Rapid Ship Design Evolution Using Computer Algorithms Rapid Ship Design Evolution Using Computer Algorithms Rapid Ship Design Evolution Using Computer Algorithms Rapid Ship Design Evolution Using Computer Algorithms ----

A Scientific Approach to A Scientific Approach to A Scientific Approach to A Scientific Approach to SEA 1180SEA 1180SEA 1180SEA 1180

Aidan Depetro BEng (Hons), MIEAust CPEng

BMT Design & Technology, Australia

adepetro@bmtdt.com.au

INTRODUCTION

The SEA 1180 Program

In 2009, the Government expressed its intent to rationalise the Navy's Patrol Boat,

Hydrographic, and Mine Countermeasures (MCM) ships in efforts for greater operational

efficiencies and reduced cost of ownership [1]. The SEA 1180 project seeks to replace 4 single-

role vessel classes, comprising of 26 ships, with one multi-role vessel class of around 20

Offshore Combatant Vessels (OCV), initially estimated to cost $3-$5B [2]. Important details

regarding the immediate plans for the program are expected to be released in the

forthcoming 2015 Defence White Paper, as yet unreleased at the time of writing.

Although clear in its plans to procure one class of OCV, during the early stages of an

acquisition program it is difficult to determine whether building all capability requirements

into one class of multi-role vessel is the most cost effective solution, or if a fleet of two or

more classes of vessel is more suitable. Further, as a result of the time consuming nature of

ship design, determining the optimal fleet size and vessel mix is problematic due to the

number of different possibilities and the investment required to understand the pros, cons

and effectiveness of each option.

Challenges: Determining the Optimal Solution in a Vast Solution Space

Due to the extensive time and resource investment required to generate multiple design

concepts, the traditional early stage design approach does not easily lend itself to rapid trade-

off, cost-benefit and options analyses where many different solutions are required to be

synthesised and evaluated. Despite significant developments in early stage design tools and

software applications, the level of manual user input and the degree of input information

required still prevents extensive exploration of the solution space, particularly in the case of

multi-role vessels.

This is the challenge currently being faced by the SEA 1180 and similar international programs

which seek to consolidate minor warships into a more coherent and adaptable fleet.

Determining the most ideal solution requires exploration of numerous multi-role

combinations (e.g. patrol-hydrographic survey, patrol-minehunting, minehunting-

hydrographic survey), analysis of the pros and cons of varying performance parameters for

each multi-role concept (e.g. range, speed, endurance) and evaluation of different fleet mixes

featuring one or more multi and single-role vessel types. Exploration of such a vast solution

space requires a more intelligent, efficient and scientific approach.

A Scientific Approach

If the early stage design process is able to be automated, a significant number of different

capability and mission role combinations are able to be evolved and evaluated in a short

period of time for a substantially lower upfront engineering support cost. Such a method

provides the basis for informed decision making during the crucial early stages of the

acquisition cycle and enables designers and stakeholders to discover ship designs offering the

optimal balance between cost, performance and other unique customer requirements. By the

use of the “inside-out” design approach, intelligent computer algorithms and application of

computing power, this approach is able to execute typically man driven processes in a matter

of minutes rather than days.

TRADITIONAL DESIGN METHODS

The Ship Design Process

As discussed by Sanders and Guedj [3], the traditional ship design approach is logical, well

proven and effective in most cases, however can be inefficient during the early stages of the

design process. In summary, the design process starts with the definition of capability

requirements of the ship, normally expressed as an operational concept, which gives rise to a

corresponding set of functional requirements. Functional requirements are met by

specification of various different ship system configurations and equipment options. The

designer then estimates the weight of the ship and every system to be included in the ship

and balances this quantity with the underwater volume of the hull. The weight and

underwater volume of the hull are distributed so that the operating ship floats upright and

meets performance requirements. The overall design process requires iterative calculation

and adjustment of many interrelated ship parameters, and is commonly depicted as a spiral,

as shown in Andrew’s ship design process, in Fig 1 below.

