UNIVERSITY OF HAWAI'I LIBRARY

ADVANCED MARINE VEIDCLE PRODUCTS DATABASE -A PRELIMINARY DESIGN TOOL

A THESIS SUBMITTED TO THE GRADUATE DMSION OF THE UNIVERSITY OF HAWAI'I IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF

MASTER OF SCIENCE

IN

OCEAN ENGINEERING

AUGUST 2003

By

Kristen A.L.G. Woo

Thesis Committee:

Kwok Fai Cheung, ChaiIperson Hans-Jurgen Krock John C. Wiltshire ACKNOWLEDGEMENT

I would like to thank my advisor Prof. Kwok Fai Cheung and the other committee members Prof. Hans-Jiirgen Krock and Dr. John Wiltshire for the time and effort they spent with me on this project. I would also like to thank the MHPCC staff, in particular,

Mr. Scott Splean for their advice and comments on the advanced-marine-vehicle products database. Thanks are also due to Drs. Woei-Min Lin and JUll Li of SAIC for their advice on the neural network and preliminary ship design tools. I would also like to thank Mr.

Yann Douyere for his help with MatLab.

The work described in this thesis is a subset of the project "Environment for Design of

Advanced Marine Vehicles and Operations Research" supported by the Office of Naval Research, Grant No. NOOOI4-02-1-0903.

iii ABSTRACT

The term advanced marine vehicle encompasses a broad category of ship designs typically referring to multihull ships such as catamarans, trimarans and SWATH (small waterplane area twin hull) ships, but also includes hovercrafts, SES (surface effect ships), hydrofoils, and advanced monohulls. This study develops an early stage design tool for advanced marine vehicles that provides principal particulars and additional parameters such as fuel capacity and propulsive power based on input ship requirements. This is accomplished by compiling a product database of existing advanced marine vehicles for the development of relationships between ship characteristic parameters. The relationships are analyzed using both the multiple regression analysis and a neural network. Because of the scatter of the data, the first order or linear multiple regression analysis is adopted. The neural network, on the other hand, is a non-linear learning tool that uses existing ship parameters to predict future ship designs. The results are compared with the actual ship parameters to validate the preliminary design tools. The Maui High

Performance Computing Center has developed a geographic information system interface for the statistical tools and databases with the additional capabilities to analyze ocean wave environment for the definition ofship design requirements.

IV TABLE OF CONTENTS

Acknowledgement iii

Abstract iv

List ofTables vii

List ofFigures viii

I Introduction 1 1.1 Advanced Marine Vehicles 1 1.2 Ship Design Database .3 1.3 Objectives and Approach 5

2 Advanced Marine Vehicle Products Database 8 2. I Data Sources 8 2.1.1 Ship Data 8 2.1.2 Wave Data 8 2.2 Database Structure 9 2.2.1 SWATH 10 2.2.2 Catamaran and Trimaran II 2.2.3 Hovercraft and SES II 2.2.4 Hydrofoil I2 2.2.5 Sailing Catamarans and Trimarans .1 3 2.3 GIS Interface I5 2.3.1 Informational Features 15 2.3.2 Feature Controls 16

3 Multiple Regression Analysis I8 3. I Theoretical Background I8 3.2 Program Structure I9 3.3 Analysis Routines .20

4 Neural Network .23 4.1 Theoretical Background 23 4.2 Program Layout 25 4.3 Analysis Routines 27 4.3.1 Setup the Data 27 4.3.2 Design the Neural Network 29 4.3.3 Train the Neural Network .30 4.3.4 Test the Neural Network .3 I 4.3.5 Using the Trained Neural Network .3 I 4.3.6 Setup ofthe Trained Neural Network for the User. 32 4.3.7 New Data 32

v 5 Results and Discussion 33 5.1 Multiple Regression Analysis .33 5.1.1 User Instructions .33 5.1.2 Results .34 5.1.2.1 Catamaran Results 34 5.1.2.2 SWATH Ship Results 36 5.1.2.3 SES Results .37 5.1.2.4 Hydrofoil Results .39 5.1.2.5 Hovercraft Results 41 5.2 Neural Network .43 5.2.1 General Setup .44 5.2.2 General Instructions to Operate the Excel Output File .44 5.2.3 Ship Type Recommendations .45 5.2.3.1 User Instructions .46 5.2.3.2 Results 46 5.2.4 Ship Parameters .47 5.2.4.1 General User Instructions .48 5.2.4.2 Catamaran Results .48 5.2.4.3 Hovercraft Results .49 5.2.4.4 SWATH Ship Results 50 5.2.4.5 Hydrofoil Results 51 5.3 Comparison ofResults .52

6 Conclusions and Recommendations 55 References 88

Vi LIST OF TABLES

1. Multiple Regression Analysis 1: Results for Catamarans with 3 Inputs 35 2. Multiple Regression Analysis 2: Results for Catamarans with 2 Inputs 36 3. Multiple Regression Analysis Results for SWATH ships 37 4. Multiple Regression Analysis 1: Results for SES with 3 inputs 38 5. Multiple Regression Analysis 2: Results for SES with 2 inputs 38 6. Multiple Regression Analysis 3: Results for SES with 2 inputs 39 7. Multiple Regression Analysis I: Results for Hydrofoils with 3 inputs 40 8. Multiple Regression Analysis 2: Results for Hydrofoils with 3 inputs 40 9. Multiple Regression Analysis 3: Results for Hydrofoils with 2 inputs 41 10. Multiple Regression Analysis 1: Results for Hovercrafts with 4 inputs 42 11. Multiple Regression Analysis 2: Results for Hovercrafts with 3 inputs 42 12. Multiple Regression Analysis 3: Results for Hovercrafts with 3 inputs 43 13. Multiple Regression Analysis 4: Results for Hovercrafts with 2 inputs 43 14. Neural Network Results for the Recommendation ofShip Types 47 15. Neural Network Results for the Preliminary Design ofCatamarans 49 16. Neural Network Results for the Preliminary Design ofHydrofoils 50 17. Neural Network Results for the Preliminary Design of SWATH ships 51 18. Neural Network Results for the Preliminary Design ofHydrofoils 52 19. Catamaran: Neural Network verses Multiple Regression 53 20. SWATH: Neural Network verses Multiple Regression 54 21. HYdrofoil: Neural Network verses Multiple Regression 54 22. Hovercraft: Neural Network verses Multiple Regression 54

vii LIST OF FIGURES

1. Types ofAdvanced Marine Vehicles (with permission from Mustafa Insel) 57 2. ENDEAVOR GIS Website: AMV Homeports 57 3. ENDEAVOR GIS Website: AMV Routes 58 4. ENDEAVOR GIS Website: Significant Wave Height 58 5. ENDEAVOR GIS Website: Peak Wave Period 59 6. ENDEAVOR GIS Website: Peak Wave Direction 59 7. ENDEAVOR GIS Website: Wind Speed 60 8. ENDEAVOR GIS Website: Wind Direction 60 9. ENDEAVOR GIS Website: Topographic and Bathymetry Data 61 10. ENDEAVOR GIS Website: Query Setup 61 II. ENDEAVOR GIS Website: Query Output... 62 12. ENDEAVOR GIS Website: Ship Photo from AMV Database 62 13. Analyse-It: Tutorial Example ~ 63 14. Analyse-It: Set Dataset Properties Toolbar 64 15. Analyse-It: Dataset Properties Dialog Box 64 16. Analyse-It: Variable Properties Dialog Box 65 17. Analyse-It: Linear Regression Menu 65 18. Analyse-It: Linear Regression Dialog Box 66 19. Analyse-It: Multiple Regression Analysis Report Table 66 20. Analyse-It: Multiple Regression Analysis Report Graphs 67 21. Biological Neuron (from Anderson and McNeil, 1992) 68 22. Neural Network Processing Element (from Anderson and McNeil, 1992) 68 23. Sigmoid Transfer Function (from Anderson and McNeil, 1992) 69 24. Artificial Neural Network (from Anderson and McNeil, 1992) 69 25. Multilayer Perception Model (from Anderson and McNeil, 1992) 70 26. Back-propagation Learning Process (from Anderson and McNeil, 1992) 70 27. NeuroSolutions NeuralExpert Design Tool 7l viii 28. NeuroSolutions NeuralBuilder Design TooL 71 29. NeuroSolutions Training Report Produced in Excel 72 30. NeuroSolutions Preprocess Data Menu 73 31. NeuroSolutions Tag Data Menu 74 32. NeuroSolutions Create/Open Network Menu 75 33. NeuroSolutions Breadboard 75 34. NeuroSolutions Train Network Menu 76 35. NeuroSolutions Train Menu 76 36. NeuroSolutions Train N Times Function 77 37. NeuroSolutions Vary A Parameter Function 78 38. NeuroSolutions Test Network Menu 79 39. NeuroSolutions Apply Production Dataset Function 79 40. NeuroSolutions Input for the Production Dataset.. 80 41. NeuroSolutions Output for the Production Dataset. 81 42. Excel User Application: Introduction Page 82 43. Excel User Application: Input Page 83 44. Excel User Application: Output Page 84 45. Multiple Regression Analysis Output.. 85 46. Neural Network User Application: Ship Type Recommendation Input.. 86 47. Neural Network User Application: Ship Type Recommendation Output 87

