Parametric OSV Design Studies precision and quality assurance via updated statistics

Ali Ebrahimi1, Per Olaf Brett1,2, Henrique M. Gaspar3 , Jose Jorge Garcia1, yvind Kamsvg1,3

ABSTRACT

More accurate parametric estimations during the conceptual phase are of paramount importance for proper

early ship design. Balancing vessel designs means ensuring that a vessel particulars are set to represent a

conceptual design format, which will meet the performance yield expectations of involved stakeholders at an

earliest possible stage of the design process. This paper addresses how different early rule of thumb

approaches to offshore support vessel design solution balancing have been out of date, or rather outdated,

regarding quality assurance and statistical accuracy. The paper suggests ways of handling such a quality

assurance issue by introducing and integrating parametric OSV design studies with multivariate data

analysis MDA techniques to properly extract the more accurate estimating transfer functions in the earlier

stages of the vessel design process.

KEY WORDS

Offshore Support Vessel Design, Parametric Vessel Design Analysis, Quality assurance of regression equations

INTRODUCTION PARAMETRIC OSV DESIGN

The objective of the parametric design procedure is to establish a consistent parametric description of the vessel in the early

stages of vessel design, starting from the basic design principle that a certain description of a vessel should be able to perform

efficiently a given mission (Parsons 2004; Gaspar 2013; Gaspar and Erikstad, 2009). Our paper discusses recent studies of such

methods applied to the design of offshore support vessels (OSVs).

The early stages of ship design, requires a consistent definition of a candidate base design in terms of just its main dimensions

and other shape factors. This description can be then optimized with respect to some measure(s) of merit or subjected to various

parametric trade-off studies to establish the basic definition of the design to be developed in more detail. In determining the

main dimensions for a new ship, guidance can be taken from a similar ship for which basic details are known. This is known

as a base vessel and must be similar in type, size, speed and power to the new vessel, which is constantly referred to as the

new design is being developed (Watson 1977).

Parametric models already exist within the marine design literature for common class of vessels such as Watson and Gilfillan

(1998), for commercial ships; Nethercote and Schmitke, for SWATH vessels ; Fung for naval auxiliaries and Schneekluth and

Bertram parametric models (Parsons, 2004). Any design models from the literature are, however, always subject to

obsolescence as transportation practices, regulatory requirements, and other factors evolve over time (Watson, 1977).

Considering current literature review and naval architecture books they still shows there is lack of updating of comprehensive

parametric models for initial design of different OSV segments, with punctual exceptions such as Erikstad and Levander (2012).

Our work demonstrates how utilization of different Multivariate Data Analysis techniques can be useful to extract more accurate

parametric design knowledge from available OSVs data sources. To produce knowledge, different relevant data analysing and

statistical data mining methods are applied. This paper contains some results of parametric studies on an Ulstein internal data

set which is an integrated data base from most recent updated IHS Fairplay (IHS Fairplay, 2014), Marine Base (IHS Petrodata,

2014) and Construction Vessel Base (IHS Petrodata, 2014), data bases for OSVs and OCVs.

1 Ulstein International AS, Norway 2 Norwegian University of Science and Technology, Marine Systems Group, Norway 3 Aalesund University College, Faculty of Maritime Technology and Operations, Norway

A significant portion of collated data from these resources are raw data which has not been subjected to processing or any other

manipulation, containing errors and missing values, not validated in different (colloquial) encoded formats, and we suspect

the confirmation or citation (Barrass, 2004). Information used in all further parametric design calculation is the preface of a

consistent and more robust result, which shows the importance of where this information comes from, how accurate are the

values and how reliable are the sources. Due to the importance of utilizing appropriated data to produce conceptual vessel

designs, it is essential to clean these data. Data cleaning is the process of detecting and correcting (or removing) corrupt or

inaccurate records from a record set, table, or database, which refers to identifying incomplete, incorrect, inaccurate or

irrelevant parts of the data and then replacing, modifying, or deleting this dirty data or coarse data. Data cleaning will increase

data quality in the preprocessing stage, which will improve robustness of achieved parametric design knowledge. Data quality

is a state of completeness, validity, consistency, timeliness and accuracy that makes data appropriate for a specific analysis.

