All Words
Exact Phrase
Title Search Only
advanced search
Digital Archives Initiative
Memorial University - Electronic Theses and Dissertations 3
Anthropology
Aquaculture
Archaeology
Biochemistry
Biology
Biopsychology
Chemistry
Classics
Community Health
Computational Science
Computer Science
Counselling Centre
Earth Sciences
Economics
Education
Educational Administration
Educational Psychology
Engineering
English
Environmental Science
Folklore
French and Spanish
Geography
German and Russian
History
Human Kinetics and Recreation
Linguistics
Marine Studies
Mathematics and Statistics
Medicine
Nursing
Pharmacy
Philosophy
Physics and Physical Oceanography
Political Science
Psychology
Religious Studies
Social Work
Sociology
Toxicology
Women's Studies
home
browse
preferences
my favorites
about/feedback
recent uploads
help/search tips
Français
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
View:
document description
page description
page & text
previous page
:
next page
Document Description
Title
A
method
for the
analysis
of the
MDTF
data
using
neural
networks
Author
Ibrahim
,
Mohamed
,
1972-
Description
Thesis
(M.Eng.)--Memorial
University
of
Newfoundland
,
2001.
Engineering
and
Applied
Science
Date
2000
Pagination
xiii, 129 leaves : ill.
Subject
Neural
networks
(Computer
science);
Submarines
(Ships)--Hydrodynamics--Simulation
methods
Degree
M.Eng.
Degree Grantor
Memorial University of Newfoundland. Faculty of Engineering and Applied Science
Discipline
Engineering and Applied Science
Language
Eng
Notes
Bibliography:
leaves
94-96
Abstract
Numerical
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.
Type
Text
Resource Type
Electronic
thesis
or
dissertation
Format
Image/jpeg;
Application/pdf
Source
Paper copy kept in the Centre for Newfoundland Studies, Memorial University Libraries
Local Identifier
a1522160
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.
Collection
Electronic
Theses
and
Dissertations
Scanning Status
Completed
PDF File
(14.35
MB)
--
http://collections.mun.ca/PDFs/theses/Ibrahim_Mohamed.pdf
CONTENTdm file name
209144.cpd