IX 1 INTRODUCTION

1.1 Advanced Marine Vehicles

Advanced marine vehicle (AMV) encompasses a broad category of ship designs typically referring to multihull ships, but also includes hovercrafts, SES (surface effect ships), hydrofoils, and advanced monohulls. Figure I shows the different types of AMVs. A subset of AMVs, covering SWATH (small waterplane area twin hull) ships, SES, and hydrofoils are, considered advantageous for a number of naval missions (Lavis et aI.,

1990). These vessels sustain most of their weight by dynamic lift or by powered aerostatic lift (Eames, 1985). Each ship design category has its own advantages over the widely used monohull design that is seen in many ships today.

The first category of advance marine vehicles is multihull vessels, which consist of catamarans, trimarans, and SWATH ships. Catamarans are vessels with twin hulls, while trimarans have two outriggers attached to a center hull. Skinner (200 I) pointed out that catamaran and trimaran hull designs are faster, more stable in high sea states, have similar internal volumes as single-hull ships, and are more fuel-efficient than conventional hull designs. The layout of a trimaran is advantageous for military applications in that the increased beam, compared to typical monohulls, provides an excellent area for a flight deck; the cross deck also provides additional space in the center ofthe ship. The beam ofa trimaran is typically 45 to 60% greater than a monohull with a similar displacement (Greig et a!., 1995). SWATH ships have two lower hulls that are completely submerged below the water surface. This allows the vessel to have the steadiness of a big ship and the ability to sustain its normal cruising speed in rough seas that would normally hinder other types of advanced marine vehicles (Wright, 1990; and

Seidl et al., 1993). The second category is hovercrafts, which include air cushion vehicles (ACV) and surface effect ships (SES). ACVs can be designed for high speeds and have a smoother ride compared to any other ship because they move over the water, not through it, resulting in less drag and less horsepower to operate. ACVs are important to military applications because of their low underwater acoustic, magnetic, and pressure signatures, which makes it suitable for mine countermeasures (Lavis, 1985). Another advantage is that they are amphibious and able to operate in a variety of wave conditions and land configurations. SES technology combines the designs of ACVs and catamarans.

Eggington et al. (1975) states that the SES is the only advanced craft that does not suffer from either size or speed limitations. The cushion of air supports the majority of the weight ofthe vessel, reduces the resistance to forward motion at high speeds and helps to mitigate the effects on craft motions in rough seas (Lavis et al. 1991). According to Butler (1985), in comparison to the ACV, the SES uses water propulsion systems, which are more efficient than air propulsion. The SES hull has less air leakage and better longitudinal stability. An SES typically requires 5 to 10% less enclosed volume to carry a given payload compared to a typical monohull vessel.

The third category contains hydrofoils, which consist of two types: deeply submerged

(jetfoil) and surfacing piercing. The foils on a deeply submerged hydrofoil are always under the water surface. Johnston (1985) pointed out that this design allows the vessel to operate foilbome at high speed in practically any sea conditions normally encountered while maintaining a comfortable motion environment for the crew and passengers.

According to Wright, (1990), this type ofvessel provides the smoothest ride ofany high­ speed vessel. The second type is surface piercing where parts ofthe foil operate in the air and water. An increase in speed will cause the ship to rise, thus decreasing the underwater foil area, until the lifting force equals the weight carried by the foils.

2 The fourth category is advanced monohulls. This category consists of vessels with hard

chine planing hulls or round bilge semi-displacement hulls. According to McKesson

(1996) a planing hull vessel is buoyant at rest, but at high speeds half of the vessel's

weight is carried by buoyancy and the other half by dynamic lift. This type of vessel is meant only for high speeds and can retain a large portion oftheir calm water operational

speed capability in moderate to severe sea conditions. A semi-displacement hull vessel is

one that is able to perform at any speed (Savitsky, 1985). It has a keel that is not as deep

as a full displacement hull allowing it to travel through the water easier. This vessel is slower, but it has a more stable and easier ride than the planing hull vessel.

Conventional monohull design is being reconsidered in exchange for a more advanced design that results in better ship performance at a more economical cost. Currently there

is not much experience with advanced ship design tools. The need for preliminary design tools would be the first step to a detailed advanced ship design. A database of available advanced ships would be a good starting point.

1.2 Ship Design Database

Ship design databases exist in different forms. A few researchers have analyzed and derived empirical relationships among key ship parameters such as propulsion, weight

and speed. Kennell's (1998) Transport Factor (TF) sought the relationship between weight, design speed and installed power. He maintained that each ship design had a

unique TF, which provides insight into the interaction between the vessel's major

subsystems. Gabrielli and von Karman's (1950) Transport Efficiency (TE) also results in a value that is unique for each vehicle. TE relates the sum of a vessel's installed

propulsion and auxiliary power to its weight and maximum speed. Gabrielli and von

Karman also analyzed the ratio of Lift to Drag (LID). This method differs from the

3 previous two in that it excludes the hydrodynamic and mechanical losses due to the propulsion and power transmission system. Unlike TE and TF, which is unique for each vessel, LID varies with speed. TE and LID are used to establish minimum power requirements for each type of vessel over a wide range of speeds. TF is used as a design tool to evaluate vessel designs as it affects weight, speed and installed power. In order to separate and evaluate different AMV types, a simple database is established for a vast number ofvessels.

There are also detailed databases encompassing complete ship major component designs.

M. Rosenblatt & Son, Inc. under the direction of the University of New Orleans, Gulf

Coast Region Maritime Technology Center developed a Portfolio of World Class Ship

Designs (Schiller et aI., 2000). This design tool allows the user to use existing ship designs and modify certain parameters to calculate adequate cargo deadweight, volume, or area depending on the ship type and design. The objective ofthis database is to allow

US shipbuilders and designers to access data quickly and accurately for competitive bidding purposes. This database only considered three types of ships: tank ship, container ship, and ROIRO passenger ship. Lavis et al. (1999) described a similar design tool with a database containing 22 vessels that can model SWAill, semi-SWATH, catamarans, trimarans, slender displacement monohulls, planning monohulls, and semi­ displacement monohulls. This model, called PASS (Parametric Analysis of Ship

Systems) uses cost-benefit analysis ofemerging technologies and considers the impact of changing operational requirements. It also has the flexibility to do a design-to-cost trade­ off for determining the selection of hullform and subsystem choices. These two databases have extensive modeling tools that allow the user to input design parameters and requirements to produce a ship design, along with its related systems.

4 There are few tools for the concept-level and preliminary design of one or two types of ships, depending on the needs of the designer. A database of available ship designs that could model and calculate ship requirements for all types of vessels and produce new designs would be an extensive endeavor. The existing tools do not contain enough

AMVs because of their detailed modeling functions. A good place to start would be a general database that contains ship parameters in empirical relations without detailed modeling programs. This database can provide recommendations for AMV types and general design parameters based on input requirements.

1.3 Objectives and Approach

This study is a subset of the project "Environment for Design of Advanced Marine Vehicles and Operations Research (ENDEAVOR)" funded by the Office of Naval Research. The primary objective ofthis study is to develop an early stage design tool for advanced marine vehicles. This is accomplished by

• compiling a product database ofexisting advanced marine vehicles, • compiling a global wave climate database,

• adapting statistical tools for data analysis, and • developing relationships between ship characteristics parameters. The Maui High Performance Computing Center (MHPCC), a collaborating organization of the project ENDEAVOR, assisted in the development of a geographic information system (GIS) interface.