Fig 1 Andrew's Overall Ship Design Process [19]

As suggested by Fig 1 above, the ship design process has several distinct stages most of which

must complete at least one cycle of the above spiral, before feeding into the next stage. Fig 2

below summarises the key stages and activities of a ship acquisition program.

Fig 2 Ship Acquisition Process [4]

The ship design process comprises stages 1-5 and requires several iterations of the design

spiral with concurrent consideration for all applicable aspects of the ship. The focus of the

concept proposed in this paper is on early stage ship design which is covered by stages 1-2

above and discussed further in the next section.

Compartment Arrangement

Compartment arrangement is key to establishing ship size, weight and hull form. Ship design

in this regard, conventionally follows an ‘outside-in’ approach, where the compartments to

be included are arranged after a hull form has already been determined. This approach can

lead to various issues (as noted by Keane [5]) due to the possible compartment arrangements

being restricted by the hull form. Satisfying adjacency requirements, location preferences and

maintenance/repair access considerations may not be simultaneously achievable within the

given hull, but this may not be apparent until the later stages of the design process, when it

is difficult to change the hull form.

Traditionally, the task of compartment arrangement is performed by a ship designer who

employs a manually executed, iterative approach. Due to the labour involved, the number of

different configurations that can be considered is limited. If this process is automated, a large

number of different potential compartment configurations can be evaluated, covering many

more possibilities than a manual approach and potentially discovering a superior solution to

the manually designed configuration [7].

EARLY STAGE SHIP DESIGN

Early stage ship design requires translating a coarse set of capability requirements into a

narrow set of defined concept solutions for further analysis and transition into detailed

design. As shown in stages 1-2 of Fig 2, this involves needs analysis, definition of capability

and functional requirements, exploratory concept design and initial system design

specification. Essentially, the objective of the early stage design process is explore an initially

vast solution space and significantly reduce the solution space size to a small number of

broadly specified concepts in preparation for market approach or detailed design.

During the early design stages, requirements are normally quite broad, can be satisfied in

many ways and the potential solutions can take many different forms. Therefore the key to

successful early stage design is in discovering the most suitable concepts in the solution space

and providing enough information about their relative costs, capabilities, advantages and

disadvantages to make the right decisions moving forward into subsequent design stages. As

noted by Doerry [9], it is important to note that while the objective of later design stages is

to synthesise a ship design to meet requirements, the goal of early stage design is to

synthesise and refine requirements based on insight gained from developing multiple concept

designs and performing other analysis.

It is well known that decisions made in the early stages of ship design have a significant impact

on functional outcomes, through-life cost and the overall effectiveness of the resultant vessel

which makes the early stage ship design process critical to the success of any ship program.

However early stage design is subject to the same constraints as all other design phases;

namely time, resources and budget. This drives a need for innovative approaches and the

development of intelligent tools to aid in the early stage ship design process.

Approaches

Approaches to early stage ship design have evolved immensely over the last 50 years, with

particularly significant steps being taken over the last decade. Regardless of the tool used, the

general approach to early stage design involves capture and definition of requirements and

translation of requirements into some sort of roughly specified, multi-faceted entity

comprising space, weight, power and other demands. The balancing and arrangement of

these entities, whether they be compartments, zones, functional blocks or otherwise, gives

rise to a ship concept capable of meeting the specified requirements.

Initially, early stage ship design was a product of the traditional design process whereby

concept exploration would normally comprise a scaled down or shortened version of the

design spiral. At this point, several key decisions (or assumptions) had already been made

about capability requirements and general ship type such that only a small number of possible

solutions remained. Engineers and naval architects would then proceed with hull selection

and manual compartment arrangement for candidate designs. Due to the time intensive

nature of the process and limitations imposed by program budgets, only a few concepts were

able to be explored before committing to a solution to take forward to detailed design.

