HANSA 01-2021

SCHiFFStECHNiK | SHiP tECHNoloGY
A hybrid hull performance prediction
Finnish technology company Wärtsilä Voyage developed a new model for predicting hull
performance that could improve accuracy while keeping costs low enough to make the
system viable fleetwide
The topic »fusion of high-frequency
navigational data and noon-reported
data to predict hull condition«
was presented and discussed during recent
Hull Performance & insight Conference
2020.
accurately predicting the impact of
hull condition on vessel performance allows
ship operators to better understand
and reduce their fuel consumption. But
typical methods of predicting hull performance
are far from ideal. our paper
explained how a new model for predicting
hull performance had been developed
and verified that could improve accuracy
while keeping costs low enough to
make the system viable fleetwide.
Hull performance predictions are
typically based either on high-frequency,
sensor-based data or low-frequency
noon report data. Methods using sensor
data are more accurate but are expensive
because they must be tailored
to each vessel and sensors require calibration.
Noon report-based methods can be
more easily implemented but are less
accurate due to averaging of inevitable
changes in speed, weather and other
factors between reporting intervals.
Wärtsilä Voyage’s third way combines
high-frequency data available through
standardised navigation equipment (such
as ECdiS) with noon reports and data
from third-party sources. By applying machine
learning to these data sources, this
approach achieves data quality similar to
high-frequency data logging, but with low
capital and operational costs comparable
with noon report-based logging.
Aggregation error
Hull performance prediction can suffer
from errors in measuring, modelling,
implementation or statistical analysis.
one significant statistical source of error
is introduced when basing hull performance
model calculations on noon
report data. Noon report data provides
average readings of various variables
affecting vessel performance, including
speed, fuel consumption and weather.
But these factors are rarely consistent
across a day. The more they fluctuate,
the less accurate the averaged readings
are and the greater the statistical error
that is introduced into the readings and
any subsequent calculations. as an example,
the charts on the left in the figure
shows timeline for speed and consumption
for non-constant speed operation.
Blue dots mark the high-frequency data.
The average and standard deviation are
plotted as orange lines. The speed-consumption
graph is given on the right,
plotting speed and aggregated consumption
in orange.
The aggregated consumption lies significantly
above the high-frequency data
points. This error is known as aggregation
error. When variables have changed
often throughout the aggregation period,
this can lead to a deviation of as much as
30% from the true consumption.
A timeline for speed and consumption.
Blue dots mark the high-frequency
data. The average and standard deviation
are plotted as orange lines
© Wärtsilä Voyage
48 HaNSa – international Maritime Journal 01 | 2021