Ship Design I - · PDF file2 Ship Dimensioning M.Ventura Ship Dimensioning 4 Preliminary...

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Ship Design I

Manuel [email protected]

MSc in Naval Architecture and Marine Engineering

M.Ventura Ship Dimensioning 2

Summary

• Ship Dimensioning• Owner’s Requirements• Traditional approach• Generic Ship Dimensioning Process• Most common implementation methods:

– Systematic parametric variation– Optimization methods

• Some Optimization Software Tools– Excel Solver– Matlab fmincon() function

Annex A. Ships Statistical Data Gathering and Processing Annex B. Physical Limitations to ship dimensionsAnnex C. Economical Measures of Merit

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Ship Dimensioning


Preliminary Design Process

Yang & al (2006)

The determination of the main dimensions and characteristics of the ship is the first step of the preliminary design stage.

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Owner’s Requirements

Example of requirements:

Type of ship: Container-carrier, with cell guidesMission: Service Line Setubal - AntwerpDeadweight: 9,500 dwtMax. Draught: 8.0 mCargo capacity: 750 TEU, including 20 reefersService speed: 17 knotsAutonomy: 20,000 milesCargo Equipment: 2 cranes of 40 t x 26.5 mOther: Accommodations for 15 people

The starting point is a set of Owner’s requirements defining mainly the ship type, cargo capacity and speed


Ship Dimensioning – Traditional Process

• DW (input)• Assumed a (DW/ Displacement) ratio empirically• Displacement = DW / (DW/ Displacement)• Lpp = f (Displacement, Vs )• Cb = f ( Fn, Displacement, Vs )• B, T, D are functions of:

– Space requirements (cargo and ballast volumes, max. dimensions)

– Intact stability– Free Board– Reserve of flotation

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Selection of the Form Coefficients

• Selection of the Cb– In diagrams similar to the one in the figure, as a function of the Froude

Number


Selection of the Main Dimensions

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Ship Initial Dimensions

• Watson and Gilfillan (1976) presented the following procedure to obtain the main dimensions of a ship with the required displacement ∆

( ) ( )( )

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2

1025.1 ⎟⎟⎟

⎠

⎞

⎜⎜⎜

⎝

⎛

⋅+⋅

⋅⋅Δ=

BCsT

BB

LL

( )

( )( )TDTD

TBBT

BLLB

⋅=

=

=

• The ratios (L/B), (B/T) and Cb are obtained from statistical data of similar ships

• (D/T) is initially assumed as 1.20

• (1+s) is a coefficient related to the hull appendages


Generic Ship Dimensioning

Process

Modern approach

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Ship Model

Lpp, B, D, T, CbEtc.

Type of Propulsion SystemSpecific Fuel Oil ConsumptionEtc.

DisplacementCm, Cwl, Kb, Lcb, BMT, BML, SwLightship Weight, Kg, LcgGMtDW, CDWCargo CapacityBallast CapacityPropulsion PowerLength of Engine RoomLength of Cargo AreaEtc.

Ship Model

Design Variables

Possible Solution

Technical Design

Parameters

Mission Requirements

Type of ShipCDW, TEU, Lane LengthVsAutonomyEtc.


Size Measures for Specific Ship Types

• Weight based design (Ex.: Tankers, bulk-carriers,..)– Cargo capacity depends mainly of the displacement– Homogeneous cargoes– CDW is the measure of cargo capacity– Depth = f(Vcargo)

• Volume based design (Ex.: Container carriers)– Unitized or packed cargo– Number of TEU is the measure of cargo capacity

• Area based design (Ex.: Ro/Ro ships)– Lane length for vehicle stowage is the most common measure of

cargo capacity

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Constraints


Economical Assessment

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Economical Measures of Merit

• Initial ship cost– The initial ship cost is not by itself a good indicator, some

design options only become economically advantageous on the long run

• Other criteria can be used to take into consideration the running costs of the ship along its entire operational life

• The most common are:– Required Freight Rate (RFR)

– Present Value (PV)

– Internal Rate of Return (IRR)

• To evaluate these criteria the knowledge of the typical ship voyage is required


Typical Voyage

• The specification of the typical ship voyage allows a more comprehensive analysis of the economic aspects

• It may include:– The number of ports visited during the round trip

– The distance between ports

– The cargo-handling capabilities available and the corresponding handling rates and costs

– Port fees and taxes

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Example of a Typical Voyage Specification

