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Date
2012
Author
Naaijen, P. and E. BlondelCouprie
Address
Delft University of
Teciinology
Stiip
Hydromechanics and Structures Laboratory
IMekelweg
2, 2628 CD Delft
Delft
University of Technology
TUDelft
Wave induced
motion
prediction as operational
decision
support for
offshiore
operations
by
P. Naaijen and E. BlondelCouprie
Report No.
1836P
2012
Published
in Proceedings of the International Conference
Marine
Heavy Transport & Lift III, RINA, London, UK,
ISBN:
9781909024052.
Page /of 1/1
INTERNATIONAL
CONFERENCE
MARINE
HEAVY
TRANSPORT
&
LIFT
III
2425
OCTOBER
2012
RINA
HQ, LONDON
PAPERS
THE ROYAL INSTITUTION
OF
NAVAL
ARCHITECTS
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BELGRAVE
STREET,
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The Royal Institution of
Naval Architects
INTERNATIONAL
CONFERENCE
MARINE
HEAVY
TRANSPORT
&
LIFT
III
2425
October
2012
RINA HQ,
London
©
2012:
The Royal Institution of Naval Architects
The Institution is not, as a body,
responsible
for the
opinions
expressed
by the individual
authors
or
speakers
THE
ROYAL
INSTITUTION
OF
NAVAL
ARCHITECTS
10
Upper
Belgrave
Street
London
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8BQ
Telephone:
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7235
4622
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ISBN
No:
9781909024052
All
the
papers
in this
proceeding
have
been
reviewed
FSC"
0020438
Marine Heavy Transport & Lift
III,
2425 October
2012,
London,
UK
CONTENTS
The
LCAC
Carrier:
A
HeavyLift
Ship for the Transportation Of
ACVS
G.
Gougoulidis,
Hellenic Navy,
Greece
The
Turbine Foundation
Liner
Concept
J.W
Brouwer, Dutch Offshore Innovators
BV,
The Netherlands
Wave Induced Motion Prediction as Operational Decision Support for Offshore
Operations
P.
Naaijen,
E.
BlondelCouprie,
Delft
University of Technology, the Netherlands
FeasibUity
of
Electric
Propulsion for SemiSubmersible Heavy
Lift
Vessels
K.
Kokkila,
ABB,
Finland
Enhancements in Monte
Carlo
Simulation in Safetrans 5
R.
Grin,
Maritime
Research
Institute Netherlands, NL
NonLinear
Hydrostatic Analysis of the Floating
Crane
Considering the Large Angle
oflnchnation
*
K.Y
Lee,
N.
Ku,
J.H
Cha,
K.P
Park,
Seoul National
University,
Korea
Effect
of Porosity on the Hydrodynamic Behaviour of Oscillating Structures During
Lifting
Operations
H.
Wadhwa,
Intecsea,
Australia
Removal and Installation of Modules Onto
Truss
Spar With DP Heavy
Lift
Vessel
Z.
Ayaz, G. Mclelland, P. Smith, V.
McCarthy,
Saipem
Ltd.,
UK
Operability of Ballasting and Lifting Operations of Extreme Loads with Integrated
Hydrodynamics (Obelics)
Drs.
G. de
Vries,
Maritime
Research
histitute
Netherlands
(MARIN),
the Netherlands
Ir.
E.J.P.M.
Frickel,
Marithne
Research
histitute Netherlands
(MARIN),
the Netherlands
Technical
CapabiUties
and Cost Benefits of Dockwise Vanguard
M.
J M
Seij,
Dockwise, The Netherlands
First
Heavy
Lift
Super Fly Jib with
Fibre
Rope Stays
G.
Wender,
BigLift
Shipping, the Netherlands
W.
van Zonneveld, FibreMax, the Netherlands
M.
van Leeuwen,
Teijin
Aramid,
the Netherlands
M.
te Velthuis, Huisman Equipment, the Netherlands
Developments
in Heavy Transport Design Calculations
M.