Moving on from man-driven methods, computer aided design methodologies began to

emerge as early as the late 1960s, and development has continued to this day. Nick [8]

provides a summary of developments as concepts moved from two dimensional layout tools

to volume-based design applications and semi-automated arrangement programs.

Current Tools

The modern day computer and the unprecedented levels of computing power now

commercially available have led to the development of a wide range of increasingly intricate

ship design tools. Currently available tools are too numerous to detail in this paper, however

there are two noteworthy tools with particular application to early stage design: the

Advanced Surface Ship Evaluation Tool (ASSET), used by the US Navy, and SURFCON,

developed by University College London (UCL) and implemented in software package,

Paramarine.

Fig 3 A coarse layout of Building Blocks for a frigate design in Paramarine [11]

ASSET has been used for early stage design by the US Navy for a number of years now. As

described by Chalfant et al. [10], ASSET is a powerful tool which can be used to rapidly

generate and analyse multiple early-stage ship designs. The tool is based on parametric

relationships derived from an extensive database of previous ship designs and is therefore is

not well suited for exploration of new-concept designs that differ significantly from past

practice. ASSET also requires extensive training and experience to be used effectively, and

due to distribution restrictions and the native dataset, is only suitable for use in the US, for

US naval ships.

Paramarine is a commercially available tool used by the British Ministry of Defence and a wide

range of commercial clients. The tool features many other in-built analysis modules and

routines suitable for extended definition of concept designs stretching into the preliminary

design stage. Its early stage design module utilises the “Functional Building Block”

methodology developed by UCL [11]. This defines the requirements of the ship as a set of

functional blocks, each possessing a number of attributes, including weight, space, services

and personnel. Paramarine does not rely on parametric relationships or a specific dataset,

and therefore grants the user a great degree of freedom in concept exploration. As noted by

Chalfant et al. [10] however, the strength of Paramarine can also be a weakness: its extreme

flexibility requires a firm grasp on requirements and definition of a range of parameters.

Although the process can be relatively efficient, Paramarine still requires manual input from

a qualified and experienced user to generate each concept as key mission and ship

parameters are varied.

THE SEA 1180 PROBLEM SPACE

Multi-role Vessels

A changing threat landscape, technology advancements and global power shifts have

drastically changed the nature of naval and border protection operations in recent years.

Austerity measures in response to difficult economic conditions have seen typical big

spenders slash defence budgets and consolidate naval fleets whilst many developing

countries now seek to boost defence in efforts for greater stability to foster economic growth

[12].

Evans [13] highlights the emerging operational needs that have driven the development of

multi-role vessels over the last decade, termed collectively as military operations other than

war:

• Humanitarian relief:

o Emergency medical care;

o Amphibious operations;

o Supply of food, water, temporary accommodation and ongoing replenishment; and

o Evacuation.

• Maritime security:

o Border protection;

o Piracy interdiction;

o Long-range counter-terrorism operations;

o Mine detection and countermeasures; and

o Hydrographic survey.

The concept has proven popular with several navies already, and will only grow in popularity

whilst the current political and economic climate persists. Multi-role vessels already in

operation include the Royal Danish Navy's (RDN) Flyvefisken Class, also known as the Standard

Flex or STANFLEX, New Zealand's HMNZS Canterbury and the US Navy’s Littoral Combat Ship

(LCS). Concepts include Austal’s MRV 80, BAE’s Global Combat Ship and BMT’s Venator Minor

Warship.

Fig 4 STANFLEX Concept of the Royal Danish Navy

The greatest advantage of the multi-role vessel is the ability to rapidly respond to one or more

of these situations with the first available asset in the fleet. Providing the same range of

capability and rapid response capacity with single role vessels requires several classes of

vessel as part of an overall larger and more complex fleet.

Unique Challenges for Early Stage Design of Multi-role Vessels

Simply combining as many of the required roles as possible into a single vessel isn’t necessarily

the best solution however, as one particular role or capability can drive the size of the vessel

and result in a design that is particularly good for one mission role, and very inefficient for

performing several others.

