CSCMulti Hull Ships

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LEADING EDGE FORUM CSC PAPERSCopyright 2009 Computer Sciences Corporation. All rights reserved.

MULTI-DISCIPLINARYSYNTHESIS DESIGN ANDOPTIMIZATION FOR MULTI-HULLSHIPS

ABSTRACT

Keywords: Multi-disciplinary Design and Optimization (MDO), Neural Networks,

Pareto Optimum Solutions

This paper1describes a synthesis level multi-disciplinary design and optimizatio

(MDO) method developed for multi-hull ships. The method is unique in two

respects. First, it uses advanced multi-objective optimization methods (in its broascope), integrating powering, stability, sea keeping, hull forms definition, cost, an

payload capacity into a single design tool. Second, it uses neural networks as a

response surface method. More specifically, the paper discusses the use of neu

networks, trained based on sets of Computational Fluid Dynamics (CFD) data, fo

estimation of powering and sea keeping through the optimization loop. The pape

presents details of the method and multi-objective optimization results in the form

Pareto optimum solutions for multi-hull concepts

1The latest version of this paper, which reflects progress during the last two yea

is MULTIDISCIPLINARY SYNTHESIS OPTIMIZATION PROCESS IN

MULTIHULL SHIP DESIGN by Hamid Hefazi, Adeline Schmitz, Igor Mizine

Steve Klomparens, and Stephen Wiley. This new paper is available from Igor

Mizine, [email protected].

Hamid HefaziCalifornia State University,

Long Beach, Long Beach/USA

[email protected]

Adeline SchmitzCalifornia State University,

Long Beach, Long Beach/USA

[email protected]

Igor [email protected]

Geoffrey [email protected]

CSC

CSC Papers

2009


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MULTI-DISCIPLINARY SYNTHESIS DESIGN AND OPTIMIZATIONFOR MULTI-HULL SHIPS

INTRODUCTION

The vast majority of current U.S. naval auxiliary ships are relatively large mono-h

with limited speed capabilities. The OSD guidance known by the rubric 10-30-3

cites goals for the speeds at which deployments have to be executed that canno

met with existing transportation vehicles, particularly the ships on which over 90%the materiel needed by ground forces has to move. The desire for high-speed

transit capabilities has resulted in increased interest in non-traditional and multi-

platforms for naval missions. Multi-hull ships have many potential advantages o

mono-hull ships; however, their design procedures are not as mature. Further,

multi-hull ships also offer avenues of hydrodynamic design optimization that are

found on mono-hull shipssuch as optimizing of hull spacing or relative hull

proportions. Achieving many desirable sets of performances requires advances

our ability to predict (and explore) hydrodynamic effects in conjunction with othe

constraints such as dynamic structural loads when operating in high sea states a

cost.

Synthesis tools that are used to explore the ship design trade space in the conce

design phase (ASSET, PASS) have been around for many years and are used

widely by industry for mono-hull ships. While some synthesis tools have been

developed for multi-hulls, they are not nearly comparable in depth or level of fide

to the mono-hull tools. They are used to develop point solutions of ship designs

populate and study the trade space, but the difference in the point designs are

determined by the design team. This process could be substantially enhanced b

the application of multi-disciplinary design and optimization (MDO) tools to the

design problem, and by further development of multi-hull synthesis tools.

Comprehensive, computational MDO tools however can be prohibitively expensi

considering the complexities that are involved in accurate analysis of

hydrodynamics, structural loads, cost, etc. Advanced multi-objective optimization

methods in conjunction with advances in our ability to accurately and efficiently

predict these performances are needed if these tools are to be of practical value

the designer. Such advanced multi-disciplinary ship hull design/optimization too

will be a valuable resource equally applicable to the design of future commercial

military high speed vessels (dual-use). The advanced hull forms designed there

potentially offer the advantage of reduced drag at a given speed, and thus

increased fuel efficiency and range, and/or reduced structural weight and thus

increased cargo lift capacity while meeting stability and seakeeping criteria.

Most of the MDO works to-date are focused on application to mono-hulls. For

example, Zalek(2007) describes multi-criterion evolutionary optimization of ship

hullforms for propulsion and seakeeping. The problem formulation and developm

is applicable to mono-hull frigate type naval surface vessels. Harries et al. (2001

investigate optimization strategies for hydrodynamic design of fast ferries. A

commercial optimization system is used to integrate various CAD and CFD code

for calm water resistance and seakeeping. The method is applied to Ro-Ro ferry

Campana et al.(2007) present results of the MDO of the keel fin of a sailing yac


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accounting for hydrodynamic and elasticity. Different MDO formulations are stud

in the context of global optimization (GO) frame work.

