The development of intelligent hull forms of large ships for energy efficient transportation

Student thesis: Doctoral Thesis

Abstract

It is not usual that the interests of ship owners are aligned with maritime legislation requirements with the incentives to make ships more efficient. Owners seek to reduce the ship operating costs whilst entities, such as the IMO, push for a more environment friendly marine industry.A ship's efficiency can be improved by optimising the hull form and/or by installing Energy Saving Devices (ESD) in order to improve the hydrodynamic performance and fluid flow of a vessel. These two areas are not new to the industry and have been investigated using various methods, including computational fluid dynamics (CFD) procedures. The use of CFD for ship performance analyses is becoming more popular in the maritime industry due to its cost-effective capabilities. The continuous development of numerical simulation (CFD) as well as high performance computing opens doors to new areas of investigation and allows research to further study topics that have been previously looked into as well as research questions that have never been explored.The general aim of this PhD thesis is to contribute to the body of knowledge by shedding light on improving the hydrodynamic performance of large ships for energy efficient transportation. This was achieved by accomplishing a series of objectives and case studies that addressed various research questions.This study initially proposes an intelligent state-of-the-art methodology for predicting and enhancing the hull-propeller interaction of a vessel using numerical techniques (CFD), optimisation procedures and high-performance computing to identify optimal stern designs as well as understanding the physics and impact of Energy Saving Device/s that could help improve the stern flow characteristics.The CFD method, that was used to predict the performance for all the various case studies, was validated and verified using well established procedures. The implementation of the recently available curvature correction feature in the numerical model and its enhanced wake prediction capabilities were clearly demonstrated.Moreover, various types of post-processing CFD analyses that were deemed useful to understand the hydrodynamic performance of a vessel were listed and outlined.A practical full-scale stern form optimisation procedure for a bulk carrier was developed and demonstrated. Furthermore, whether the quality of a nominal wake can provide any insight into the propeller performance of a vessel was investigated and evaluated.With regards to the case studies investigating Energy Saving Devices, Propeller Boss Cap Fins were analysed in open water full-scale conditions to understand the physics, function and working principles of such technologies. A state-of-the-art full-scale PBCF optimisation procedure was also demonstrated. The performance of a Wake Equalising Duct at different scale conditions was investigated and compared. The study indicated that the duct is not as effective in full-scale. The reasons and findings were thoroughly discussed and outlined.The thesis also focused on the research questions regarding the installation of multiple Energy Saving Devices on a ship system. Whether the benefits are directly cumulative or whether the ESDs affect the flow regimes of one another? Thus, a case study was carried out by investigating seven combinations using PBCF, a duct and stator fins. The performance of each condition was clearly outlined, discussed and explained.This author believes that this study has exhibited and proven the ability and applicability of computational fluid dynamics (CFD) to better understand the hydrodynamics of a ship system and improve hull-propeller interaction dynamics. The studies and research in this thesis contribute to the industry as well as academia by shedding more light on large ship hydrodynamic systems.
Date of Award31 Jul 2020
Original languageEnglish
Awarding Institution
  • University Of Strathclyde
SponsorsUniversity of Strathclyde
SupervisorOsman Turan (Supervisor) & Panagiotis Kaklis (Supervisor)

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