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1996-07
Predicting Ship Fuel Consumption: Update
Schrady, David A.
Monterey, California. Naval Postgraduate School
http://hdl.handle.net/10945/29469
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NPS-OR-96-007
NAVAL POSTGRADUATE SCHOOL
Monterey, California
Predicting Ship Fuel Consumption: Update
by
David A. Schrady
Gordon K. Smyth
Robert B. Vassian
July 1996
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Predicting Ship Fuel Consumption:
6.
Technical
FUNDING NUMBERS
8.
PERFORMING ORGANIZATION
REPORT NUMBER
Update
AUTHOR(S)
David A. Schrady, Gordon K. Smyth, and Robert
7.
5.
B.
Vassian
PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES)
Naval Postgraduate School
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unlimited.
ABSTRACT (Maximum 200 words)
concerned with the prediction of ship propulsion fuel consumption as a
function of ship speed for U.S. Navy combatant and auxiliary ships. Prediction is based on
fitting an analytic function to published ship class speed-fuel use data using nonlinear
regression. The form of the analytic function fitted is motivated by the literature on ship
powering and resistance. The report discusses data sources and data issues, and the impact of
ship propulsion plant configuration on fuel use. The regression coefficients of the exponential
function fitted, tabular numerical comparison of predicted and actual fuel use data, the
standard error of the estimate, and plots of actual and fitted data are given for 22 classes of
This report
is
Navy ships.
14.
SUBJECT TERMS
Operational
17.
Navy Logistics;
SECURITY CLASSIFICATION
OF REPORT
Unclassified
NSN 7540-01-280-5500
1
8.
1
5.
NUMBER OF PAGES
1
6.
PRICE
Ship Fuel Use Prediction
SECURITY CLASSIFICATION
OF THIS PAGE
Unclassified
19.
SECURITY CLASSIFICATION
70
CODE
20. LIMITATION
OF ABSTRACT
OF ABSTRACT
Unclassified
UL
Standard Form 298 (Rev. 2-89)
Prescribed by ANSI Std. 239-18
Table of Contents
1.
INTRODUCTION
2.
SHIP FUEL
2.1
page
CONSUMPTION DATA
2
Data Sources
2
2
2.2 Data Issues
3.
METHODOLOGY
3.1
4
Conventional
Wisdom
4
3.2 Theory of Ship Powering and Resistance
3.3 Theoretical
4.
5
Model of Ship Fuel Consumption verses Speed
9
DATA FITTING AND RESULTS
10
Data Source Utilized
4.2 Zero Points
10
4.3 Plant Configuration
1
4.1
4.4
4.5
5.
1
The Regression Software
The Results
CONCLUSION
Initial
Ship Class Fuel
Distribution List
12
12
13
REFERENCES
APPENDIX:
10
15
Use Prediction Results
17
63
PREDICTING SHIP FUEL CONSUMPTION
1.
INTRODUCTION
This report
concerned with predicting the
fuel
A data analysis approach is taken with reference made to the hydrodynamics of ship resistance
and powering requirements as
data.
Given that sea
trials
it
motivates the analytical form of the function which
are conducted and that data
speed and class of ship, and that these observations are
can attempt to
is
consumption (DFM/F-76) of U.S. Navy
Fuel consumption will be stated in gallons per hour as a function of speed for each class of
ships.
ship.
is
what
is
fit
is
to the
taken on fuel consumption for a given
made
for a
number of different speeds, one
the speed-fuel use data with an analytic function using nonlinear regression. This
meant by a data
The reason
analysis approach.
this analysis
was undertaken
relates to operational logistics
estimate ship and battle group/battle force endurance, fueling-at-sea
shuttle ship requirements to sustain the
is
is fitted
combat
logistics force
(CLF)
and the need to
(FAS) requirements, and tanker
station ship.
One of the
authors
involved in the development of a computer-based battle group tactical logistics support system
concerned with planning, tracking, and predicting
ment.
The system
fuel
and ordnance consumption and replenish-
requires analytical functions from which to
compute predicted
ship fuel
consumption.
This report
omits
is
much of the
an update of an
earlier
(1990) report on the same subject, Ref (1). This report
analysis detailed in the earlier report, omits results
on
ship classes
decommis-
sioned by the Navy, and includes results for ship classes brought into service since 1990. Also the
form of the
analytic fuel use prediction function fitted
by regression has been changed from a power
function to an exponential form resulting in smaller standard error of the prediction.
2.
SHIP FUEL CONSUMPTION DATA
Data Sources
2.1
Data on ship
published.
NWP
consumption as a function of speed for
fuel
Sources include the old
NWIP
11-2 (Auxiliaries), Ref (4), and the
class ships
was obtained from
11 -20(D),
new
Ref
(7),
NAVSEA
2.2
and Ref
NWP 65
series.
NWP
ship classes are
11-1 (Combatants),
Ref (3),
on the DD-963
and
PHTORONs
7 and
9,
Ref
Data on newly commissioned ship classes has been provided by the
(8).
Propulsion Branch.
Data Issues
amount of speed-fuel use data
range of ship speeds in the data, and 3) the consistency of the data
of data for the same ship
pairs for each class of combatant ship.
each class of auxiliary
NWP
ship.
series has only
auxiliary ship classes.
when
available, 2) the
there are multiple sources
class.
With respect to the amount of data
its
USN
Newport Ref (5), and data on
COMNAVSURFPAC
Issues regarding such data include 1) the
the
major
Additionally, data
the Surface Warfare Officer School,
amphibious warfare ships was obtained from
(6),
Ref (2),
all
available,
NWP
NWP
11-1 generally has 7 to 9 speed-fuel use
11-2 generally gives 3 to 6 speed-fuel use pairs for
In fitting any sort of analytical function the
more data the
better,
and
minimal amounts of data; actually insufficient amounts of data for the
The
NWP 65
series is for
combatant ship classes only and
is
treatment of ship fuel consumption. For some ship classes speed-fuel use data
inconsistent in
is
provided, for
one ship
class a series
configurations), and for
at
all.
The
older NWIP
of
some
1
fuel
consumption curves
1-20 provided
included in this publication.
NWP 65
ship classes the
more
Of course
is
provided (curves depend on plant/shaft
document does not address
data, generally
important,
newer
fuel
consumption
15-20 speed-fuel use pairs, for the ships
classes are not included in
Because the method of fitting a continuous function to the data
is
regression, the
essentially remains a methodological problem affecting the robustness
NWIP
11-20.
amount of data
of the fuel consumption
estimation equations derived.
the ship speed ranges for which data exists. Generally
NWP
11-1
data exists for combatant speeds above 12 knots, sometimes well above 12 knots, and
NWP
11-2
The second data
issue
is
data exists for auxiliary speeds above 10 knots.
ranges from 6 to 12 knots.
The lowest speeds
The speed range of the data
is
for
which
NWIP
data exists
important in terms of the behavior of the
regression equation at low ship speeds.
