ISO 19030-1:2016

INTERNATIONAL ISO
STANDARD 19030-1
First edition
2016-11-15
Ships and marine technology —
Measurement of changes in hull and
propeller performance —
Part 1:
General principles
Navires et technologie maritime — Mesurage de la variation de
performance de la coque et de l’hélice —
Partie 1: Principes généraux
Reference number
©
ISO 2016
© ISO 2016, Published in Switzerland
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ii © ISO 2016 – All rights reserved

Contents Page
Foreword .iv
Introduction .v
1 Scope . 1
2 Normative references . 1
3 Terms and definitions . 1
4 General principles . 2
4.1 Hull and propeller performance . 2
4.2 Ship propulsion efficiency and total resistance . 3
4.3 Primary parameters when measuring changes in hull and propeller performance . 4
4.4 Secondary parameters . 5
4.5 Measurement procedures. 5
4.5.1 General. 5
4.5.2 Data acquisition . 6
4.5.3 Data storage . 6
4.5.4 Data preparation . 6
5 Performance indicators . 6
5.1 Dry-docking performance: Change in hull and propeller performance following
present out-docking as compared with the average from previous out-dockings . 7
5.2 In-service performance: The average change in hull and propeller performance
over the period following out-docking to the end of the dry-docking interval . 8
5.3 Maintenance trigger: Change in hull and propeller performance from the start of
the dry-docking interval to a moving average at any chosen time . 9
5.4 Maintenance effect: Change in hull and propeller performance measured before
and after a maintenance event .10
6 Measurement uncertainties and the accuracy of the performance indicators .11
Annex A (informative) Method and assumptions for estimating the uncertainty of a
performance analyses process .13
Bibliography .30
Foreword
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The committee responsible for this document is ISO/TC 8, Ships and marine technology, Subcommittee
SC 2, Marine environment protection.
A list of all parts in the ISO 19030 series can be found on the ISO website.
iv © ISO 2016 – All rights reserved

Introduction
Hull and propeller performance refers to the relationship between the condition of a ship’s underwater
hull and propeller and the power required to move the ship through water at a given speed. Measurement
of changes in ship specific hull and propeller performance over time makes it possible to indicate the
impact of hull and propeller maintenance, repair and retrofit activities on the overall energy efficiency
of the ship in question.
The aim of the ISO 19030 series is to prescribe practical methods for measuring changes in ship specific
hull and propeller performance and to define a set of relevant performance indicators for hull and
propeller maintenance, repair and retrofit activities. The methods are not intended for comparing the
performance of ships of different types and sizes (including sister ships) nor to be used in a regulatory
framework.
The ISO 19030 series consists of three parts.
— ISO 19030-1 outlines general principles for how to measure changes in hull and propeller performance
and defines a set of performance indicators for hull and propeller maintenance, repair and retrofit
activities.
— ISO 19030-2 defines the default method for measuring changes in hull and propeller performance
and for calculating the performance indicators. It also provides guidance on the expected accuracy
of each performance indicator.
— ISO 19030-3 outlines alternatives to the default method. Some will result in lower overall accuracy
but increase applicability of the standard. Others may result in same or higher overall accuracy but
include elements which are not yet broadly used in commercial shipping.
The general principles outlined, and methods defined, in the ISO 19030 series are based on
measurement equipment, information, procedures and methodologies which are generally available
and internationally recognized.
INTERNATIONAL STANDARD ISO 19030-1:2016(E)
Ships and marine technology — Measurement of changes
in hull and propeller performance —
Part 1:
General principles
1 Scope
This document outlines general principles for the measurement of changes in hull and propeller
performance and defines a set of performance indicators for hull and propeller maintenance, repair and
retrofit activities.
The general principles outlined and performance indicators defined are applicable to all ship types
driven by conventional fixed pitch propellers, where the objective is to compare the hull and propeller
performance of the same ship to itself over time.
NOTE Support for additional configurations (e.g. variable pitch propellers) will, if justified, be included in
later revisions of this document.
2 Normative references
There are no normative references in this document.
3 Terms and definitions
For the purposes of this document, the following terms and definitions apply.