Itinerário 1 2 3

Carga 1-2 2-3

Ritmos de carga/descarga 1 2

Termos de carga/descarga Custos portuários do navio

1 2

Custos portuários da carga 1 2

Frete

Setúbal Antuérpia Sines 600 teu’s x 14 t 400 teu’s x 16 t + 200 teu’s vazios 60 teu’s/hora shinc 70 teu’s/hora shinc Li-Lo €10,000 + 0.5xGT €30,000 + 0.5xGT €100/teu cheio, € 50/teu vazio €120/teu cheio, € 70/teu vazio RFR (frete mínimo requerido)


Some Common Freight Conditions

• fio (free in and out)• fiost (free in and out stowed and trimmed)• li-lo (liner in and liner out)• shinc (Sundays and holidays included)• sshex (Saturdays, Sundays and holidays excluded)

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Other Generic Requirements

Ship Registry (conventional flag/convenience flag) MARDuration of the Investment (ship economic lifetime) 20 yearsCapital Interest Rate (bank loans) 10%Working days /year (Off hire days/year) 355 d (10 d)


Generation Engine for Design Variables

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Determination of the Design Variables

• Parametric Studies– The independent variables are obtained by variation between

the lower and upper limits assumed– Require more computing time when the number of design

variables is high– No guarantees that the solution found is the optimal

• Optimization Methods– The independent variables are obtained from an optimization

algorithm– Possible to find a better and faster solution– Only provides information about the optimal point found (single

objective methods)

Parametric Studies

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Parametric Study Methodology

• System of 5 equations• 11 variables• 3 variables fixed based on

the Owner requirements (DW, CCAP, V)

Experience shows that these three relations, which are relatively stable for each ship type, are suited for a good initial estimate

Introducing these additional three relations, the solution of the displacement equation it is transformed in the solution of system of eight non-linear equations.

( )( )

( )

, , , , ,

, , , ,

, , , ,

WT

WT MCR

MCR

MCR CAP

L B T CbL DW

L f L B D T Cb P

P f L B T Cb V

D f L B Cb P C

γΔ = ⋅ ⋅ ⋅ ⋅Δ = +

=

=

=


Functional Diagram of the Dimensioning by

Systematic Variation

The system of non-linear equations is solved by an iterative process

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Main Dimensions

• The main dimension can be obtained from the ratios and coefficients used as independent variables

• For example:

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1 DWk

TL B CbB TBB TTLL BB

γ

⎛ ⎞Δ = ⋅⎜ ⎟⎝ ⎠

Δ=

⎛ ⎞ ⎛ ⎞⋅ ⋅ ⋅⎜ ⎟ ⎜ ⎟⎝ ⎠ ⎝ ⎠

⎛ ⎞= ⋅ ⎜ ⎟⎝ ⎠⎛ ⎞= ⋅ ⎜ ⎟⎝ ⎠

DWk ⎛ ⎞= ⎜ ⎟Δ⎝ ⎠

The ratio k can be obtained from statistics:

Optimization Methods

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Types of Optimization Problems

• Single-Objective– Simplified process in which one only objective, considered the

most important, is selected

• Multi-Objective– Closer to the reality– Several objectives can be in conflict between them

• Hybrid– A multi-objective problem is transformed into a single

objective– One of the objectives is selected as the most important and

the other are converted into a set of constraints that are varied parametrically


Types of Methods and Algorithms

Linear Methods:• Linear Programming (LP)• Newton

Non-Linear Methods:• Gauss-Newton• Levenberg-Marquardt• Sequential Quadratic Programming (SQP)• Artificial Neural Networks (ANN)• Genetic Algorithms (GA)• Simulated Annealing (SA)• Particle Swarm Optimization (PSO)

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Non-Linear Methods (1)

• Linear Successive Approximations– The process starts from an initial feasible point– The functions are expanded in Taylor series around the initial

point, considering only the linear terms– The constrains and the objective functions are linearized in a

similar way and the problem is solved as linear.• Random Search

– The values of the design variables are generated randomly between the lower and upper limits.

– The values that do not comply to the constrains are eliminated and are not used in the next functions.

– The process stops when all the variables comply to the criteria defined.

• Direct Search– The process starts from an initial point and generates a

sequence of point that converge to an optimal point where the function has a minimum.


Non-Linear Methods (2)

• Advantages/Disadvantages– Linear Successive Approximations – Fast process but where

the non-linear behavior of the relations is lost due to the linearization of the initial stage.

– Random Search – Slow process where the optimum point can be missed due to the contraction process. It can be applied to multi-modal functions.

– Direct search – Based on local search and global movements.