The third data
issue
Obviously data validity
is
is
the consistency of ship fuel consumption data from different sources.
a serious issue but one that cannot be resolved here. In actuality there are
precious few sources of ship fuel consumption data and, in addition to limitations
on the amount of
data and the speed range covered by the data, none of the data available includes information about
how the
propulsion plant and shafts were being operated or the condition of the ship's
different sets
of data were obtained with the
ship's plant in different configurations
hull.
Where
or with a fouled
rather than clean hull, or in different sea states or temperatures, one should expect different fuel
consumption
data.
3.
METHODOLOGY
3.1
Conventional
Wisdom
In fitting an analytic function to ship speed-fuel use data,
sort
of function
it
is
supposed to be.
The conventional wisdom
function of ship speed. In connection with their
cubic polynomial regression to
fit
it
own
studies, the
speed-fuel use data.
is
is
helpful to
know
a priori what
that ship fuel use
a cubic
is
Center for Naval Analyses has used
This produces relatively high coefficients
of determination, r-squared values; generally 0.97 or higher. Residuals, the differences between the
actual fuel use at a given speed
at that speed,
and the fuel use predicted by the cubic regression equation evaluated
were generally acceptable with maximum errors being on the order of
range of ship speeds contained
equation
at
in the data.
low ship speeds. In the
However
there
CNA report, Ref (9),
is
10%
within the
the problem of controlling the cubic
ship speed-fuel use data
is fitted
with the
cubic polynomial equation,
F=
where F
regression
speeds
is fuel
is
(e.g.,
go negative
co + c
use in gallons per hour and
V
+ c
V
is
x
2
V2
+ c
3
V3
(1)
When
ship speed in knots.
cubic polynomial
used and data exists only for higher ship speeds, the equation can curve upward
predicting that the ship will use
(e.g.,
the coefficient c
reports get around this
by noting
with ship speeds above, say
,
one must be able to predict
Our attempts
is
more
fuel at 5 knots than at
1
negative, at slow speeds the ship
that the fuel
at
low
5 knots) or the curve can
is
making
fuel!).
Some
consumption prediction equations should only be used
14 knots. In reality however, speeds below 14 knots are important and
fuel
use for speeds below 14 knots.
to control the
low speed behavior of the cubic polynomial included using
port fuel allowances as the amount of fuel used at zero speed and spline
fit
in-
routines to generate
missing low speed data, as described
in
Ref (1). None of these attempts
to control the
behavior of the cubic polynomial was satisfactory. Because of this and because
satisfying to try to
made
consumption, some effort was
3.2
Theory of Ship Powering and Resistance
1 is
to study the subject of ship powering and resistance.
intended to illustrate the relationship between fuel input and ship speed. Fuel
consumed by a prime mover
(fossil fuel
steam turbine, gas turbine, or diesel) and the output
horsepower (BHP). This power generally
acts through gearing to a shaft or shafts
to propellers, the output being effective horsepower (EHP).
the water at
simply more
determine the theoretical relationship which should exist between ship speed and
fuel
Figure
it is
low speed
some speed completing the chain from
The
is
slow speeds
brake
and ultimately
EHP acts to move the hull through
fuel input to ship
speed achieved. The
EHP must
equal the total resistance generated by the ship moving through the water. For displacement
total resistance
is
hulls,
has two principal components: friction resistance and wave-making resistance. At
friction resistance dominates, but at higher
and increases rapidly as
hull
speed
is
approached.
speeds wave-making resistance dominates
EHP
is
the horsepower required to equal the
ship's total resistance at a given speed.
In 1876, William Froude in England gave the formula for
EHP
=
-L
p
2
—
EHP as, Ref (10):
V3
(2)
550
where
C T = coefficient of total
p
= fluid
resistance,
density in slugs per cubic foot,
S = wetted area of the
hull in square feet,
550 = one horsepower
in
and
foot-pounds per second.
CO
8
>
DD
c
»' C
_ c 3
£ §5"S
o o
L>
raH
» 2
_
CO
2$
•
•
1
M
0)
©
c
©
•::::-^j::
TJ
©
:•
Q.
::•:
essssss^s*
?**&#
-effic
-slip
fc
i
C
3=
Icle
ura
"•X-ji'iX
eff
;<5
z
config
CREW(S
s
Inherent
plant
:•:(!>
-
'
a
A
u
3
WW::
-
tB
w.
BO
.•:•:•:•:•:•:•?-':•::.
11.
K4W&S&. tZm-
BHP
l >
Figure
1.
Ship Propulsion System
2w
Thus we know
An
that the
EHP
required
alternate explanation
is
theoretically a cubic function
of this relationship
squared, and that resistance times velocity
is
depends on the cube of ship speed. Further
a constant fraction of BHP.
The
by
is
that hull resistance is proportional to speed
definition
assumed
it is
of ship speed.
power. Either
way
in current practice,
it
follows that
Ref (1 1),
EHP is
that
ultimate relationship between fuel input and ship speed
cubic in ship speed, but further depends
on the
relationship
between
fuel input
by the prime mover. Thus more must be said about the relationship between
output power for the types of prime movers used in U.S.
Navy
ships; diesel,
and
fuel
EHP
is
then
BHP produced
consumption and
steam turbine, and gas
turbine.
One would
know
ideally like to
the theoretical relationship between fuel input and
horsepower output. After extensive discussion with mechanical and naval engineers and a review
of applicable
result)
literature,
was determined
mover (steam
question (manufacturer,
operated in
its
specific
exists.
model (something
The
relationship
and 3)
there
is
no
fuel
single theoretical
model for
prime movers. The approach to describing
given
horsepower
is
how
the prime
like
Froude's
always specific
turbine, gas turbine, or diesel), 2) the particular
size, specific characteristics),
to use specific fuel consumption
is
single theoretical
prime mover
mover
is
actually
consumption as a function of power
some indication of this relationship can be gained from examining the
output,
SFC
no
application.
Even though
was
that
of fuel consumption as a function of power output
to 1) the type of prime
in
it
in,
or can be converted
yields the desired fuel
(SFC)
to, units
vs.
fuel
characteristics
of some
consumption as a function of horsepower
horsepower curves for each type of prime mover.
of gallons per horsepower-hour. Multiplying SFC by
consumption measure, gallons per hour.
A typical SFC versus
horsepower curve shows a function
falls rapidly
modestly as
which SFC
in
is
quite large for
low output (horsepower) and
with increasing output reaching something of a lower bound, and possibly rising
maximum
the relationship
is
output
is
approached.
typically a function
turbine and diesel prime movers.
maximum output. The gallons
When converted to gallons
which
a
is
monotone
A gas turbine, however,
per hour versus horsepower
increasing
is different in
convex curve for steam
that
it
is
most
per hour versus horsepower relationship for a gas turbine
efficient at
is
concave.
In general, in no case are such curves linear.
It is
may be
assumed that the concave or convex
fuel gallons per
hour verses horsepower function
described by the equation
F=b,+b/' BHP
where F
is
fuel use in gallons per
BHP
hour and
intermediate form which will affect the
form of the
is
(3)
brake horsepower.