J A van
Exsel
MSc and
J.B.
de
Jonge
MSc,
Doclcwise,
The Netherlands
Marine
Domain Awareness And Safety Of Heavy
Lift
Operation
S.
Yasseri,
Safe
Sight Technology, UK
©
2012:
Tiie Royal Institution of Naval Arcliitects
Marine Heavy Transport & Lift
IIL
2425 October 2012, London, UK
Marine
Design
Aspects
For Large
Modules
On
Heavy
Transport
Vessels
105
A.P Crowle,
CB&I,
UK
Heavy
Lift Transport Of 231m Lhd From
Spain
To Australia 113
A.
Van
Ginkel,
Dockwise,
Holland
A.
Méndez and F. Lago,
Navantia,
Spain
Authors'Contact
Details
229
©2012:
Tlie Royallnstitution
of Naval Architects
Marine Heavy Transport
&
Lift III, 2425 October
2012.
London,
UK
WAVE
INDUCED
MOTION
PREDICTION
AS
OPERATIONAL
DECISION
SUPPORT
FOR
OFFSHORE
OPERATIONS
P.
Naaijen,
E.
BlondelCouprie,
Delft
University
of
Technology, the Netherlands
SUMiMARY
This
paper
considers the motivation and approach of a real time predication system of
vessel
motions. A short term
motion
forecast
would
enable
to anticipate on
vessel
motions in the
near
future during motion
critical
offshore
operations such as heavy
lifting,
helicopter landings etc.
A
case
study is
presented
to illustrate the possibility of
shifting
fiom
a
workability
analysis
based
on statistics only to
one
involving
a
deteiministic
approach.
The approach to use remote wave
obseivations
by Xband
radar
for deterministic prediction is explained.
1.
MTRODUCTION
It
has
been
common practice for many
years
to
assess
operability
for offshore operations that can be
critical
with
regard to
vessel
motions in waves, by considering
statistical properties
like
significant
motion amplitudes or
most probable maximum values.
These
are
typical
properties related to the sea surface elevation described
as a
stociiastic
process.
With
the development of various
remote surface elevation
sensors
like
lidar
([1],
[5]) and
Xband
radar
[3] a relatively new
research
field
was
initiated
considering the surface elevation
fiom
a
deterministic
point of view. It has
been
shown that in
principle,
for relatively small time
scales
(in the order of
tens
of
seconds),
with
this approach it is feasible to
accurately predict wave elevation and related properties,
like
vessel
motions, in a
deteraiinistic
way
([8],
[11],
[9],
[7],
[2], [13],
[12],
[6], [4]). The main objective of the
application
of deterministic wave and motion prediction
(DWMP)
on board offshore
vessels
is to improve
operability
and safety. Instead of only
indicating
operability
by statistical
parameters
(like
most probable
maximum
values or significant values), it provides a
means
to
influence
the operability: a prediction of wave
elevation and induced
vessel
motions
enables
optimal
timing
of
critical
phases
within
offshore operations.
The
first
order forces
cause
structure motions at
frequencies in the
same
range
as the wave frequencies.
For many offshore operations
involving
floating
structures mainly the
l"
order motions in waves are the
cause
of a
limited
operability.
Oil/LNG
tandem
off
loading
hookon, helicopter / UAV landing, topside
installation
/ removal, cutter dredger operation, offshore
wind
turbine installation and pipe transfer are a few
examples.
In
all
these
operations, certain risks exist due to wave
induced motions:
snap
loads in
offloading
hoses
just after
connecting,
collision
of
lifted
items, crashing of
helicopters /
UAV.
In
case
of dredging operation in environments that are
exposed to waves, vertical motions of the
vessel
directly
result in vertical motions of the cutter
head.
When
exceeding certain
limits,
collision
of the cutter
head
with
the bottom results in large loads that are tiansferred via
the cutter ladder to the connection
joint
and stud pole.