Multivariate statistics can extract better information and produce required knowledge for further designs and marketing

strategies. Generated knowledge from this process is important for proper decision making processes to achieve better market

based features of OSVs, which is calibrated according to statistical models of existing fleet and observed market trends.

There are some differences between the presented approaches with traditional parametric methods, mainly regarding the sample

size in a pre-set database, depth of utilization of statistics in the study process and multivariate aspects in data processing and

regression models applied. Results of our study demonstrates that, to increase the robustness of parametric vessel design in a

preliminary conceptual design phase, updated statistics of available world fleet is an appropriate source of information which

will help designers to perform more accurate OSV design equations, compared to utilizing generally published design equations

in naval architect books. For instance, Figure 1 demonstrates how the result of block coefficient (Cb Equations 1 to 4)

estimation for the PSV- segment based on generally published equations, cannot straight forward be applied for Cb evaluation

of OSVs. Therefore, developing specific equations based on nonlinear regression models can be useful to achieve more accurate

and reliable results.

= . Equation 1: Alexander

= . + . . + . Equation 2: Gillfilan = . + . ! " # Equation 3: Japanese study, commented by Jensen = . + . %&. . '. + . %&. . '. Equation 4: Ulstein developed

Obtained results due to practical OSV design experiences mainly reflects lower influence of Froude Number Fn in Cb

estimation of OSV segment, which is shown in Figure 2. There are some reasons that justifies the result of different design

routines of OSVs compared to commercial vessels. According to Ulstein internal design experience, lower proportion of middle

parallel body to total length in addition to a lower speed range of OSV segment compared to other commercial vessels, have a

vital role, which is observed in Cb and further displacement estimations. Lower parallel middle body in this segment makes

better maneuverability and sea keeping behavior, which is essential in the operation of OSVs. However, it may reduce Cb of a

vessel and as a consequence OSVs will have lower payload capacity compared to similar length size cargo carrying commercial

vessels such as tankers.

Figure 1: Cb calculated based on different equations Figure 2: Froude Number vs Cb different equations

We, therefore, state the importance of a deeper statistical multivariate study on OSV segments in conceptual design phase rather

than solely relying on published literatures for commercial vessels in the same size ranges.

MULTIVARIATE DATA ANALYSIS WITHIN OSV DATA Multivariate Data Analysis is providing a relevant statistical method to be able to extract proper knowledge from these sources

of available data according to defined dependent and independent variables (Hair et al., 2010). Figure 3 presents the Ulstein

process flowchart for applying multivariate data analysis in conceptual design phase of OSVs, based on the knowledge

discovery from data base concept from Piatetsky-Shapiro (Fayad et al., 1996). The procedure consists of the following elements:

i) Data base integration; ii) Data cleaning; iii) Clustering analysis; iv) Multivariate regression analysis; v) Parametric Equation

development and parametric study of clusters; and vi) Data validity and sensitivity analysis.

Multivariate Data Analysis MDA refers to any statistical technique used to analyze data that arises from more than one variable.

This essentially models reality where each situation, product, or decision involves more than a single variable. Despite the

quantum of data available, the ability to obtain a clear picture of what is going on and make intelligent decisions is a challenge.

When available information is stored in database tables, Multivariate Analysis is used to process the information in a

meaningful fashion. Applied multivariate data analysis methods utilized for making inferences regarding the mean and

covariance structure of several variables, for modeling relationships among variables, and for exploring data patterns that may

exist in one or more dimensions of the data (Timm, 2002).