Equation (3)
is
only an
analytic function actually fitted to the speed-fuel
use data. In the earlier report, Ref (1), Equation (3) had the form
F=
While the form of Equation (3)
is
b +
*,
[BHPf
arbitrary so long as the
.
form can produce monotone increasing
convex or concave functions over the appropriate ranges, use of the exponential form for Equation
(3),
when combined with Froude's
3.3 Theoretical
result,
Equation
Model of Ship Fuel Consumption
In Section 3.2
it
was shown
it
was
Horsepower
is
stated that there
a constant fraction
is
no
produces superior prediction functions.
verses Speed
that theory dictates that the
displacement hull through the water at velocity
Effective
(2),
power
V was proportional to V 3
less
.
It
was
than on of Brake Horsepower.
single theoretical relationship for the conversion
8
required to
move
a
also indicated that
Also
in that section,
of fuel to horsepower
in a
prime mover. The relationship depends on
the prime
all
mover
specifics
and
how it
is
actually
operated in a given application.
Referring again to Figure
1
and Equation
EHP
C
—
=
—
c
p
2
If we
we know that
(2),
V3
-
dV>
.
assume
where
0<a<
1,
=
a-BHP
then
BHP
™L
-
=
C
1L
a
if,
a
as in Equation (3),
F=
r-,
it
cV3
550
EHP
Then
=
,
,
b 72
BHP
L
M
**
bQ + b x e
follows that
L
17
1
or finally
F=
p,e
e2
p n + Pi
Po
2
where
p
= b
x
/>2
,
and
l
=
M-
(4)
In application the coefficients p
,
p,
,
and p 2
ship class speed-fuel use data. Equation (4) will
will
be determined by regression performed on
be referred to as an exponential model of fuel use
as a function of ship speed.
4.
4.1
DATA FITTING AND RESULTS
Data Source
The source of the data used
indicated
on the data page
in
developing the fuel use prediction for each ship class
for the ship class in the Appendix.
number of speed-fuel use data pairs is used. For the newer
obtained from the
4.2
is
Generally the source with the largest
classes of ships, however, ship trials data
NAVSEA Propulsion Branch is used and is the only known source.
Zero Points
The problem with
already been discussed.
how much
low speed behavior of the cubic polynomial prediction function has
the
An
early attempt to control
fuel a ship (ship class actually)
burned
at
low speed behavior lead us to
zero speed. Such data
is
not available and
took as a surrogate the published In-Port Steaming Allowances obtained from
These were referred to as "zero
very
much
predicted
points".
Though the power
functions controlled
better than the cubic polynomial functions, there
low speed
fuel
try to determine
we
CINCLANTFLT.
low speed behavior
was a minor problem
in that
the
use values were excessive for the CV-63, DD-963, and CG-47/52 classes.
Zero points were not used
in
producing the power function results
in
Ref (1) because they
improve low speed behavior significantly and did tend to produce predictions
in
the speed range for
which data was available which were not as good as the regressions run without zero
10
did not
points.
Still
the excessive low speed fuel use of the three ship classes noted above, lead to
reevaluating the use of zero points and a decision to use
for the four ship classes introduced since
of doing
this
NPS
this time; this
Data Analysis course
Thus the form of the
and resulted
first
time
1990 (LHD-1, DDG-51, AOE-6, and PC-1). In the process
in the
Operations Research and Operational Logistics curricula
and that he found that an exponential functional form produced better
functions.
time was the
one of the authors, Gordon Smyth, came along and said that he had been using Ref (1)
in teaching the
at
them
relationship
in the exponential expression
use and ship speed. As before the
final
between
shown
decision
is
fuel input
in
fits
than did the power
and horsepower output was changed
Equation (4) for the relationship between fuel
that the best results obtain
from not using the
In-
Port Steaming Allowances as a proxy for fuel use at zero speed.
4.3 Plant Configuration
All ship classes
whether steam,
of engines. Plant configuration
refers to the
propel the ship through the water.
operated with a single engine,
diesel or
two
A
gas turbine are powered by pairs or multiple pairs
number of engines which
ship with, say, four
engines, or four engines
are 'on line' and
working to
LM 2500 gas turbine engines may be
on
line.
In general a ship must use
more
power, and more engines to make greater speed, but the speed ranges of each mode of plant
configuration overlap.
(single engine,
two
For a
ship with four
LM 2500 gas turbines and three plant
configurations
engine, and four engine), there are really three different speed-fuel use curves.
While this phenomenon
is
real
and
exists for
all
ship classes regardless
of their type of prime mover,
the regressions produced here correspond to a single speed-fuel use relationship
the transitions between plant configurations.
11
which "smoothes"
Still
study,
one could
separate fuel prediction functions depending on plant configuration.
fit
Ref (9), suggested using three
different fuel
One
use prediction functions depending on ship speed.
This would require more detailed information and more computational complexity for each ship
class.
However, given the
configuration, and
many
total
number of
variables involved (sea state, hull condition, plant
others for which specific information will not exist off-ship), these
complications seem unwarranted.
4.4
The Regression Software
The
statistical
package used to perform the regressions for each ship class was S-PLUS
published by Statistical Sciences, Inc.,
Ref (12). This PC-based software computes
the values of
the three parameters of Equation (4) using the minimization of the standard error of the estimate as
its fit criterion.
4.5
The Results
The
results, values
of the
fitted
regression parameters, tabulation of the numerical actual and
predicted fuel use in gallons per hour as a function of speed, and plots of the actual data and the
prediction function for 21
6.
USN ship classes is presented in the Appendix.
CONCLUSION
As
stated in the Introduction, the authors' use of ship fuel use prediction functions
connection with a battle group logistics support system.
is in
The support system allows the planning
for,
tracking of) and prediction of future fuel and ordnance consumption and replenishment requirements.
It
can be argued that
if one
such prediction capability
is
can predict the daily
fuel
use of a given ship to within 1-2% of capacity,
adequate and useful for the purposes intended. With fuel reserve levels
12
of 50% or more, fueling-at-sea (FAS)
will
be required every 3-7 days for most surface combatants
depending on ship class and speed. Prediction errors of 1-2% of capacity per day are small enough
that
FAS requirements planning would
FAS was required by
a given ship.
indicate the correct
Of course the
exact hour
tactical situation allows daily ship reporting, daily
difficulties
of a given ship on a given day
prediction function
little
is
is
is
of little
real interest.
Further, if the
updates of predicted values to actual values can
be made eliminating the compounding of prediction
Review of the
day (but not the correct hour) on which
errors.
involved in ever making truly accurate predictions of the fuel use
instructive.
First there are
based: few sources of data, relatively
problems with the data on which any
little
data available from any source,
or no low speed data, and inconsistency between different data sources.
