposed. Finally, the obtained CFD results for the hydrodynamic performance of ships are presented. Full article
(This article belongs to the Special Issue Performance Assessment of Ship Energy Efficiency)
Show Figures

Figure 1

19 pages, 7052 KiB  
Article
State-of-the-Art Methods to Improve Energy Efficiency of Ships
by Johannes Hüffmeier and Mathias Johanson
J. Mar. Sci. Eng. 2021, 9(4), 447; https://doi.org/10.3390/jmse9040447 - 20 Apr 2021
Cited by 17 | Viewed by 5123
Abstract
Generating energy efficiency through behavioural change requires not only understanding and empathy with user interests and needs but also the fostering of energy saving awareness, a technique and framework that supports operators and ship owners. There is strong potential to make use of [...] Read more.
Generating energy efficiency through behavioural change requires not only understanding and empathy with user interests and needs but also the fostering of energy saving awareness, a technique and framework that supports operators and ship owners. There is strong potential to make use of different technical solutions to increase energy efficiency, but many cost-efficient solutions relate to carrot-and-stick incentives for operators to minimise energy consumption. These incentives range from voyage planning with weather routing eco-driving bonus, to torque limitations and changes in company policies, all of which demonstrate that the operators’ on-board importance for the energy consumption has been identified. Data collection will allow operators to make better decisions in the lifecycle of the ship from knowledge-driven design to operation, redesign and lifetime extension. Various systems are available for data acquisition, storage and analysis, some of which are delivered by well-known marine suppliers while others are stand-alone systems. The lack of standardization for data capture, transmission and analysis is a challenge, so systematic improvement is required in shipping companies to achieve energy savings. When these are achieved, they will be the result of customer requirements, cost pressure or individual driving forces in the companies. The potential energy savings, brought up in interviews, shows up to 35% on specific routes and up to 60% in specific maneuvers. These savings will be made feasible by operators and crews being involved in the decision-making process. Full article
(This article belongs to the Special Issue Performance Assessment of Ship Energy Efficiency)
Show Figures

Figure 1

26 pages, 12532 KiB  
Article
Evaluation of Different Deep-Learning Models for the Prediction of a Ship’s Propulsion Power
by Panayiotis Theodoropoulos, Christos C. Spandonidis, Nikos Themelis, Christos Giordamlis and Spilios Fassois
J. Mar. Sci. Eng. 2021, 9(2), 116; https://doi.org/10.3390/jmse9020116 - 24 Jan 2021
Cited by 25 | Viewed by 3925
Abstract
Adverse conditions within specific offshore environments magnify the challenges faced by a vessel’s energy-efficiency optimization in the Industry 4.0 era. As the data rate and volume increase, the analysis of big data using analytical techniques might not be efficient, or might even be [...] Read more.
Adverse conditions within specific offshore environments magnify the challenges faced by a vessel’s energy-efficiency optimization in the Industry 4.0 era. As the data rate and volume increase, the analysis of big data using analytical techniques might not be efficient, or might even be infeasible in some cases. The purpose of this study is the development of deep-learning models that can be utilized to predict the propulsion power of a vessel. Two models are discriminated: (1) a feed-forward neural network (FFNN) and (2) a recurrent neural network (RNN). Predictions provided by these models were compared with values measured onboard. Comparisons between the two types of networks were also performed. Emphasis was placed on the different data pre-processing phases, as well as on the optimal configuration decision process for each of the developed deep-learning models. Factors and parameters that played a significant role in the outcome, such as the number of layers in the neural network, were also evaluated. Full article
(This article belongs to the Special Issue Performance Assessment of Ship Energy Efficiency)
Show Figures

Figure 1

Back to TopTop