Comments on Reliable Prediction
Accuracy
A HydroComp Technical Report
Report 103
Product link: NavCad,
SwiftCraft
Introduction
A traditional
part of design has been the use of model testing to predict fullscale
performance. In the arena of speed and power prediction, however,
many designers are finding that predicting vessel performance
with contemporary software, such as NavCad or SwiftCraft, is creating
opportunities for shorter development cycles and improved designs
with a prediction accuracy typically associated with model testing.
The purpose
of any performance prediction is to accurately match the asbuilt
delivered ship  not its model  so the historical precision of
fullscale craft is a reasonable guide. One author cites deviations
in wetted surface of 4% and displacement of 5% to be common [Schneiders,
1989]. Since the ITTC lists standard model test errors at about
half of this figure [ITTC, 1978], there is an overall precision
in model testing that is significantly better than the typical
asbuilt vessel.
As can
be seen from the above evidence, the institutional precision of
model testing is quite reasonable, and well developed numerical
methods  like those in NavCad and SwiftCraft  share this accuracy.
An evaluation of the historical difficulties with numerical performance
prediction, and how the software addresses these problems to provide
a reliable, technically sound analysis platform will be described
below.
A history of numerical performance
prediction
The numerical
prediction methods found in the software are based on the statistical
manipulation of empirical (test) data where hull, appendage and
propeller data are based on "parametric" values. The exact threedimensional
description of the vessel or propulsor is not required. Rather,
the data is described by hull parameters such as length on waterline,
displacement and wetted surface, or by propeller parameters like
diameter, pitch and blade area ratio. (To simplify the following
comments, only hull performance  such as resistance and shaft
power  will be addressed further since the history and basic
procedures for propeller performance prediction parallel those
for hull performance.)
The use
of parametric prediction actually goes back some hundred years
or more [Froude, 1888]. The techniques employed today however
 while still utilizing solid fundamentals established long ago
 are far removed from the original method of Froude. Advances
in basic methodology, test procedures and numerical analysis have
greatly enhanced the breadth and accuracy of parametric prediction.
One important
contribution was the introduction of the "methodical series" or
"statistical series". By testing a series of hull models in a
systematic manner, one could assess the effect of particular hull
changes on performance. In a typical series  Series 60, for example
[Todd, 1963]  a "parent" hull is created that has certain design
features, such as sectional area curve, turn of bilge, bow flare,
entrance angle, for example. A matrix of models is then built
that vary the principal shape parameters (L/B, B/T, Cp, etc.)
in a systematic fashion to determine the effect on resistance
of each of these principal parameters.
During
the historical development of these model test series, prediction
methodologies also went through an evolutionary process. One significant
example the change in technique is with the friction line. No
fewer than four different recommended skin friction lines have
been established during the last fifty years [SNAME, 1988]. This
may not seem like many, but each different friction line has required
a different recommended value for the model scale to ship scale
correlation allowance.
Another
maturing technique has to do with the analysis of the wavemaking
(or residuary) component of resistance. This was originally segregated
as a twodimensional residuary resistance [Froude, 1888], and
grew into the currently recommended practice of treating this
as threedimensional viscous and wavemaking resistances [ITTC,
1978]. Like the different skin friction methods, each of the wavemaking
approaches utilizes different recommended values for the modelship
correlation allowance.
One significant
advance in the parametric prediction of hull performance came
with the use of statistical regression as a means to develop the
results of systematic series into numerical formula. Since its
introduction [Doust, 1959], a broad range of vessel types and
tests have been regressed into numerical relationships.
Of course,
performance prediction is not complete without the significant
"added resistances", so numerical methods have been developed
for added appendage, wind, seas and shallow water resistances.
These items are often difficult to predict through a model test
program and are frequently derived numerically.
Typical difficulties
with numerical performance prediction and proposed solutions
Due to
less than successful experiences, many designers are justifiably
skeptical about the ability to accurately predict performance.
However, poor prediction accuracy is generally due to misuse of
the methodologies, rather than inherent problems with the methodologies
themselves. Some of these typical problems  and how the software
addresses and corrects them  are outlined below.
PROBLEM: Incorrect selection
of a parametric prediction model (e.g. series or method)
Without
a doubt, the most significant contributor to poor prediction accuracy
is due to inappropriate selection of the basic parametric model.
For example, it is unwise to use a cruiserstern statistical series
like the Taylor series [Taylor, 1943] to evaluate highspeed,
transomstern displacement vessels. However, many designers do
just that.