None of the
sources provide information on the plant condition, hull condition, temperature, sea
of which
effect fuel
consumption. Difficulties
in
data
state, etc., all
using any fuel use prediction function at sea in real
operations include knowing sea state, ship speed (something that varies often throughout a given day
depending upon the assigned activities of a given ship), and operational specifics which
that the ship has
is in
more horsepower on
plane guard role, the ship
is in
line
than
is
required for
by a planner or
For
all
above
These factors
will not
e.g.,
is
dictate
the ship
navigating
be known with any
afloat logistics coordinator.
these reasons the question
accurately, but rather
speed at a given time;
an underway replenishment evolution, the ship
restricted waters, the ship is in a high threat situation, etc.
certainty
its
may
is
not whether one can predict ship propulsion fuel usage
whether on can predict ship fuel use to a useful approximation.
that predicting ship fuel use to within
there are a plethora of reasons
why the
It
was argued
1-2% of capacity per day was adequate. Thus while
fuel use prediction functions in this report will not
13
produce
"spot on" accurate estimates of the fuel use of a given ship
in fact
on a given day,
produce operationally adequate and useful estimates.
14
it
is
asserted that they
do
REFERENCES
1.
D.A. Schrady, D.B. Wadsworth, R.G. Laverty, and W.S. Bednarski, Predicting Ship Fuel
Consumption, Naval Postgraduate School Technical Report NPS-OR-91-03, October 1990
2.
Naval Warfare Information Publication
of U.S. Navy Ships and Aircraft
3.
Naval Warfare Publication
Combatant Ships
4.
1 1
-20(D),
Volume
Missions and Characteristics
May
1974
Characteristics
and
(U), Confidential,
11 -1(B),
II,
Capabilities
of
U.S.
Navy
(U), Confidential, January 1983
Naval Warfare Publication
1
1-2(B), Characteristics
and Capabilities of U.S. Navy Auxiliary
Ships (U), Confidential, January 1983
5
Surface Warfare Officers School,
DD-963
Class Speed/RPM/Pitch Tables
and Fuel Curve
Tabular Data, undated (circa 1989)
6.
Interview between
COMNAVSURFPAC Operations Officer and LT Bednarski,
23 February
1990
7.
Telephone conversation between
LCDR Cate, COMPHIBRON 7, and LT Bednarski,
28 June
1990
RMC Provost, COMPHIBRON 9, and LT Bednarski, 23
8.
Interview between
9.
Jodi Tryon, CubeS:
A Model for
Calculating Fuel Consumption for Gas-Turbine Ships,
Center for Naval Analyses Research
10.
February, 1990
Memorandum
CRM 86-213,
October 1986
T.C. Gillmer and B. Johnson, Introduction to Naval Architecture, Chapter 11 "Ship
Resistance and Powering", Naval Institute Press, 1982
1 1
P.F. Pucci, Supplemental
Notes for Marine Gas Turbines,
class notes,
Naval Postgraduate
School, January 1990
12.
Statistical Sciences, Inc.,
S-PLUSfor Windows, Reference Manual, 1993
15
16
APPENDIX
This Appendix presents the fuel use prediction functions for 22 U.S.
is
organized as follows:
Ship Class
CV-63/67
CG-47/52
DDG-51
DD-963/DDG-993
FFG-7
PC-1
LCC-19
LHD-1
LHA-1
LPH-2
LPD-4/AGF-11
AGF-3
LSD-41
LSD-36
AD-37
AOE-6
AOE-1
AOR-1
TAE-26
TAFS-1
AO-177(J)
TAO-187
17
Navy
ship classes
and
Class: CV-63/67
Source: NWIP 11-20
(D)
Speed KGal.Hr Predicted
NA
1928 .3
.0
1928 .6
1 .0
NA
NA
1931 .1
2 .0
1937 .9
NA
3 .0
1954 .6
4 .2
1653
1972 .7
NA
5 .0
6. .4
2021 .7
1905
7. .0
2050 .7
NA
2194
2164 .6
8 .7
2190 .1
9. .0
NA
2289 .1
10, .0
NA
2482
2424 .7
11 .1
2559 .3
12, .0
NA
2816, .8
2887
13, .4
2947, .3
14, .0
NA
3363, .0
15, .6
3392
3483, .9
16, .0
NA
4086, .1
17. .7
4143
4208, .7
18, .0
NA
5118, .6
19. .9
5081
5173, .5
20. ,0
NA
5766, .9
21. ,0
NA
6522, .0
22. .1
6510
7231, .2
23. ,0
NA
24. ,2
25. ,0
26. ,3
27. ,0
28. ,3
29. ,0
30. ,3
31. ,9
32. ,0
33. ,2
34. ,2
35. ,0
Formula
8503
26662
31672
NA
8325, .9
9164. .8
10747. ,9
11730. ,8
13845, .6
15165. .4
18022. .4
22438. ,4
22753. ,9
26973, .4
31201, ,4
35151, .4
KGal.Hr
-
NA
11014
NA
14146
NA
17842
21941
NA
cbind
Parameters
Value Std. Error
32.6666
1.41696
b
-8937.6000 894.86500
10865.9000 822.30100
exp(b
*
(Speed/100) "3)
t value
23.05400
-9.98766
13.21400
Residual standard error: 264.591 on 13 degrees of freedom
U
1
I
^f(lo z
t
t$9tt
CV-63/67
O
O
o
o
CO
(0
o
o
o
o
CM
o
o
o
o
20
10
Speed
(Knots)
19
30
Class: CG-47/52
Source: NAVSEA Trials
Speed KGal.Hr Predicted
786.4
NA
786.4
1
NA
787.0
NA
2
788.6
NA
3
791.7
4
NA
796.8
NA
5
804.4
NA
6
7
NA
815.0
NA
829.3
8
847.7
NA
9
871.0
10
NA
899.7
11
NA
934.6
12
NA
976.5
13
NA
NA
1026.3
14
1085.1
15
NA
1076
1154.0
16
17
1172
1234.3
1327.6
18
1287
1435.8
19
1414
1700
1561.0
20
1705.7
21
1827
1873.0
22
1966
2116
2066.5
23
2272
2290.5
24
2427
25
2550.2
2852.1
26
2605
3204.0
27
3364
3687
3615.2
28
4097.7
29
4065
4666.0
30
4622
5372
31
5338.2
6137.0
32
NA
7091.3
33
NA
34
8237.4
NA
NA
9621.9
35
Formula: KGal.Hr ~ cbind(l, exp(b
:
Speed/100) "3)