An advanced marine vehicle is usually designed to operate in a specific environment for a given purpose. The design is initiated by defining certain key parameters. Typical input parameters consist of operational speed, maximum speed, payload, and range. Using these parameters as a starting point, a set of output design parameters of the ship can be

5 determined. For this study the design parameters consist of displacement, length overall, length at waterline, beam, draft, fuel capacity, and propulsive power. To reduce the number of ships considered, this study excludes the fourth category, which is advanced monohulls. The vessels in this category are the newest addition to the AMV designation. The advanced marine vehicle database is divided into six vessel types (SWATH, hydrofoil, SES, hovercraft, trimaran, and catamaran) that contain the as-built ship parameters, wave conditions at their location of operation, and other information that helps to define the vessel. Another stand-alone category is sailing catamarans and trimarans, which contains as-built parameters, cost and retailer. An extensive physics­ based modeling tool ofa majority of ship designs would be considered in parallel.

Correlation among ship characteristic parameters such as range, payload, maximum and operational speed, displacement, length overall, length at waterline, beam, draft, fuel capacity and propulsive power are considered to find a common trend in each type of AMV design. From the results of these correlations, a conceptual model and design guidelines will be presented for each AMV category. This will allow designers to enter ship requirements at one end and obtain conceptual design parameters at the other. This can be achieved by a multiple-regression routine that allows the user to weigh the different requirements to arrive at a conceptual design. Add-in software for Microsoft Excel written and distributed by Analyse-It® is used to perform the analyses. A neural network is also used to evaluate the ship categories with more entries. In order to result in a more accurate training session, neural networks need a lot of data to learn; this is why the neural network is only being considered for the ship categories with a lot of vessels.

The gathered ship information, including available photos, is entered into a GIS database along with the gathered wave data on a MHPCC server. This database provides a basic

6 starting point for the design of ships in certain areas by displaying the current ship types and their operating locations over the world. The structure ofthe database allows general parameters (e.g. length overall, beam, draft, displacement) to be searched along with the desired type ofvessel (e.g. SWATH, catamaran) resulting in a list ofships that fall within the desired parameters. The use of GIS also allows the user to select a certain region on the globe and access all the ship and wave information for that area. GIS allows the selected information to be filtered or sorted according to certain parameters.

7 2 ADVANCED MARINE VEHICLE PRODUCTS DATABASE

2.1 Data Sources

2.1.1 Ship Data

The ship data is obtained from various sources such as journals and printed publications.

Jane's High-Speed Marine Transportation 29th (1996) and 35th (2002) editions edited by

S.J. Phillips and published by Abeking & Rasmussen in Lemwerder, Germany provide the ship parameters for SWATH, SES, hovercraft, hydrofoil, catamarans, and trimarans.

The Sailor's Multihull Guide 2nd edition (1998) edited by K. Jeffrey and C.E. Kanter and published by Avalon House Publishing, USA provides the sailing catamaran and trimaran ship parameters and costs.

The data recorded in these publications is not entirely complete for the present application. Information posted on the following Internet sites is also used to update and complete the ship parameters:

• http://swathocean.com/

• http://www.swath.com/

• http://www.nis-nor.no/

2.1.2 Wave Data

The global wave conditions are forecasted by the National Oceanic and Atmospheric

Administration (NOAA) using the spectral wave model Wavewatch III (NWW3) and the archived wave data in terms of the significant wave height and peak period is available from the NOAA website (http://polar.wwb.noaa.gov/waves/Welcome.html). NWW3 replaced the Wave Model (WAM) as NOAA's National Centers for Environmental

Prediction (NCEP) operational global wave model in 2000, but it was in operation from

8 1997. According to Chen et al. (1999), NWW3 can be applied to waves that are outside

the surf zone. This model applies to everywhere in the world between latitude 78° north

and 78° south to a minimum depth of 25 meters. The grid points are spaced at 1.25°

interval longitude and I° latitude.

2.2 Database Structure

There are six individual Excel databases respectively for SWATH, catamaran, trimaran,

hovercraft, SES, and hydrofoils. The basis structure of the database is the same. Each

database contains the following common parameters:

• SEQ - generic number used to designate individual ships

• Type - ship type designated by the builder or designer

• Name_Class - ship name or class designated by the builder or operator

• Bldr_Dsgnr - ship builder or designer

• Country - country where the ship was built or designed

• Yr_built_d - year the ship was built or deployed

• Photo name - ship photo name. This is used to link the ship photo to the database entry in the GIS database.

• Lgth_overa - ship length overall in m

• Beam_m - ship beam in m

• Crew - number ofcrew required to operate the ship

• Passengers - maximum number ofpassengers allowed

• Fuel_cap_l- ship fuel capacity in liters

• Water_cap_- ship fresh water capacity in liters

• Proppwr_kw - ship propulsive power in kW

• Max_speed_- maximum speed in kts

• Op_speed_k - operational speed in kts

9 • Range_mile - range ofthe ship in nmi

• Strucmatrl - ship primary structural material

• Propulsion - number and type ofpropulsive devices (e.g. diesels, gas turbines, etc.)

• Output - output ofthe propulsion device in kW

• Thrust_dev - number and type ofthrust devices (e.g. propellers, water-jet, etc.)

• Transmissn - number and type ofgearboxes

• Homeport, Port_I, Port_2, and Port_3 - ship port locations

• Hmport_lat, Portl_lat, Port2_Iat, and Port3_Iat - port latitude

• HmportJon, Port1Jon, Port2Jon, and Port3_Ion - port longitude

• (monthLavg_hs - monthly average significant wave height along the operating route

or at the homeport. This data was obtained from a Matlab program designed to

calculate the average significant wave height by month from the six years ofcollected data (1997 - 2002).

• (monthLavg_tp - monthly average peak period along the operating route or at the

homeport. This data was also obtained from a Matlab program designed to calculate

the average peak period by month from the six years ofcollected data (1997 - 2002).

• Owner_oper - ship owner or operator

• Other info - other information about the ship

• Classifica - ship classification, ifany

• Classif_by - classifying agency

• Ref- reference source for the information

Each database also contains additional ship parameters specific to that ship type.

2.2.1 SWATH

Additional information in the SWATH database includes:

• Lght water - ship length at waterline in m

10 • Draught_m - ship draft in m

• Displmt_t - ship displacement in t

• Deadwgth_t - ship deadweight in t

• Vehicles - maximum number ofvehicles (cars and/or busses) allowed

• Electrcsys - number and type ofgenerators used to run the electrical systems

• Aux_system - ship auxiliary system

• Oper_limit - ship operational wave height limitation

2.2.2 Catamaran and Trimaran

Additional information for the catamaran and trimaran databases includes:

• Lght_water - ship length at waterline in m

• Hull_beam - ship hull beam in m

• Draught_m - ship draft in m

• Displmt_ma - ship maximum displacement in t

• Displmt_mi - ship minimum displacement in t

• Deadwgth - ship deadweight in t

• Vehicles - maximum number ofvehicles (cars and/or busses) allowed

• Aux_system - ship auxiliary system

• Cost - estimated cost ofthe ship

2.2.3 Hovercraft andSES

Additional information for the hovercraft and SES category includes:

• Lght_water - ship length at waterline in m

• Hull_beam - ship hull beam in m

• Height -height ofthe air cushion

• Draught_m - ship draft in m when only one draft parameter is avaliable

11 • Draughthul - ship hullborne draft in m

• Draught_on - ship draft on the air cushion in m

• Displaceme- ship displacement in t

• Weight_max - ship maximum (loaded) weight in t

• Weight_min - ship minimum weight in t

• Payload_t - ship payload

• Lift---'pwr_ k - ship lift power in kW

• Obstacle c - ship obstacle clearance height in m

• Skirtmatrl- ship primary skirt material

• Propulsion - ship combined propulsion and lift power

• Output - ship combined propulsion and lift output

• Lift - only the ship lift power

• Lift_outp - only the ship lift output

• Lift_devic - number and fan specification for the ship

• Aux_system - ship auxiliary system

• Cost - estimated cost ofthe ship

2.2.4 Hydrofoil

Additional information in the hydrofoil database includes:

• Foilwdth m - ship foil width in m

• Draughthul- ship hullborne draft in m

• DraughtJo - ship draft on the foils in m

• Displmt_rna - ship maximum displacement in t

• Displmt_mi - ship minimum displacement in t

• Payload_t - ship payload

• Electrcsys - number and type ofgenerators used to run the electrical systems

12 • Range_frpo - range from point ofrefuge in nmi

• Oper_waveh - operational wave height limitation in m

• Oper_winds - operational wind limitation in Beaufort scale

• Oper_seast - Sea State restriction

2.2.5 Sailing Catamarans and Trimarans

The sailing catamaran and trimaran database has a different structure from the other six databases. This database contains:

• Type -ship type designated by the builder or designer

• Designer - ship designer

• Builder - ship builder, manufacturer, or retailer

• Country -country the ship is sold from

• Class - ship classification (e.g. semi-custom, stock, or production)

• Class2 ~ ship type (e.g. cruiser, racer, etc.)

