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Springer Power Prediction Modeling of Conventional High-Speed Craft

Amazon

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Description

The proposed book addresses various power prediction methods, a principal design objective for high-speed craft of displacement, semi-displacement, and planing type. At the core of the power prediction methods are mathematical models based on experimental data derived from various high-speed hull and propeller series. Regression analysis and Artificial Neural Network (ANN) methods are used as  extraction tools for this kind of models. The most significant factors for in-service power prediction are bare hull resistance, dynamic trim, and the propeller’s open-water efficiency. Therefore, mathematical modeling of these factors is a specific focus of the book. Furthermore, the book includes a summary of most of the power-prediction-relevant literature published in the last 50 years, and as such is intended as a reference overview of the best high-speed craft modeling practices. Once these mathematical models have been developed and validated, they can be readily programmed into software tools, thereby enabling the parametric analyses required for the optimization of a high-speed craft design. The proposed book is intended primarily for naval architects who design and develop various types of high-speed vessels (yachts, boats etc.), as well as for students who are interested in the design of fast vessels. The book includes useful Excel Macro Codes for the outlined mathematical models. Moreover, software for all considered models is provided.  From the Back Cover The proposed book addresses various power prediction methods, a principal design objective for high-speed craft of displacement, semi-displacement, and planing type. At the core of the power prediction methods are mathematical models based on experimental data derived from various high-speed hull and propeller series. Regression analysis and Artificial Neural Network (ANN) methods are used as  extraction tools for this kind of models. The most significant factors for in-service power prediction are bare hull resistance, dynamic trim, and the propeller’s open-water efficiency. Therefore, mathematical modeling of these factors is a specific focus of the book. Furthermore, the book includes a summary of most of the power-prediction-relevant literature published in the last 50 years, and as such is intended as a reference overview of the best high-speed craft modeling practices. Once these mathematical models have been developed and validated, they can be readily programmed into software tools, thereby enabling the parametric analyses required for the optimization of a high-speed craft design. The proposed book is intended primarily for naval architects who design and develop various types of high-speed vessels (yachts, boats etc.), as well as for students who are interested in the design of fast vessels. The book includes useful Excel Macro Codes for the outlined mathematical models. Moreover, software for all considered models is provided. About the Author Professor Dejan Radojčić is a Naval Architect with 40 years of academic and industrial experience, covering research focused on ship design and hydrodynamics. Academic experience (1979-2014) rising from teaching assistant to full professor and Head of the Naval Architecture department at the Faculty of Mechanical Engineering, University of Belgrade. Participated in a series of international (EU) and domestic projects on ship design, ship hydrodynamics, shallow draught vessels, optimization of propulsion systems etc., frequently focused on special high-speed craft of displacement, semi-displacement or planing type. Collaborated with leading European universities and research institutions. Published a number of often cited scientific papers, including some on the hydrodynamics of high speed craft currently implemented in commercial CAD software. As a member of a research team, received in 2015 SNAME’s The Vice Admiral E.L.Cochrane Award for best peer-reviewed paper pu

Product Specifications

Format
paperback
Domain
Amazon UK
Release Date
08 November 2020
Listed Since
13 October 2020

Barcode

No barcode data available

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