Therefore a balance between through-life cost, flexibility, manning requirements and

capability must be reached which presents some unique challenges to effective early stage

design of multi-role vessels. In the case of multi-role vessels, designers must not only consider

hull types, performance parameters and compartment configurations within a singular vessel

type, but must also consider multiple mission role combinations giving rise to further vessel

types, as well as different fleet configurations of the multiple possible vessel types. An

example of this is depicted in Fig 5.

Variables:

- Speed

- Range

- Endurance

- Other performance characteristics

Roles:

Patrol, minehunting

Roles:

Patrol, hydrographic

survey

Roles:

Patrol, hydrographic

survey, minehunting

Roles:

Patrol

Vessel Options Multi-role Options Fleet Mix Options× ×

Fig 5 The Multi-Role Vessel Solution Space

Depending on the array of capabilities required, the possible solutions can quickly become

too numerous to explore in detail using conventional methods.

A SCIENTIFIC METHOD: RAPID SHIP DESIGN USING COMPUTER ALGORITHMS

Relating Capability to Compartments

Referring back to the traditional approach, the precursor to arrangement of compartments

requires intrinsically linking key capability requirements and mission roles with platform and

mission specific equipment and subsequently the compartments required to house the

equipment. A combination of first principles, parametric and engineering methodologies are

used to calculate the required compartments and associated space demand based on basic

user inputs of speed, range, endurance and mission role(s).

Relating capability to equipment, space requirements and compartments allows the solution

to be independent of any specific ship type or hull form which enables free exploration of the

solution space. With particular application to multi-role vessels, it allows the generation of

any possible multi-role option without the need for rework or specification of additional

parameters.

Inside-out Design Approach

Of the various limitations of the traditional ‘outside-in’ approach, the most critical to

automation of the early stage design process is the operational restrictions placed on the

design by having a predetermined and fixed hull form [4]. Such an approach precludes the

variation of ship operational parameters and mission roles to explore the multi-role vessel

solution space.

The alternative approach involves arranging the compartments first (the insides), then

‘wrapping’ a hull form around them (the outside), so that the issue mentioned above can be

avoided. This ‘inside-out’ approach prioritises the arrangement of the ship’s systems to best

achieve the required operational effectiveness, treating the hull as a means of supporting the

ship’s systems, as opposed to a primary, defining aspect of the design [1]. The rapid ship

evolution concept proposed in this paper is based on this idea, as it allows free permutation

of ship operational parameters and mission roles.

Arrangement of Compartments

Arrangement Algorithms

The approaches taken in the use of computers in ship design and compartment configuration

range from providing varying degrees of computer assistance to a human designer, to

completely automating the compartment arrangement task. (A comprehensive review of

much of the recent work in this area is given by van Oers [14].)

The problem of compartment arrangement is essentially a particular example of the layout

problem (alternately referred to as the packing, packaging, configuration, container stuffing,

pallet loading or spatial arrangement problem throughout the literature [15]). This problem

has been extensively studied, and a multitude of approaches exist for producing solutions.

The ship compartment configuration problem may be regarded as a variation of the bin-

packing problem (a particular type of layout problem), which consists of packing a number of

objects within one or more larger spaces. It has been studied extensively and there exist

numerous heuristics and rules to generate solutions, in one, two and three dimensions [16].

The use of bin-packing algorithms is particularly useful when the optimal use of available

space is a high priority, an example is shown in Fig 6.

Fig 6 Allocation of extreme points to define remaining usable space as compartments are progressively packed [16]

Genetic Algorithms

Arranging compartments into a space is only the beginning of the problem for ship designers.

When a naval architect sets about arranging compartments, there are many factors that

require consideration, including weight distribution, buoyancy, damage control, seakeeping

and stability, compartment adjacency, functional flow and design rules. The number of

possible ways even a small number of compartments can be arranged into a hull is

prohibitively large (even for today’s most advanced computers) and precludes simply trying

every possible way until a correct solution can be found. Therefore any computer driven

automated method must be capable of assessing generated solutions against multiple

criteria, then intelligently converge on an arrangement that sufficiently satisfies those criteria.