Studies applicable to multi-hull ships include, Tahara et al. (2007) who present a

multi-objective optimization approach for a fast catamaran via a Simulation-BaseDesign (SBD) framework. A variable fidelity concept is also presented which allo

for integration of accurate, yet time consuming RANS predictions together with fa

potential flow results for optimization. The MDO method only considers resistan

and seakeeping. Another study funded by the Office of Naval Research at

University of Michigan, Beck (2007) is also focusing on the hydrodynamic

(seakeeping and resistance) optimization of multi-hulls. Brown and Neu, (2008)

the phase I of a study entitled Naval Surface Ship Design Optimization for

Affordability have applied a multi-objective optimization method to a number of c

studies using a simple ship synthesis model, and the US Navys Advanced Ship

Submarine Evaluation Tool (ASSET) in the PHX ModelCenter (MC) design

environment,ASSET(2008), ModelCenter (2008). Their case studies include

LHA(R), a replacement for the US Navy amphibious assault ship, and DDG-51, destroyer class vessel. Phase II of their study will include response surface

modeling (RSM), a more detailed design of experiments (DOE) and focus on mu

hull high speed ships.

Since 1998, CSULB, under programs funded by the Office of Naval Research

(ONR), Besnard et al. (1998), and the Center for Commercial Development of

Transportation Technology (CCDoTT) has been developing advanced automate

optimization methods and computational fluid dynamics (CFD) methods for

applications to fast ship design. Originally, the focus of these programs was sha

optimization of underwater hull forms, such as the Pacific Marines blended wingbody (BWB) which was optimized for its lift to drag ratio, Hefazi et al.(2002), He

et al.(2003). Having demonstrated the feasibility of automated hydrodynamic sh

optimization for lifting bodies using advanced methods such as neural networks,

Schmitz(2007), CSULB in collaboration with Computer Science Corporation (CS

initiated the current program to extend these technologies to multi-disciplinary

design and optimization (MDO) of multi-hull ships. Our approach is unique in i ts

broad scope and use of neural networks as a response surface method.

Generally, the MDO design system consists of synthesis design method (SDM),

hullforms definition and optimization sub-system, seakeeping, structural design

optimization, general & cargo arrangement design optimization, propulsion

machinery sub-systems and more local sub-systems such as: outfit, electrics,

handling systems, etc. Seakeeping, power, and payload are primary functional

relationships, which depending on the stage of the design, are analyzed at vario

degrees of fidelity.

Two major challenges of MDO design system are:

MDO needs to formulate a design in which there are several criteria or design

objectives, some of which are conflicting.

Subsystem performance evaluations (such as powering, seakeeping, etc) are of

very complex and (computationally) intensive. Direct evaluation of these


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performances as part of the optimization process, may make the MDO method t

costly and out of reach of most practical design problems.

To overcome these limitations, our approach, uses advanced multi-objective

optimization methods such as Neighborhood Cultivation Genetic Algorithm (NCGfor optimization. Unlike traditional design spiral approaches, multi-objective

optimization keeps various objectives separate and concurrent in order to find th

best possible design, which satisfies the (opposing) objectives and constraints. T

address the subsystem performance evaluation challenge, artificial neural netwo

are trained based on model tests or computed data bases and are used in the

optimization process to evaluate various subsystem performances. This innovati

approach replaces the use of highly idealized or empirical methods for evaluatio

subsystem performances (such as powering, seakeeping, etc) during the

optimization process.

The overall MDO process is schematically shown in Figure 1. It consists of vario

models to evaluate powering, cost, stability, seakeeping, structural loads, etc. Toutcomes of these models are then used by a multi-objective optimization metho

such as MOGA to perform optimization. The entire process is managed by

commercially available software, iSIGHT(2008), or ModelCenter (2008) designe

for optimization applications. Various models and subsystems are briefly describ

in subsequent sections. Some of the applications of the method are presented in

section 6.

Figure 1: MDO process

SYNTHESIS LEVEL MDO MODEL

This model includes various design relationships for calculating areas, volumes,

sizes, weights, stability and costs of multi-hull (trimaran) ships. These relationsh

are based on many technical literature sources and practical design experiences

New D. V.

Initial Design Variables

Optimum Design

Neural Network for

Powering prediction

Define

Configuration

Optimum

?