ISO and IEC maintain terminological databases for use in standardization at the following addresses:
— IEC Electropedia: available at http://www.electropedia.org/
— ISO Online browsing platform: available at http://www.iso.org/obp
3.1
hull and propeller performance
relationship between the condition of a ship’s underwater hull and propeller and the power required to
move the ship through water at a given speed
3.2
delivered power
P
D
power delivered to the propeller (propeller power)
3.3
speed through the water
V
ship’s speed through water for a given set of service (environmental) and loading (displacement/trim)
conditions
3.4
accuracy
described by trueness and precision, where trueness refers to the closeness of the mean of the
measurement results to the actual (true) value and precision refers to the closeness of agreement
within individual results
Note 1 to entry: See ISO 5725-1:1994, 3.6 and Introduction 0.1.
3.5
uncertainty
probability that the measurement of a quantity is within the specified accuracy to that quantity’s actual
(true) value
3.6
filtering
method of removing unwanted data
3.7
normalization
refers to the creation of shifted and scaled versions of statistics, where the intention is that these
normalized values allow the comparison of corresponding normalized values in a way that eliminates
the effects of specific influences
3.8
performance indicators
PIs
used to evaluate the effectiveness of, or to trigger, a particular activity
3.9
dry-docking
bringing the ship onto dry land to maintain, repair and/or retrofit the parts of the hull that are
submerged while the ship is in service
3.10
out-docking
period immediately following a dry-docking
3.11
dry-docking interval
period between two consecutive dry-dockings
4 General principles
4.1 Hull and propeller performance
Hull and propeller performance refers to the relationship between the condition of a ship’s underwater
hull and propeller and the power required to move the ship through water at a given speed. Hull and
propeller performance is related to variations in power, because ship hull resistance and propeller
efficiency are not directly measurable quantities.
2 © ISO 2016 – All rights reserved

4.2 Ship propulsion efficiency and total resistance
Hull and propeller performance is closely linked to the concepts of ship propulsion efficiency and ship
resistance. The performance model is based on the relation between the delivered power and the total
resistance where delivered power, P , can be expressed as Formula (1):
D
RV×
T
P = (1)
D
η
Q
where
R is the total in-service resistance (N);
T
V is the ship speed through water (m/s);
η is the quasi-propulsive efficiency (-).
Q
The total resistance consists of several resistance parts and can be written as Formula (2):
RR=+ RR++ R (2)
TSWAAAWAH
where
R is the still-water resistance (N);
SW
R is the added resistance due to wind (N);
AA
R is the added resistance due to waves (N);
AW
R is the added resistance due to changes in hull condition (fouling, mechanical damages, bulging,
AH
paint film blistering, paint detachment, etc.), (N).
Likewise, the quasi-propulsive efficiency consists of different efficiency components expressed as
Formula (3):
ηη= ηη (3)
Q0 HR
where
η is the open-water propeller efficiency;
η is the hull efficiency;
H
η is the relative rotative efficiency.
R
The added resistance due to changes in hull condition can be expressed as Formula (4):
P ×η
DQ
R = −+()RR +R (4)
AH SW AA AW
V
where
V is the ship speed through water, can be measured directly;
P is the delivered power, must be approximated – for example based on calculations of shaft
D
power;
P is from measurements of shaft torque and shaft revolutions or, alternatively, from calculations
S
of brake power;
P is from SFOC reference curves, measurements of fuel flow and temperature and data on calo-
B
rific value, density and density change rate for the fuel being consumed.
Variations in the delivered power required to move the ship through water at a given speed, and the
same environmental conditions and operational profile, are due to changes in the underwater hull
resistance and/or propeller efficiency. Changes in underwater hull resistance are due to alterations in
the condition of the hull. Changes in the propeller efficiency are due to both alterations in the condition
of the propeller and to modifications to the flow of water to the propeller (the hull wake) as consequence
of alterations to the hull condition.
For a vessel in service, both environmental conditions and operational profile (e.g. speed, loading, trim)
vary. In order to measure changes in the speed-power relationship for a vessel in service, it is necessary
to compare two periods (a reference period and an evaluation period) where the environmental
conditions and the operational profile are adequately comparable (filter the observed data) and/or
apply corrections (normalize the observed data).
There are a number of alternative procedures for filtering and normalizing observed data. These
procedures each have advantages and disadvantages in terms of the resulting accuracy of the
measurements. This document prescribes a practical blend of filtering and normalization procedures
found to yield sufficient accuracy.