Failure of
these
components
resufts
in expensive repair
work
and downtime
which
is why
these
dredging
operations are often restricted to very
mild
wave
conditions.
It
is
viitually
impossible to influence/decrease
first
order
motions,
meaning that when they exceed certain
operational
limits
it is inevitable to abort or cancel the
operation, resulting in expensive downtime.
In
2012 a
research
project
PROiVIISED
Operations
(PRediction
Of wave induced
iVIotions
and forces In
Ship,
offshore and Dredging Operations) was launched,
initiated
by
Delft
University of Technology. One of the
main
objectives of
this
project is to predict wave induced
vessel
motions and related
1^'
order quantities resulting
from
wave frequent wave forces real time
with
a forecast
horizon
in
tiie
order of 100 s in order to
enable
anticipation
on
those
predicted motions during motion
critical
offshore operations.
This
article aims to report on the
backgiound
and
progress
within
this
research
project.
First
a
case
study
will
be
presented
illustrating the
possible use of a deterministic
vessel
motion prediction,
followed
by an explanation of the approach using remote
wave observations by Xband
radar
to
initialize
a wave
propagation model.
2.
CASE
STUDY
In
order to illustrate this objective, a
case
study has
been
done
in cooperation
with
one of the participants in order
to
assess
the possible benefits of a DSS for a
typical
operation that can be
critical
with
respect
to downtime:
transfer of pipes
fiom
a supplying
barge
to a pipe laying
vessel
(schematically depicted in Figure 1)
The operability of such
lifting
operations is
detemined
by
the expected probability of
collision
of the pipe
crate
©2012:
The
Royal Institution of Naval Architects
11
Marine Heavy Transport
&
Lift
III,
2425 October
2012,
London,
UK
with
the
barge
once
it has
been
lifted.
Having pre
calculated transfer ftinctions of the vertical motions of
the pipe laying
vessel
relative to the
barge
and knowing
the incoming waves, the
distance
between pipe
crate
and
barge
can be simulated during a
lift.
Figure 1, pipe transfer
This
is visualized in Figure 8 where the blue line
represents
the vertical position
of
the
crane
hook
of
pipe
laying
vessel
relative to the supply
barge
(positive
=
upward) while the pipe
crate
is on the
barge.
(An offset
of
5m has
been
applied in order to account for slack in
the hoisting line.) Indicated in green and red are the
position
of the
crane
hook during a
lift.
Crossing the
zerolevel
conesponds
with
the actual takeoff of the
pipe crate. Crossing this level downwards after takeoff
means
the pipe
crate
collides
with
the
barge
which
occurs
for
the red
line.
By
simulating a
sufficient
number of
lifts,
the probability
of
occurrence
of a
collision
can be determined.
Depending on the criterion for this probability the
considered sea
state
can be indicated as workable or not
workable.
It
is obvious that when being
able
to predict the
veitical
relative motions and simulate future pipe
lift
with
an on
board real time
DSS, a
crane
operator
will
be
able
to
pick
a
safe
moment in time to start the
lifting
operation.
A
reliable DSS therefore can
justify
a
less
severe
workability
criterion than is
used
now.
Besides,
the
safety of
lifting
operations is
enhanced
regardless
of the
applied criterion when using a DSS.
As
mentioned the prediction of the relative motion of
supply
barge
and
crane
hook of the discharging pipe
laying
vessel
can be
used
to simulate a
pipelift
at
eveiy
instant in time, realtime. That way possible future
starting points of the
lifting
operation can be indicated to
be
safe
(gieen)
or not
safe
(red)
giving
the operator the
oppoitunity
to
choose
a
safe
starting point without
having to rely on inaccurate visual
obseivation.
This is
illustrated
in Figure 9.
It
is obvious that
increased
operability has a direct
economic impact. In order to
quantify
this
aspect,
a study
has
been
carried out into the effect of
increased
operability.
The objective of this study was to quantify
the
increase
of
workability
due to
less
severe
statistical
criteria
for
typical
operations in several
different
working
areas.