MDA analyses are typically applied in practical OSV design processe with N observations which constitute the world OSV

segment fleet data base on p variables (vessel particulars). As with univariate data analysis, probability distribution of the

Figure 3 - Multivariate parametric OSV design process

population is the multivariate normal (MVN) distribution. Multivariate techniques are used to classify or cluster objects into

categories, via cluster analysis, classification and regression trees (CART), classification analysis and neural networks (Timm,

2002). MDA helps to the detection and description of relationships among variables in large population spaces by categorizing

objects into proper clusters.

The most accomplished database used for OSV multivariate studies is an integration of different databases (based on IMO

number and vessel name) in the way to achieve more complete and useful starting point information. Available data depends

mainly on the vessel segment, since there exist general information for all segments such as delivery year, main dimensions,

particulars, capacities and owners, besides specific information for each segment such as winch pull, survivors, J-lay tension

and so on.

Available OSV data sets, mostly contains huge amount of missing values and inaccuracies, varying in different segments.

Observation in current vessel data, for instance in DWT part of PSV segment, shows 17% of missing values as well as around

14% of inaccurate data. While in AHTSs 94% of vessels contains DWT information, around 25% of provided values are

inaccurate. Generally main dimensions have highest accuracy and availability in data set with more than 90% of reliability for

different segments while combination of availability and accuracy of data for main particulars such as DWT, deck area and

installed power generally is not more than 75%. Vessel price and main machinery information available data for different

segment generally does not exceed 50% of fleet. The density of wrong values and outliers in an original database will influence

the final result and mislead design decisions. According to data quality literature, we are dealing with issues such as: Accuracy,

Integrity, Cleanliness, Correctness, Completeness and Consistency (Dongre, 2004). Evaluating data quality enables to

determine the usability of data and to establish the processes necessary for improving data quality to makes data appropriate

for a specific use.

A cleaning process can be carried out in automated, manual or combined format, which depends on the type and volume of

data (Dongre, 2004). Practical experience shows, for our OSV parametric study, that performing combined data cleansing is

the most effective way where logical error types in data structure are corrected through programmed cleansing process. Manual

intervention is used to deal mainly with missing data and correcting outliers which are detected in automated process wherein

neither a logical conclusion can be drawn nor rules can be formulated about the value that a particular field will take.

There are several data cleaning methods that can be used, including Statistical, Clustering, Pattern-based and Association rules

(Maimon, 2010). According to the nature of available data in the OSV-base all these methods are applied by Ulstein to clean

up different objects. To identify outlier fields and records using the values such as mean, standard deviation, range is the first

statistical step to find variation of each field in the data base. 1 or 1.5 standard deviation area in normally distributed dimensions

and particulars is acceptable range for cleaning outliers. Dimensional ratios such as L/B, B/T and L/D are better cleaned up

based on statistical methods, which requires no specific pattern and are usually normally-distributed around the average value.

Figure 4 demonstrates examples of applying statistical cleaning on L/B ratios for OCV ship types.

Figure 4: B/D ratio range over average Figure 5: DWT cleaning standard deviation from trend

line

Finding patterns among datasets, which shows similar characteristics or behavior of variables, is another way of data cleaning

data. Utilizing trend line in 2-D scatter plot will help to fill out missing data. For instance to determine missing DWT values

for PSVs, regression line for DWT with Lbp*B*T can be a very useful cleaning object. The same procedure can be used to

detect outliers and extremes from the standard deviation, coming up with a more uniform data set. Figure 5 presents an example

of applying standard deviation on trend line for PSV segment larger than 2000 ton. The mentioned pattern is used to fill out

missing values at an initial stage.

Classification and statistical data grouping is another useful multivariate tool to clean data and detect more accurate patterns

inside clusters. Data clustering is a data exploration technique that allows objects with similar characteristics to be grouped

together in order to facilitate their further processing. Discriminant analysis is used to evaluate group separation and to develop

rules for assigning observations to groups. Cluster analysis is concerned with group identification. The goal of cluster analysis

is to partition a set of observations into a distinct number of unknown groups or clusters in such a manner that all observations

within a group are more similar, while observations in different groups are less similar (Bischof, 1999). Identifying groups of

individuals or objects that are similar to each other but different from individuals in other groups is intellectually satisfying and

profitable (Dongre, 2004). Using initially, cleaned OSV data and implementing clustering analysis enables designers and

analysts to find out statistically, similar mission requirements and functionalities where the target is the expectations in

subgroups that are most likely to be representative for a certain market segment to be targeted in the analysis. Figure 6 is an

example of applying cluster analysis on the world PSV fleet larger than 2000 tonnes (metric) DWT.