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Types of Optimization Methods

Global• Is able to search through the entire design space to find

the optimal solution

Local• Can converge to a local solution, missing possible solutions in

other regions of the design space

Some Optimization Software Tools:Excel Solver

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EXCEL Solver (1)

• Available algorithms:– LP - Linear Programming (assumed only if selected in <Options>)– Non-Linear Programming (assumed by default)

• GRG2 - Generalized Reduced Gradient (Lasdom et al, 1998)• The Solver approximates the Jacobian matrix (partial

derivatives) using finite differences and re-evaluates it at the beginning of each iteration

• Limits– 1 objective (Single Objective algorithm)– 200 variables– 100 implicit restrictions– 400 simple restrictions (upper/lower limits)

• Usage:– <Tools>/<Solver>


EXCEL Solver (2)

• <Target cell> is the one where the objective function is evaluated• <Equal To> define the type of problem (maximize, minimize, equal to)• <Changing Cells> are the ones that contain the design variables (to be

optimized) and must be all in the active sheet• <Constrains> list the constraints to be applied

Multiple range <Changing Cells> can be indicated separated by commas:

$C$4, $C$6:$C$8

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Example: Bulk Carrier Dimensioning

• The simplified Model used on the example is based on the one presented in Xuebin (2009)

Objective Function


Example: Problem Constraints

• The following set of 14 constraints is applied:

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Example: Bulk Carrier Dimensioning


Notes on the Spreadsheet Design (1)

• In order to make the model formulas more readable and easy to debug and maintain, cell and range names should be used instead of just references

• Cell names are created by:<Insert/Name/Define>

• The use of cell names avoids the need to use absolute cell references (Example: ‘Lpp’ instead of C$4$)

• Define all the cell names BEFORE entering the formulas• Define explicitly the units of all the values

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• Constraints associated to intervals must be split in two. Example:

25000 <= DW <= 500000To be split into:

DW >= 25000DW <= 50000

• Use color codes to identify the different types of cells. For example:– <yellow> input cells– <orange> constraints– <red> objective function– <gray> values computed by the model



• Container Carriers have dimensions external (breadth of ship) and internal (inner breadth of cargo hold) multiple of the width of the standard container (8.0ft = 2.44m)

• These conditions can be converted into additional constraints • For example for the Breadth of the ship:

Module(B/2.46) < 0.01

NOTES:• The value 2.46 results from taking into consideration the width of

the container plus the interval between containers (abt. 25 mm)• In Excel the expression will be:

mod(B; 2.46)where the function mod(a;b) returns the remainder of the divisiona/b

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• Sometimes it is convenient to be able to use more than one objective in the optimization process

• Although the <Solver> is a single objective method, multiple objectives can be taken into consideration by creating an objective function which is the result of a weighted sum of several contributions:

Fobj = w0xF0 + w1xF1 + w2xF2 …• The weights wi will be assigned by the designer in accordance to

the relative importance of each contribution and their sum will be always equal to 1.0:

w0 + w1 + w2 + … = 1.0• The sign of each weight will be positive, if the corresponding

contribution is to me minimized, or negative, if it is to be maximized

• It is convenient to scale the different contributions to the same order of magnitude. For example each contribution can be scaled to be in the interval [0, 1]



• The initial values for the variable cells should be representative of the values expected at the optimal solution, rather than arbitrary values such as all zeroes.

• The Excel Solver is a local optimizer -> different sets of initial variables values should be tested to check the consistency of the results and to help to find a global optimum

• The process can be made automatic by creating a macro to define the design variables, the constraints and the objective function and to run the Solver

• The macro can be converted into a VBA (Visual Basic for Applications) function

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VBA Programming in Excel

• The first draft of a program can be obtained by recording a sequence of commands (macro) using the macro recorder:<Tools/Macro/RecordNew Macro>

• Next the macro code can be run and edited in the VBA Editor<Tools/Macro/Macros/Run> or /Edit>

• The code should be extensively commented in order to make its debugging and maintenance easier


VBA Function for Dimensioning (1)

Sub OptimumShip()'' OptimumShip Macro' Macro recorded 2010-09-22 by Manuel Ventura'' Keyboard Shortcut: Ctrl+Shift+S'

' Clear Solver optionsSolverReset

' Minimize Objective FunctionSolverOk SetCell:="$K$17", MaxMinVal:=2, ValueOf:="0", _

ByChange:="$C$4:$C$9“

' Constraints' Lpp <=SolverAdd CellRef:="$C$4", Relation:=1, FormulaText:="$C$24"