Considering the pipe transfer, a
workability
criterion of
98 % probability of successful
lift
is considered common
practice. This
means
that when starting a
lift
at an
arbitraiy
moment in time the probability of
collision
must be
less
than 2 %.
For
three
areas
of expected operation (in this
speciftc
case
Campos
Basin
Brazil,
Block31
Angola and Gorgon
Australia)
the
increase
of
workability
is considered when
we would
assume
an allowable
collision
probability of
10% in combination
with
a DSS system (instead of the
mentioned 2% without DSS). (This implies that the
assumed
probability of the DSS wrongly predicting a
successful
lift
amounts
to 20%.)
For every location, historical wave
data
of the
15
years
between 1992 and 2006 has
been
used,
during which a
wave spectrum was recorded every 3
hours
resulting in
roughly
40000
simulations. Each of the simulations
resulted in a
percentage
of successful
lifts
and a resulting
workability
given the criterion of a
success
rate
of
minimum
98%.
The table below indicates the
percentage
of
increase
of
workability
under
the
above
mentioned
assumptions
resulting
from
applying a DSS on a yeariy
base
(i.e. for
the
case
the
vessel
would
operate
a
full
year
at the
considered location).
Location
Workability
Increase
Campos
Basin,
Brazil
33%
Block
31,
Angola
112%
Gorgon, Australia 245%
Table 1,
Workability
increase.
These
numbers
should be interpreted carefully: factors
having a favourable effect on the estimated
workability
increase
are the underiying
assumptions
that for the
situation
without DSS the
crane
driver is not
able
to
anticipate on the
waves
by visual observation and that the
vessel
operates
365
days
/
year
at the
same
location.
Besides,
the
assumed
20% probability of the DSS
wrongly
predicting a
successfial
lift
is a rough estimate.
However, the figures clearly indicate the very significant
increase
in operability due to only a slight relaxation of
the
workability
criterion.
3.
APPROACH
The
chosen
approach to obtain a prediction of the
waves
and the resulting motions of a
vessel
is to use a remote
wave
measurement
and
propagate
the
measured
wave
field
towards the location
of
the
vessel.
One of the
recent
developments into remote wave
sensors
is the Xband
radar,
the navigational
radar
that basically all
ships
are
12
©
2012: The
Royal Institution
of
Naval Architects
Marifie Heavy Transport
&
Lift
III,
2425 October
2012,
London,
UK
equipped
with.
Being relatively inexpensive and having
the
advantage
of large spatial range, it has
some
promising
feahires
for the intended
research
objective.
The idea of the application of the Xband radar as a
remote
sensor
is depicted in Figure
10.
Waves measured in the indicated observation
zone
are
used to
initialize
a wave propagation model
which
provides a prediction of the waves and, in combination
with
the required transfer functions, any wave related
property
at the ship location.
3.1 PRINCIPLES OF
WAVE
SENSE^G
BY X
BAND
RADAR
The navigational Xband radar is normally used for
navigation
puiposes:
avoiding collisions
with
the
suiTounding
traffic.
Reflections of the radar on the wavy
sea surface, socalled back
scatter,
are usually
filtered
out.
However, this back
scatter
reveals a lot of valuable
information
about the waves. It is important to note
though
that the Xband radar
does
not provide a
direct
measurement of the wave elevation. Wave elevation can
only
indirectly
be derived
from
it.
A
commonly applied analysis technique that is used in
Xband
radar remote wave sensing is the three
dimensional
Fourier transform (3D FFT).
Applying
a 3D
EFT
to a
series
of backscatter images obtained in an
observation
zone
(a part of the entire radar
view
indicated,
indicated in Figure 11) yields a number of
backscatter components
each
having a twodimensional
wave number and frequency. Using the linear dispersion
relation
as a
filter
and applying modulation transfer
functions
(MTF) to translate backscatter
data
into wave
elevation
data, yields a frequency domain description of
the surface elevation
which
could
directly
be used for the
initialization
of
a
wave propagation model.