NOTE The relative importance of the different resistance components varies to certain degree with
the operational and environmental condition the vessel is exposed to. Also, the accuracy of the models to
correct/normalize for such variations depends on the operational and environmental conditions. These
dependencies impact the accuracy of the hull and propeller performance indicators as described in the current
standard. Therefore, in the estimation of the accuracy of the performance indicators and for the intended use
comparable operational and environmental conditions over the reference and evaluation period (see Annex A)
are assumed. Future revisions of this document will re-evaluate if more accurate correction formulae are
available that take the above mentioned dependencies into consideration.
Hull and propeller maintenance, repair and retrofit activities have an effect on the energy efficiency
of a ship in service. An indication of these effects can be obtained by measurement of changes in the
delivered power required to move the ship through water at a given speed between two periods for
which the environmental conditions and operational profiles have been made adequately comparable
through filtering and/or normalization of the observed data.
4.3 Primary parameters when measuring changes in hull and propeller performance
The above definition gives ship’s speed through the water and delivered power as the two primary
parameters when measuring changes in hull and propeller performance.
NOTE If hull performance is to be separated from propeller efficiency, propeller thrust would also have to be
measured.
For these parameters, different measurement approaches, and for each approach, different sensors with
different signal qualities are available. In ISO 19030-2, default measurement approaches and associated
“minimum required” signal quality values are specified.
If sensors with the minimum required signal quality are not available, alternative measurement
approaches can be used, but they introduce additional uncertainty. In ISO 19030-3, alternative
4 © ISO 2016 – All rights reserved

measurement procedures are described. For each alternative, the minimum required signal quality is
specified together with an estimation of the additional uncertainty introduced.
4.4 Secondary parameters
In order to apply the filtering and normalization procedures necessary to make the reference period
and evaluation period adequately comparable, measurements of both the environmental conditions
and the ship’s operational profile are required. Relevant environmental factors are as follows:
— wind speed and direction;
— significant wave height, direction and spectrum;
— swell height, direction and spectrum;
— water depth;
— water temperature and density.
Relevant operational factors are as follows:
— speed;
— loading conditions (static draught, static trim, heel);
— dynamic floating conditions (motions, dynamic draught, dynamic trim);
— rudder angle / frequency of rudder movements.
If reliable sensor signals are not available for all parameters, either signals from alternative sensors can
be used to approximate and/or for practical purposes one must assume their effects “average out over
time”. Using alternative sensors or relying on an equal distribution assumption introduces additional
uncertainty.
In ISO 19030-2, a “minimum set” of sensor signals and the “minimum required” signal quality for each
sensor are specified for the default method for measuring changes in hull and propeller performance.
In ISO 19030-3, alternative sets of sensor signals and “minimum required” signal quality are defined,
together with estimations of their effect on the expected accuracy of the performance indicators.
4.5 Measurement procedures
4.5.1 General
There are three basic procedural steps involved when measuring changes in hull and propeller
performance. Figure 1 summarizes these three steps.
Figure 1 — Procedural steps when measuring changes in hull and propeller performance
The accuracy of a measurement is determined by both its trueness and its precision (see ISO 5725).
Trueness refers to the closeness of the mean of the measurement results to the actual (true) value.
Precision refers to the closeness of agreement within individual results and is a function of both
repeatability and reproducibility. Reproducibility refers to the variation arising using the same
measurement process among different instruments and operators, and over longer time periods.
Measurement procedures have a considerable impact on the reproducibility of, and therefore on the
accuracy of, the performance indicators.
NOTE The procedural steps do not have to be conducted in the above sequence. For example, some
preparation of the data can be done as a part of data acquisition.
4.5.2 Data acquisition
Data acquisition refers to the systematic process of recording (manually and/or automatically)
signals/data from the relevant sensors, equipment installed on the vessel and external information
providers. Manual data collection is typically performed once every day (noon data). Generally,
automated data collection occurs at a much higher frequency.
4.5.3 Data storage
Data storage refers to the saving and retention of collected data in a suitable format. This process
should allow previously stored data to be kept together with new data, and ordering it in a sequence so
that it is easy to retrieve when required.
4.5.4 Data preparation
Data preparation includes extracting, compiling, screening and validating the data to give it a structure,
format and quality suitable for further processing. A set of non-dimensional performance values,
that reflect the changes in the hull and propeller performance over the given period of time, are then
calculated. Different sub-sets of the performance values are used to calculate the various performance
indicators. Data preparation can be partially or fully automated.