• Year_intra - year the ship was introduced into the market

• Built_Cost - price range ofa built ship

• Kit cost - cost ofthe kit

• Plans_cost - cost of only the plans for the ship

• Currency - currency used to define the costs ofthe ship

• Displacment_kg - ship displacement in kg

• Std_aux_hp - ship standard auxiliary

• Out_in - outboard or inboard motor

• Lgth_avera - ship length overall in m

• Lght_water - ship length at waterline in m

• Lgth_bridge - length ofthe bridge deck in m

• Wing_clear - wing deck clearance in m

13 • Beam_max - ship maximum beam in m

• Beamjold - ship folded beam in m

• Beam trail - ship trailered beam in m

• Draft_up - ship boards up draft in m

• Draft_dn - ship boards down draft in m

• Draft max - ship maximum draft in m

• Draft_min - ship minimum draft in m

• Draft hull - ship hull draft in m

• Draft keel- draft ofthe fixed keels in m

• Mast deck - height ofthe mast offthe deck in m

• Mast water - height ofthe mast offthe water in m

• Wing_mast - area ofthe wing mast in m2

• Jib - area ofthe jib in m2

• Mainsail - area ofthe mainsail in m2

• Genoa - area ofthe genoa in m2

• Staysail- area ofthe staysail in m2

• Total_area-total area ofthe wing mast, jib, mainsail, genoa, and staysail in m2

• Spinnaker - area ofthe spinnaker in m2

• Screacher - area ofthe screacher in m2

• Reacher - area ofthe reacher in m2

• Drifter - area ofthe drifter in m2

• Berth_s - number ofsingle berths

• Berth d - number ofdouble berths

• Stateroom - number ofstaterooms

• Passengers - number ofpassengers for the commercial ships

• Heads - number ofheads

14 • Fuel - fuel capacity in liters

• Water - fresh water capacity in liters

• Payload - ship payload in kg

• Steering_t - type ofsteering (e.g. wheel or tiller)

• SteeringJ -location ofthe steering

• Engine - number and type ofengines (e.g. inboard or outboard)

• Engine_I-location or location ofthe access to the engine

• Op_Ioc - ship operational location

• Strucmatrl - primary ship structural material

• Photo name - ship photo name

2.3 GIS Interface

The database currently contains about 600 ships. To better view this amount of data, a geographic infonnation system (GIS) interface was developed. This interface portrays the infonnation in the database and wind and wave conditions on a map of the world.

The map provides a graphical representation of the ship locations and routes along with the existing ship parameters from the database.

2.3.1 Informational Features

The Maui High Performance Computing Center (MHPCC) developed an ArelMS

(internet map server) website using the infonnation contained in six of the seven databases (SWATH, catamaran, trimaran, hovercraft, SES, and hydrofoil). ArclMS is a program distributed by ESRI's (Enviromnental Systems Research Institute, Inc.) that enables GIS data to be displayed and analyzed on the Internet. The ArelMS software enables users to integrate local data with Internet data sources for displaying, querying, and analyses in an easy-to-use web browser.

15 The database information on the website is separated into six AMV ship types: SWATH, catamaran, trimaran, hovercraft, SES, and hydrofoil. Each ship type has two separate layers for homeport shown in Figure 2 and route information for the North Sea area as seen in Figure 3 along with country boundaries. Also contained in this website are layers of ocean wind and wave conditions archived from NOAA (http://polar.wwb.noaa.gov).

Figure 4 to Figure 6 show the 2000 winter significant wave height, peak period, and direction respectively, while Figure 7 and Figure 8 show the corresponding wind speed and direction. All NOAA parameters (HS, TP, DP, WindJ, and Wind_d) are mapped as

"winter" seasonal averages. The website also has a layer of global topographic and bathymetry data as seen in Figure 9.

2.3.2 Feature Controls

The controls for the website are available in a toolbox in the upper left comer of the display window and in the menu on the right side of the map. Changing what layers are displayed on the map requires the user to select the checkbox in the "Visible" column for the desired layer(s) and clicking on the "Refresh Map" button in the layer list. The toolbox contains buttons that will toggle the layer and legend map and the overview window. The basic tool controls allow zooming, panning, and measuring. The "Selection" tool allows a rectangular, line, or polygon selection ofthe map features with the associated records displayed in a table below the map.

Queries can be done spatially by keying on an "Active" layer and selecting the "Identify" tool and choosing a feature on the map. The data for the records is returned in a table form below the map. Figure 10 shows the setup for the query command. The menus generate a Boolean logic query interface (i.e. Field: "MAX_SPEED", Operator: ">", , Value: "30"). Clicking on the "Add to Query String" button and then on the "Execute"

16 win return an the selected features on the map and records in a table as seen in Figure 11.

Clicking on the blue URL in the table for Photo_Name win display the AMV photo in a separate browser window as shown in Figure 12. Layer attributes can be queried by invoking the "Query" tool. The user can zoom to any ofthe selected records on the map.

Boolean connectors (AND, OR, etc.) allow additions to the query strings.

17 3 MULTIPLE REGRESSION ANALYSIS

3.1 Theoretical Background

Multiple regression analysis considers the relationship between three or more variables.

The purpose of this analysis is to establish a quantitative relationship between a group of predictor variables and a response. According to Sanders et al. (1976), multiple regression analysis has been used extensively to build forecasting models. Because ofthe large scatter of the data, a linear multiple regression analysis is considered here. The predicted response is expressed in terms ofa linear polynomial as

k Y=bo+ ~)jXj [3.1 ] j=1

where ..1:;. are the predictor variables, k is the number of predictor variables, and bi are coefficients determined from the method ofordinary least squares.

The method of ordinary least squares assumes that the best-fit solution for all given data sets has the minimal sum ofthe deviations squared (least square error) such that

~t (r; - y)2 =0 forj = I, ..., k [3.2] 8b j '=1 where Y, is the dependent variable of the i-th data set and n is the number of data sets used in the regression. This gives rise to

n L:(Xij -xJr; -f) b = -'.:'="'1 _ [3.3] J n L:(Xij -x} i=l in which

18 [3.4a, b]

where Xij are independent variables in the i-th data set. Once bi are known, bo can be computed from

k bo =y - ~)jXi [3.5] Fl

With the coefficients bi determined from the data sets (Y" Xij), Equation [3.1] can be applied to predict the response variable Y based on input predictor variables J0.

3.2 Program Structure

The accuracy ofMicrosoft Excel's statistical capabilities has been called into question by several authors. McCullough (1998) proposed a method of assessing the reliability of statistical software in three areas: estimation, statistical distributions, and random number generation. In particular, estimation is assessed with the Statistical Reference Datasets produced by the National Institute of Standards and Technology using four tests: univariate summary statistics, one-way analysis ofvariance (ANOVA), linear regression, and nonlinear least squares. McCullough and Wilson (1999) evaluated Excel 97 and found it to be deficient in all three areas. They concluded that Excel should not be used for statistical analysis of data. With the release of Excel 2000 and Excel XP, the deficiency found in Excel 97 was still not fixed (McCullough and Wilson, 2002). To perform the multiple regression analysis on the AMV database, another program IS necessary to compensate for the insufficiency ofExcel's statistical analysis.

