Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.489750
Title: The development of the first Deep-V catamaran (DVC) systematic series
Author: Mantouvalos, Antonis
ISNI:       0000 0001 3618 5324
Awarding Body: Newcastle University
Current Institution: University of Newcastle upon Tyne
Date of Award: 2008
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Abstract:
It is a well-known fact that relatively large and fast mono-hull vessels with Deep-V hullforms have been recently employed for fast ferry and naval applications in order to improve their transport eHiciency and performance with a specific emphasis on their seakeeping characteristics. It is also a well-known fact that catamarans need more attention to improved seakeeping behaviour in rough seas more than monohulls, due to their inherently poor motion characteristics. Based on the above rationale and on the numerous earlier undergraduate research projects carried out in the School of Marine Science and Technology, this research project involves the development of the Ist systematic Deep-V Catamaran (DYC) series and investigates the resistance and seakeeping characteristics using numerical and experimental techniques. This thesis, therefore, presents the research carried out in this project describing how these series has been developed in similar way to the well-known round bilge NPL series. The main naval architectural features of the series members are described along with quantifiable parameters and design guidelines for the antiSlamming bow, the hard chine and the transom stern. Calm water resistance data have been calculated using CFD techniques and have been validated with experimental studies. The experiments helped into validating the CFD on the one hand and systematically evaluate the hullform performance on the other allowing the observation of the resistance performance and wave formation in the tunnel region and the transom. Motion responses and acceleration responses have also been calculated using CFD methods. All the performance characteristics along with the varying parameters of the series have been regressed using Artificial Neural Networks (ANN) to provide an early performance prediction algorithm in the initial design stages of Deep-V catamarans. The regression equations have been used for the estimation of main hull particulars of the optimum catamaran vessel by performing multi-objective optimisation technique.
Supervisor: Not available Sponsor: Not available
Qualification Name: Newcastle University, 2008 Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.489750  DOI: Not available
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