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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
Adeline SchmitzCalifornia State University,
Long Beach, Long Beach/USA
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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