Analyse-it® is an add-in for Microsoft Excel. This software is a professionally developed statistical package written and compiled in c++ (www.analyse-it.com).

Unlike other Excel add-ins that are macro based, this software does not use any of

19 Excel's built-in statistics functions. Other statistical packages such as SPSS and SAS were tested by the American Statistical Association and did not perform well in many cases (McCullough, 1998, 1999). According to Newell (1999), Analyse-it shows a "considerable improvement on the statistical modules available in Excel to date in terms of accuracy and precision." Analyse-it employs the least-squares method to predict a response based on the linear relationship with one or more predictor variables. This software calculates a regression line intercept and slope coefficients with confidence intervals. The output of the multiple regression analysis is shown on an ANOVA table that shows the variation due to regression. The related charts show standardized residual plot and a residual histogram with a normal-curve overlay. The maximum number of variables that Analyse-it can handle is determined by the maximum size of an Excel worksheet (256 columns); thus Analyse-it supports regressions containing up to 255 predictor variables with 1 column for the response variable.

3.3 Analysis Routines

The worksheets in Excel have to follow a specific format in order for Analyse-it to understand the data. Figure 13 shows an actual worksheet used. 1. All the data cells (Rows 4+ in the figure) that will be used in the analysis should only

contain numerical data. The worksheet shown here contains the actual AMV database information. Some of the data that is used in the analysis is shown with

column headings. For example some ships have options as to the amount of

propulsive power that can be used in a certain design. Make sure that the value is a

single number and not a range.

2. Cell Al should contain the title ofthe study.

3. Row 2 should be left blank.

20 4. The next row(s) should contain the column headings, which are already set in the

AMV database. In the example, the column-heading row is Row 3 in the figure. 5. Set the data set properties by first selecting a column-heading cell (cell B3 in Figure

13) and using "Data" menu - "Dataset" in the toolbar as shown in Figure 14. A dialog

box as shown in Figure 15 with the "Name" section varying depending on the

analysis will show up. The layout should display "list (columns are variables; rows

are cases)" and the "repeat measures" box should not be checked. Under "Format" check both the "First column contains identifier label" and "Apply AutoFormat" box.

6. The variable properties for each column that will be used in the analysis have to be set by selecting the first data cell of each column (cell B4 in Figure 13) and using the

"Data" - "Variable" menu shown in Figure 16. The numerical data should have a "Measurement scale" as "Continuous." Set the format ofthe column, along with the

necessary number ofdecimals by using the "Format. .. " button. 7. The next step is to perform the multiple regression analysis by using "Analyse" ­ "Regression" - "Linear..." in Figure 17 to open the Linear regression toolboxes

shown in Figure 18. The independent variables are a combination of maximum

speed, operational speed, range, and payload. The single dependent variable consists of either displacement, length overall, length at waterline, beam, draft, fuel capacity,

or propulsive power. The multiple regression analysis is conducted for each

dependent variable and for each ship type and combination of independent variables, depending on the number on entries for each ship type.

8. Figure 19 shows the analysis report, which is opened in a new window with the

option of adding it into the current Excel file or deleting it. This report contains the

coefficient of determination, adjusted coefficient of determination, standard error,

parameter coefficients, probability for each coefficient, sum of squares, degrees of

21 freedom, mean square, ratio of mean square to error variance and the p-value for the

analysis. 9. Figure 20 shows the plot of the actual ship parameter versus predicted value (Y) for

the parameter, which is determined by the coefficients. The second plot shows the plot ofthe residuals, which is the vertical distance between the actual parameters and

the regressed solution. The histogram shows the frequency of residuals, with a

superimposed normal curve. When new data is added to the database, all ofthe reports in the file can be updated at the same time by using the "Update All" button on the "Report" toolbar.

22 4 NEURAL NETWORK

4.1 Theoretical Background

A neural network is like a human brain; learning involves adjusting the synaptic connections between the neurons (Battelle Memorial Institute, 1997). Each neuron is linked to its neighbors with varying coefficients of connectivity that represent the strengths ofthese connections. Learning is the result ofadjusting these strengths to cause the overall network to process the appropriate results. According to Anderson and

McNeil (1992), the biological neuron is a cell that provides us with the ability to remember, think, and apply previous experiences to every action. The power ofthe brain is the result of the connections between up to 200,000 neurons. Each neuron has four parts: the dendrite, soma, axon, and synapse as shown in Figure 21. The dendrites receive inputs from other sources; the soma processes these inputs and transfers them to the axon, which turns the processed inputs into outputs. The synapses function as the connection between the neurons.

An artificial neuron is much simpler than a biological neuron, but it still simulates the basic functions of the four parts of the biological neuron. Artificial neurons in neural networks are called "processing elements" as shown in Figure 22. The inputs entered into the processing element are multiplied by their weight factors (wn); these inputs then enter the summation function where the statistical properties are determined. The data processed by the summation function is sent to the transfer function, where it is turned into an output via a set of available algorithms. For example, a sigmoid transfer function uses the data from the summation function to calculate a value between zero and one as seen in Figure 23. The results from the transfer function can be displayed as output to the user or further processed by neurons in another layer.

23 In neural network, the neurons can be grouped into layers. These layers, along with the connection between the layers and the summation and transfer functions compose a functioning neural network as illustrated in Figure 24. Each layer of neurons performs a specific function. Some ofthe neurons interact with the real world to receive inputs while others provide outputs. The hidden layers receive signals from all the neurons in the above layer; when a neuron performs its function, it passes the results to all the neurons in the next layer. Learning for a neural network typically occurs by example through training with the exposure to a set of input/output data, where the training algorithm iteratively adjusts the connection weights (synapses denoted by wn). These connection weights store the knowledge necessary to solve specific problems. The output is automatically compared with the actual data and the difference is used to adjust the weights.

Neural networks have the ability to represent linear and non-linear relationships directly from the data being modeled. The most common model is the multilayer perception

(MLP) model, which requires specific sets of input and output to learn. Figure 25 illustrates a multilayer perception model with two hidden layers. The inputs fed into the input layer are multiplied by interconnection weights as they are passed to the first hidden layer. Within the first hidden layer, the inputs are processed by the summation and transfer functions. As the processed data leaves the first hidden layer, it is again multiplied by interconnection weights and then processed by the second hidden layer.

Finally the data is multiplied by interconnection weights and then processed one last time within the output layer to produce the neural network output.

The main advantage of the multilayer perception model is that it is easy to use and can approximate any input/output relation. The main disadvantage is that it trains slowly and requires a lot of input and output training data to properly learn. According to Rumelhart

24 et al. (1986), this model uses a back-propagation algorithm, shown graphically in Figure

26, meaning that input and the desired output is needed to train the network. In this figure, the data is repeatedly presented to the neural network. With each iteration, the error between the network output and the actual data is computed and back-propagated to the neural network. The neural network uses this error to adjust its weights so the error will be decreased with each iteration. This sequence ofevents is usually repeated until an acceptable error has been reached or until the network no longer appears to be learning.

4.2 Program Layout

The neural network that is used in this study is produced by NeuroSolutions®. There are basically four types of neural network applications: classification, function approximation, prediction, and clustering. NeuroSolutions can solve any of the four problems. The goal of classification problems is to label each input pattern as belonging to a certain class. For example, given certain ship parameters, the neural network would be able to predict what type of ship (e.g. catamaran, hovercraft, etc.) relates to those parameters. Function approximation determines what numerical values relate to the input pattern. This is used to predict ship parameters (length, beam, etc.) using given input parameters (speed, range, payload). Prediction problems are those where the goal is to determine a future output given a set of inputs and the past history of the inputs as the training data. Clustering problems are used to extract information from only the input data, such as surveys. The last two problem types are not considered for this study.

There are two main functions in NeuroSolutions to design a neural network: the

NeuralExpert or the NeuralBuilder. The NeuralExpert, shown in Figure 27, designs a neural network by the type of problem that needs to be solved. Input and output are entered into this network via a text file. The designer then designates which columns in

25 the file are input and output and ifany ofthe columns contains non-numerical data. The designer then chooses the complexity of the network as low, medium, or high. Low complexity networks are preferable for small data sets and train faster and produce better results ifthey are sufficiently powerful enough to do so.

The NeuraIBuilder, seen in Figure 28, lets the designer create his or her own network.