YES

NO

Structural

design &

optimization

Stability and

Neural Network

for Seakeeping

Payload

capacity

determination

Cost

Model

Hull form

definition

model


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They are consistent with Navys, USCG, ABS regulations, and operational

requirements for specific planned applications. They are organized in various Ex

spreadsheets. Synthesis design model, in short, achieves a weight - buoyancy,

required - available area/volume balanced design, with required propulsion and

auxiliary machinery and with a check on stability. The flow chart in Figure 2 show

the synthesis model process. A comprehensive description of the SDM is given i

Hefazi (2006). The overall process includes the following calculations

Speed-power and endurance fuel calculations.

Area/volume calculations including required length, height and volume formachinery spaces for required propulsion plant and auxiliary machinery.

Required tankage volume for required endurance fuel.

Determines remaining hull area/volume available for payload items.

Sizes superstructure and deckhouse above the main deck to exactly provide

area/volume for the remainder of required payload and crew.

Electric load calculations.

Weight and center of gravity calculations.

Required vs. available GM per USCG windwheel criteria.

COST MODEL

The build strategy and cost estimate analysis for multi-hull (trimarans and

catamarans) and mono-hull ships is performed using SPAR Associates proprieta

cost estimating model called PERCEPTION ESTI-MATE. SPARs PERCEPTIO

ESTI-MATE cost model has evolved over nearly two decades of algorithm

development and shipyard return cost data collection and evaluation,perception

Esti-mate(2008).

The cost models approach for an estimate is based first upon the composition o

the hulls structural components (decks, bulkheads, shell, double bottoms, etc.),

then the ship systems (mechanical, piping, electrical, HVAC, etc.), and finally oth

ship characteristics. Factors considered, and applied, if relevant, are the genera

build strategy for on-unit, on-block and on-board construction; the type of shipya

and its established product line, its facilities and production capabilities; and the

expected competence of the shipyard to plan and manage its resources, costs, a

schedules.

Each cost model employs a comprehensive set of cost estimating relationships,

CERs. They reside on SPARs estimating system called PERCEPTION ESTI-

MATE and represent a wide cross-section of current and historical shipyard

construction costs at many levels of detail. Adjustments can be made (and were

made for the HALSS estimate) as necessary to reflect differing shipyard product

factors, construction methods, and material costs. These CERs, while parametr

nature, focus on a specific area of cost (labor and material) and each reflects the

specific material and the manufacturing and assembly processes required.

Specialized CERs focus on structural component fabrication, assembly, and


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erection for installation of propulsion systems and for various support activities. T

CERs are based on many different metrics, such as weld length, deck area,

compartment volumes, number of crew (by type crew), kW of propulsion (by type

etc. Hull structural component costs are based upon component weight by type

structure and material.

The cost estimates, applicable to a lead ship, are believed to be fair representatof anticipated true costs based upon the design information. Material costs have

been adjusted to reflect a common year (2007) value. This assumes that for a

multi-year program, appropriate contract escalation clauses have been defined t

index actual costs relative to the base year.

The cost estimates are based upon typical contract cost and schedule performa

for three types of shipbuilders and shipbuilding processes: so-called Virtual

Shipyard (US National Ship Research Program (NSRP) terminology), Dual Use

Shipyard, and Large US Mid Tier Shipyard, as well as shipyards in other countrie

USING NEURAL NETWORKS IN NUMERICAL

OPTIMIZATIONAs mentioned earlier, a unique feature of our approach is the utilization of artifici

neural networks as a response surface method (RSM) to replace time consumin

and costly direct CFD calculations of powering and seakeeping in the optimizatio

loop. The method has wide range of other potential applications and is briefly

reviewed here.

The modern approach used in the design of a complex system (the ship or

component inside the ship) usually includes at some level an optimization. In

practical cases, the design tool may either be an optimization or design-of-

experiment software, or a set of test cases identified by an experienced designe

interested in conducting trade studies. The analyses performed at each subsyste

level rely, in general, on a combination of semi-analytical models, advanced

numerical methods such as computational fluid dynamics (CFD) and finite eleme

analysis (FE), and use of existing databases.