This
process
is schematically depicted in Figure
11
and
Figure
12.
Obtaining
the
infoiTnation
in the radar images in the
wavenumherfrequency domain by applying a 3D FFT
basically
has three objectives:
1.
enable
application of the frequency domain
modulation
transfer functions (MTF) that
translate radar backscatter
data
into wave
elevation
data
2.
distinguish real wave phenomena
from
noise by
using
the linear dispersion relation as a
filter.
3. remove the ambiguity in the directional
sense
resulting
from
analysing just 1
snapshot
of a
wave
field
Also
transfer functions of wave related properties
like
e.g. ship motions could be directly applied to the
obtained wave components before applying the inverse
transformation
into timespace domain.
3.1
DETERME^IISTIC
WAVE
PREDICTION
FROM
RADAR
OBSERVATIONS
In
order to
assess
the possibilities to
initialize
a linear
wave propagation model by using the 3D FFT of a
series
of
subsequent
radar images, focussing on the effect of the
3D
FFT procedure, simulations
have
been
earned
out
assuming 'prefect' observation data, i.e. assuming an
actual observation of the wave elevation in
space
and
time
wouldbe
available
(without
taking into account the
fact
that in reality the observation consists of the radar
back
scatter
that in its tum
will
have
to be converted into
wave elevation by the mentioned
MTF).
A
3D Fourier
transfonn
of a
series
of
2D
snapshots
of a
wave
field
is a Fourier transform of the surface elevation
7]
applied subsequently over the spatial x, and y domain
and over time.
From
the 3D FFT, the complex amplitudes
4„„;,
of
the
wave elevation
rj,
sampled at x spatial points at
time
steps,
can be derived that relate to the wave
elevation
as
follows:
m=0 11=0 p=0
Where:
/f„„,
is the 2D wave number vector
^/c^,
j
(Dp
is the wave frequency
A
consequence
of
this
3D FFT approach is that equation
(1)
represents
a wave elevation that is periodic in
x,
y,
and
/.
See Figure 2.
Suppose
waves
with
a main
propagation direction
in
positive
xdirection
are
considered (see Figure 10) that are observed in the 3D
obseivation
domain indicated in Figure 2. An attempt to
use equation (1) to
fmd
the wave elevation for future
times and/or down'stream' locations, indicated by the
suiTounding
boxes,
would
result in exact copies of the
observation:
because
of
the
periodicity
introduced by the
3D
FFT procedure, the wave pattern
will
repeat
in
space
and time.
A
y
obsen'alioD
•
/
•— — —•
t
Figure
2,
periodicity
of wave elevation representation
from
3D
FFT
©
2012:
Tlie Royallnstitution
of
Naval Architects
13
Marine Heavy Transport
&
Lift III, 2425 October 2012, London,
UK
3.1
SIMULATIONS
WITH
'PERFECT'
DATA
A
related
issue
is that the obtained wave components in
equation (1) do not satisfy the dispersion relation.
An
example of the result of a 3D FFT is visualized in
Figure
3 where
each
dot
represents
one wave component
from
a 3D FFT whose colour indicates its magnitude.
(Only
components
with
energy above a threshold value
are shown) The
solid
line
represents
the dispersion
relation.
As can be
seen
some
dots that are not exactly on
the dispersion
line
represent
wave energy even though
the
data
on
which
the 3D FFT was applied is perfectly
linear
synthetic wave data.
relation.
In
tenns
of equation (1) this
means
that all
frequencies
(O^
are tumed into
ty„„^,
where
equals
the frequency that
conesponds
with
/c^,
This
results in a wave representation as given in equation
(2)
where
p^s.nd p^refcr
to the lower and upper
frequency
following
from
the chosen
filter
bandwidth.