The designer chooses the Neural Model from a list of eleven different models. Accessibility to a model is determined by which level of NeuroSolutions that is purchased. The level that is used for this study only contains the multilayer perception model. The number of hidden layers needs to be specified by the designer; for this level there is a maximum oftwo hidden layers and a minimum ofzero. Each hidden layer and output layer has specific parameters that can be adjusted by the designer. The default settings for these layers are set by the program and are dependent on the amount of training data. Each layer has its own learning parameters that can be modified even after the network is built.

NeuroSolutions also has an add-in to Microsoft Excel that uses Excel's environment to organize the data. With this add-in all the features ofNeuroSolutions are in one easy pull down menu. The training results for each individual run ofthe neural network are saved in worksheets within the same Excel file. The report worksheet seen in Figure 29 contains a plot showing the decrease of the average MSE (errors) with an increasing number of epochs. An epoch is one complete presentation of all the data to the neural network. The table below lists the average minimum MSE and final MSEs. The following table contains the run and epoch, at which the lowest MSE occurred along with the minimum and final MSE. The next plot displays the average training MSE versus epochs.

26 The finalized neural network can be made available to persons who do not have the

NeuroSolutions software. The Custom Solution Wizard uses a completed neural network to generate a dynamic link library (DLL). This DLL can also be save in Visual Basic

5/6/7, Visual C++ 6, Excel XP, or Access XP. This way the user will not need to have a license to NeuroSolutions, but it also means that the user cannot modify and retrain the completed neural network.

4.3 Analysis Routines

The design and training of the neural network is completed using the add-in to Excel. The AMV database is separated by ship type into individual Excel files. The database is set up according to the parameters listed in Section 2.2. The menus detailed below are all found in the "NeuroSolutions" pull-down menu in Excel. The "designer" is considered as a person who designs or has the ability to modifY the neural network, meaning he or she has access to a copy of NeuroSolutions. The "user" is a person who does not own

NeuroSolutions, but is still allowed to use the results of a trained neural network. The steps to setup, design, train, and produce the output are as follows.

4.3.1 Setup the Data

NeuroSolutions does not process incomplete record; it treats the missing data as an error. In order to correct this, the neural network designer must first edit the data by making sure that all the cells in the worksheet that will be used by the neural network contain data. The designer should also check that the data in a column is ofthe same type, either numerical or text. The steps required for the preparation ofthe training data are detailed in the following section.

I. "Preprocess Data" menu in Figure 30.

27 a. Select "Randomize Rows" to re-arrange the data ofthe original worksheet so that the

selections in the next step are not grouped together (i.e. the data was sorted by max speed). This data from the original worksheet is copied into a new worksheet named "[the original name1Randomized." b. Select "Translate Symbolic Columns" to change textual data, if any, into data that

NeuroSolutions can understand. This is explained further in Section 2.c below.

2. "Tag Data" menu in Figure 31. Before NeuroSolutions can design a neural network

the designer must first define what in the data sheet is to serve as input and output, training, and testing. The designer can also provide a list of input parameters for

production. a. Select the coluruns on the worksheet that will serve as the input data. Using "Column(s) As Input" will define the input columns in NeuroSolutions. b. Use "Column(s) As Desired" to define the output columns. c. The "Column(s) As Symbol" is used to define either input or output non-numerical

data (e.g. the type of ship in the ship recommendation neural network). When symbols are used, go to the "Preprocess Data" menu and select "Translate Symbolic

Columns." d. Select the rows in the worksheet that will be used to train the data. Use "Row(s) As

Training" to tag this data. e. The "Row(s) As Cross Validation" may be used if there are enough records. Cross

validation sets aside part ofthe data and uses this data to monitor the training process.

This feature is used to guard against over training the neural network. A neural

network can be over trained so that it actually memorizes the individual training set

rather than trends in the data.

28 f. "Row(s) As Testing" is used to set aside data to use to test the performance of the

network. Once the network has been trained, the test data is fed into the network and

the output is compared with the actual values. The designer does not have to use this

function, especially ifthere are not a lot ofcomplete data sets. g. Instead the designer could use the "Row(s) As Production" to tag rows in which the

neural network will use the inputs of that row(s) to produce the output based on the

trained neural network. This feature can be used with non-complete data sets that

have, at least, all of their input data. The designer can then compare the neural

network output with the incomplete data. For example if the record has all of the

input data (e.g. max speed and operational speed) and only some of the output data

(e.g. length overall, beam, draft, and propulsive power); the designer can use the input

data and compare the neural network output data (which contains all the output

parameters) to the available actual data that does exist.

4.3.2 Design the Neural Network

"Create/Open Network" menu in Figure 32. After the data has been tagged, the designer can build the neural network. Excel uses the NeuralBuilder to design the neural network.

In the present educational version, the designer can only use the multilayer perception layer model and can adjust the number ofhidden layers between zero and two to optimize the network performance. NeuralBuilder automatically determines the training parameters based on the number of inputs. Ideally these parameters, especially the number ofprocessing elements, are based on the complexity ofthe input-output mapping of the data, but the optimal settings can only be determined through fine-tuning by the designer. The created neural network is called a "Breadboard" and is shown in

NeuroSolutions window as seen in Figure 33. This breadboard must be saved before the

29 neural network is trained. Each symbol of the neural network represents the functions and processing ofthe neural network.

4.3.3 Train the Neural Network

"Train Network" menu in Figure 34. After the input and output parameters have been set and the neural network designed, it must be trained to optimize the output.

I. The "Train..." function trains the neural network once; the informational dialog box

is shown in Figure 35. The name of the training session along with the number of epochs can be set in the Train window. The "Use Cross Validation" checkbox will

only be available if there are tagged cross validation rows in the Excel worksheet. The next checkbox is used if the designer wants to terminate the training run if the

cross validation error has not improved in the set number of epochs. After the network is trained, the error data is displayed in a separate Excel worksheet. If the results are not satisfactory, the network can be trained again. If a cros.s validation

data set is used, the best weights of the training run and epoch are saved, when the cross validation error is minimum. If only a training data set is used, the weights are

saved at the run and epoch at which the training error is minimum. 2. The "Train N Times" function, in Figure 36, allows the network to be trained a

certain number of times. This is easier than using the "Train..." function over and

over agaill. 3. The "Vary A Parameter" function, as seen in Figure 37, contains the same settings as

the "Train N Times" plus a few more. This function allows the designer to change

the training parameters that are displayed in the "Component.Action" pull-down

menu. Ifthe neural network does not learn a problem after a number ofepochs (i.e. the learning curve is not approaching zero), this might mean that the network has not been trained

30 long enough, the network is stuck in a local minimum, or the network does not have the capability to learn the problem. To increase the amount oftraining time, use the "Train N

Times" function and increase the number ofepochs and runs. Ifthe network is stuck in a local minimum, there are three things that can be done. First, on the NeuroSolutions screen (where the built neural network is displayed) click on "Jog;" this will randomize the weights about their present values. Ifthis does not work, try the "Randomize" button, which randomizes the weights using mean and variance. When randomization does not work, the designer should reset the epoch and "examplar" counters and randomize the weights using the "Reset" button. Ifnone ofthese work, the neural network might not be powerful enough to solve the problem. If this is the case the complexity of the neural network will have to be modified. This is accomplished by changing the settings in the

"Vary A Parameter" window.

4.3.4 Test the Neural Network

"Test Network" function menu, shown in Figure 38, tests the network using either the rows that are designated as "testing," or if there are no set testing rows, all the training records. The designer has the option of which weights to use and the report type; the

"Load Best" and "Regression" are selected respectively for this study. The test report displays a plot ofthe desired output verses the actual neural network output.

4.3.5 Using the Trained Neural Network

The "Apply Production Dataset" function, seen in Figure 39, is only available if the designer has designated rows in the Excel worksheet as "Production" rows. The best weights of the training runs are used to determine the output based on the data in the

"Input" columns. The results are displayed in the "Output" columns on the same worksheet. A sample of the input and output results are shown in Figure 40 and Figure

31 41. The black data on the sheet is the training data and the green data is the production

data set of which the outputs are seen in bold. This function is only available to the

designers of the neural network, who has to setup an interface in order for a user to use

the trained neural network.