Practical approaches to data acquisition, data storage and data preparation that yields a high expected
accuracy is defined in ISO 19030-2, the default method for measuring changes in hull and propeller
performance.
In ISO 19030-3, alternatives to the measurement procedures are defined and the impacts on the
expected accuracy of the performance indicators are described.
5 Performance indicators
Measurements of ship specific changes in hull and propeller performance can be used in a number of
relevant performance indicators to determine the effectiveness of hull and propeller maintenance,
repair and retrofit activities. Table 1 outlines four basic hull and propeller performance indicators.
6 © ISO 2016 – All rights reserved

Table 1 — Basic hull and propeller performance indicators (PIs)
Performance indicator Definition
Dry-docking performance: Change in hull and propeller performance following
present out-docking (evaluation period) as compared
Determining the effectiveness of the dry-docking (re-
with the average from previous out-dockings (refer-
pair and/or retrofit activities)
ence periods).
In-service performance: The average change in hull and propeller performance
from a period following out-docking (Reference peri-
Determine the effectiveness of the underwater hull
od) to the end of the dry-docking interval (evaluation
and propeller solution (including any maintenance
period).
activities that have occurred over the course of the full
dry-docking interval)
Maintenance trigger: Change in hull and propeller performance from the
start of the dry-docking interval (Reference period)
Trigger underwater hull and propeller maintenance,
to a moving average at any chosen time (evaluation
including propeller and/or hull inspection
period).
Maintenance effect: Change in hull and propeller performance measured
before (Reference period) and after (evaluation peri-
Determine the effectiveness of a specific maintenance
od) a maintenance event.
event, including any propeller and/or hull cleaning
5.1 Dry-docking performance: Change in hull and propeller performance following
present out-docking as compared with the average from previous out-dockings
The change in hull and propeller performance following present out-docking as compared with the
average from previous out-dockings (where data/measurements are available) is useful for determining
the effectiveness of the dry-docking.
Key
H hull and propeller performance
t time
DDn present dry-docking
DDn+1 next dry-docking
DDI dry-docking interval
R reference period: average hull and propeller performance following previous out-dockings
E evaluation period: hull and propeller performance following present out-docking
PI-1 performance indicator 1: dry-docking performance
Figure 2 — Dry docking performance
During a dry-docking, the propeller(s) are typically cleaned, polished and/or repaired and the
underwater hull is typically cleaned, spot or fully blasted, repaired and re-coated. In addition, retrofits
may be undertaken to improve the performance of the hull, propeller or both.
It is not possible to accurately isolate individual effects (for example impact of differences in level or
quality of pre-treatment, quality of application or surface characteristics of paint). But, if only a sub-
set of effects are expected to differ between the dry-dockings and everything else can reasonably be
assumed to be the same, the performance indicator can serve as an indicator for this sub-set of effects.
The procedures for calculating this performance indicator are provided in ISO 19030-2 and ISO 19030-3.
NOTE Damage to, and deformation of, the hull occurring during the dry-docking, for example, bulging
caused by improper placement of supporting blocks, will affect measured hull and propeller performance and,
unless accounted for, is a source of uncertainty in this performance indicator.
5.2 In-service performance: The average change in hull and propeller performance
over the period following out-docking to the end of the dry-docking interval
The average change in measured hull and propeller performance over the period from the out-docking
to the end of the dry docking interval can be used to determine the effectiveness of the underwater hull
and propeller solutions including hull coatings used and any maintenance activities that have occurred
over the course of the dry-docking interval.
8 © ISO 2016 – All rights reserved

Key
H hull and propeller performance
t time
DDn present dry-docking (or in the case of a new ship, date of entry into service)
DDn+1 next dry-docking
DDI dry-docking interval
R reference period: hull and propeller performance following present out-docking
E evaluation period: avg. hull and propeller performance over remainder of dry-docking interval
PI-2 performance indicator 2: in-service performance
Figure 3 — In-service performance
The procedures for calculating this performance indicator are provided in ISO 19030-2 and ISO 19030-3.
NOTE 1 Damage to, and deformation of, the hull occurring during the dry-docking, for example bulging caused
by improper placement of supporting blocks, will affect measured hull and propeller performance and, unless
accounted for, is a source of uncertainty in this performance indicator.