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' T <=SolverAdd CellRef:="$C$7", Relation:=1, FormulaText:="$C$25“' T <=SolverAdd CellRef:="$C$7", Relation:=1, FormulaText:="$C$26"' L/B >=SolverAdd CellRef:="$C$11", Relation:=3, FormulaText:="$C$27"' L/D <=SolverAdd CellRef:="$C$12", Relation:=1, FormulaText:="$C$28"' L/T <=SolverAdd CellRef:="$C$13", Relation:=1, FormulaText:="$C$29"' Cb >=SolverAdd CellRef:="$C$8", Relation:=3, FormulaText:="$C$30"' Cb <=SolverAdd CellRef:="$C$8", Relation:=1, FormulaText:="$C$31"' Fn <=SolverAdd CellRef:="$G$15", Relation:=1, FormulaText:="$C$32"



' GMT >=SolverAdd CellRef:="$G$31", Relation:=3, FormulaText:="$C$33"' DW >=SolverAdd CellRef:="$G$20", Relation:=3, FormulaText:="$C$34“' DW <=SolverAdd CellRef:="$G$20", Relation:=1, FormulaText:="$C$35"' Vs >=SolverAdd CellRef:="$C$9", Relation:=3, FormulaText:="$C$36“' Vs <=SolverAdd CellRef:="$C$9", Relation:=1, FormulaText:="$C$37"

' Run Solver and ‘ allow the user to decide to keep or not the obtained resultSolverSolve userFinish:=False

End Sub

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Solver Options (1)

• The Solver can be fine-tuned by changing the default options


Solver Options (2)

• The <Max Time> and the <Iterations> edit boxes control the Solver’s running time.

• The <Show Iteration Results> check box instructs the Solver to pause after each major iteration and display the current "trial solution" on the spreadsheet. In alternative the user can simply press the ESC key at any time to interrupt the Solver, inspect the current iterate, and decide whether to continue or to stop.

• The <Assume Linear Model> check box determines whether the simplex method or the GRG2 nonlinear programming algorithm will be used to solve the problem.

• The <Use Automatic Scaling> check box causes the model to be rescaled internally before solution.

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Solver Options (3)

• The <Assume Non-Negative> check box places lower bounds of zero on any decision variables that do not have explicit bounds in the <Constraints> list box.

• The <Precision> edit box is used by all of the optimizers and indicates the tolerance within which constraints are considered binding and variables are considered integral in mixed integer programming (MIP) problems.

• The <Tolerance> edit box is the integer optimality or MIP-gap tolerance used in the branch and bound method.

• The GRG2 algorithm uses the <Convergence> edit box and <Estimates>, <Derivatives>, and <Search> option button groups.

Some Optimization Software Tools:Matlab Optimization Toolbox

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MatLab Optimization Toolbox fmincon()


MatLab Optimization Toolbox fmincon()

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Objective Function


Non-Linear Constraints

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Example: Bulk Carrier Dimensioning (1)

• The simplified Model used on the example is based on the one presented in Xuebin (2009)

• This is a part of the Matlab code to call the optimizer:

% Initial pointLpp = 185.0;B = 26.0;D = 14.5;T = 10.5;Vs = 15.0;Cb = 0.70;

x0 = [Lpp B D T Vs Cb];

% Call optimizer [x, acc, exitflag, output] = fmincon( @CalcModel, x0, [], [], ...

[], [], [], [], @mycon, options );



The file <CalcModel.m> defines the sequence of the calculations required to compute the objective function:

function [annualCargoCost] = CalcModel( x )

% Design independent variables Lpp = x(1);B = x(2);D = x(3);T = x(4);Vs = x(5); Cb = x(6);

displ = 1.025*Lpp*B*T*Cb;

% Froude NumberFn = 0.5144*Vs/sqrt(9.8065*Lpp);

…………

annualCargoCost = aoc/nvr/cdw;

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The file <mycon.m> contains the definition of the constraints:

function [c, ceq] = mycon( x )

global Fn dw;

Lpp = x(1);B = x(2);D = x(3);T = x(4);Vs = x(5); Cb = x(6);

% Stabilitykb = 0.53*T;bmt = (0.085*Cb - 0.002)*B*B/T/Cb;kmt = kb + bmt;gmt = kmt - (1.0 + 0.52*D);



% Inequality Constraints defined as% ax + b <= 0

c = [-Lpp/B+6.0 Lpp/D-15.0 Lpp/T-19.0 ...T-0.45*dw^0.31 T-0.7*D-0.7 ...25000-dw dw-500000 ...0.63-Cb Cb-0.75 ...14.0-Vs Vs-18.0 ...Lpp-274.32 Fn-0.32 ...-gmt+0.07*B];

% NO equality constraintsceq = [];

The file <mycon.m> with the definition of the constraints:

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Final results of the optimization :

Algorithm used : medium-scale: SQP, Quasi-Newton, line-searchNo. of iterations = 18No. function calls = 133

Optimum Ship:Lpp = 221.855 mB = 36.976 mD = 19.821 mT = 14.575 mVs = 14.000 knotsCb = 0.720

ACC = 7.972 US$/t

The results are quite similar to those obtained from the Excel spreadsheet using the Solver.