Parameters
Value Std. Error t value
5.77862
37.4831
6.4865
b
-1429.0400
727.9950 -1.96298
646.2490 3.42807
2215.3900
Residual standard error: 113.925 on 13 degrees of freedom
20
CG-47/52
o
o
o
CO
o
o
o
CD
CO
O
O
O
o
o
o
o
20
10
Speed (Knots)
21
30
Class DDG-51
Source: NAVSEA Trials
Speed KGal.Hr Predicted
NA
615.2
NA
615.3
1
NA
615.8
2
NA
617.1
3
NA
619.8
4
NA
624.1
5
NA
630.7
6
7
NA
639.8
NA
652.1
8
613
668.1
9
:
658
700
741
784
832
886
950
1025
1115
1222
1348
1496
1669
1920
2070
2280
2460
2780
3730
4220
4800
5600
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
Formula
NA
NA
NA
NA
:
KGal.Hr
:
688.2
713.3
743.8
780.8
825.0
877.6
939.8
1013.2
1099.5
1200.9
1320.1
1460.2
1625.3
1820.1
2050.7
2324.9
2652.0
3044.3
3517.3
4091.0
4791.2
5651.6
6716.8
8045.6
9716.9
11837.3
-
cbindd, exp (b
Parameters
Value Std. Error
b
51 .5925
3 .76872
-764 .4330 220 .15400
1379 .6200 191 .05800
t
(Speed/100)
A
3)
value
13 .68960
-3 .47226
7
.22095
Residual standard error: 98.2004 on 20 degrees of freedom
22
DDG-51
03
CD
Speed (Knots)
23
Class: DD-963/DDG-993
Source: NWP 11-1 (B)
Speed KGal.Hr Predicted
NA
1285 .0
NA
1285 .1
1
1285 .7
NA
2
1287 .3
NA
3
NA
1290 .4
4
NA
1295 .5
5
NA
1303, .2
6
7
NA
1313. .9
NA
1328. .3
8
NA
1346. .8
9
1370, ,0
10
NA
1398, .7
11
NA
1433, .4
12
NA
1474, .9
13
NA
1523, .9
14
NA
1581, .4
15
NA
1648, .3
1600
16
17
1725, .7
NA
1814, .8
1800
18
1917, .0
NA
19
2034, .0
20
2100
2167, .6
21
NA
2319, .9
22
2350
2493, .3
23
NA
2700
2690, .9
24
2915, ,8
25
NA
26
3172. .3
3150
3464, .8
27
NA
3799, ,1
28
3750
NA
4181, .8
29
4620, ,8
4650
30
5125, .6
31
NA
5707, .9
32
NA
NA
6381, .4
33
34
NA
7163, .3
8074, .2
NA
35
Formula: KGal.Hr ~ cbind(l, exp(b
*
(Speed/100) "3
)
Parameters
Value Std. Error t value
27.0667
5.42175 4.99225
b
-1812.9200 951.22300 -1.90588
3097.9700 898.85400 3.44658
Residual standard error: 48.2 814 on
5
24
degrees of freedom
DD-963/DDG-993
O
O
o
CO
o
o
o
o
CO
o
o
o
CO
Speed
(Knots)
25
Class FFG-7
Source: NWP 11- KB)
Speed KGal.Hr Predicted
NA
405.4
1
NA
405.5
405.8
2
NA
NA
406.7
3
NA
408.6
4
NA
411.6
5
416.1
6
NA
NA
422.5
7
NA
431.0
8
NA
442.1
9
NA
456.1
10
NA
473.4
11
472
494.6
12
13
NA
520.2
553
550.9
14
587.4
15
NA
16
649
630.6
17
NA
681.6
764
741.5
18
19
NA
811.9
894.7
20
914
21
NA
992.1
1087
1106.9
22
23
NA
1242.4
24
1313
1402.9
NA
1593.8
25
26
1917
1821.7
27
2095.2
NA
28
2400
2425.1
NA
2825.5
29
NA
3314.6
30
31
NA
3916.1
NA
4661.4
32
33
NA
5592.0
6763.5
34
NA
8251.4
NA
35
Formula: KGal.Hr
~
cbindd, exp(b
*
(Speed/100) ^3
)
)
Parameters
Value Std. Error t value
51.8843
11.1081 4.67084
b
-545.7160
382.7230 -1.42588
951.1170
344.6340 2.75979
Residual standard error: 57.6276 on
6
26
degrees of freedom
FFG-7
o
o
o
CO
o
o
in
c\j
CO
o
o
o
CM
O
o
o
o
o
o
o
o
in
Speed
(Knots)
27
Class: PC-1
Source Ship tests
Speed KGal.Hr Predicted
11.0
NA
11.0
1
25
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
Formula
11.2
11.8
12.8
14.5
17.0
20.5
25.2
31.2
38.6
47.5
58.2
70.7
85.0
101.4
119.7
140.1
162.6
187.2
213.7
242.2
272.6
304.7
338.4
373.5
409.9
447.2
485.4
524.1
563.1
602.1
640.9
679.3
716.9
753.6
25
25
25
25
25
25
25
25
33
47
48
54
61
2
83
107
132
159
186
216
246
277
310
344
378
414
450
487
525
564
603
642
683
710
750
:
KGal.Hr
~
cbind
(Speed/100) ~3)
Parameters
Value Std. Error t value
-24.3044
1.30277 -18.6560
b
41.26510 28.0692
1158.2800
-1147.2600
40.20400 -28.5360
Residual standard error: 9.24413 on 32 degrees of freedom
28
PC-1
o
o
CO
o
o
CO
o
o
o
CM
o
-
Speed
(Knots)
29
Class: LCC-19
Source: NWP 11- KB)
Speed KGal Hr Predicted
791.6
NA
791.7
NA
1
792.2
2
NA
793.7
NA
3
796.7
4
NA
801.6
5
NA
NA
808.9
6
7
NA
819.2
833.3
8
NA
NA
851.6
9
875.3
10
873.6
905.1
11
NA
12
945.0
942.4
NA
988.6
13
1045.8
1045.8
14
1116.2
15
NA
1201.2
1203.1
16
17
NA
1310.5
1444.8
1443.8
18
1610.0
NA
19
1818.6
1818.8
20
NA
2083.1
21
2420.7
22
NA
NA
2856.5
23
3425.4
24
NA
4177.4
25
NA
5184.4
26
NA
27
NA
6552.6
8439.7
28
NA
11084.6
29
NA
NA
14854.7
30
31
NA
20325.2
28411.5
32
NA
40598.4
33
NA
34
59340.1
NA
NA
88773.2
35
.
Formula
i
:
KGal Hr
.
-
cbind
(Speed/100)
A
3)
Parameters
Value Std. Error t value
2 80061 40.32750
b 112.9410
92.0583
29.36670 3.13479
27.14310 25.77270
699.5530
Residual standard error: 2.18812 on
3
30
degrees of freedom
LCC-19
O
o
o
CO
CD
o
o
m
o
o
o
Speed
(Knots)
31
Class: LHD-1
Source: NAVSEA Propulsion Branch
Speed KGal.Hr Predicted
NA
1338. .6
NA
1338. .8
1
NA
1339, ,9
2
NA
1342. .9
3
1348, .8
NA
4
NA
1358. .6
5
NA
1373, .3
6
7
NA
1394, .0
1421, .9
NA
8
NA
1458, .3
9
1504, .5
NA
10
1562, .3
NA
11
1633, .7
1489
12
1720. .9
NA
13
1845
1826, .8
14
NA
1954, .6
15
2080
2108, ,7
16
2294, .1
NA
17
2517, .2
2700
18
2786, .4
NA
19
3280
3111, .9
20
3507, .0
NA
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
lUla:
3893
NA
5000
6433
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
KGal Hr
.