Our internal Ulstein practice shows that one of the most effective clustering methods to group the vessels based on main

particulars is density clustering. Density clustering groups aggregates elements that have statistically closer distance to the

mean value of the group. Density clustering is originated based on K number of vessel designer specified clusters. The K initial

centroids are chosen in parameters and each point is then assigned to the closest centroid. A case is assigned to the cluster for

which its distance to the cluster mean is the smallest. The action performed in the algorithm centers around finding the K-

means (Pelleg and Moore, 2000). Eventually each collection of points assigned to a centroid generates a descriptive cluster.

Density clustering approach have been applied in parametric study of different OSV segments at Ulstein multivariate parametric

design model. Continual results from studies on PSV larger than 2000 tonnes DWT is demonstrated in Figure 8 and Figure 9.

To perform a clustering study and achieve more accurate results, we selected variables with lower internal correlation. DWT

and Engine HP are selected for clustering study, where DWT as one of the main revenue earning factors of PSVs and engine

power contributing to the capital costs and operational cost. These driver factors have significant role in the decision making

process of this segment. On the other hand, significant influence of engine HP on the vessel speed could not be neglected in

variable selection process. The result of extensive clustering and further statistics manipulation of the existing OSV fleet is

depicted in Figure 6 where, after running some iterations, eventually six clusters are selected, which provides acceptable

statistical results for each cluster. This study covers all PSV fleet larger than 2000 tonnes built after 1975. A second iteration

contains vessels built after 2005, which covers 75% of large size PSVs. It is demonstrated that a substantial improvement since

2005 in the OSV market is the case, therefor to reflect the influence of market change in parametric evolution of the fleet,

vessels built after 2005 have been selected for the second part of the study.

PSV

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Figure 6: PSV fleet K-means cluster Figure 7: Distribution of generated

cluster

We obtain a better understanding of existing trends among different clusters and finding similar groups of PSVs, based on

average parameters by generating frequency histogram and presenting the most popular clusters among data populations. As

observed in Figure 10, Clusters 2, 5 and 6 from clustering of the total fleet and clusters 1, 3 and 5 from clustering of vessels

built after 2005, they have a higher population in the overall market sample. The next step is to find out similarity among

extracted groups, via hierarchical clustering. Hierarchical clustering considers every case being a cluster in itself. However at

successive steps, similar clusters are merged together and clusters are formed (Maimon, 2010). The algorithm loses information

at every step, ending with every previous division back to one common cluster (Figure 11).

cluster2-5cluster2-1cluster1-6cluster1-5cluster2-3cluster1-4

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Figure 10: Different clusters frequency histogram variation among clusters

Figure 11: Hierarchical clustering of groups

Figure 8: Main dimensions variation among

clusters Figure 9: Main particulars variation among clusters

As it is observed in the Dendrogram (Figure 11), three categories of PSVs have higher population in the market, indicated in

Table 1 by different colors. Considering characteristics of these three categories it is conspicuous that, due to size, only two

groups of vessels are highly populated. LOA around 75 m and LOA around 89m. Third category is the result of higher Engine

horsepower as it is shown in Table 1. Three cluster groups of PSVs with higher population, have been identified as shown in

Table 2.