Linear Programming (LP) Methods Applied to Ship Dimensioning

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Introduction

• Linear Programming (LP), is an Operations Research technique that was first applied during the Second World War to help solve troop-supply problems.


LP Software Tools Available

• MatLab Optimization Toolbox– Quadric and Linear Programming

• LP Solve (ANSI C)– Current version: 5.5 (CD-ROM#68)

• Clp - COIN-OR Linear Programming Solver (C++)– Current version: 1.10 (CD-ROM#68)

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Bibliography


Bibliography (1)

Artana, Ketut Buda and Ishida, Kenji (2003), "The Determination of Optimum Ship’s Design and Power Prediction Using Spreadsheet Model", Journal of the JIME Vol. 37, No. 6.Artana, K.Buda and Ishida, Kenji (2003), "Spreadsheet Modeling to Determine Optimum Ship Main Dimensions and Power Requirements at Basic Design Stage", Marine Technology, Vol. 40, No. 1, Jan.2003, pp. 61–70. (CD-ROM#51)Brinati, HL; Augusto, AO and Conti, MB (2007), “Learning Aspectsof Procedures for Ship Concept Design Based on First Principles”, International Conference on Engineering Education – ICEE’2007, Coimbra, Portugal.Burgos, D. and Martins, M. (2008), "Projeto Preliminar de Embarcações Usando Algoritmos Genéticos", SOBENA 2008.Chao, Chen (2009), "The Container Shipping Network Design under Changing Demand and Freight Rates", The Eighth International Symposium on Operations Research and Its Applications (ISORA’09) Zhangjiajie, China, September 20–22, pp. 245–262. (CD-ROM#68)

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Bibliography (2)

Chryssostomidis, Chryssostomos (1967), "Optimization Methods Applied to Containership Design", MsC Thesis, MIT. (CD-ROM#67)Cudina, Predrag (2008), "Design Procedure and Mathematical Models in the Concept Design of Tankers and Bulk Carriers", Brodogradnja, Vol.59, No.4, pp.323-339. (CD-ROM#70)De, Abhijit and Kumar, Ashish (2006), "OPTI-MARINE-WARE (Optimization of Vessel's Parameters Through Spreadsheet Model)", Journal of Naval Architecture and Marine Engineering. (CD-ROM#51)Dobie, Thomas (2002), “The Importance of the Human Factors in Ship Design”Frank, Darko; Klanac, Alan and Bralic, Svemir (2008), "A Concept for Concurrent Group Design of Ships", Proceedings of COMPIT'08, Liege, pp.450-459. (CD-ROM#68)


Bibliography (3)

Frank, D.; Klanac, A. and Bralic, S. (2008), "ng.zine - A New Design System for Naval Architecture", Proceedings of SORTA'08, Pula. (CD-ROM#68)Ganesan, Vikram (2001), "Global Optimization of the NonconvexContainership Design Problem Using the Reformulation-linearization Technique", MSc Thesis, Virginia Polytechnic Institute and State University. (CD-ROM#67)ISSC 2003, Technical Committee IV.2 Report

• Jensen, G. (1994), “Moderne Schiffslinien”, Handbuch der Werften, Vol.XXII, Hansa, pp.93.Klanac, A. and Jelovica, J. (2007), "A Concept of Omni-Optimization for Ship Structural Design", Advancements in Marine Structures, Guedes Soares & Das (eds), Proceedings of MARSTRUCT 2007, The 1st International Conference on Marine Structures, 12-14 March 2007, Glasgow, UK. p. 473-481. (Taylor & Francis: London). (CD-ROM#68)

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Bibliography (4)