77{x,t)=n\^'X
£
4™/^"'""'^"""4
(2)
m=0
11=0 p=p^
Figure
3, wave component amplitudes against modulus
of
wave number and frequency
It
is
assumed
that the main
cause
for the
occuiTence
of
these
components is the fact that the 3D FFT is applied
on
a
finite
spatiotemporal measurement domain. This
results in:
1.
a
grid
of
/c,

^
combinations that simply
does
not
provide a
(o^
for
eveiy
A:„,„
that satisfies the
dispersion relation
2.
spectral leakage: (a classical problem ti'eated by
e.g.
[18])
Energy of wave components
present
in
the
data
whose frequency is not a harmonic of
the record length or whose wave number is not a
harmonic of the spatial domain length is
assigned to adjacent harmonics.
These
'nonphysical' wave components and their
periodicity
are obviously a problem when the obtained
components are to be used as the
initialization
of a
(linear)
wave propagation model.
Figure
4,
filtering
using
dispersion
relation
For
a more detailed description of the
process
reference
is
made
to
[7,
2].
This
way the
periodicity
in time
will
be removed. This is
visualized
in Figure 5 where the blue boxes
represent
the
propagated wave
field
in time.
As
mentioned, in the
process
of analysing radar images
to
obtain a wave obseivation, the linear dispersion
relation
is used in order to distinguish noise
from
real
waves:
only
those
components
within
a certain band
width
around the dispersion relation are considered as
indicated
by the
dashed
lines in
Figure
4.
In
the approach where we want to use the obtained
remaining
components
fov propagation,
on top of the
filtering
process,
a procedure is applied to the wave
components in order to map them onto the dispersion
relation:
all wave components
within
the
filter
bandwidth
(Figure
4) are
'vertically'
shifted onto the dispersion
Figure
5,
periodicity
of wave elevation after
shifting
components onto dispersion relation
For
prediction
purposes,
only
the upper
.right
box is
relevant. It should be noted however that
based
on the
lower
left
observation, it is not possible to obtain an
accurate
prediction in the entire upper
right
prediction
box.
Prediction is
only
possible in the socalled
predictable zone. This is explained in more detail in [10]
and [2]
14
©
2012:
Tlie Royal Institution of Naval Arcliitects
Marine Heavy Transport
&
Lift III, 2425 October 2012, London.
UK
4.
RESULTS
AND
CONCLUSIONS
In
order to
assess
the accuracy and vahdity of the
proposed approach, a large number of simulations
have
been
conducted. Linear synthetic short crested waves
have
been
generated.
(An
average
directional spreading
for
wind
waves has
been
used.)
Part of the
generated
wave
field
was
used
as an obseivation, indicated by the
left
square
zone
indicating 'observation zone' in Figure
6. From 32
subsequent
snapshots
taken
with
a time
interval
of 1.5 s in this obseivation
zone
the wave
field
was predicted at other locations ('prediction') and future
times. (The two outer summations over m and n in
equation (2) can be computed
efficiently
by
means
of an
inverse 2D FFT
from
the wave number
(/c
) domain into
the spatial
(X)
domain, resulting in a spatial prediction
domain
(indicated by the rightmost
square
zone
in Figure
6)
of equal
size
as the
ohseivation
domain.) The two
snapshots
shown in Figure 6 are schematically
represented
by the vertical
planes
indicated
with
'Obs
zone' and
'Pred.zone'
in Figure 5.
The colour in Figure 6 indicates the prediction error
according to equation (3), where
^ ^
indicates the
ensemble
average
over 260 realizations and
cr^
equals
the variance of the
generated
waves,
fj
is the
generated
('bue')
wave elevation
while
T]
is the prediction
following
from
equation (2). The result is shown for 2
moments in time (t=
51
s and
t=78
s, being 3 s and 30 s
respectively after the last
snapshot
of the obseivation was
taken).
500 0 500
X
Figure 6,
reconstiuction
/ prediction accuracy
The simulations were carried out using temporal and
spatial resolutions for the observations that are realistic
for
radar
capabilities: the spatial
step
amounted to 7.8 m
in
both
X
and y direction and the
size
of the observation
zone
was 128 x 128 points.