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Monohull-Trimaran

Design Synthesis Model

Design Input?Crew?Cargo & Other Payload

?Range

?Launch & Operations Limits

?Rules & Standards

?Electric Power Required

SpeedPowerSpeed for Installed Power

OrPower for Required Speed

AreaVolumesMachinery Spaces

Hull Tanks

Deckhouse

Superstructure

Electric LoadIn Transit

Load/Unload

Select Gen Size

7 Weapons Weight

6 Outfit Weight

5 Auxiliaries Weight

4 Command Weight

3 Electric Weight

2 Propulsion Weight

1 Structure Weight

8 Deadweight Weight

Output & FeasibilityWeight vs. Displacement

Speeds Requirements

Stability

(Seakeeping Ranks)

Cost

Balances:

Cargo Area/Volume

Electric Power

TankageVolume

Machinery Installation

VariablesDimensions

Hulls Configuration

Hull Forms integral parameters

Internal spaces arrangement

Hull Forms Generation?Basic hull forms lines and profiles

?Assumed Displacement

Table of offsets & Hydrostatics

Figure 2: Synthesis Model Process

Such optimization or trade study usually has to be able to handle a large numbe

design variables and explore the entire design space. Advanced analysis tools fo

function evaluation such as CFD and FE are very demanding in terms on compu

requirements and when they are used, the cost associated with their use, both in

terms of man and computing power required, usually limits the exploration of the

design space. Regression models like neural networks (NN) can be used to redu

some of these limitations. They basically seek to reduce the time associated with

extensive computations by estimating the functions being evaluated in the

optimization loops.

Figure 3 shows how neural networks can be inserted in the design process by

generating a database outside the design loop or make use of a large available

database and then use those to train one or several NNs. In practical terms, the

introduction of NNs allows extracting the time-consuming or difficult operations

(performing an advanced numerical analysis or extracting information from a larg

and evolving database) from the design loop while still keeping their influence on

the outcome of the design process via the NN. The cost has thus been moved (apossibly reduced in the process) to the training set generation (if it was not alrea

available) and to the training of the network. The result is a NN which can estim

the function or functions over the design space it has been trained on. This abilit

quickly evaluate new designs allows in turn for the use of global optimization too

such as Genetic Algorithms instead of having to rely on local optimization metho

or exploring a restricted part of the design space.


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The neural network methodology that is developed is a constructive algorithm

based on cascade correlation (CC). Instead of just adjusting the weights in a

network of fixed topology, cascade correlation begins with a minimal network, the

automatically trains and adds new hidden units one-by-one in a cascading mann

This architecture has several advantages over other algorithms: it learns very

quickly; the network determines its own size and topology; it retains the structure

has built even if the training set changes; and it requires no back-propagation of

error signals through the connections of the network. In addition, for a large num

of inputs (design variables), the most widely used learning algorithm, back-

propagation, is known to be very slow. Cascade correlation does not exhibit this

limitation. This supervised learning algorithm was first introduced by Fahlman an

Lebiere(1990).

Figure 2: System design loop utilizing Neural Networks. The NNs are generated

outside the design loop based on computationally extensive models and/or large

databases.

The original CC algorithm has been modified in order to make it a robust andaccurate method for function approximation. The modified algorithm, referred to

modified cascade correlation (MCC) in this paper, is an alternative committee NN

structure based on a constructive NN topology and a corresponding training

algorithm suitable for large number of input/outputs to address the problems whe

the number of design parameters is fairly large, say up to 30 or more. Details of

MCC algorithm are presented in Schmitz(2007). The method has been validated

using a mathematical function for dimensions ranging from 5 to 30, Schmitz(200

Besnard et al.(2007). Overall results indicate that it is possible to represent

complex functions of many design variables, with average error of close to 5%. T

number and distribution, within the design space, of training data points have so

impact on the accuracy of the network predictions. Our validation studies sugges

also that an optimum number of training data points is approximately 100*N wheN is the number of design variables. Furthermore a Latin Hypercube distribution

the data points within the design space also tends to improve accuracy.

In practical applications such as optimization loops, this approximation is much

better than resorting to empirical or highly idealized approximation of complex

function evaluations such as powering or seakeeping of multi-hull ships. The NN

approach allows the optimization process to utilize the results of highly

sophisticated CFD or experimental analysis in the process without limitations

imposed by computational costs.

Subsystem 1Semi-analytical

model

Design Tool(DOE or

optimization)

New Design

Subsystem 2NN-2

Subsystem 3NN-3

Object ive(s) &

Constraints

Training setgeneration forsubsystem 2

analysis

Subsystem 2NN-2

Largedatabase forsubsystem 3

analysis

Subsystem 3NN-3


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MDO SUBSYSTEMS

HULLFORMS DEFINITION

At the present stage of this work, in order to allow practical applications by avera

users at an early stage of design, it is decided that the MDO process and all its

models must be able to run on a workstation computer but be scalable to operat

on a server type system. Therefore a commercially available CAD based hullform

definition program is most appropriate. The naval architecture tools of Rhinocer

and Rhino Marine, that is similar to Fast Ship, have been selected for this purpo

RhinoMarine(2008). The standard, Rhino Marine process requires the user to

manually enter the waterline heights and to select the hullform that the hydrostat

is to be performed. This manual procedure is replaced by an automated procedu

in order to allow for incorporation into our optimization application. The process

starts with selection of a parent hullforms for center hull and side hulls. A geome

modeler interface automatically produces a model of scaled proportions to that o

the desired parent hull selection through the optimization loop. Using RhinoMarin

the geometric modeler also produces various hydrostatic data and the minimum

wetted surface as output. This information is incorporated into the synthesis des

model for stability calculations.

