Digital Archives Initiative
Memorial University - Electronic Theses and Dissertations 3
menu off  add document to favorites : add page to favorites : reference url back to results : previous : next
 Search this object:
 0 hit(s) :: previous hit : next hit
  previous page : next page
Document Description
TitleA method for the analysis of the MDTF data using neural networks
AuthorIbrahim, Mohamed, 1972-
DescriptionThesis (M.Eng.)--Memorial University of Newfoundland, 2001. Engineering and Applied Science
Paginationxiii, 129 leaves : ill.
SubjectNeural networks (Computer science); Submarines (Ships)--Hydrodynamics--Simulation methods
Degree GrantorMemorial University of Newfoundland. Faculty of Engineering and Applied Science
DisciplineEngineering and Applied Science
NotesBibliography: leaves 94-96
AbstractNumerical simulation techniques are widely used to investigate the behavior of submarines during the design stage. The accuracy of these techniques depend upon the accurate determination of the hydrodynamic coefficients for the model. -- The Marine Dynamic Test Facility (MDTF) is a new-six-degree-of-freedom forced motion testing rig. The rig has the ability to test underwater vehicles in a manner that makes it possible to determine the hydrodynamic coefficients in the equations of motion. Multi-variant linear regression is used to obtain the hydrodynamic coefficients from the experimental data. -- In this study a neural network technique to identify the hydrodynamic model from experimental data is investigated. The technique uses the model trajectory (motion history) to predict the hydrodynamic coefficients of the model. A single MDTF generated maneuver was used to train the network. The trained network was then tested using different maneuvers and the network predictions were compared to the actual MDTF measured forces and moments. -- Results obtained from the neural network technique indicate that the technique can be used to predict the hydrodynamic model of underwater vehicles. The use of this technique can dramatically cut the running costs to conduct experiments on new models.
Resource TypeElectronic thesis or dissertation
FormatImage/jpeg; Application/pdf
SourcePaper copy kept in the Centre for Newfoundland Studies, Memorial University Libraries
Local Identifiera1522160
RightsThe 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.
CollectionElectronic Theses and Dissertations
Scanning StatusCompleted
PDF File(14.35 MB) --
CONTENTdm file name209144.cpd