4.3.6 Setup o/the Trained Neural Network/or the User

The Custom Solutions Wizard takes an existing neural network and generates and

compiles a dynamic link library (DLL). This DLL can also be integrated into a working

sample application in Visual Basic, Access, Excel, and Visual C++. The designer has to

select the desired sample application and the custom solutions wizard will generate the

DLL and sample application. This sample application is available for anyone with access to one of the above programs. The Excel sample application is used to distribute the

trained neural network for this study. The results are contained in one file with three

worksheets in the sequence shown in Figure 42 to Figure 44. The designated inputs are

entered into the "Input" worksheet as seen in Figure 43. The user then has to click on the

"Launch Demonstration" button on the "Introduction" tab in Figure 42 to get the results

on the "Output" tab shown in Figure 44.

4.3.7 New Data

When new AMV data becomes available the neural network needs to be re-trained to

accommodate the data. There is no instant, easy way to re-train the network with the

additional data as there is for the multiple regression analyses. Ideally it might be

beneficial to design a new neural network because re-training the old neural networks might favor the old data and not give adequate weight to the new data.

32 5 RESULTS AND DISCUSSION

The preliminary design tools generated from the database are available on Excel spreadsheets. The user can perform linear and nonlinear analyses using respectively the multiple regression and neural network output applications. The number of ships and the completeness ofthe data sets determine the number ofinput and output parameters. In the present application, the user can enter up to four input parameters: maximum speed, operational speed, range, and payload depending on the ship type. The output, also dependent on the ship type, will consist of combinations of displacement, length overall, length at waterline, beam, draft, fuel capacity, and propulsive power.

5.1 Multiple Regression Analysis

The multiple regression analysis is performed on the SWATH, catamaran, hovercraft,

SES, and hydrofoil categories. The computed coefficients are entered into an Excel spreadsheet to be used as a preliminary design tool. The user should understand that the results ofthe multiple regression analysis are linear. Also, the trimaran category does not contain enough records to perform an analysis.

5.1.1 User Instructions

The file "RD_output.xls" contains the output application of the multiple regression analysis generated by the Analyse-it software. The worksheet is shown in Figure 45.

Column A contains the ship type. Columns B-E, if highlighted, are input parameter fields. "Not used" means that these parameters are not used in the regression analysis because there are too few records. The numbers in the highlighted cells are automatically copied into the cells below for the same ship type to provide additional combinations of input parameters. If a highlighted cell is not filled in, the row will not calculate the

33 preliminary design parameters. The output parameters (columns F+) are only calculated if the row contains all the required input parameters. If one is missing, then "n/a" will appear in the output cells. "n/a" in the output cells also signifies that there are too few records to perform the regression analysis.

5.1.2 Results

The number of output parameters for each ship type varies depending on the available data sets. The input parameters are a combination ofmaximum speed, operational speed, range, and payload. Analyses are performed with different combinations of input and output parameters to identi1)r the combination that produces the most accurate results for each ship type. In most cases, each output parameter has its own optimal combination of inputs. The following sections detail the optimal combinations for each ship type analyzed.

5.1.2.1 Catamaran Results

Two different analyses are performed on the catamaran data. The first uses three input parameters: maximum speed, operational speed, and range to determine the length overall, length at waterline, beam, draft, fuel capacity, and propulsive power. The second analysis uses inputs of maximum speed and operational speed resulting in outputs for maximum and minimum displacement as well as the parameters from the first analysis.

The performance data of the two analyses in relation to the actual ship parameters is summarized in Table 5-I and Table 5-2. The number ofrecords in the testing data set is shown in the "Total cases" row. The records that are used to test the data are the actual records used to calculate the regression coefficients. The bold headings indicate the

2 better results between the two analyses as determined by the adjusted R-square (R ). The

34 adjusted R-square measures the overall goodness of the regression model. The closer to one the better; a value close to zero means no correlations between dependent and independent variables. The tables also show the performance of the multiple regression analysis in terms ofthe number and percentage ofthe predictions that have less than 25% difference, between 25% and 50% difference, and greater than 50% difference from the actual values.

Table 5-1 Multiple Regression Analysis I: Results for Catamarans with 3 Inputs

Inputs: Maximum Length_ Length_ Power speed, Operational overall Beam m Draft m Fuel I - water m kw Speed, and Range m Total cases 21 13 21 21 19 12

Adjusted R2 0.19 0.12 0.11 0.02 0.Q9 0.88

<25% 15 7 13 9 3 II

50%>x>25% 4 4 6 7 6 0

>50% 2 2 2 5 10

<25% 71% 54% 62% 43% 16% 92%

50% >x > 25% 19% 31% 29% 33% 32% 0%

> 50% 10% 15% 10% 24% 53% 8%

35 Table 5-2 Multiple Regression Analysis 2: Results for Catamarans with 2 Inputs

Inputs are: Displae Displae Length Length Maximum speed Beam_ Draft Power ement_ ernent overal _water Fuel I and Operational m m kw sneed max m min m 1m m Total cases 7 12 113 58 110 101 72 60

AdjustedR' -0.10 -0.14 0.20 0.22 0.13 0.15 -0.01 0.38

<25% 2 2 66 22 60 46 18 4

50% >x>25% 3 2 33 23 35 35 14 20

>50% 2 8 14 13 15 20 40 36

<25% 29% 17% 58% 38% 55% 46% 25% 7%

50%> x> 25% 43% 17% 29% 40% 32% 35% 19% 33%

>50% 29% 67% 12% 22% 14% 20% 56% 60%

5.1.2.2 SWATH Ship Results

There is only enough data for one multiple regression analysis to be performed on the SWATH ship data. The input data consists of maximum and operational speed. The outputs are displacement, length overall, beam, draft, fuel capacity, and propulsive power. Table 5-3 shows the results for the SWATH ship data. The data and the regressed solution do not have strong correlation as indicated by the low values of the adjusted R-square, probably because ofthe limited size ofthe data set and the significant design variations among SWATH ships. Even though the adjusted R-square value is the highest for fuel capacity, only 17% ofthe data is within a 25% difference from the actual data. The displacement parameter has the lowest adjusted R-square value and this can be seen in the comparison, in which 11 % of the data is within a 25 % difference from the actual data.

36 Table 5-3 Multiple Regression Analysis Results for SWATH ships

Inputs are: Displaceme Length_ov Maximum speed and Beam m Draft m FueU Power_kw Operational SIleed nt m erall_m Total cases 9 15 15 13 6 6

AdjustedR' 0.09 0.11 0.30 0.32 0.45 0.20

<25% 5 9 7 2

50%>x>25% 4 8 4 6 2 2

>50% 4 2 2 0 3 2

<25% 11% 33% 60% 54% 17% 33%

50%>x> 25% 44% 53% 27% 46% 33% 33%

>50% 44% 13% 13% 0% 50% 33%

5.1.2.3 SESResults

Three different analyses are performed on the SES data. The first uses three input parameters: maximum speed, operational speed, and range to determine length overall, beam, and hullbome draft and the performance data is shown in Table 5-4. The second analysis uses inputs of maximum speed and range resulting in outputs for the parameters from the first test as well as draft on-cushion, fuel capacity and propulsive power. The third analysis uses maximum and operational speeds as inputs resulting in the same outputs as the first test with the addition of propulsive power. Table 5-5 and Table 5-6 summarize the results for the second and third analyses respectively.

The bold headings in the tables indicate the better adjusted R-square results among the three analyses for each parameter. The best parameters can be compiled from the results of all three analyses. The data sets have good correlation among themselves as well as with the regressed solution. This results in R-square values close to one for all three analyses and there are high percentages ofaccurate test results to within a 25% difference from the actual parameters.