There is a type of algorithm that meets these requirements and is often used for this type of

problem. Genetic algorithms are a class of evolutionary search algorithms loosely based on

biological processes. Within a genetic algorithm, each solution to the problem is represented

as a string of numbers (or other characters) known as a genome (or chromosome). Each

genome uniquely encodes a single solution (and vice versa). As in most optimisation

problems, an objective function is defined, which the algorithm will attempt to maximise. This

function contains a mathematical representation of the relevant criteria, called an Objective

Function, and is evaluated for each genome, yielding a ‘fitness’ score which represents how

desirable the solution it represents is.

Depending on the ‘fitness’ score, operators such as ‘mutation’ and ‘crossover’ are used to

generate alternative solutions for evaluation, as shown in Fig 7. The mutation operator causes

a random change to some part of a genome, while the crossover operation exchanges one or

more sections of two parent genome strings to produce one or more child genomes.

Crossover is designed to combine good solutions in order to potentially generate better ones,

gradually increasing the fitness of the population, while mutation introduces random

variation into the population, forcing the algorithm to search a greater range of the solution

space and helping to avoid convergence at inferior maxima. The algorithms are also able to

deal with preferred absolute and relative locations of compartments by using fuzzy

preferences (which allow for compromise between conflicting allocation objectives [8]).

Fig 7 Pictorial representation of Crossover and Mutation functions within a genetic algorithm [8]

In basic terms, if ship compartment arrangement criteria can be represented mathematically,

the algorithm can be programmed to apply design rules to the arrangement routine and

consider things like stability, seakeeping, damage control and rules of thumb that govern non-

critical compartment placement. The arrangement process is summarised in Fig 8.

Fig 8 Compartment Arrangement Process using Computer Algorithms

Fig 9 Automated Compartment Arrangement with hull (left), without hull (right)

Fig 9 shows an automated compartment arrangement for a minor warship, completed by a

combination of bin-packing and genetic algorithms, starting with no more than a list of

compartments including information on area, volume and function. The hull has been suitably

sized to accommodate the necessary fuel capacity (fuel storage shown in red), and the bridge

(purple) and propulsion compartments (blue) have been correctly positioned in accordance

with design rules.

Multi-Criteria Methods

As discussed above, a genetic algorithm has the ability to consider multiple criteria via its

objective function. Providing the criteria can be expressed mathematically, technically there

is no limit to what the algorithm is able to incorporate into its evolution process. This lends

itself to multi-criteria approaches such as Multi-Criteria Decision Analysis (MCDA).

MCDA is a systematic framework for decision making, providing a rational, objective means

of comparing the available choices and their consequences in order to rank them from most

to least preferred. In the context of early stage ship design, particularly for military or

defence-related vessels, MCDA typically involves evaluation of operational effectiveness and

cost to determine the best value for money solution. Use of computer algorithms in this way

can allow the rapid evolution and evaluation of multiple vessel types, multi-role combinations

and fleet mixes.

SIMPLIFIED PROBLEM - MULTI-ROLE VESSEL ACQUISITION

SEA 1180 presents a complex design challenge, however a simplified problem can be used to

demonstrate the use of the proposed methodology on a similar program. The below example

problem is based on the SEA 1180 program.

Aim

To demonstrate the use of computer algorithms in rapid generation and evaluation of multi-

role vessel designs in order to provide recommendations on vessel type, multi-role

combinations and fleet mix based on Life Cycle Cost and capability.

This is typical of the advice sought by stakeholders at the inception of a vessel acquisition

program, however with use of the method described in this paper, detailed analysis can be

undertaken in a fraction of the time usually taken to complete a task of this nature to yield

enhanced outcomes at a lower engineering development cost.