Table 2: Analysis of the categories of PSVs larger than 2000 tonnes

To eliminate the influence of time in cluster analysis all prices are adjusted to 2015, based on globally published historical data

of industrial inflation rate (Bureau of Labor Statistics, 2014)

The rule length constraint (89,9 m) in design explicitly demonstrated by the population of Cluster 2 and 3. Vessels larger than

90m Loa should fulfil higher safety requirements, which increase building cost, so designer mainly attempt to define their new

designs within this limitation, which has increased the population of large size PSVs around 89m.

Category 2 and Category 3 clusters have similar size but a significant difference in installed power that increases considerably

around 40%. That power difference is reflected in the final price, with an increment around 3 Mill USD.

The cluster examples demonstrate how applying statistical classification methods will lead to better understanding of market

behavior for designs and main particular decision making processes at initial conceptual design phase. Compared to traditional

vessel classifying-methods, usually based on single variable such as vessel size or capacity, this method provides the possibility

of considering more variables in a grouping procedure. The overall process of multivariate data clustering analysis is a statistical

process, which is more robust compared to traditional classifying methods. For instance, in the case of the PSV fleet, if

traditional vessel classification is applied based on vessel cargo carrying capacity, the results would not differentiate large size

PSVs regarding the different power range, which influences the significant differences in price and operability of vessel as

main decision making factors.

MULTIVARIATE REGRESSION In OSV parametric design studies multiple linear regression is utilized to determine the most appropriate linear model to predict

only one dependent random variable y from a set of fixed, observed independent variables x1, x2,..., xk measured without

error. Different parameters, for instance DWT as dependent and main dimensions as independent variables, are considered.

Linear model can be fitted using the least squares as an initial model to the data.

Formal tests and numerous types of plots have been developed to systematically help one evaluate the assumptions of

multivariate normality; detect outliers, select independent variables, detect influential observations and detect lack of

independence. Single and multiple linear and nonlinear regression analysis, besides time series analysis and trend analysis of

clusters and segments are applied in this part of multivariate parametric OSV study. In the following, we demonstrate some

examples of applying multivariate regression analysis in different OSV segments. Some Ulstein OSV developed design

equations are also commented.

Built LOA (m) DWT (Tonnes) Deck Area (m2) Speed (knots) Power (HP) Price (M USD)

Category 1 After 2005 75 - 78 3500 740 13 below 7000 23 - 27

Category 2 After 2010 87 - 89 5000 970 13 7000 - 7900 43 - 45

Category 3 After 2010 87 - 89 5000 970 13,5 10500 47

Fig 10: Different clusters frequency Fig 11; Hierachical clustering of groups Table 1: Three main category PSVs larger than 2000 tonnes

Single regression analysis example: PSV, GT vs DWT

Figure 12 shows that DWT and GT are highly correlated to each other. Payload capacity of vessels are considered based on

these two important parameters. In addition to value of R-Square to evaluate significance of correlation among variables,

residual plot and normality check of residuals is important in statistical study, which should be considered in regression based

design estimations.

Another example of applying regression modeling to identify dimensional ratios is shown in Figure 14, which presents ship

shape OCV L/B ratio time series. As it is depicted for the OCV segment, L/B ratio has a negative trend in time series, which

means newer designs have larger beam compared to older vessels. Main reason, which justifies such a trend, is high deck load

capability and stability requirement of OCVs which convinces designers to have larger improvement in the beam of the vessels

compared to length. However, similar studies for other dimensional ratios are possible.

Figure 14: OCV L/B ratio time series study

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Figure 12: GT vs DWT regression Figure 13: Residual analysis of regression GT DWT curve

MULTIVARIATE NONLINEAR REGRESSION

In addition to normal single regression analysis in 2-D plot, between two variables, multivariate regression is one the most

powerful methods to generate proper equations for a particular estimation. There are many examples in ship parametric design

which shows importance of utilizing multi regression rather than single regression which can miss lead the designer to wrong

points and conclusions. Figure 15 shows significant correlation between length and DWT in PSV segment, which is statistically

correct. However, it should be considered as it is demonstrated in Figure 16 and 17, there is a significant correlation between

Beam and Length and Beam with DWT on the other hand, which means all influencing variables on the dependent variable

(DWT) should be considered together and single regression is not a so precise tool for design estimation.