As
can be
seen,
using the proposed method to map the
wave components onto the dispersion relation (next to
the band
pass
filtering
around the dispersion relation), an
accurate
prediction can be obtained. The forecast horizon
depends
on the
size
of the observation
zone
and the
temporal length of the observation, the position of the
vessel
relative to the observation zone, and the wave
condition.
This is explained in detail in
[2].
In
order to give an indication of required
radar
ranges.
Figure 7 shows the optimal distance between the
observation
zone
(i.e. the distance between the
right
most
edge
of the observation
zone
and the
suggested
vessel
location
in Figure 6) for the
case
of the aforementioned
observation
zone
size
and length. The figure shows
results for JONSWAP wave
spectra
whose
peak
period
Tp
is
used
as the
abscissa
and
which
again
have
an
average
directional spreading for
wind
waves.
600
Figure 7, optimal observation location
The obtained optimal
distances
between observation
zone
and
vessel
location are all
well
within
the
capabilities
of
Xband
radar
wave
sensors.
The principles of wave prediction by using a 3D FFT
have
been
shown to be able to give very
accurate
prediction
results as shown by Figure 6.
Having
available the relevant transfer functions as a
function
of wave number k , a
vessel
motion prediction
can easily be obtained.
However,
these
results are obtained by using perfect
observation
data.
Future
research
will
focus on
research
using more realistic wave observations obtained by
actual Xband
radar
simulations,
involving
the effects of
shadowing (parts of the observation
zone
being blocked
©2012: Tlie Royal Institution of Naval Architects
15
Marine Heavy Transpori & Lift III, 2425 October 2012, London,
UK
for
the
radar
by
wave
crests,
resuhing
in
gaps
in the
observation)
and
measurement
noise.
Possible
alternatives
for
the 3D FFT
approach
will
be
investigated.
5.
ACKNOWLEDGEMENTS
This
research
on
which
this
paper
is
based
has
been
carried
out
within
the
research
project
'PROMISED
Operations'
(PRediction
Of
wave
induced
Motions
and
forces
In
Ship,
offshorE
and
Dredging
Operations),
funded
by
'Agency
NL',
a
department
of the
Dutch
Ministery
of
Economic
Affairs, Agriculture
and
Imiovation
and
coflmded
by
Delft University
of
Technology,
University
of
Twente,
Maritime
Research
Institue
Netherlands,
Ocean
Waves
GMBH,
Allseas,
Heerema
Marine
Contractors
and
IHC.
6.
REFERENCES
[1]
M.R.
Belmont,
J.M.K. Horwood, R.W.F.
Baker,
and
J.
Baker.
Shallow
angle
wave
profiling
lidar. Journal
of
Atmosperic
and
Oceanic
Technology,
24:11501156,
2007.
[2]
E. Blondel
and P.
Naaijen.
Reconstruction
and
prediction
of
shortcrested
seas
based
on the
application
of a
3dfft
on
synthetic
waves,
part
1:
Prediction.
In
Proceedings
of the 31st
International Conference
on
Ocean, Offshore
and Arctic Engineering, 2012.
[3]
J.
C.
Nieto
Borge,
K.
Reichert,
and J.
Dittmer.
Use
of
nautical
radar
as a
wave
monitoring
inshument.
Coastal Engineering,
37(34):331

342, 1999.
[4]
D.R.
Edgar,
J.M.K. Horwood, R.
Thurley,
and
M.R.
Belmont.
The
effects
of
parameters
on
the
maximum prediction
time
possible
in
short
term
forecasting
of the sea
surface
shape.
International Shipbuilding Progress,
47:287¬
301,2000.
[5]
S.
T.
Grilli,
C.
Guerin,
and
B.
Goldstein.
Oceanwave
reconstruction
algorithms
based
on
spatiotemporal
data
acquired
by a
flash
lidar
camera.