POWERING

As mentioned earlier, throughout the optimization loop, the powering (coefficient

residual resistance) is evaluated with a trained neural network. The neural netwo

approach encompasses three steps:

1. Generation of the training set (TS) & validation set (VS).

2. Neural network training to obtain a NN evaluator(s).

3. Integration of the trained NN evaluator(s) in the optimization process.

A training set (TS) is a set of known data points (design variables and their

associated values, such as objective function(s) and constraints). The training

algorithm attempts to achieve an output, which matches these inputs. A validatio

set (VS) is a set which, unlike the TS, is not used for training, but rather is used f

stopping the training. The purpose of the VS is to avoid over-fitting which can oc

with the MCC algorithm. Accurate prediction of the training data is not a valid

measure of NN accuracy. Theoretically it is possible to drive this error to zero. H

well the network represents data that it has not been trained on (VS) is a proper

representation of accuracy. In the absence of access to an existing

comprehensive powering data base for multi-hull configurations of interest

(trimaran), in this study the TS data was generated using the MQLT,Amromin e

(2003). Based on a quasi linear theory, MQLT is a CFD code, which has beenverified by comparison with trimaran model test results and proved to be reliable

assess complex problem of multi-hull interference, Mizine et al. (2004). In view o

reduced CFD cost due to application of neural networks, methods with higher lev

of fidelity (such as RANS) can also be used for generating TS data. The TS

database in this work consists of 578 number of CO values computed for three

design variables (separation, stagger and length of center hull). Seventeen

additional data points are used as validation set. The training program is a C++

software in which the MCC algorithm is programmed. The outcome of the trainin

a NN in the form of an executable in which the proper number of hidden units an

corresponding weightsfound during traininghave been implemented. This


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executable is integrated in the optimization process. The CO determines the

powering requirement and the numbers of engines required which is used by the

SDM model.

SEAKEEPING

Similar to the powering, neural networks are used to predict seakeepingperformance in the MDO process. Using advanced numerical motions analysis,

TS has been generated using a series of geometrical configurations to evaluate

log the effects of size, stagger and separation of the side hulls on the motions of

vessel.

The hull form and hydrostatic conditions were developed with the program

FASTSHIP. The hydrodynamic analysis has been performed with the WASIM

software, Wasim (2008). WASIM is a hydrodynamic program for computing glob

responses and local loading on displacement vessels moving at any forward spe

The simulations are carried out in the time domain, but results may also be

transformed to the frequency domain using Fourier transformations. WASIM is

capable of both linear and non-linear time domain simulations. However, it has bassumed that the non-linear hydrostatic effects on this trimaran hull form are

negligible, and the motions analysis has been performed with a linear simulation

The training set data base consists of trimarans ranging from 100 m to 300 m in

length. To evaluate the impact of the geometrical hull variations on the trimaran,

analysis has been performed with various longitudinal and transverse relative

locations of the side hulls, as well as displacement ratios between the side hull a

the main hull. The stagger of the side hull describes the longitudinal location of t

side hulls relative to the main center hull. The separation describes the transvers

spacing between the side main hulls. An example of a configuration (stagger and

separation) is shown in Fig 4 and 5.

Figure 4Stagger Case 0.00


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Figure 3- Separation Case 9.075 / 25.00 = 0.36

Overall sixteen ship responses for trimaran vessel are evaluated. They include ro

pitch, vertical and transverse accelerations, bending moment, shear force, prope

emergence, etc. These responses are evaluated at sea states 4, 5, 6 and 7, thre

speeds of 15, 25 and 35 knots and 5 headings of 0, 45, 90, 135 and 180 degree

Hull configurations consisted of the following variations:

Stagger of side hulls 0.00, 0.24, 0.40 & 0.80

Separation of side hulls 0.36, 0.75, 1.25

Overall vessel size 150m, 200m, 250m & 300m

Displacement ratio (side hull/center hull) 0.1015

The range of these parameters were decided upon after reviewing the initial res

in order to avoid studying options that were undesirable or unreasonable. Theseconfigurations represent a total of 48 hull variations for both vessel types for 60

environments leading to a total of 2,880 data points for the training set (for each

the 16 criteria). Details of computations and analysis of results are presented in

Hefaziet al. (2008).