Scope and Requirements

The scope of this simplified problem includes the generation of multiple multi-role ship

designs based on specified capability requirements and the recommendation of the most

suitable vessel concepts, multi-role combinations and fleet mix.

The capability requirements expressed by the primary stakeholders for the vessel or group of

vessels include:

• Patrol:

o Interception;

o Boarding;

o Surveillance;

o Counter-piracy operations.

• Hydrographic survey:

o 2-D and 3-D mapping;

o Search and salvage.

• Minehunting:

o Detection and classification; and

o Mine clearance.

The minimum capability mix requires that at least 10 ships be suitable for patrol, 4 for

hydrographic survey and 4 for minehunting. Fleet Mix 1, shown in Table 2 below, provides a

baseline single role vessel fleet mix. The minehunting capability is assumed to be provided by

use of Remotely Operated Vehicles (ROV).

Rapid Evolution Using Computer Algorithms

The methodology applied to this problem is an integration of the concepts discussed in this

paper and has been implemented in an internally developed early stage design tool. The core

function of the tool is the population of compartments required to meet user-input capability

needs and arrangement of compartments into a feasible design. This task is performed by a

bin packing algorithm augmented with a genetic algorithm to search the space of potential

solutions. Both of these components are implemented in C++ and the genetic algorithm uses

the GAlib library of genetic algorithm components written by Wall [17].

Each concept takes a matter of minutes to generate using a moderately powered, standard

laptop or desktop computer, after which the key parameters output by the tool are used as

the basis for costing and operational effectiveness scoring.

Costing was based on internally developed lifecycle cost models taking into account key ship

parameters including displacement, power, range and manning. Operational effectiveness

scoring was completed using a similar scoring scheme to that used in a previous BMT paper

(Glanville [18]).

Later versions of the tool will see costing and effectiveness scoring of generated designs

incorporated into the tool and the evolution process via the algorithm utility functions. Future

developments will also incorporate more complex features of the ship arrangement including

passage ways and enhanced compartment alignment.

Rapid Ship Evolution Results

For the purposes of demonstration, ten concepts were generated each with varying degrees

of patrol, hydrographic survey and minehunting capability. Based their performance

specifications, the necessary compartments were generated and computer algorithms were

used to automatically arrange compartments into feasible configurations within a suitably

sized hull. The ship arrangements for each concept are shown in the figure below.

Fig 10 Arrangements for all 10 concept vessels (in ascending order from left to right, top to bottom)

The output concepts from the rapid evolution process, their associated costs and capability

scoring are summarised in Table 1.

Table 1 Rapid generation multi-role vessel concepts

Concept Description Mission

Roles

Speed

(kn)

Range

(nm)

Endurance

(days)

Length

(m)

Cap.

Score

Acq.

Cost

($M

AUD)

LCC

($M

AUD)

1 Dedicated Fast Patrol P 25 3000 20 48.2 34 39.6 616.3

2 High Capacity Fast Patrol-Hydro P-H 25 15000 50 111.9 68 192.9 1,108.0

3 Fast Patrol-Mine P-M 25 3000 20 52.6 58 44.2 665.5

4 High Capacity Fast Omni-class P-H-M 25 10000 50 93.1 92 120.8 964.4

5 Dedicated Hydro H 12 15000 50 83.6 48 95.4 449.9

6 Dedicated Mine M 15 1500 20 46.4 40 37.9 191.8

7 High Capacity Hydro-Mine H-M 12 10000 40 70.7 76 69.4 260.2

8 Medium Capacity Mid-speed Omni-class P-H-M 20 8000 30 61.0 70 54.4 639.5

9 Medium Capacity Hydro-Mine H-M 15 8000 30 61.0 58 54.4 389.9

10 Low Capacity Hydro-Mine H-M 15 1500 20 55.3 44 47.3 213.4

A number of fleet mixes were trialled and evaluated, each meeting the minimum capability

mix and collectively providing roughly equal patrol, hydrographic survey and minehunting

capability. The results are shown in Table 2.