Considering the impact of different dimensions, nonlinear multivariate regression analysis is applied on the development of

DWT equation, since single variable regression analysis is deprived of having comprehensive consideration of all influential

variables on dependent parameter. The result is considering the influence of L, B and T conjointly as main drivers for DWT,

but with different correlation significance. Equation 5 depicts PSVs DWT estimation equation, which is calibrated to real vessel

data and Figure 18 presents validation of the generated points based on DWT equation over Loa diagram for the PSV fleet

larger than 2000 tonnes DWT.

()' = , +%&, , ', + , +%& ' Equation 5: PSV parametric equation for DWT

The same procedure is applied to calculate other particulars including LWT, Engine power and Deck area for different OSV

segments. Results after calibration with internal source of design data have a variance lower than 5% from real data, which is

theoretically acceptable for early estimation processes.

Sensitivity analysis and cost impact of small changes of each dimension on main particulars (DWT, LWT, power, deck area,

building cost) is the last stage of parametric design which is significantly important for decision makers in early design phase.

Figure 19 demonstrates sensitivity analyze on ship shape OCV segments for 10% change in beam, keeping length and draft

Figure 15: PSV DWT vs Lbp Figure 16: PSV Beam vs Lbp Figure 17: PSV DWT vs Beam

Figure 18: PSV DWT vs Loa, based on real data and developed equation

constant compared to base vessel. It is demonstrated that a 10% increment on Beam will have different impacts on DWT, LWT,

Engine power and Deck area. Figure 20 shows the impact of changes on main dimensions of OCV segments on design

particulars. Three steps of changes 5, 10 and 15% has been considered in this study.

Figure 19: Example of sensitivity OCV study 10% beam

Figure20: OCV particular sensitivity to L,B, T changes

COMBINING PARAMETRIC STUDY AND MARKET ANALYSIS TOWARDS A DESIGN STRATEGY

Due to the volatility of the OSV market, as well as the evolution of the OSV fleet through the last decades, it is important to

consider, analyses and understand the reasons of evolutions, most influential parameters and their significance of influence

which may impact final decision dramatically. A time history plot helps to identify external factors like: economic crisis, low

oil price, new regulations, and relation of these with relevant changes in the tendency of the evolution.

For the combination of parametric study with market analysis included, we have chosen, as example, the work performed for

AHTS with more than 10.000 BHP. In Figure 21 relationship between building price and units delivered is reflected. It is

clearly shown in the figure that the vessels delivered in good market years (years with a high delivery ratio) have a higher cost

than those delivered in bad years.

On other hand, Figure 22 shows the evolution of the cost from two points of view: one where we connect the revenue indicators

(bollard pull and deadweight, for AHTS) with building price (adjusted based on the evolution of industrial inflation rate (Bureau

of Labor Statistics, 2014)) and other where revenue indicators are connected with cost driver indicators (as L*B*D and power).

It is clear the opposite direction of both tendency lines.

The negative tendency of the blue lines reflects that to get the same bollard pull and deadweight, it is required a higher inversion

at the same time and from another point of view, it is required a smaller hull and less power to achieve that bollard pull and

power combination when compared to older vessels. In another words, we can say that the efficiency of the vessels have

increased, but this technological and efficiency improvement have also increased the price in recent years.

The two figures above show in a time history plot, the evolution of non-dimensional ratios of AHTS with more than 10.000

BHP. Figure 23 shows that it is possible to conclude that new regulationsas example, do not show any clear effect in the

tendency lines (NMD, 2007).