NOTE 2 Fouling of the propeller(s) (and / or tip damage) can have a significant influence on hull and propeller
performance. If an indication of the change in hull performance is required in isolation, it is necessary that the
propeller(s) be clean and un-damaged during both reference and evaluation periods.
5.3 Maintenance trigger: Change in hull and propeller performance from the start of
the dry-docking interval to a moving average at any chosen time
The measured change in hull and propeller performance from the start of the dry-docking interval to a
moving average at a chosen time during the same interval can be used as a trigger for underwater hull
and propeller maintenance, including propeller and/or hull cleaning.
Key
H hull and propeller performance
t time
DDn present dry-docking (or in the case of a new ship, date of entry into service)
DDn+1 next dry-docking
DDI dry-docking interval
R reference period: hull and propeller performance following present out-docking
E evaluation period: moving average hull and propeller performance at any chosen time
PI-3 performance indicator 3: maintenance trigger
Figure 4 — Maintenance trigger
The procedures for calculating this performance indicator are provided in ISO 19030-2 and ISO 19030-3.
5.4 Maintenance effect: Change in hull and propeller performance measured before
and after a maintenance event
The change in hull and propeller performance measured before and after a maintenance event can
be used to determine the effectiveness of a specific maintenance activity that has taken place in the
interval between the measurements, including any propeller and/or hull cleaning.
10 © ISO 2016 – All rights reserved

Key
H hull and propeller performance
t time
DDn present dry-docking (or in the case of a new ship, date of entry into service)
DDn+1 next dry-docking
DDI dry-docking interval
e maintenance event
R reference period: hull and propeller performance before maintenance event
E evaluation period: hull and propeller performance after maintenance event
Figure 5 — Maintenance effect
The procedures for calculating this performance indicator are provided in ISO 19030-2 and ISO 19030-3.
6 Measurement uncertainties and the accuracy of the performance indicators
Consistent with ISO 5725-1, important sources of uncertainty, that influence the accuracy of the
performance indicators, include the following:
— measurement uncertainty (e.g. related to a sensor’s accuracy, both the uncertainty that might
be observed in a laboratory test in ideal conditions and any additional uncertainty that might be
related to a sensor’s installation, maintenance and operation);
— uncertainty introduced through the use of a sample, an average, or aggregate, value of a parameter
when that parameter is variable with time (e.g. using an average of the wind speed over a period
of time);
— uncertainty introduced through the use of formulas which necessarily simplify relationships
in order to manage the complexity, or because of imperfect information (e.g. use of sea trial data
for draught corrections, or the approximation in the Admiralty formula if used to normalize data
measured at a specific draught to a reference draught).
The aim of this document is to define standard procedures for the determination of performance
indicators such that:
— the uncertainties described above are at levels that enable the meaningful deployment of the
performance indicators for a variety of decision making applications (recognizing that not all
methods may be appropriate for all applications);
— all three of the uncertainties are reduced by as much as is practicable, given different availabilities
of sensors and hardware and the requirement that an ISO standard be transparent;
— the relative accuracies resulting from method differences and variations in ISO 19030-2 and
ISO 19030-3 are made transparent.
Appropriate use of the performance indicators for decision-making purposes is dependent on
understanding to what extent uncertainty influences the accuracy of each.
For the default method, guidance on the expected accuracy of each performance indicator is therefore
provided in ISO 19030-2 and ISO 19030-3. Similarly, for the alternatives to the default method, guidance
on their impact on the expected accuracy of the performance indicators is provided in ISO 19030-3.
The framework used to assess the expected accuracies is described in Annex A.
12 © ISO 2016 – All rights reserved

Annex A
(informative)
Method and assumptions for estimating the uncertainty of a
performance analyses process
A.1 General
The aim of an uncertainty analysis is to describe the range of potential outputs of the system at some
probability level, or to estimate the probability that the output will exceed a specific thresholds or
[16]
performance measure target value .