The seakeeping approach is based on computing a seakeeping index as describ

in Hefazi et al.(2008). This seakeeping index is then be minimized as one of th

objective functions in the multi-objective optimization process. The motion and

seakeeping criteria for the vessel while under transit conditions, needed to comp

the index, have been derived from the seakeeping criteria for the transit and patr

mission for a NATO Generic Frigate, Eefsen et al. (2004). The limits for the tran

condition are listed in Table 1 as single amplitude RMS values of roll motion; pitcmotion, vertical and lateral acceleration, bottom slamming and propeller emerge

Table 1: Transit Criteria

Parameter Limit Value

Roll Angle 4.0 deg

Pitch Angle 1.5 deg

Vertical Acceleration 0.2 g


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Lateral Acceleration 0.1 g

Bottom Slamming Index 20 per hour

Propeller Emergence Index 90 per hour

The roll angle criterion for the transit condition is independent of the roll period. T

pitch angle criterion is independent from the pitch period of the vessel.

APPLICATIONS

HALSS MODEL

The MDO method to-date has been applied to several High Speed Sealift Ship

(HSS) concepts such as basic Army and USMC requirements for JHSS, and Hig

Speed Connector (HSC) such as basic JHSV, where multi-objective optimization

necessary. Furthermore, each requirement has its distinct constraints which are

generally derived from mission requirements. Their purpose is to avoid exploring

unreasonable designs. A very detailed study has been conducted in order to

determine the best approach for application of the method. Results indicate that

careful optimization process, including selections of proper algorithms and prope

initial population, have to be followed in order to obtain complete and meaningfu

results. This process and results (pareto optimum) are described in detail, Hefaz

al. (2008).

The application of the synthesis level MDO tool consists of

Definition of the design space, constraints and measure(s) of merit.

Running the MDO program to search the multi-dimensional design space usinsingle or multi-objective optimization algorithms.

Construction of feasible and Pareto optimum solution sets.

Subsystem requirement definition corresponding to optimum measure(s) of m

Two cases are reported here. Other applications of the method can be found in

Hefazi et al. (2008). The first case is application to a Sealift Alternative Ship

concept. HALSS is an airlift large ship concept capable of C130 operations. Tab

and 3 contain the design variables, their description and design space limits and

design constraints.

Table 2: Design variables for HALSS

Design

Variable

Lower

Bound

Upper

Bound

Description

Lch250.0 320.0

Center Hull Length onWaterline

Bch20.0 28.0

Center Hull Beam on Water

Tch10.0 12.0

Center Hull Draft

Lsh100.0 200.0

Side Hull Length on Waterli

Bsh4.0 8.0

Side Hull Beam

Tsh7.5 10.0

Side Hull Draft on Waterline


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Dch24.0 32.0

Center Hull Depth

0.5 1.0

Separation

0.15 0.35

Stagger

Table3: List of constraints imposed on the HALSS modelConstraint Lower

Bound

Upper

Bound

Description

WSch0 17279.0

Center Hull Wetted Surfa

WSsh600.0 90000.0

Side Hull Wetted Surface

ch

bC 0.55 0.90Center Hull Block Coeffic

ch

pC

0.625 1.0

Center Hull PrismaticCoefficient

ch

mC 0.675 0.95Center Hull MaximumSection Coefficient

sh

bC 0.4 1.8 Side Hull Block Coefficiensh

pC 0 1.8Side Hull PrismaticCoefficient

sh

mC

0.7 1.8Side Hull Maximum SectiCoefficient

Wtdisplbal0 3000

Weight-DisplacementBalance

Maxspeedboost33.0 200.0

Maximum Speed Boost

The objectives of the optimization for this case are to maximize the dead weight

displacement ratio (Dwtdisplratio), maximize the maximum speed boost

(Maxspeedboost), and maximize the seakeeping index (Skpp).

Initially, each objective function (Dwtdisplratio, Maxspeedboost, and Skpp) is

optimized individually using a Multi-island Genetic Algorithm (MIGA). MIGA is a

global search algorithm and is distinguished from other genetic algorithms in tha

each population of individuals is divided into several sub-populations called

islands upon which all genetic operations are performed separately.