Table 2 Comparison of fleet mixes

Fleet Mix Composition Number

of Ships

Number

of

Classes

Manning

Req.

Capability

Score

Total Acq.

Cost ($M

AUD)

Total LCC

($M AUD)

$/Cap.

Point Rank

Fleet Mix 1 10 x C1, 4 x C5, 4 x C6 18 3 700 692 929 8,730 12.6 5

Fleet Mix 2 14 x C4 14 1 714 1288 1,692 13,502 10.5 2

Fleet Mix 3 6 x C1, 4 x C2, 4 x C3 14 3 604 708 1,186 10,792 15.2 6

Fleet Mix 4 10 x C1, 5 x C7 15 2 535 720 743 7,464 10.4 1

Fleet Mix 5 8 x C1, 8 x C8 16 2 584 832 753 10,047 12.1 4

Fleet Mix 6 10 x C1, 6 x C9, 8 x C10 18 3 596 748 803 8,577 11.5 3

Discussion

The ten nominated vessel concepts were able to be evolved via the use of computer

algorithms in less than an hour, averaging approximately 5 minutes per concept. It is

acknowledged that the output general arrangements do not include passageways and other

smaller features (this will be the subject of future tool development), however the current

arrangements were more than sufficient to establish vessel size and other parameters

required for costing and evaluation.

All fleet mixes meet the minimum capability required by the baseline case, Fleet Mix 1, and

are considered feasible from a capability point of view. It is interesting to note the contrast

between Fleet Mix 1, a fleet comprising three classes of single role vessel (one for each

capability), and Fleet Mix 2, a fleet comprising one class of multi-role vessel (to satisfy all three

capabilities). Although Fleet Mix 2 offers ultimate flexibility and versatility, it has a higher

manning requirement and is more than 50% more expensive through life.

Critical points to note from this example problem include:

• Fleet mixes with two classes of vessel generally provide the best value for money;

• Combining all three capabilities into one class of multi-role vessel, while providing the best

capability, was significantly more expensive to acquire, operate and maintain;

• Combining the hydrographic survey and patrol mission roles produced costly designs with

significantly higher operating costs;

• Adding the minehunting capability to a vessel via the use of ROVs generally provided good

value for money.

Fleet Mix 4 provided the best value for money, lowest cost and lowest manning requirement

whilst meeting the minimum capability requirement. This concept fleet maintained a single

role class for the patrol capability, providing a small vessel to conduct high speed operations

efficiently, and combined the hydrographic survey and minehunting classes into a larger,

lower speed, higher range vessel. This proved to be the most economic mix whilst still

providing the level of versatility required to meet overall capability requirements.

CONCLUSIONS

Traditional ship design approaches, whilst long-proven and effective, can be time consuming

and generally not conducive to the exploration and evaluation of a large number of possible

solutions. This is particularly relevant to the early stage, exploratory design required to advise

the SEA 1180 program where a multitude of different role combinations, performance

parameters and fleet mixes are possible. This creates the need for intelligent, rapid and,

where possible, automated design synthesisation techniques.

This paper has discussed and demonstrated the use of computer algorithms to achieve

automation in the early stage design process and by extension, rapidly generate and evaluate

many different multi-role vessel design concepts. Further, the use of genetic algorithms

within a MCDA framework allows for optimisation of designs based on stakeholder

requirements including operational effectiveness and cost.

This technique and tools developed based on the concepts discussed in this paper can be used

to assist the SEA 1180 program, and more broadly, navies and maritime security organisations

in ascertaining value for money capability solutions in a fraction of the time and at

significantly lower engineering development costs. Importantly, the depth of analysis and

design exploration achievable with this approach can significantly reduce project risk by

helping to ensure poor solutions are explored and dismissed whilst superior solutions are

discovered and developed into effective capabilities.

The results of the simplified example problem showed that combining all mission roles into

one multi-role vessel is not cost effective and that greater efficiencies can be recognised by

combining complimentary mission roles such as hydrographic survey and mine hunting.