Liang, Zheng Xuan; Yan, Lin and Shang, Ji Zhuo (2009), "Collaborative Multidisciplinary Decision Making Based on Game Theory in Ship Preliminary Design", Journal of Marine Science and Technology, Vol.1, pp.334–344. (CD-ROM#70)Murphy, R.; Sabat, D. and Taylor, R., “Least Cost Ship Characteristics by Computer Techniques” (CD-ROM#33)Na, S-S and Karr, D. (2002), “Product-Oriented Optimal Structural Design of Double-Hull Oil Tankers”, Journal of Ship Production, Vol. 18, No. 4, Nov. 2002, pp. 237–248. (CD-ROM#51)Parsons and Scott (2004), “Formulation of Design Optimization Problems for Solution with Scalar Numerical Optimization Methods”, Journal of Ship Research, Vol.48, No.1, pp.61-76. (CD-ROM#51)


Bibliography (5)

Peri, D. and Campana, E. F. (2003), “Multidisciplinary Design Optimization of a Naval Surface Combatant”, Journal of Ship Research, Vol.47, No.1, pp.1-12. (CD-ROM#51)Ross, J.; McNatt, T. and Hazen, G. (2002), “The Project 21 Smart Product Model: A New Paradigm for Ship Design, Cost Estimation, and Production Planning”, Journal of Ship Production, Vol. 18, No. 2, May 2002, pp. 73–78. (CD-ROM#33)Schiller, T.R.; Daidola, J. C.; Kloetzli, J.C. and Pfister, J. (2001), "Portfolio of Ship Designs: Early-Stage Design Tools", Marine Technology, Vol.38, No.2, April 2001, pp.71–91. (CD-ROM#51)Schneekluth, H. e Bertram, V. (1998), “Ship Design for Efficiency and Economy”, Butterworth-Heinemann.

• Watson, DGM and Gilfillan, AW (1976), “Some Ship Design Methods”, RINA Transactions, Vol.119, pp.279-324.

• Whiton, Justin C. (1967), "Some Constraints On Shipping in Linear Programming Models", Naval Research Logistics Quarterly, Vol. 14, Issue 2, pp.257-260.

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Xinlian, Xie; Tengfei, Wang and Daisong, Chen (2000), "A Dynamic Model and Algorithm for Fleet Planning", Maritime Policy & Management, Vol.27, Issue 1, pp.53-63. (CD-ROM#68)Xuebin, Li (2009), “Multiobjective Optimization and MultiattributeDecision Making Study of Ship’s Principal Parameters in Conceptual Design”, Journal of Ship Research, Vol.53, No.2, pp.83-92.Yang, Y-S; Park, C-K; Lee, K-H and Suh, J-C (2007), “A Study on the Preliminary Ship Design Method Using Deterministic and Probabilistic Approach Including Hull Form”, Journal of Structural Multidisciplinary Optimization, Vol.33, No.6, pp.529-539. (CD-ROM#65)Zanic, Vedran and Cudina, Predrag (2009), "Multiattribute Decision Making Nethodology in the Concept Design of Tankers and Bulk Carriers", Brodogradnja, Vol.60, No.1, pp.19-43. (CD-ROM#70)


Bibliography

Linear ProgrammingFerris, Michael C.; Mangasarian, Olvi L. and Wright, Stephen J. (2007), “Linear Programming with MatLab", Society for Industrial and Applied Mathematics and the Mathematical Programming Society.Luenberger, D.G. anf Ye, Y. (2008), “Linear and Non-Linear Programming ”, 3rdEd, Springer.Matousek, Jiri and Gartner, Bernd (2006), "Understanding and Using Linear Programming", Springer.

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Approach to Optimization of Ship Design and Construction Phases”, Journal of Ship Production, Vol. 24, No.1, pp. 1-6.

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Annex A. Ships Statistical Data Gathering and Processing


Ships Data Gathering (1)

• The common practice of single design or small series implies that some initial knowledge can be obtained from the analysis of the existing ships

• To improve the efficiency of the process the information about existing ships of the same type and in a similar range of cargo capacity should be structured in a small Data Base

• To improve the quality of the process, the Data Base should first be cleaned from:– Incorrect data (from wrong sources or typing mistakes)– Incomplete data (incomplete records with some missing fields)– Repeated data (from identical ships produced in series)

• Keeping track of the ship identification (Name, IMO Number, building yard and year) and data source (journal, web site, etc.) will help to check and improve the data quality

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• The registries of Lloyds Register and other classification societies are good data sources

• A Spreadsheet can be used for data storage and for the statistical analysis and graphic display of the results

• The main topics of interest are:– Hull dimensions– Propulsion machinery and electric generators– Cargo capacity and equipment– Others (ballast capacity, crew)

LwtIMONo

MCRMainEngine

BuiltYear

Electr. Power

VsDWTDBLppName



The measure(s) of the cargo capacity used depends of the ship type:

Ramps, liftsTotal lane length / number of cars / number of trailersNumber of passengers