Figure 23: Evolution of non-dimensional ratios (AHTS)

Figure 21: Building price and units delivered by year

(AHTS) case)

Figure 22: Evolution of economy ratios by year (AHTS

case)

CONCLUSION

In this paper we presented how the application of multivariate data analysis MDA in parametric design of OSVs can be useful

during initial design stage. We defend that the proper utilization of statistical methods will help the designer to have better

estimation of main particulars of final product based on main dimensions at initial design stage. It is shown by some case

studies how the methodology is more accurate compared to available analytical equations, which is mainly developed for

commercial vessels and not applicable in most cases for OSV segment. The methodology was contrasted and discussed in

relation to merchant vessel design procedures and practices.

The methodology introduced in this paper considers various MDA aspects to create meaningful knowledge from a source of

row data, such as i) OSV fleet data refining (data cleaning); ii) Clustering methods and their applications; iii) linear and non-

linear regression models; iv) data verification. Case studies demonstrated the application of methodology to find more precise

(that is, better) equations for different OSV-segments. Proper vessel type segmenting and data-updating procedures discussed

and recommendations proposed. Nonlinear regression equations are proposed for OSVs based on special characteristics of

these groups of vessels. It shown in the paper how findings are verified with OSV fleet real data and specific Ulstein designs.

The methodology is beneficial to reduce deviations from real vessel data, leading to earlier and faster design results with higher

level of accuracy. Moreover, results claim for quality control and quality assurance reviews of existing published estimating

equations for OSV fleet, together with a quantification of the level of deviations and accuracy validations among real data and

results of past equations. By this paper our proposition is that an effective parametric vessel design must apply continually

updated statistics and revised regression equations. Proper statistical methods and robust approximations must be applied in

future parametric vessel design approaches to retain sufficient rigor in the analysis and vessel design work.

REFERENCES

BISCHOF, H., LEONARDIS, A. and SELB, A. "MDL principle for robust vector quantisation", Pattern Analysis and

Applications vol. 2, p. 59-72, 1999.

BARRASS. C.B., Ship Design and Performance For Masters And Mates, 2004 ISBN 0 7506 6000 7

DONGRE, K., Data Cleansing Strategies. Information Management, InfoManagement Direct, October 2004October 2004

ERIKSTAD, S.O. and LEVANDER, K., System Based Design of Offshore Support vessels, 11th IMDC, Glasgow, UK, 2012

FAYYAD, U. PIATETSKY-SHAPIRO, G. AND SMYTH, P. From Data Mining to Knowledge Discovery in Databases, AI

Magazine 17(3): Fall 1996, 37-54

GASPAR, H. M. Parametric Ship Design: A Simple Application in HTML + Javascript, HIALS, 2013

GASPAR, H.M., ERIKSTAD, S. Extending the Energy Efficiency Design Index to Handle Non-Transport Vessels.

COMPIT09, Budapest, Hungary, 2009

HAIR, J.F., ANDERSON, R.E., TATHAM, R.L. and BLACK, W.C., Multivariate Data Analysis, Seventh edition. 2010

IHS Fairplay, IHS Fairplay, Q3 2014

IHS Petrodata, Marine Base, Q3 2014

IHS Petrodata, CVBase, 2014

MAIMON, O., ROCKACH, L. Data Mining and Knowledge Discovery Handbook, 2010

NMD (Norwegian Maritime Directorate), Guidelines for revision of ISM-manual on supply ships and tugs used for anchor

handling, 2007

PARSONS, M. G. 2004 Parametric Design, Ch. 11 in Lamb, T. (ed.), Ship Design and Construction, SNAME, Jersey City,

NJ

PELLEG, D. AND MOORE, A., "X-means: extending K-means with efficient estimation of the number of clusters" ICML

2000.

SCHNEEKLUTH, H. and BERTRAM, V., Ship Design for Efficiency and Economy, 2004 second edition ISBN 0 7506

4133 9

TIMM, N. H., Applied Multivariate Analysis, 2nd edition 2002 ISBN-13: 978-0387953472

WATSON, D. G. M., and GILFILLAN, A. W. (1977) Some Ship Design Methods, Transactions RINA, Vol. 119,

WATSON D. G. M., Practical ship design, Elsevier ocean engineering book series, Volume 1, 1998