Abstract
Traditionally seagoing vessels has been designed and optimized to operate at their boundary speeds based on hydrodynamic considerations. For any given hull form, the boundary speed area can be defined as the speed range where the resistance coefficient goes from nearly a constant, to rise rapidly and increases fuel consumption significantly. For a typical Aframax tanker, the boundary speed is around 15 knots, with a gradual increase in the resistance coefficient from 12 knots upwards. With higher fuel prices and more public focus on maritime transports emission and its contribution to climate change and local pollution, reducing fuel consumption has become a priority for shipping lines.
The three main responses for reducing fuel consumption are slower speeds, larger vessels and more hydrodynamic hull forms, i.e. more slender vessels. While larger vessels and more hydrodynamic hull forms requires new-buildings, speed reductions and optimization is applicable for the entire
eet. The key insight is that the power required for propulsion is a function of the speed to the power of three for lower speeds, four for an incremental speed increase when operating at boundary speed, and which gradually increases against infinity for higher speeds. This implies that when a ship reduces its speed, the speed consumption per freight work unit is reduced.
(Corbett et al., 2009; Seas at Risk and CE Delft, 2010; Psaraftis and Kontovas, 2010, Lindstad et al, 2011; Jonkeren et al 2012). However, the reduction in speed from a booming shipping market in 2007 to a 2012 market with overcapacity in major shipping segments and peak fuel prices was less than 10 %, i.e. from 12 to 11 knots (Smith et al. 2014). One explanation is the cargo inventory cost, which implies that with lower speeds the voyage length increases, and hence the cost for the owners of the cargo. Moreover for crude oil transport, Lindstad and Eskeland (2015) findings indicates that the additional cargo inventory cost is of the same magnitude as the savings which can be made by reducing speed. Speed decisions is also influenced by the sea conditions and the freight market (Lindstad et al. 2013) and the potential demurrage income from arriving early when there is a backlog of vessels waiting to be served. There is hence a need for a more holistic approach to optimize speed decisions for crude oil tankers. For these reasons the present study has focused on developing a model for Speed Optimization as a function of Cargo Inventory Cost, Demurrage, Freight Market and Real Sea Conditions.
The three main responses for reducing fuel consumption are slower speeds, larger vessels and more hydrodynamic hull forms, i.e. more slender vessels. While larger vessels and more hydrodynamic hull forms requires new-buildings, speed reductions and optimization is applicable for the entire
eet. The key insight is that the power required for propulsion is a function of the speed to the power of three for lower speeds, four for an incremental speed increase when operating at boundary speed, and which gradually increases against infinity for higher speeds. This implies that when a ship reduces its speed, the speed consumption per freight work unit is reduced.
(Corbett et al., 2009; Seas at Risk and CE Delft, 2010; Psaraftis and Kontovas, 2010, Lindstad et al, 2011; Jonkeren et al 2012). However, the reduction in speed from a booming shipping market in 2007 to a 2012 market with overcapacity in major shipping segments and peak fuel prices was less than 10 %, i.e. from 12 to 11 knots (Smith et al. 2014). One explanation is the cargo inventory cost, which implies that with lower speeds the voyage length increases, and hence the cost for the owners of the cargo. Moreover for crude oil transport, Lindstad and Eskeland (2015) findings indicates that the additional cargo inventory cost is of the same magnitude as the savings which can be made by reducing speed. Speed decisions is also influenced by the sea conditions and the freight market (Lindstad et al. 2013) and the potential demurrage income from arriving early when there is a backlog of vessels waiting to be served. There is hence a need for a more holistic approach to optimize speed decisions for crude oil tankers. For these reasons the present study has focused on developing a model for Speed Optimization as a function of Cargo Inventory Cost, Demurrage, Freight Market and Real Sea Conditions.