SOLUTION: A broad selection
of parametric models
In a setting
where only a few parametric models are used and designers apply
these few methods to a broad range of hull types, it is easy to
see how errors can occur. In a comprehensive software, designers
need not make compromises that lead to such errors.
HydroComp
software provide the largest commerciallyavailable library
of prediction methods for a wide range of hull types and parameters.
By appropriately selecting a method from the dozens of displacement,
semidisplacement, planing, barge train, sailing yacht and catamaran
routines, a designer can eliminate errors derived from using an
unsuitable method. This software also aids in the selection of
a parametric model by providing parameter range windows that display
limits of the available methods in relation to the subject hull.
PROBLEM: Using incompatible
components
Too often,
designers incorrectly mix prediction routines which are incompatible
with each other. For example, in an attempt to employ a recommended
contemporary approach, a designer will predict frictional resistance
with the newer ITTC friction line [ITTC, 1957]. This designer
will then use a frequently quoted correlation allowance of "0.0004",
which was derived from analyses with the older Schoenherr friction
line [Schoenherr, 1932] and is incompatible with the ITTC recommendations.
SOLUTION: Compatible
components are readily available
With properly
developed software, the calculation process naturally guides the
designer toward the appropriate choice of compatible prediction
components. For example, either the ITTC or ATTC friction lines
may be used. Then, to insure compatibility, the software offers
a selection of correlation allowance estimates with suitable values
for either friction line.
In another
instance, the software offers specific propulsive coefficient
predictions for various hull types and propeller configurations.
A designer is not forced to use an openwheel figure if a ducted
propeller is being installed, for example.
PROBLEM: Forgetting
necessary components
Wind, appendages,
seas and shallow water all contribute to the real performance
of the vessel at sea. Many times, however, designers will neglect
some or all of these, or base added resistances on simple guesses.
As these contribute significantly to total resistance  appendages
for fast craft or the effect of shallow water can be as much as
25% of the total, for example  it is apparent that poorly defining
these added resistances can greatly reduce prediction accuracy.
SOLUTION: A complete
analysis environment
One source
of prediction error is simply to forget one or more important
parts of the picture. By placing all of the necessary routines
in front of a designer, the software helps to insure that no part
is accidentally omitted.
A designer
has complete control over the analysis. No assumption is hidden
and no preset methodology or calculation path is needlessly forced
on a user. In every possible way, the software offers complete
freedom to run the predictions and analyses in any manner the
designer chooses. To further aid the designer in preparing a complete
and errorfree evaluation, the software has a comprehensive error
and data checking system that helps to discover potentially hidden
errors.
The help
system places a complete library of methodology, data definitions
and descriptions of the parametric models in the hands of the
designer. Included in this help system are comments by users and
industry experts that help a user to choose the best methods and
to guide a designer toward an accurate prediction.
PROBLEM: Using poorly
developed regression methods
Simply
because a regression formula is based on model tests is no guarantee
that the developed numerical regression will generate accurate
results. Occasionally, methods are presented that do not follow
a wellplanned statistical development, leading to what is termed
an "unstable" formula. These unstable relationships may quite
accurately predict the original test data points, but fall apart
between points and in extrapolating beyond the data set. A welldeveloped
regression, on the other hand, accurately forecasts the trends
across the range of data, as well as for the test data itself.
SOLUTION: The nature
of statistical analysis
The very
nature of the statistical manipulation of test data can help insure
good results with the software. Two features of the regression
process add to prediction reliability.
First,
regression helps to smooth the effect of any one test into the
results of the remaining tests of the series. While the results
of any one particular model or test may be suspect, the regression
process is "selfregulating" and the series as a whole remains
wellbehaved and of good quality.
Second, HydroComp has
reviewed the available statistical prediction methods, and only
those that are technically valuable to a designer and were appropriately
developed are included in the software.
PROBLEM: Using outmoded
methodologies
Evolutionary
changes in hull form have demanded that numerical methods keep
pace. Many designers, however, do not have enough time or interest
to keep abreast of contemporary methodologies which are suitable
for these newer hull forms and speed regimes. For example, the
twodimensional (CfCr) structure produced acceptable results
while vessels remained fairly slow, where friction was the major
contributor and wavemaking was wellbehaved and consistent. As
vessels became faster and new hull performance characteristics
emerged, the twodimensional approach proved unsuitable, and gave
way to the threedimensional formfactor method [ITTC, 1978] and
to recent enhancements on this theme [Holtrop, 1988].