The existing literature relevant to uncertainty quantification within the shipping industry is found
most notably in the ITTC recommended procedures. This includes established methods for estimating
[12]
the uncertainty in experimental results relating to hydrodynamic experiments , for example,
[11] [12]
in propulsion open water tests and resistance tests . Applications of these are shown in
Reference [15] in relation to towing tank tests and in Reference [10] in relation to sea trials. These
[14]
methods are based on the ISO “Guide to Uncertainty in Measurement” and the AIAA standard on
[2]
the assessment of experimental uncertainty . A key document, used as the main source to formulate
[14]
the method described in this annex, is the “Guide to the expression of uncertainty in measurement”
[6]
which provides a procedure adopted by many bodies . Reference [5] provides derivation and
discussion of the method and procedures.
A.2 Uncertainty analysis methods overview, selection
The GUM framework is itself derived in part from the work of Coleman (1990) who introduced for the
first time the balanced treatment of precision and bias errors, they also describe a method to treat
correlated errors and small sample sizes. The nomenclature and definitions of Coleman and Steele are
consistent with those of the ANSI/ASME standard on Measurement Uncertainty; precision error is the
random component of the total error, sometimes called the repeatability error, it will have a different
value for each measurement, it may arise from unpredictable or stochastic temporal and spatial
variations of influence quantities, they are due to limitations in the repeatability of the measurement
system and to facility and environmental effects. The bias error does not contribute to scatter in
the data but is the fixed, systematic or constant component of the total error; it is the same for each
measurement.
The basic premise of the GUM framework is twofold; firstly, to characterize the quality of the output in
terms of the systematic and random errors which are then combined to obtain the overall uncertainty
in a probabilistic basis and secondly, it includes representation of how well one believes they know
the true value of the measurand, quantified in terms of probabilities that express degrees of belief.
This is a refinement to traditional error analysis in which the output is in terms of a best estimate plus
systematic and random error values.
This leads to the classification of uncertainties according to the method used to evaluate them; type A
evaluation of uncertainties is based on statistical methods or repeated indication values, i.e. Gaussian
distributions derived from observed frequency distributions. Type B evaluation of uncertainties is
based on scientific judgment (any basis other than statistical), this is a priori distribution based on a
degree of belief, a feature of Bayesian inference, if there is no specific knowledge one can only assume
a uniform or rectangular distribution of probabilities to be assigned. In accordance with the second
premise, both types of evaluation are based on probability distributions (quantified by variances
and standard distributions) and the classification is not to indicate differences in the nature of the
components.
The GUM specifies three methods of propagation of distributions:
a) the GUM uncertainty framework, constituting the application of the law of propagation of
uncertainty;
b) Monte Carlo (MC) methods;
c) analytical methods.
The analytical method c) gives accurate results involving no approximations; however, it is only valid
in the simplest of cases while both methods a) and b) involve approximations. The GUM framework
is valid if the model is linearized and the input pdfs are Gaussian, this is the framework followed by
[2] [12]
the AIAA guidelines and the ITTC guide to uncertainty in hydrodynamic experiments , of which
[13] [12]
relevant examples include applications to water jet propulsion tests and resistance experiments
[15]
. In these examples, sensor measurement repeatabilities (same conditions, equipment, operator and
location) are identified as precision limits for each variable and are described by a distribution function
[4]
or simply by a standard deviation . The bias limit for each elemental input may be present as a fixed
(mean value) or as a random variable, in the latter case it would be defined by the band within which
[2]
you can be 95 % confident that the true value lies , i.e. the band in which the (biased) mean result,
would fall 95 % of the time if the experiment were repeated any times under the same conditions using
the same equipment.
If the assumptions of model linearity and Gaussian input pdfs are violated or if these conditions are
questionable then the MC method can generally be expected to lead to a valid uncertainty statement.
In the probabilistic risk assessment field, Monte Carlo Analysis is perhaps the most widely used
[7]
probabilistic method , relevant examples in the shipping industry include applications in sea trial
[10]
uncertainty analysis . A further advantage of the MC method is that the input uncertainties are
based on probability distributions (rather than associating standard uncertainties with estimates of
each input) therefore separation of the inputs into type A and type B is not necessary, finally, a more
insightful numerical representation of the output is obtained and is not restricted to a Gaussian pdf.
Because the model of ship performance is non-linear and there is no extensive evidence that both
input and output uncertainties can be represented as Gaussian pdfs, the MC method is selected for the
purpose of estimating the uncertainty of the methods described in ISO 19030-2 and ISO 19030-3. This
method also enables robust, experimental investigation of the sensitivity of the overall uncertainty to
changes in the input uncertainties. Observing the sensitivities enables the justification of assumptions
regarding which inputs can be safely assumed to have negligible influence on the outcome.
