Using machine learning to understand the behavior of added resistance of a ship in waves

Ships experience resistances of various origins. The influence of the added resistance of a ship in waves is significant enough not to be ignored. The analysis drawn to attention identifies the different relations between the added resistance and the various factors of contribution factors, direct a...

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Main Author: Ong, Rayner Junjie
Other Authors: Liu Shukui
Format: Final Year Project
Language:English
Published: Nanyang Technological University 2020
Subjects:
Online Access:https://hdl.handle.net/10356/141813
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1418132023-03-04T19:40:43Z Using machine learning to understand the behavior of added resistance of a ship in waves Ong, Rayner Junjie Liu Shukui School of Mechanical and Aerospace Engineering raynerong11@hotmail.com Engineering::Mechanical engineering Ships experience resistances of various origins. The influence of the added resistance of a ship in waves is significant enough not to be ignored. The analysis drawn to attention identifies the different relations between the added resistance and the various factors of contribution factors, direct and indirect. These factors range from the ship profile to wave character. This report analyses the waterline of the vessel at a given angle of wave. At given angles, part of the waterline will only be exposed. Therefore, this report seeks to identify and enhance on the current diffraction calculation method. This report will also attempt to work on relationship between the diffraction added resistance and wave spectra theories employed by different theories. Experimental data collected are processed using machine learning tools to aid the in-depth analysis of the added resistance. Bachelor of Engineering (Mechanical Engineering) 2020-06-11T01:42:46Z 2020-06-11T01:42:46Z 2020 Final Year Project (FYP) https://hdl.handle.net/10356/141813 en MA4079 application/pdf Nanyang Technological University
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering::Mechanical engineering
spellingShingle Engineering::Mechanical engineering
Ong, Rayner Junjie
Using machine learning to understand the behavior of added resistance of a ship in waves
description Ships experience resistances of various origins. The influence of the added resistance of a ship in waves is significant enough not to be ignored. The analysis drawn to attention identifies the different relations between the added resistance and the various factors of contribution factors, direct and indirect. These factors range from the ship profile to wave character. This report analyses the waterline of the vessel at a given angle of wave. At given angles, part of the waterline will only be exposed. Therefore, this report seeks to identify and enhance on the current diffraction calculation method. This report will also attempt to work on relationship between the diffraction added resistance and wave spectra theories employed by different theories. Experimental data collected are processed using machine learning tools to aid the in-depth analysis of the added resistance.
author2 Liu Shukui
author_facet Liu Shukui
Ong, Rayner Junjie
format Final Year Project
author Ong, Rayner Junjie
author_sort Ong, Rayner Junjie
title Using machine learning to understand the behavior of added resistance of a ship in waves
title_short Using machine learning to understand the behavior of added resistance of a ship in waves
title_full Using machine learning to understand the behavior of added resistance of a ship in waves
title_fullStr Using machine learning to understand the behavior of added resistance of a ship in waves
title_full_unstemmed Using machine learning to understand the behavior of added resistance of a ship in waves
title_sort using machine learning to understand the behavior of added resistance of a ship in waves
publisher Nanyang Technological University
publishDate 2020
url https://hdl.handle.net/10356/141813
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