After both objective functions are optimized individually using MIGA, 100 individu

from each optimization are chosen and concatenated to create a new population

such that all its members are feasible (no constraints are violated) and the entire

design space is spanned. This new population serves as the initial population fo

the multi-objective genetic algorithm (MOGA). This operation is performed in ord

to obtain a suitable initial population to begin the multi-objective optimization.

Next, a MOGA which, similarly to MIGA is a global search method, is run using

Neighborhood Cultivation Genetic Algorithm (NCGA). NCGA utilizes an initial

population upon which standard genetic operations of mutation and crossover a

performed such that a pareto set is constructed. A set is said to be pareto

optimal when no individual can be made better off without another being made

worse off. Unlike MIGA, where only one objective is to be optimized, NCGA

simultaneously attempts to optimize multiple objectives, resulting in trade-offs be

made between them.


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The results of the NCGA optimization with three objective functions are presente

Figures 6 and 7; Fig. 6 shows the pareto front for Maxspeedboost vs. Dwtdisplra

and Fig. 7 shows a three-dimensional representation of the Pareto set.

Table 4 presents the maximum values found for Maxspeedboost, Dwtdisplratio,

Skpp using NCGA. Point 1 corresponds to maximum Maxspeedboost and is

represented by the white triangle in Figures 6. Point 2 corresponds to maximumDwtdisplratio and is represented by the gray square in Figure 6. Point 3

corresponds to maximum Skpp.

Figure 8 shows a frontal (a) and top (b) view of the HALSS where the left (or top

side corresponds to point 1 in Table 5, and the right (or bottom) side correspond

point 2 in Table 4.

32.5

33

33.5

34

34.5

35

35.5

36

36.5

37

37.5

38

0.28 0.3 0.32 0.34 0.36 0.

Dwtdisplratio

Maxspeedboost(knots)

NCGA Pareto

Dead Weight to Displacement Ratio

Maximum Speed Boost

Figure 6: HALSS model NCGA optimization results (Dwtdisplratio vs.

Maxspeedboost)


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Figure 7: 3D representation of HALSS NCGA optimization results

Table 4: Maximum values for Maxspeedboost, Dwtdisplratio, and Skpp

Point Lch Bch Tch Lsh Bsh Tsh Maxspeedboost Dwtdisplratio Sk

1 313.28 22.53 11.08 180.56 5.35 8.09 0.81 0.32 37.44 0.31 0.9

2 276.92 27.46 11.08 161.29 7.90 8.64 0.77 0.15 33.03 0.37 0.9

3 319.94 26.66 11.04 119.18 4.12 8.65 0.74 0.15 36.12 0.33 0.9

(a) Frontal View (b) Side Vi

Figure 8 Comparison of two designs from extreme ends of the design space. Th

design on the right maximizes Dwtdisplratio. The design on the left maximizes

Maxspeedboost.

JHSV MODEL

The second case reported here are the results of optimization for the Sealift Ship

Joint High Speed Vessel (JHSV) type mission requirements. This mission

requirement includes:

Transit speed: Not less than 25kn Crew: 44

Boost range: Not less than 1,200nm Troops, berthed: 150

Transit range: Not less than 4,700nm Troops, seated: 312

Vehicle weight: Not less than 635t Total accommodations: 506

Vehicle area: Not less than 1,858m^2

Table 5 and 6 contain the design variables, their description and design space li

and design constraints for JHSV trimaran model

Table 5: List of design variables for the JHSV trimaran model

DesignVariable

LowerBound

UpperBound

Description

Lch100.0 150.0

Center Hull Length onWaterline

Bch7.5 12.0

Center Hull Beam onWaterline

Tch3.5 10.0

Center Hull Draft

Lsh40.0 65.0

Side Hull Length onWaterline

Bsh3.0 6.0

Side Hull Beam

Tsh1.5 4.0

Side Hull Draft on Waterl


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Dch9.0 12.0

Center Hull Depth

0.75 1.5

Separation

0.0 0.35

Stagger

Table 6: List of constraints imposed on the JHSV Trimaran model

Constraint LowerBound

UpperBound

Description

WSch Center Hull WettedSurface

WSsh Side Hull Wetted Surfacch

bC 0.550 0.625Center Hull BlockCoefficient

ch

mC 0.675 0.800Center Hull MaximumSection Coefficient

sh

bC

0.500 1.000Side Hull Block Coeffici

sh

mC

0.700 1.800Side Hull MaximumSection Coefficient

Wtdisplbal -300 300 Weight-DisplacementBalance

Maxspeedboost35.0 200.0

Maximum Speed Boost

The objectives of the optimization for this case are to maximize the dead weight

displacement ratio (Dwtdisplratio), maximize the lift to drag ratio and maximize th

cost. The optimization is run using the Darwin Genetic Algorithm in PHX

ModelCenter environment, ModelCenter (2008). Figure 9 shows the results of th

optimization in the form of Pareto optimal solutions.