3989, .2
4580
.8
5311, .6
6221, .0
7362, .0
8806. .4
10652. .4
13036, .3
16148
20258
25753
33193
43404
57614
.4
.5
.4
.9
.8
.9
~ cbii id
exp(b
*
(Speed/100) "3)
Parameters
t value
Value Std. Error
27.5816 2.835550
78.209
b
-700.811 1458.8500 -0.480386
2039.410 1276.9900 1.597040
Residual standard error: 216.814 on
5
32
degrees of freedom
LHD-1
O
O
O
ID
O
O
O
O
Us
o
o
o
to
Speed
(Knots)
33
Class: LHA-1
Source: COMPHIBRON
9
Speed KGal.Hr Predicted
952 5
NA
952 7
NA
1
4
NA
NA
NA
5
961.8
6
NA
NA
NA
NA
NA
NA
2
3
7
8
9
10
11
1398.6
1570.8
1751.4
1936.2
2100.0
2242.8
2499.0
2977.8
3498.6
3897.6
4300.8
4888.8
5703.6
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
28
29
30
31
32
33
34
35
Formula
:
KGal.Hr
954 5
959 4
968 9
984 6
1008 2
1041 2
1085 3
1142 4
1214 4
1303 4
1411 7
1541 8
1696 6
1879 3
2093 8
2344 3
2635 8
2974 4
3366 8
3821 6
4348 5
4959 5
5669 1
6494 5
7457 2
8583 .3
9905 .0
11462 .3
13304 9
15495 5
18112 9
21257 2
25056 4
29675 2
- cbii id
exp(b
*
(Speed/100) "3)
Parameters
Value Std. Error t value
8.20849 4.79093
39.3264
b
-5577.6800 1811.58000 -3.07890
6530.1500 1768.23000 3.69305
Residual standard error: 78.8921 on 11 degrees of freedom
34
LHA-1
O
O
o
o
CO
o
o
o
o
CO
o
o
o
CM
Speed (Knots)
35
Class LPH-2
Source: COMPHIBRON
9
Speed KGal.Hr Predicted
NA
475.6
475.7
1
NA
2
NA
476.5
478.8
3
NA
4
NA
483.2
5
504.0
490.5
6
NA
501.3
7
NA
516.2
8
NA
535.8
9
NA
560.5
579.6
10
590.8
11
621.6
626.8
668.7
12
663.6
714.0
716.5
13
14
768.6
769.9
15
831.6
828.5
16
898.8
891.8
17
966.0
959.0
18
1029.0
1029.4
19
1100.4
1101.8
20
1171.8
1175.3
21
1248.7
NA
22
1321.0
NA
23
NA
1391.1
24
NA
1458.1
25
1521.0
NA
26
NA
1579.3
27
NA
1632.3
28
1679.9
NA
29
NA
1721.9
1758.3
30
NA
31
1789.3
NA
32
NA
1815.3
1836.7
33
NA
34
1854.0
NA
35
NA
1867.7
Formula
:
KGal Hr - cbind
.
(Speed/100) *3)
Parameters
Value Std. Error
t value
-83.8886
b
9.50305 -8.82755
1906.9000 117.77400 16.19120
-1431.3100 113.67300 -12.59150
Residual standard error: 7.37016 on
9
36
degrees of freedom
LPH-2
o
o
CM
O
O
O
CO
O
O
O
CD
O
O
CD
Speed
(Knots)
37
Class: LPD-4/AGF-11
Source: COMPHIBRON 9
Speed KGal.Hr Predicted
442,.4
NA
NA
442 .5
1
443 .6
2
NA
NA
446 .4
3
452 .0
4
NA
461 .2
462.0
5
475 .0
6
NA
494. .5
7
NA
520, .8
NA
8
NA
555, .3
9
592.2
599, .3
10
651.0
654, .6
11
723, .4
726.6
12
808, .0
814.8
13
911. .6
14
919.8
1038. .0
15
1041.6
1184.4
1192. .1
16
17
1369.2
1380. .0
18
1608.6
1609. .6
1891, .2
19
1902.6
2238. .3
20
2234.4
2668. .5
21
NA
3205. .4
NA
22
NA
3881. ,1
23
4739. .0
24
NA
5839. .0
25
NA
7264. .5
26
NA
27
NA
9133. .6
11614. .6
28
NA
NA
14951. .4
29
NA
19502. .1
30
NA
25799. .6
31
34649. .4
32
NA
47287. ,4
33
NA
34
NA
65640. .3
NA
92761. .4
35
Formula: KGal.Hr ~ cbind(l, exp(b
*
(Speed/100 ^3
)
)
Parameters
Value Std. Error t value
b
95.4647
3.81939 24.9947
-1124.4300
94.06040 -11.9543
89.75880 17.4556
1566.7900
Residual standard error: 7.61684 on
9
38
degrees of freedom
LPD-4/AGF-1
o
o
o
CO
o
o
in
CM
o
o
o
CM
CD
o
o
o
o
o
o
o
IC
10
15
Speed
(Knots)
39
20
Class AGF-3
Source COMPHIBRON
9
Speed KGal.Hr Predicted
306.2
NA
306.3
NA
1
307.2
2
NA
NA
309.8
3
314.6
4
NA
322.7
378.0
5
334.8
NA
6
7
351.8
NA
374.6
NA
8
NA
404.2
9
441.6
399.0
10
488.0
NA
11
529.2
544.7
12
613.3
13
596.4
680.4
695.4
14
789.6
793.0
15
919.8
908.6
16
1045.0
17
1058.4
1205.5
1230.6
18
1407.0
1394.2
19
1591.8
1616.1
20
1877.3
21
NA
2185.1
22
NA
NA
2548.8
23
2979.8
24
NA
3492.7
NA
25
4105.5
26
NA
4841.3
27
NA
5729.8
28
NA
NA
6808.9
29
8128.2
30
NA
9752.3
31
NA
11766.6
32
NA
14284.5
NA
33
17458.1
34
NA
21493.5
35
NA
Formula l: KGal.Hr ~ cbind
1,
exp(b
*
(Speed/100)
A
3))
Parameters
Value Std. Error t value
20.7173 2.52152
b
52.2391
-2218.8100 1246.8100 -1.77959
2525.0000 1228.5600 2.05525
Residual standard error: 30.2495 on
8
40
degrees of freedom
AGF-3
O
O
O
CM
O
O
lO
CO
o
o
o
o
o
o
in
Speed
(Knots)
41
Class: LSD-41
Source: COMNAVSURFPAC & COMPHIBRON
7
Speed KGal.Hr Predicted
NA
238 .7
NA
238 ,8
1
2
NA
239 .5
NA
241 .3
3
244, .7
4
NA
289.8
250 .4
5
NA
258 .9
6
270 .8
7
NA
NA
286 .7
8
NA
307 .0
9
10
298.2
332 .4
NA
11
363 .5
361.2
400 .8
12
444, .9
13
NA
533.4
496 .5
14
NA
556, .0
15
596.4
624, .2
