Operational options for green ships

Sherbaz, Salma; Duan, Wenyang

doi:10.1007/s11804-012-1141-2

Cited by 17 publications

(7 citation statements)

References 13 publications

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“…Energy efficiency, and thereby the reduction of emissions per unit of transport work, can be achieved via a number of operational and technical improvements (Faber et al, 2010;Kontovas & Psaraftis, 2011a;Notteboom, 2006;Psaraftis & Kontovas, 2013;Sherbaz & Duan, 2012); slow-steaming is among the most effective and easiest to implement (Corbett et al, 2009;Cariou, 2011;Faber et al, 2012;Lindstad et al, 2011;Meyer et al, 2012;Psaraftis & Kontovas, 2010;Tai & Lin, 2013), and it often comes with a negative CO 2 emission abatement cost. Due to the non-linear correlation between speed and fuel consumption (i.e., the so-called admiralty formula, which estimates a cube function, meaning a 10% speed reduction yields a 27% fuel consumption reduction), even a marginal speed reduction will result in a substantial fuel consumption reduction (Wang & Meng, 2012).…”

Section: Slow-steaming and Its Effects On Logisticsmentioning

confidence: 99%

Swedish shippers’ strategies for coping with slow-steaming in deep sea container shipping

et al. 2018

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When container shipping lines experience over-capacity and high fuel costs, they typically respond by decreasing sailing speeds and, consequently, increasing transport time. Most of the literature on this phenomenon, often referred to as slowsteaming, takes the perspective of the shipping lines addressing technical, operational and financial effects, or a society perspective focusing on lower emissions and energy use. Few studies investigate the effects on the demand side of the market for container liner shipping. Hence, the aim of this study is to elaborate on the logistics consequences of slow-steaming, particularly the strategies that Swedish shippers purchasing deep sea container transport services employ to mitigate the effects of slow-steaming. Workshops and semi-structured interviews revealed that shippers felt they had little or no impact on sailing schedules and were more or less subject to container shipping lines' decisions. The effects of slowsteaming were obviously most severe for firms with complex supply chains, where intermediate products are sent back and forth between production stages on different continents. The shippers developed a set of strategies to cope with the low punctuality of containerised shipping, and these were categorised in the domains of transfer-the-problem, transport, sourcing and distribution, logistics and manufacturing, and product design. All firms applied changes in the transport domain, although the lack of service segmentation limited the effects of the strategy. Most measures were applied by two firms, whereas only one firm changed the product design.

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Section: Slow-steaming and Its Effects On Logisticsmentioning

confidence: 99%

Swedish shippers’ strategies for coping with slow-steaming in deep sea container shipping

et al. 2018

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“…After the ship is put into use, its performance will decrease as the service time increases. Hull fouling is an important factor leading to the increase of resistance and fuel consumption [7]. It is reported that the fouling of ship hull can increase the resistance of the ship by about 25%-50% [8].…”

Section: Introductionmentioning

confidence: 99%

“…e prediction and treatment of hull fouling are essential for fuel saving. erefore, proper and timely cleaning can significantly reduce fuel consumption and improve ship efficiency [6][7][8][9]. Ship energy efficiency monitoring can support data analysis, identify abnormal states of ship energy efficiency, and guide crew operation.…”

Section: Introductionmentioning

confidence: 99%

A Novel Big Data Collection System for Ship Energy Efficiency Monitoring and Analysis Based on BeiDou System

Zeng

Chen

2021

Journal of Advanced Transportation

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The call for green shipping is increasing, and the reduction of greenhouse gas emissions from ships becomes more and more important. Traditional ship energy efficiency monitoring is based on the noon reports, which are susceptible to human error and have a time delay. Many ship energy efficiency monitoring systems have been designed and developed, but they usually cannot send data to the shore in time. In order to identify abnormal fuel consumption in time, this paper realizes a big data collection system for ship energy efficiency monitoring based on the BeiDou System. The system installed on two sister container ships has already collected a lot of data. Big data analysis methods, such as principal component analysis (PCA) and correlation analysis, are applied in the system to realize data visualization and analysis. Using PCA, it turns out that the shaft power of the main engine is related to a certain ship speed, which is also affected by load and weather conditions, and is the biggest factor in determining fuel consumption. To realize the assessment of hull fouling and the optimization of ship trim, a useful physics-based analysis is proposed. The analysis shows that the fouling of ship body greatly increases its resistance. Our analysis method can also find the best trim under specific loading condition. All these points are important for reducing fuel consumption and improving ship efficiency.

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“…For ships with large transom sterns and bulbous bows, the power requirements for the best and worst trim may differ by more than 10% [5]. Model tests or CFD simulations have been used to assess the best trim and the effect of different trim conditions [6,7]. However, model tests are too costly and further efforts are required to improve the reliability and effectiveness of CFD simulations.…”

Section: Introductionmentioning

confidence: 99%

Trim Optimization of Ship by a Potential-Based Panel Method

Sun

et al. 2013

Advances in Mechanical Engineering

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A surface panel method for a boundary-value problem with the free surface is proposed to predict ship wave resistance under different trim conditions based on a so-called double-model solution. The free surface boundary condition is linearized with respect to the oncoming flow and computed by a four-point finite difference scheme. Sample computation for Wigley hull is carried out to demonstrate the effectiveness and the robustness of the method. A hull model is taken into account at two different displacements with respect to trim conditions of lower wave resistance. It is demonstrated by calculation and experiment that the wave resistance under the trim conditions provided by the proposed method is lower than that under the initial conditions.

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Operational options for green ships

Cited by 17 publications

References 13 publications

Swedish shippers’ strategies for coping with slow-steaming in deep sea container shipping

Swedish shippers’ strategies for coping with slow-steaming in deep sea container shipping

A Novel Big Data Collection System for Ship Energy Efficiency Monitoring and Analysis Based on BeiDou System

Trim Optimization of Ship by a Potential-Based Panel Method

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