In Proceedings
oflSOPE,
2011.
[6]
E.L.
Morris,
H.K.
Zienkiewicz,
and M.R.
Belmont.
Short
tenn
forecasting
of the sea
surface
shape.
International Shipbuilding
Progress,
45:383^00,
1998.
[7]
P.
Naaijen
and
E. Blondel.
Reconstruction
and
prediction
of
shortcrested
seas
based
on the
application
of a
3dfft
on
synthetic
waves,
part
I:
Reconstruction.
In
Proceedings
of the
31st
International Conference
on
Ocean, Offshore
and Arctic Engineering, 2012.
[8]
P.
Naaijen
and
R.H.M.
Huijsmans.
Real
time
wave
forecasting
for
real
time
ship
motion
predictions.
In Proceedings
of
tlie
ASME 2008
27th International
Co7iference
on
Ocean,
Offshore and Arctic Engineering,
2008.
[9]
P.
Naaijen
and
R.H.M.
Huijsmans.
Real
time
prediction
of
second
order
wave
drift
forces
for
wave
force
feed
forward
in
dp. In Proceedings
of
the
ASME
2010
29th International
Conference
on
Ocean, Offshore
and
Arctic
Engineering,
2010.
[10] P.
Naaijen,
K.Trulsen,
and
E.BlondelCouprie.
On
the
deteiministic
predictability
of
long
crested
waves.
ISP, to be
published.
[11]
P.
Naaijen,
R.R.T.
van
Dijk,
R.H.M.
Huijsmans,
and A.A.
ElMouhandiz.
Real
time
estimation
of
ship
motions
in
short
crested
seas. In
Proceedings
of the
ASME 2009 28th
International Conference
on
Ocean, Offshore
and Arctic Engineering,
2009.
[12] K.
Tmlsen.
Spatial
evolution
of
water
surface
waves.
In
Fith International Symposium WAVES
2005, Madrid,
July
2005.
[13]
K.
Tmlsen
and C.T.
Stansberg.
Spatial
evolution
of
water
surface
waves:
Numerical
simulation
and
experiment
of
bichromatic
waves.
In
Proceedings
of
the Eleventh
(2001)
International Offshore
and
Polar Engineering
Conference,
Stavanger,
June
2001.
7.
AUTHORS'
BIOGRAPHY
Peter
Naaijen
holds
the
cunent
position
of
Assistant
Professor
at
Delft University
of
Technology.
He is
responsible
for
education
in the
field
of
ship
and
offshore
hydromechanics
and
research
in the
field
of
deteiministic
wave
and
vessel
motion
prediction.
Elise
BlondelCouprie
holds
the
current
position
of
Post
Doc
at
Delft University
of
Technology
following
up on
her PhD
research
at
Ecole
Cenhale
de
Nantes
on
non
linear
deterministic
wave
prediction.
16 ©2012: The Royal Institution of Naval Architects
Marine Heavy Transpori & Lift III,
2425
October
2012,
London,
UK
APPENDIX,
FIGURES
2
SucMSSful
Lift
I
•
t
t
f\J I
fAAJ
V
1 1 1
i
1 1
SO
60
Figure
8,
Simulation
of
pipe
lift
80
100
«mé
[s]
120
1S0
130 200
\ \L
Indication
of safe
and
unsafe
future
sterling points
Time
Now
Figure
9, possible
Decision
Support System forecast
information
Figure
10,
Remote wave sensing by radar.
©2012: Tlie
Royal Institution of Naval Architects
Marine
Heavy Transport & Lift III, 2425 October
2012
London,
UK
I'
0
1
2
xfkm]
Figure
II,
radar
observation
zone
3D
FFT
on reflection intensity
inverse
3D FFT
apply
MTF's:
translate radar reflection
into surface elevation
Figure
12,
schematic process
transfonnation
back scatter data
into
wave
elevation
filter using linear
dispersion relation: match
kand a
18
©2012:
T/te
Royal Institution of Naval Arcliitects