Ro/RoRoPaxFerries

Number of passengersPassenger Ships

CranesCell guides

Total Number of TEUs (in holds, on deck, reefers)Container carriers

Cargo pumpsCranes

No. of Tanks / HoldsVolume cargo tanks/holds

TankersBulk carriers

Cargo EquipmentMeasures of Cargo CapacityShip Type

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• Other information also useful about ship systems, crew, etc. (if available)

Ballast Pumps

Vol. DO CrewVol. FOVol. Ballast


Process the Compiled Data

• Based on the data compiled, a set of ratios can be computed• These ratios help to characterize the ship class• Allow the definition of the bounding limits to the variation

of the design variables• Support the estimative of values for which there is no

information to support a computation, even if approximated

L/D %TEUdeck%TEU hold

CSR WB/DWB/T %TEUrefLWT/(L.B.D)CbT/DL/B

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Ship Data Sources – Web Sites (1)

Data about ships built in Polish shipyards since 1992polship.cto.gda.pl

Lloyds Register Data about existing ships (paid)www.sea-web.com

DNV Registry of ships and characteristics. Free access. Search by Name or IMO Numberexchange.dnv.com

NotesData Source


Ship Data Sources – Publications (2)

Annual publication from RINA with good descriptions of the most representative ships of each year, including General Arrangement drawing and lightship weight information. Also available in CD-ROM.

Significant Ships

RINA Journal that contains some ship descriptionsNaval Architecture

Journal of the association of Spanish naval architects that contains some ship descriptions (in Spanish). Digital version available only by subscription.

Ingenieria Navalwww.ingenierosnavales.com

Journal that contains some ship descriptionsDigital version available only by subscription.

Motor Shipwww.motorship.com

NotesData Source

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Annex B. Physical Limitations to the Main Dimensions of the Ship


Physical Limitations

• Physical limitations can be associated to the geographical route that the ship uses

• Limitations can be due to the existence of canals, straights, bridges, ports, locks systems

• The dimensions affected can be the Length, the Breadth, the Draught and the Air Draught

41


Air Draught

• Designation given to the vertical distance measured from the load waterline up to the upper extremity of the ship (top of the mast, chimney,..)

• Limited to 4.50 m in many inland waterways in central Europe due to the existence of bridges


Some Physical Limitations in Canals

--

--

35.50

--

--

--

Air Draft

max [m]

12,00018.0055.00427.00Panama Canal (after 2014)

21.00

20.12

9.10

9.50

12.04

Tmax[m]

300,000

240,000

65,000

DW Max. [t]

18,000------Strait of Malacca

17,000-----Suez Canal

22.86222.50St. Lawrence Canal

40.00315.00Kiel Canal

4,00032.31294.13Panama Canal

TEU Max.Bmax[m]

Lmax[m]

Updated on Jan. 2010

42


Strategic Points for the Marine Transportation


Panama Canal

43


Enlargement of the Panama Canal

Adapted to Post-Panamax ships

Dimensions of the new locks (eclusas):

L = 427 m

B = 55 m

T = 18 m

Cost: 5.5 billion US$

Beginning of work: 2007

Conclusion: 2014


Suez Canal – Navigation Chanel

Updated on Jan. 2010

44


Suez Canal – Evolution of the Cross Section Dimensions


Strait of Malacca

45


Restrictions in Portuguese Ports

Petrol.= 23 mGrain.= ?

Petrol.= 28 mGrain.= 17 mGen.Cargo= 5.5 m

Gen.Cargo = 125 mSines

Barra= 9.5 m(dep. on the tide)250 mSetúbal

Barra= 10.5 m(dep. on the tide)Liscont = 10 mSta.Apol. = 8 m Trafaria = 12 mBarreiro = 9 mSeixal = 5m

Trafaria = 235 mLisboa

4.7 m100 mFigueira da Foz

8 m140 mAveiro

???Leixões/Other ships

Station B = 9 mStation C = 5.8 m

Station B = 200 mStation C = 100 m

Leixões/tankers

Air DraughtDraughtBreadthLength


Marine Ports in Portuguese Coast

• Viana do Castelo

• Leixões

• Aveiro

• Figueira da Foz

• Peniche

• Lisboa

• Cascais

• Sesimbra

• Setúbal

• Sines

• Lagos

• Faro

• V. R. Sto. António

46


Links – Portuguese Ports

• www.portosdeportugal.pt• www.portodeaveiro.pt• www.portodelisboa.pt• www.portodelisboa.pt• www.portodesines.pt• www.portodesetubal.pt• www.apdl.pt


