Identification of ship coupled heave and pitch motions using neural networks
Thesis (M. Eng.) --Memorial University of Newfoundland, 1997. Engineering and Applied Science Bibliography: leaf 69 Investigation of the ship motion behavior in irregular sea states is an important step for ship seakeeping performance research. Ship motion identification from the full scale measurem...
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ftmemorialunivdc:oai:collections.mun.ca:theses4/177901 2023-05-15T17:23:34+02:00 Identification of ship coupled heave and pitch motions using neural networks Xu, Jinsong Memorial University of Newfoundland. Faculty of Engineering and Applied Science 1997 98 leaves : graphs Image/jpeg; Application/pdf http://collections.mun.ca/cdm/ref/collection/theses4/id/177901 Eng eng Electronic Theses and Dissertations (12.28 MB) -- http://collections.mun.ca/PDFs/theses/Xu_Jinsong2.pdf http://collections.mun.ca/cdm/ref/collection/theses4/id/177901 The author retains copyright ownership and moral rights in this thesis. Neither the thesis nor substantial extracts from it may be printed or otherwise reproduced without the author's permission. Paper copy kept in the Centre for Newfoundland Studies, Memorial University Libraries Ships--Seakeeping Stability of ships Neural networks (Computer science) Text 1997 ftmemorialunivdc 2015-08-06T19:22:48Z Thesis (M. Eng.) --Memorial University of Newfoundland, 1997. Engineering and Applied Science Bibliography: leaf 69 Investigation of the ship motion behavior in irregular sea states is an important step for ship seakeeping performance research. Ship motion identification from the full scale measurements is the only way to study the actual motion behavior and verify the motion predictions after ship constructions. A particular identification method for coupled heave and pitch motions was developed and validated in this research. The two-degree Random Decrement technique and the Neural Networks technique were combined in identification process. -- This developed method was applied to several motion systems to test its effects. The random motion data were obtained from the ship model experiments and numerical simulations. The coupled heave and pitch Random Decrement signatures obtained from the random motion histories were used as the Neural Networks training data to identify the Random Decrement equations. The identification results were verified by comparing the predictions with the actual Random Decrement signatures, and with the free response signatures. -- The application results suggested that the validation of the identified equations was mainly dependent on the nature of the Random Decrement signatures and the quality of the Neural Networks training. Only White Noise or broad-band spectrum excitations could yield the required agreement between identified Random Decrement equations and motion free response equations. Text Newfoundland studies University of Newfoundland Memorial University of Newfoundland: Digital Archives Initiative (DAI) |
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Memorial University of Newfoundland: Digital Archives Initiative (DAI) |
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English |
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Ships--Seakeeping Stability of ships Neural networks (Computer science) |
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Ships--Seakeeping Stability of ships Neural networks (Computer science) Xu, Jinsong Identification of ship coupled heave and pitch motions using neural networks |
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Ships--Seakeeping Stability of ships Neural networks (Computer science) |
description |
Thesis (M. Eng.) --Memorial University of Newfoundland, 1997. Engineering and Applied Science Bibliography: leaf 69 Investigation of the ship motion behavior in irregular sea states is an important step for ship seakeeping performance research. Ship motion identification from the full scale measurements is the only way to study the actual motion behavior and verify the motion predictions after ship constructions. A particular identification method for coupled heave and pitch motions was developed and validated in this research. The two-degree Random Decrement technique and the Neural Networks technique were combined in identification process. -- This developed method was applied to several motion systems to test its effects. The random motion data were obtained from the ship model experiments and numerical simulations. The coupled heave and pitch Random Decrement signatures obtained from the random motion histories were used as the Neural Networks training data to identify the Random Decrement equations. The identification results were verified by comparing the predictions with the actual Random Decrement signatures, and with the free response signatures. -- The application results suggested that the validation of the identified equations was mainly dependent on the nature of the Random Decrement signatures and the quality of the Neural Networks training. Only White Noise or broad-band spectrum excitations could yield the required agreement between identified Random Decrement equations and motion free response equations. |
author2 |
Memorial University of Newfoundland. Faculty of Engineering and Applied Science |
format |
Text |
author |
Xu, Jinsong |
author_facet |
Xu, Jinsong |
author_sort |
Xu, Jinsong |
title |
Identification of ship coupled heave and pitch motions using neural networks |
title_short |
Identification of ship coupled heave and pitch motions using neural networks |
title_full |
Identification of ship coupled heave and pitch motions using neural networks |
title_fullStr |
Identification of ship coupled heave and pitch motions using neural networks |
title_full_unstemmed |
Identification of ship coupled heave and pitch motions using neural networks |
title_sort |
identification of ship coupled heave and pitch motions using neural networks |
publishDate |
1997 |
url |
http://collections.mun.ca/cdm/ref/collection/theses4/id/177901 |
genre |
Newfoundland studies University of Newfoundland |
genre_facet |
Newfoundland studies University of Newfoundland |
op_source |
Paper copy kept in the Centre for Newfoundland Studies, Memorial University Libraries |
op_relation |
Electronic Theses and Dissertations (12.28 MB) -- http://collections.mun.ca/PDFs/theses/Xu_Jinsong2.pdf http://collections.mun.ca/cdm/ref/collection/theses4/id/177901 |
op_rights |
The author retains copyright ownership and moral rights in this thesis. Neither the thesis nor substantial extracts from it may be printed or otherwise reproduced without the author's permission. |
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1766113358079066112 |