16
701, .7
17
NA
831.6
789, .0
18
NA
886 .8
19
978.6
995, .9
20
NA
1116. .8
21
1250, .3
22
NA
NA
1397, .2
23
1558, .1
24
NA
NA
1733, .9
25
NA
1925, .3
26
2133, .2
27
NA
NA
2358, .6
28
NA
2602, .2
29
2865, .1
30
NA
NA
3148, .4
31
32
NA
3453, .0
3780, .2
NA
33
34
NA
4131, .0
4506, .8
35
NA
Formula: KGal.Hr ~ cbind
(
1
,
exp(b
*
(Speed/100)
A
3
)
Parameters
Value Std. Error
t value
b
2.86188
62.5409 0.0457601
-32454.80000 722767.0000 -0.0449035
32693.50000 722740.0000 0.0452355
Residual standard error: 46.2148 on
4
degrees of freedom
42
LSD-41
O
o
CM
O
O
O
O
O
co
CO
O
o
o
O
o
"3-
o
o
CO
Speed
(Knots)
43
Class: LSD-36
Source: COMNAVSUEFPAC & COMPHIBRON 7
Speed KGal.Hr Predicted
NA
409.0
409.1
NA
1
409.9
2
NA
411.9
NA
3
415.8
4
NA
453.6
422.3
5
432.0
NA
6
7
445.8
NA
NA
464.3
8
NA
488.6
9
519.7
10
491.4
NA
11
558.8
607.4
12
588.0
NA
13
14
15
16
17
739.2
NA
961.8
NA
1239.0
18
19
20
21
22
23
24
25
26
27
28
29
30
31
NA
1688.4
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
32
33
34
35
ormula
:
KGal.Hr
667.3
740.8
830.7
940.5
1074.7
1239.1
1441.4
1691.6
2003.0
2393.2
2886.6
3516.3
4328.1
5386.6
6783.6
8651.0
11181.7
14661.1
19518.3
26407.9
36344.4
50927.0
72719.0
~
cbind
(Speed/100) "3)
Parameters
Value Std. Error t value
98.678
20.8562 4.73136
b
-657.897
343.2460 -1.91669
328.0590 3.25227
1066.930
Residual standard error: 25.6232 on
4
degrees of freedom
44
LSD-36
o
o
o
CM
O
o
in
CO
o
o
o
o
o
Speed
(Knots)
45
Class: AD-37
Source NWP 11- 2(B)
Speed KGal.Hr Predicted
306.8
NA
306.9
1
NA
NA
308.0
2
311.0
NA
3
NA
316.8
4
326.4
5
NA
NA
340.7
6
360.7
7
NA
387.5
8
386
NA
422.1
9
465.7
10
466
519.4
11
NA
584.7
12
584
663.0
13
NA
760
755.8
14
15
NA
865.0
16
992
992.6
17
1141.0
NA
1312.9
18
1310
19
NA
1511.3
1741
1739.8
20
21
NA
2002.7
2305.0
22
NA
2652.5
23
NA
24
3052.3
NA
3512.6
25
NA
26
NA
4043.6
27
4657.3
NA
28
NA
5368.3
29
NA
6194.5
7157.6
30
NA
NA
8284.4
31
32
NA
9608.0
NA
11169.4
33
34
NA
13019.9
15223.9
NA
35
Formula: KGal.Hr ~ cbindd, exp(b
*
(Speed/100) ^3
)
Parameters
Value Std. Error t value
2.15993 15.4721
b
33.4188
-4368.6500 348.04700 -12.5519
4675.4300 346.06200 13.5104
Residual standard error: 2.76573 on
4
46
degrees of freedom
AD-37
o
o
o
CM
O
O
CO
O
o
o
o
o
m
10
15
Speed
(Knots)
47
20
Class: AOE-6
Source NAVSEA 03XN
Speed KGal.Hr Predicted
-115.2
0.0
NA
-114.8
1.0
NA
-112.6
NA
2.0
-106.6
3.0
NA
-95.0
4.0
NA
-75.8
5.0
NA
-47.2
6.0
NA
-7.4
7.0
NA
NA
45.3
8.0
112.6
9.0
NA
NA
196.2
10.0
11.0
NA
297.6
NA
417.9
12.0
13.7
580
669.6
720.5
14.0
NA
15.0
NA
904.4
1292.7
16.8
1420
17.0
NA
1340.4
1592.8
18.0
NA
NA
1925.5
19.2
2165.0
20.0
NA
21.0
NA
2483.4
22.0
NA
2821.9
3178.9
23.0
NA
24.2
3575
3629.4
3941.5
25.0
NA
4390
4342.6
26.0
27.2
4695
4836.9
28.3
5298.8
5435
29.8
5910
5935.0
30.0
NA
6019.9
6443.3
31.0
NA
6862.5
32.0
NA
7274.9
33.0
NA
7677.6
34.0
NA
8068.1
35.0
NA
Formulc l: KGal.Hr ~ cbind
(Speed/100)^3)
Parameters
Value Std. Error t value
-25.7866
8.92627 -2.88885
b
12117.2000 2993.10000 4.04836
-12232.3000 2873.71000 -4.25663
Residual standard error: 131.131 on
4
48
degrees of freedom
AOE-6
O
o
o
10
CO
o
o
o
CO
o
o
o
o
-
Speed
(Knots)
49
Class: AOE-1
Source: NWIP 11-20
(D)
Speed KGal.Hr Predicted
267.8
NA
NA
268.1
1
270.5
NA
2
NA
277.0
3
NA
289.6
4
NA
310.5
5
6
NA
341.6
7
NA
385.0
NA
443.0
8
517.5
9
NA
10
NA
610.9
11
NA
725.4
12
NA
863.4
1027.2
13
NA
14
1259
1219.5
15
1470
1442.9
1712
16
1700.3
17
1980
1994.8
2300
18
2329.5
19
2690
2708.1
20
3100
3134.3
21
3560
3612.3
22
4150
4146.7
4750
23
4742.5
24
5440
5405.2
25
6130
6140.9
7000
26
6956.4
27
7930
7859.3
28
8780
8858.1
29
NA
9962.2
30
NA
11182.4
31
NA
12530.5
32
NA
14020.3
15667.0
33
NA
34
NA
17488.1
35
NA
19503.5
Formula
:
KGal.Hr
~
cbind
(Speed/100) ~3)
Parameters
Value Std. Error t value
12.2579
1.7891
b
6.85145
-27553.4000 4703.1800 -5.85846
27821.2000 4668.4700 5.95939
Residual standard error: 42.9629 on 12 degrees of freedom
50
AOE-1
o
o
o
o
o
o
o
00
CO
o
o
o
CO
(3
o
o
o
o
o
o
CM
o
-
Speed
(Knots)
51
Class: AOR-1
Source: NWIP 11-20
(D)
Speed KGal Hr Predicted
NA
280.3
280.5
1
NA
NA
282.2
2
286.8
NA
3
295.7
4
NA
NA
310.4
5
332.3
6
321
363.0
7
358
403.9
404
8
462
456.5
9
.