A.3 Method description
This work adopts the following approach:
a) identify each elemental uncertainty source, classify, define probability distribution parameters;
b) simulate the ship’s operating profile and performance trend, and representation of data acquisition,
sampling and filtering;
c) propagate the errors through the model and simulation using the Monte Carlo method and defined
probability distributions for key sources of uncertainty [from step a)];
d) formulate the output distribution of the result, report overall uncertainty.
The details associated with these steps are also shown diagrammatically in Figure A.1.
14 © ISO 2016 – All rights reserved

Speed, V
Daily speed variability
Operating Proile
Draught, D
Time, i
Performance model
P
true,i D
V true,i
true,i
Average Frequency, f
Sample Averaging algorithm
ave
Random Sample, N
V measurement Measurement
P
meas
V
V exp,f
meas,f
uncertainty uncertainty
D
meas
V = 100.(V – V )/V
meas,f exp,f exp,i
df
V ~ N(µ ,s )
d vd vd
MC: n reps
Figure A.1 — Diagrammatic presentation of the simulation method employed to derive
estimates of performance value uncertainty
A.4 Sources of uncertainty in ship performance monitoring
A.4.1 General
A number of sources of uncertainty in the performance indicator are identified in Figure A.2.
Figure A.2 — Uncertainty sources
The leading components of uncertainty: model uncertainty, sampling error, instrument error and
human error, are discussed in greater detail below.
A.4.2 Instrument uncertainty
For each of the sensors included in the analysis (speed/power/draught) the following sensor
properties apply:
— Precision: included in the analysis.
— Bias: excluded because, provided the sensor bias is constant, then this will cancel out when % speed
loss for consecutive time periods are compared. There is a small, insignificant effect if the vessel is
operated in constant power mode owing to the speed reduction between periods; this is assumed
to be negligible.
— Drift: will affect the change in % speed loss between periods. The analysis assumes that the sensors
are maintained and within calibration limits and so drift is assumed to be negligible. The potential
effects of drift are discussed in greater detail in Reference [3].
The effect of crew measuring the BF (wind speed) instead of using an anemometer and weather vane
on the uncertainty of the % speed loss through potentially inaccurate filtering of the weather effects is
considered negligible and not represented in the results of method c) and d). This is justified because
the BF parameter is a filtering parameter rather than a primary variable required for the speed loss
performance value extraction.
The estimation of the different levels of precision of the primary parameters and the proxies defined in
ISO 19030-3 are derived in greater detail below.
A.4.3 Sampling error
A.4.3.1 Overview
The effects of sampling error are related to the sample size and the impacts of averaging, which in turn
is related to sample frequency. Estimates of representative assumptions for each of these effects are
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obtained by investigating the statistics of operation, environmental condition and performance of a
representative ship.
A.4.3.2 Sample size
The proportion of the data that are removed due to filtering (according to the filter criterion defined
in ISO 19030-2 and ISO 19030-3) depends on the environmental/operational conditions experienced
by the ship. For example, if a ship spends 80 % of its time in weather conditions where wind speed is
greater than BF4, then at least 80 % of the measurements of V will be omitted from the estimation of
d
the performance value, thereby reducing the sample size used for its calculation. If everything else is
equal, a lower sample size will result in a greater performance value uncertainty. The amount of data
that is rejected by the filtering process is also a function of the sample frequency. If a low frequency (e.g.
daily) sampling is used, then typically a greater proportion of data will be filtered out due to the use of
average values in the filter criterion.
Table A.1 — Percent of data remaining after sequential filtering steps (order matters with
regards to the relative significance of each filter)
Number of observations Cumulative remaining proportion
of at-sea data (%)
All data (397 d, 38 112
1 sample/15 min)
At sea data (and after 19 717 100
exclusion of obvious
outliers/missing data)
Wind speed < 7,9 m/s 12 881 65,3
Power > 0,3 8 643 43,8
Abs(SOG-STW) < 1 knot 7 344 37,3
Sea depth > criteria 5 636 28,6
Speed within limits 5 625 28,5
Estimates of the effect of filtering on sample size were obtained by inspecting the data measurements
of the representative ships. 70 % of the data is assumed to be
...