Links – Ports and Canals

• www.pancanal.com (Panama Canal)• www.suezcanal.gov.eg (Suez Canal Authority)• www.kiel-canal.org (Kiel Canal)• www.greatlakes-seaway.com• www.atlas.com.eg/scg.html• www.nnc.egnet.net/suezrules.htm• www.portguide.com

47

Annex C. Economical Measures of Merit


Measures of Merit

• The type of measure of merit used depends on the previous knowledge of the earnings of the ship

• Known Results– Net Present Value (NPV) – Internal Rate of Return (IRR)

• Unknown Results– Required Freight Rate (RFR) – Present Value (PV)– Average Annual Cost (AAC)

48


Net Present Value (NPV)

• Often used when the funds for investment are limited and the maximum income tax possible is required.

( ) ( ) ( )01

N

N P V P W Q F R P W A O C P W C= ⋅ − −⎡ ⎤⎣ ⎦∑

where:N - No. years of ship’s lifePW() - Present WorthQ - Total quantity of cargo carried annually FR - Freight TaxAOC – Annual Operating CostsC0 - Initial ship cost


Internal Rate of Return (IRR)

• Represents the tax of return which originates equal values for the Present Value of the results and of the costs, i.e., for which NPV = 0.

• Allows more effective comparisons between entirely different alternatives

• While NPV is expressed in currency units (Euro, US$), the IRR is expressed in percentage (%)

• One advantage of the IRR is that it can be computed without the need to estimate the cost of the capital

• When the IRR is used, the criterion is to select the projects whose IRR exceeds the cost of the capital

49


Required Freight Rate (RFR)

• Used when the data necessary to determine accurately the exploitation results is not available.

• Specially advantageous when comparisons are made between ships of different sizes.

• Represents the cost per unit of cargo, necessary to cover entirely the operation costs and to guarantee the specified income tax from the capital invested.

QCAOC

RFR i+=

where Ci is the annual cost of the capital and VR is the residual value of the ship

( )0i RC CRF C PW V= − ⋅ CRF = Capital Recovery Factor


Permissible Price (PP)

• Represents the maximum admissible price of the ship that still guarantees a specified income tax.

• With the exception of the cases where the ship is paid in a single installment, it is determined by an iterative process.

• Can be used to evaluate prices in proposals of new buildings or in the acquisition of second-hand ships, and in the comparison of those prices with the current ship prices and freight values.

50


Some Elementary Concepts

• Capital Recovery Factor - is factor that converts a present value into a stream of equal annual payments over a specified period of time at a given interest rate.

( )( )

11 1

N

N

i iCRF

i+

=+ −

• Present Worth Factor - is a multiplier which converts a future amount into a present amount

( )PW i N= + −1

where:N No. years of ship’s lifei Interest rate


Bibliography

BTE (1982), “An Estimate of Operating Costs for Bulk, RoRo and Containers Ships”, Bureau of Transport Economics, Camberra.Watson, D.G.M. (1998), “Practical Ship Design”, Vol.1, Elsevier.Y-S Yang, C-K Park, K-H Lee and J-C Suh (2007), “A Study on the Preliminary Ship Design Method Using Deterministic Approach and Probabilistic Approach Including Hull Form”, Structural and Multidisciplinay Optimization, Vol.33, No.6, pp.529-539. (CD-ROM#50)

51


Some Relevant Links (1)

Ship search by IMO Numberwww.equasys.org

BRS Barry Rogliano Salles Shipbrokers – statistics about the marine transport marketwww.brs-paris.com

Updated prices of the different types of Fuel Oil.Historic record and trends.

www.bunkerworld.com

Prices of second-hand shipswww.priyablue.com

Routes and distances between portswww.dataloy.com

CESA – Committee of European Shipyards’Associationswww.cesa-shipbuilding.org

AWES – Association of European Shipbuilders and Shiprepairerswww.awes-shipbuilding.org

Description / NotesWeb Site


Some Relevant Links (2)

Ship search by IMO Number, Name, Owner (freeregistration)www.shippingdatabase.com

World Shipping Register (subscription)Search by Name, Type, DW, etc.

e-ships.net

Description / NotesWeb Site

52


Some Portuguese Links

• www.imarpor.pt (Instituto Portuário dos Transportes Marítimos)• www.ancruzeiros.pt (Lista de Legislação Náutica de Recreio)• www.fpvela.pt (Federação Portuguesa de Vela)• www.hidrografico.pt (Instituto Hidrográfico)• www.isn.org.pt (Instituto de Socorros a Náufragos)

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