Figure 9: The results of optimization using the Model Center Darwin genetic

algorithm

Table 7 shows the specifications of the extreme corners of the pareto surface.

Detailed comparisons of various design points and their implications are currentl


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on-going and will be reported later. Overall however, results presented here sho

that the MDO method can determine the significant impact of various criteria. Th

provides valuable, input for functional and design space exploration analysis. Th

presented MDO tools allow systematic parametric study of different requirement

and design options by means of optimization routine and synthesis design mode

procedures.

Table 7: Extreme design points identified as the corners of the pareto surface

Point Lch Bo Lsh Maxspeedboost DwtdisplratioLift To

Drag

Production

Cost

1 118.3 19.9 40.1 0.75 0.35 36.6 0.41 29.53 89.6

2 152.6 26.7 65.0 1.50 0.31 36.8 0.35 43.46 97.5

3 100.3 21.0 42.9 0.80 0.15 36.1 0.44 20.21 94.4

CONCLUSION AND FUTURE WORKCCDoTT/CSC team has made substantial progress in developing comprehensiv

practical computational tools for applications to multi-hull vessels. The MDO tool

allow systematic parametric study of different requirements and design options b

means of optimization routine and Synthesis Design Modeling procedures. The

method has been applied to several High Speed Sealift applications for testing.

number of possible extensions of the method are under study and review. They

include future development of an advanced structural optimization sub-system to

investigate the impact of variations in the vessel configurations on the structural

design and weight. The structural MDO will use the loads generated by the

hydrodynamic analysis to evaluate the impact of changes in the vessel configura

on the structure, for example, the structural implications of the pinching and pryinmoments induced on the side hull for vessels with various breadths. The curren

hullforms definition sub-system is based on scaling a selected parent hul l from a

existing hullforms library. Incorporation of a parametric, non-dimensional offset

representation of the ship hulls in the MDO along with means to transform offset

for variations in block and midship coefficients, center of buoyancy, widths and

depth of transom length, area of bulb, etc are another significant improvement th

are being considered. Enhanced seakeeping computations to include more train

set data for trimarans and also catamarans are currently underway. Finally, the

synthesis design model (SDM) utilized in this work has been developed in house

over the course of past three years with focus on multi-hull applications. Ideally t

SDM model could be incorporated with the US Navys Advanced Ship and

Submarine and Evaluation Tool (ASSET). Integration of our multi-objective

optimization, neural networks and an advanced SDM such as ASSET will provid

powerful design tool applicable to both military and commercial applications.


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ACKNOWLEDGMENT

This work is supported by the US Office of Naval Research, under cooperative

agreement No. N00014-04-2-0003 with the California State University, Long Bea

Foundation Center for the Commercial Deployment of Transportation Technolog

(CCDoTT). The authors would like to sincerely thank the program manager Dr.Paul Rispin and Mr. Dan Sheridan from ONR for their support, and many import

inputs. Mr. Steve Wiley from CSC has been the primary developer of the SDM.

experience with many Navy and commercial ships have been essential

contributions to this work. We also thank Viking Systems of Annapolis Maryland

pioneering the systematic seakeeping calculations for multi-hulls. Their professio

contribution helped to incorporate these comprehensive results in MDO process

Finally we would like to thank CCDoTTs Principal Investigator Mr. Stan Wheatle

program coordinator Mr. Steven Hinds, and program administrator Ms. Carrie

Scoville for their supports.

Glossary of Acronyms:

ABS - American Bureau of Ships

CFD - Computational Fluid Dynamics

HSS - High Speed Sealift Ship

JHSS - Joint High Speed Sealift Ship

JHSV - Joint High Speed Vessel

LWT - Light Weight

MCC - Modified Cascade Correlation

MDO - Multi-disciplinary Design and Optimization

MOGAMulti-objective Genetic Algorithm

MIGAMulti-island Genetic Algorithm

NN- Neural Network

NLPQL - Sequential Quadratic Programming

NCGANeighborhood Cultivation Genetic Algorithm

TSTraining Set

VSValidation Set

USCGUS Coast Guard

USMC- US Marine Corp


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