FUTURE WORK

BMT is continually developing its ship design tools and working towards greater flexibility and

automation whilst refining algorithms to achieve an improved level of optimisation. There is

much scope for further work in the improvement of arrangement algorithms to capture more

design rules and in evolutionary algorithms to balance and manage multiple criteria through

the optimisation process. With further work, this approach could be adapted and used on

many warship acquisition programs.

ACKNOWLEDGEMENTS

Algorithm development by Rhyan Hoey, tool development by William Irvin, Callan Bird and

Mitchell Jones, funding from the BMT Group Innovation Board and other contributions from

students and staff at BMT Design & Technology are all gratefully acknowledged.

REFERENCES

1. Commonwealth of Australia, ‘Defending Australia in the Asia Pacific Century: Force 2030’,

Australia 2009.

2. Commonwealth of Australia (Capability Development Group), ‘Offshore Combatant

Vessel (OCV) Initial Capability Description’, Australia 2011.

3. P Sanders and Y Guedj, ‘Ownership Cost Drivers in Ship Design & Construction’, BMT

Design & Technology, Victoria, Australia 2012.

4. BMT Design & Technology, ‘Whole Life Warship Capability Management - Training

Course’, Victoria, Australia, 2015.

5. R G Keane, ‘Reducing Total Ownership Cost: Designing Inside Out of the Hull’, Ship Design

USA Inc., Maryland, USA 2011.

6. J H Evans, ‘Basic Design Concepts’, Naval Engineers Journal, Vol. 21, November 1959.

7. A Depetro and R Hoey, ‘Rapid Generation and Optimisation of Ship Compartment

Configuration based on Life Cycle Cost and Operational Effectiveness’, BMT Design &

Technology, Victoria, Australia 2013.

8. E K Nick, ‘Fuzzy Optimal Allocation and Arrangement of Spaces in Naval Surface Ship

Design’, University of Michigan, Michigan, USA, 2008.

9. Capt N H Doerry, ‘Systems Engineering and Zonal Ship Design’, ASNE Engineering the Total

Ship Symposium, Naval Sea Systems Command, USA, 2006.

10. J Chalfant, B Langland, S Abdelwahed, C Chryssostomidis, R Dougal, A Dubey, T El Mezyani,

J Herbst, T Kiehne, J Ordonez, S Pish, S Srivastava and E Zivi, ‘A Collaborative Early-Stage

Ship Design Environment’, Massachusetts Institute of Technology, Cambridge, USA, 2012.

11. M Bole and C Forrest, ‘Early Stage Integrated Parametric Ship Design’, International

Conference on Computer Applications in Shipbuilding 2005, Busan, Korea, pp 447-460

(August 2005).

12. Deloitte, ‘Global Defense Outlook 2015: Adapt, collaborate and invest’, Deloitte Touche

Tohmatsu, 2014.

13. Dr G Evans, ‘The rise of the multirole vessel (MRV)’, naval-technology.com, Kable

Intelligence Limited, 2012.

14. B J Van Oers, ‘A Packing Approach for the Early Stage Design of Service Vessels’, Delft

University of Technology, Delft, Netherlands, 2011.

15. J Cagan, K Shimada and S Yin, ‘A Survey of Computational Approaches to Three-

dimensional Layout Problems’, Department of Mechanical Engineering, Carnegie Mellon

University, Pennsylvania, USA, 2000.

16. T G Crainic, G Perboli and R Tadei, ‘Extreme point-based heuristics for three-dimensional

bin packing’, Informs Journal on computing, 20(3), pp 368-384, (2008).

17. M B Wall, ‘Overview of GAlib’, http://lancet.mit.edu/ga/, Massachusetts, USA.

18. J Glanville, ‘Capability Analysis of Modular Multi-Role Warships for Australia’, BMT Design

& Technology, Melbourne, Australia, 2010.

19. D Andrews, ‘Creative Ship Design’, Transactions of RINA, Vol. 123, pp 447-458, 1981.