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
538
619
707
778
965
1120
1290
1517
1760
2010
2340
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
Formula: KGal.Hr
522.6
603.7
701.5
817.9
954.8
1114.2
1298.4
1509.8
1751.0
2024.9
2334.6
2683.7
3076.1
3516.2
4008.9
4559.9
5175.4
5862.6
6629.7
7485.9
8442.1
9510.5
10705.2
12042.7
13542.0
15225.3
-
cbindd, exp(b
*
(Speed/100) ^3
)
)
Parameters
Value Std. Error t value
b
16.3917
6.05011 2.70932
-14380.5000 5760.32000 -2.49647
14660.8000 5754.35000 2.54777
Residual standard error: 15.4594 on 12 degrees of freedom
52
AOR-1
O
o
o
CO
o
o
in
CM
o
o
o
C\J
CO
O
o
o
o
o
o
o
o
m
Speed (Knots)
53
Class: AE/TAE-26
Source: NWIP 11-20
(D)
Speed KGal.Hr Predicted
NA
193 4
NA
1
193 6
NA
194 6
2
NA
197 3
3
NA
4
202 6
NA
211 3
5
NA
6
224 3
7
NA
242 5
NA
266 6
8
NA
297 5
9
NA
10
336
11
NA
382 9
NA
439
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
505
581 6
669 5
769 4
881 8
1007 3
1146 3
1299 3
1466 5
1648 3
1844 9
2056 4
2282 7
2523 8
2779 6
3049 7
3333 9
3631 6
3942 3
4265 3
4600
4945 3
5300 6
520
600
660
750
860
990
1140
1325
1530
1600
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
Formula: KGal.Hr
~
cbind(l, exp(b
*
(Speed/100) ^3
)
Parameters:
Value Std. Error
t value
-8.86595
26.0028 -0.340962
b
16343.70000 44805.3000 0.364772
-16150.30000 44747.7000 -0.360919
Residual standard error: 35.6091 on
7
54
degrees of freedom
AE/TAE-26
O
O
CO
o
o
CVJ
CO
o
o
CO
o
o
CO
o
o
o
o
CM
Speed
(Knots)
55
Class: AFS/TAFS-1
Source: NWIP 11-20
(D)
Speed KGal Hr Predicted
255.8
NA
NA
255.9
1
NA
256.6
2
258.4
3
NA
NA
261.9
4
NA
267.8
5
276.6
6
NA
7
NA
289.0
289
305.6
8
327.1
321
9
354.4
10
353
.
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
396
433
490
546
620
700
803
910
1040
1210
1429
1650
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
Formula: KGal.Hr
388.3
429.7
479.9
540.0
611.7
696.8
797.4
916.2
1056.3
1221.6
1416.9
1648.1
1922.5
2249.5
2640.8
3111.2
3679.9
4371.4
5217.8
6261.1
7557.0
9179.4
11228.1
13838.1
17194.6
-
cbindd, exp(b
Parameters
Value Std. Error
55.5118
4.56171
b
-1471.6600 191.51800
1727.4600 186.86200
*
(Speed/100) "3
)
)
t value
12.16910
-7.68422
9.24459
Residual standard error: 10.0922 on
12
56
degrees of freedom
AFS/TAFS-1
o
o
CD
O
O
CM
CO
O
o
o
00
o
o
CD
O
O
Speed
(Knots)
57
Class: AO-177(J)
Source: NAVSEA Trials
Speed KGal Hr Predicted
.
0.0
1.0
2.0
3.0
4.0
5.0
6.0
7.3
8.6
9.0
10.5
11.0
12.0
13.4
14.0
15.8
16.0
17.0
18.0
19.3
20.4
21.5
22.0
23.0
24.0
25.0
26.0
27
28
29
30
31
32
33
34
35
NA
NA
NA
NA
NA
NA
NA
400..3
400
401
402
405
411
419
434
457
466
506
523
563
412
425
NA
537
NA
NA
663
.4
.0
.7
.9
.3
.4
.9
.5
.1
.7
.5
.0
633, .6
NA
670
.3
837
809, .4
NA
NA
NA
932, .4
1058, .6
828
1212
1487
1775
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
Formula: KGal.Hr
1263
1483
.0
.8
.7
1758, .5
1905
.8
2252, .3
2684, .2
3227, .0
3915, .4
4797, .0
5938, .0
7431, .5
9409, .5
12062, .7
15668
.9
20638, .9
27588
37454
~
,4
.0
cbindd, exp(b
*
(Speed/100) "3
)
)
Parameters
Value Std. Error t value
83.9283
25.061 3.34896
b
-642.2810
476.553 -1.34776
1042.5700
457.702 2.27784
Residual standard error: 37.6589 on
5
degrees of freedom
58
AO-177(J)
O
O
m
CO
CD
o
o
o
o
o
IT)
Speed
(Knots)
59
Class: TAO-187
Source: MSC Trial s
Speed KGal.Hr Predicted
219.7
NA
NA
219.9
1
221.4
2
NA
NA
225.3
3
266
233.0
4
5
NA
245.6
288
264.3
6
7
NA
290.4
314
324.8
8
NA
368.5
9
422.6
10
388
487.8
11
NA
564.7
12
515
13
NA
653.8
14
760
755.4
15
NA
869.5
16
1024
996.0
17
NA
1134.4
1310
1284.2
18
19
NA
1444.4
1614.0
20
1594
1791.5
21
NA
22
NA
1975.5
2164.2
23
NA
NA
2356.0
24
NA
2548.8
25
2740.7
26
NA
27
2930.0
NA
28
NA
3114.7
29
NA
3293.2
3464.0
30
NA
31
NA
3625.6
3777.1
NA
32
NA
3917.5
33
4046.3
34
NA
4163.3
35
NA
Formula
:
KGal Hr
.
~
cbind(l
exp
(b
[
Speed/100)
A
3)
Parameters
Value Std. Error t value
-44.9642
23.4124 -1.92053
b
4834.5400 2036.3200 2.37415
-4614.8100 2024.1100 -2.27992
Residual standard error:
3
4.8975 on
6
degrees of freedom
60
TAO-187
O
O
O
CM
O
O
CO
O
o
o
o
o
o
in
Speed (Knots)
61
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20.
AUSTRALIA
Professor Charles Calvano
Department of Mechanical Engineering
Naval Postgraduate School
Monterey, CA 93943-5000
21.
Professor David Schrady
50
Department of Operations Research
Naval Postgraduate School
Monterey, CA 93943-5000
65
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