Vessel energy efficiency uncertainty optimization analysis in ice zone considering interval parameters. (15th July 2021)

• Record Type: Journal Article
• Authors: Hou, Yuanhang; Xiong, Yeping; Zhang, Yonglong; Liang, Xiao; Su, Linfang
• Is Part Of: Ocean engineering. Volume 232(2021)
• Publication Date: 2021-07-15
• Subjects: EEOI -- Vessel speed -- Ice area -- Interval optimization -- Uncertainty analysis; Ocean engineering -- Periodicals; Ocean engineering; Periodicals; 620.4162
• DOI: 10.1016/j.oceaneng.2021.109114
• Languages: English
• ISSNs: 0029-8018

Abstract

Abstract: Searching for the optimal engine speed to reduce the Energy Efficiency Operation Index (EEOI), which is an important design guide for vessels in operation, is a significant way to achieve intelligent navigation control, aiming for energy savings and emission reduction. Due to high uncertainty in the ice environment, optimization and analysis that consider uncertainty are necessary for the energy efficiency design of vessels sailing in this kind of zone. Vessels in ice areas are affected by the impact of ice loads and water velocity during navigation, which can be described by interval numbers for their simple meaning and clear boundary. This research concerns uncertainty of ice loads and water velocity in both single and multiple segments in the vessel speed optimization design process. Random statistics distribution models of the involved factors are then introduced into the interval optimization system, whose randomness is analysed. A series of cases is tested, using different interval radiuses and algorithms and other configuration parameters to analyse the influence of uncertainty. The results of optimization, with about 15% reduce of EEOI, and analysis can provide feasible guidance for vessel energy efficiency design, taking into account the uncertainty of the actual environment. Highlights: Vessel engine speed optimization design is conducted to find a minimum EEOI in ice zone. Uncertainty of ice loads and water velocity both in single- and multiple-segment are quantified by interval parameters. Random statistics distribution models of the involved factors are introduced into the optimization system. The uncertain influence of interval number and statistics is revealed by tests of series cases.