SOLUTION: Stateoftheart
methodologies
Most of
the methods in the software are based on collections of model
tests, so the mathematical accuracy is comparable to that of model
testing. Many of the routines also include the results of sea
trials, thereby enhancing the ability to predict values more in
line with fullscale vessels. They includes these newer parametric
models and insures their best use as described in the following
examples.

Some
of the available methods are based on older techniques, such
as the two dimensional Cr approach. To take full advantage
of current recommended practices (like the recent ITTC threedimensional
formfactor method) the software numerically recreates model
scale results via the original tested model length and friction
line. Then, the software allows the user to build these results
to fullscale based on the designer's preference to utilize
traditional approaches or to fully exploit contemporary practices
(twodimensional or threedimensional, ITTC or ATTC friction
line and correlation allowance).

For
planing craft, the software extends the simple prediction
of barehull resistance into an equilibrium trim analysis.
Here the effect of hull, flaps, propulsor, appendages and
wind are resolved into an equilibrium sum of all forces and
moments at each speed, resulting in a true dynamic trim. Each
component is then evaluated at this trim, insuring realistic,
not simplified, results.

The
shaft power analysis offers the use of a wake fraction scale
correction [ITTC, 1984], and establishes equilibrium state
propeller RPMs for both fixed and controllablepitch propellers
in a manner similar to selfpropelled model tests. Openwater
propeller efficiency is determined at these equilibrium RPMs,
thereby presenting true system efficiencies and powers, not
a value derived from a rudimentary formula.
Anchoring to realworld
data
Even though
NavCad offers a comprehensive collection of prediction methods,
a designer may encounter a vessel that does not neatly fit into
one of the available parametric models. In these cases, a designer
typically has had few options but to accept the results of the
closest method with some margin to cover the differences. It is
possible in many of these design situations to improve prediction
accuracy by using NavCad's ability to correlate a resistance prediction
to a model or fullscale test of a design variant or suitable
"family" hull.
Without
going into great detail, this feature "realigns" a prediction
to suit a particular set of test results. In other words, NavCad
first sees how well the prediction fares against the model and
then applies a correction to the prediction of the subject hull
based on this information. The resulting resistance values then
reflect the basic resistance "magnitude" of the selected parametric
method, in concert with the hydrodynamic "character" of the correlated
model.
Conclusion
HydroComp
software clearly offers an extremely costeffective means to predict
performance with an accuracy comparable to model testing  especially
when aligning to test data as described above. With a) complete
control of the methodology in the hands of the user, b) the ability
to rapidly evaluate many design options and c) a modest onetime
capital investment, the software can be a valuable alternative
to a designoriented model test program  particularly during
early and intermediate design stages.
Doust, D.J. and O'Brien,
T.P., "Resistance and Powering of Trawlers", NECIES, Vol. 75,
1959.
Froude, R.E., "On the 'Constant'
System of Notation of Results of Experiments on Models Used at
the Admiralty Experiment Works", Transactions INA, 1888.
Holtrop, J., "A Statistical
Resistance Prediction Method with a Speed Dependent Form Factor",
SMSSH, Varna, Bulgaria, 1988.
International Towing Tank
Conference, Proceedings of the 17th ITTC, Goteborg, Sweden, 1984.
International Towing Tank
Conference, Proceedings of the 15th ITTC, The Hague,
The Netherlands, published by the Netherlands Ship Model Basin,
Wageningen, 1978.
International Towing Tank
Conference, Proceedings of the 8th ITTC, Madrid, Spain, published
by Canal de Experiencias Hidrodinamicas, El Pardo, Madrid, 1957.
Schoenherr, K.E., "Resistance
of Flat Surfaces Moving Through a Fluid", SNAME Transactions,
Vol. 40, 1932.
Schneiders, C.C., "The
Prediction of Ship Performance: by Calculation or by Measurement?",
7th Lips Propeller Symposium, 1989.
SNAME, Principles of Naval
Architecture, Lewis E.V., Editor, 2nd Rev., Vol. 2, 1988.
Taylor, D.W., The Speed
and Power of Ships, 2nd Rev., U.S. Maritime Commission, 1943.
Todd, F.H., "Series 60
 Methodical Experiments with Models of SingleScrew Merchant
Ships", TMB Report No. 1712, DTRC, 1963.