37 Table 5-4 Multiple Regression Analysis I: Results for SES with 3 inputs

Inputs are: Maximum speed, Length_overall Draft Operational speed, and Beam_m Range - m hullborne m Total cases 5 5 5

AdjustedR' 0.99 0.92 1.00

<25% 5 5 5

50%>x>25% 0 0 0

>50% 0 0 0

<25% 100% 100% 100%

50%>x> 25% 0% 0% 0%

>50% 0% 0% 0%

Table 5-5 Multiple Regression Analysis 2: Results for SES with 2 inputs Draft Inputs are: Length_ Draft_on_ Maximum speed Beam m hullborne FueU Power_kw overall m cushion m and Range m Total cases 10 10 10 8 7 6

Adjusted R' 0.80 0.63 0.30 0.41 0.96 0.66

<25% 10 10 10 6 4 5

50%>x>25% 0 0 0 2

>50% 0 0 0 0

<25% 100% 100% 100% 75% 57% 83%

50%>x> 25% 0% 0% 0% 13% 29% 17%

>50% 0% 0% 0% 13% 14% 0%

38 Table 5-6 Multiple Regression Analysis 3: Results for SES with 2 inputs

Inputs are: Maximum speed Draft Length_overall_m Beam_ffi Power_kw and Operational Speed hullbome m Total cases 7 7 7 3

AdjustedR' 0.88 0.91 0.72 0.42

<25% 7 7 7 2

50%>x>25% 0 0 0 0

>50% 0 0 0

<25% 100% 100% 100% 67%

50% >x > 25% 0% 0% 0% 0%

>50% 00/0 00/0 00/0 33%

5.1.2.4 Hydrofoil Results

Three different analyses are also performed on the hydrofoil data. The first uses the input parameters of maximum speed, operational speed, range, and payload to determine the output parameters of displacement, length overall, beam, foil width, hullborne and foilborne draft, and propulsive power. The second analysis uses inputs of maximum speed, operational speed, and range resulting in outputs for the displacement, length overall, hullborne and foilbome draft, and propulsive power. The third analysis uses maximum and operational speeds as inputs resulting in the same outputs as the first test.

Table 5-7 to Table 5-9 contains the comparison results of the analyses. The best parameters are the results of all three analyses and are shown as bold headings in the tables. These multiple regression analyses are performed on a small amount of data; the most data used in an analysis is 12 records. Table 5-7 uses maximum speed, operational speed, range, and payload as inputs and the result for length overall is the best for all three analyses. Table 5-8 has the best adjusted R-square value for maximum displacement when maximum speed, operational speed, and range are used as inputs.

39 Table 5-9 produces the best results out of the three analyses for beam, foil width, hullbome and foilbome draft, and propulsive power.

Table 5-7 Multiple Regression Analysis I: Results for Hydrofoils with 3 inputs

Inputs are: Displace Draft Draft Maximum speed, Length_o Foil_ Power k ment Beam m hullborne foilborne Operational speed, verall m width_m w Rang;e max m m m Total cases 10 10 5 9 9 9 10

Adjusted R' 0.16 0.42 -0.76 -0.28 0.30 0.30 0.31

<25% 6 10 5 8 8 9 6

50% >x>25% 0 0 0

>50% 3 0 0 0 0 0 3

<25% 60% 100% 100% 89% 89% 100% 60%

50% >x > 25% 10% 0% 0% 11% 11% 0% 10%

>50% 30% 0% 0% 0% 0% 0% 30%

Table 5-8 Multiple Regression Analysis 2: Results for Hydrofoils with 3 inputs

Inputs are: Maximum Displacement Length_ Draft_ Draft speed, Operational Power kw speed, and Pavload max m overall_m hullborne_m foilborne_m Total cases 5 5 5 5 5

AdjustedR' 0.86 0.89 0.96 0.87 0.86

<25% 5 5 5 5 5

50%>x> 25% 0 0 0 0 0

>50% 0 0 0 0 0

<25% 100% 100% 100% 100% 100%

50%>x>25% 0% 0% 0% 0% 100%

>50% 0% 0% 0% 0% 0%

40 Table 5-9 Multiple Regression Analysis 3: Results for Hydrofoils with 2 inputs

Inputs are: Displace Length_ Foil_ Draft Draft_ Maximum speed Beam Power k ment overall_ width hullborne foilborne_ and Operational m - w sneed max m m m m m Total cases 12 12 6 10 II 10 12

AdjustedIf 0.05 0.17 0.58 0.01 0.30 0.32 0.37

<25% 3 11 6 8 9 9 4

50%> x > 25% 5 0 2 5

>50% 4 0 0 0 0 3

<25% 25% 92% 100% 80% 82% 90% 33%

50%> x > 25% 42% 8% 0% 20% 9% 10% 42%

>50% 33% 0% 0% 0% 9% 0% 25%

5.1.2.5 Hovercraft Results

Four analyses are also performed on the hovercraft data. The first uses four input parameters: maximum speed, operational speed, range, and payload to determine length overall and beam. The second analysis uses inputs of maximum and operational speed and payload resulting in the same outputs as the first analysis with the addition of propulsive power. The third analysis uses maximum and operational speeds and range as inputs resulting in the same outputs as the second test and hullborne draft. The fourth test uses maximum and operational speeds as inputs with length overall, beam, hullborne and foilborne draft, fuel capacity, and propulsive power as the output parameters. Table 5-10 to Table 5-13 shows the comparison of the analyses to the actual data. The best parameters are shown as bold headings in all the tables. The best adjusted R-square values are found from three of the analyses, one that uses maximum speed, operational speed, range, and payload as inputs; another that uses maximum speed, operational speed and payload as inputs, and the analysis that uses maximum and operational speeds as inputs.

41 Table 5-10 Multiple Regression Analysis I: Results for Hovercrafts with 4 inputs

Inputs are: Maximum speed, Operational speed, Range, Payload

Total cases 8 8

Adjusted R' 0.77 0.98

<25% 7 8

50%>x > 25% o o >50% o

<25% 88% 100%

50% >x> 25% 0% 0%

>50% 13% 0%

Table 5-11 Multiple Regression Analysis 2: Results for Hovercrafts with 3 inputs

Inputs aTe: Maximum Length_ speed, Operational speed, Beam_m Power kw and Pa 'load overall m Total cases 23 22 18

AdjustedR' 0.86 0.98 0.92

<25% 17 21 7

50% > x > 25% 4 0 4

>50% 2 I 7

<25% 74% 95% 39%

50% > x > 25% 17% 0% 22%

>50% 9% 5% 39%

42 Table 5-12 Multiple Regression Analysis 3: Results for Hovercrafts with 3 inputs

Length~ Draft Inputs are: Maximum speed, Beam m Power kw Operational speed, and Range overall m hullborne m Total cases 16 16 5 12

AdjustedR' 0.34 0.65 -0.56 0.27

<25% 5 7 4 0

50%>x>25% 5 7 0 3

>50% 6 2 9

<25% 31% 44% 80% 0%

50%>x>25% 31% 44% 0% 25%

>50% 38% 13% 20% 75%

Table 5-13 Multiple Regression Analysis 4: Results for Hovercrafts with 2 inputs Draft_ Draft_ Inputs are" Length_ Maximum speed and Beam m bullhorne on Fuel I Power kw overall m - Operational speed m cushion Total cases 37 36 9 8 11 28

AdjustedR' 0.26 0.58 0.24 -0.18 0.76 0.44

<25% 10 17 6 3 3 2

50%> x > 25% 10 16 2 0 7

>50% 17 3 4 8 19

<25% 27% 47% 67% 38% 27% 7%

50%>x>25% 27% 44% 22% 13% 0% 25%

>50% 46% 8% 11% 50% 73% 68%

5.2 Neural Network

A neural network application is created to recommend the ship type based on input requirements. Additional applications are generated as preliminary design tools for the catamaran, hovercraft, hydrofoil, and SWATH categories. The SES and trimaran categories do not contain enough complete data sets to perform an analysis using a neural

43 network. The results of these analyses are saved on macro-enabled Excel spreadsheets.

The neural network results are based on a non-linear analysis.

5.1.1 General Setup

The resulting DLL files created by NeuroSolutions that allow any user with Excel to run the completed neural network requires a few setup steps on the user's computer:

I. Copy the "neurosolutionsol.dll" file to the c:\windows\system32 folder 2. From the Windows Start menu, select "Run..."

3. Enter "regsvr32 c:\windows\system32\neurosolutionsol.dll" (or wherever the dll file is currently located in) in the Run Menu.

These steps need to be performed only once. A folder containing the individual output folders from NeuroSolutions should be placed directly into the C drive in order for the macros in the Excel file to function properly. If the folder is placed within any other folder or drive then uses the following steps to change the paths:

I. Open the Excel file. 2. From the Excel menu, select "Tools" then "Macro" then "Visual Basic Editor" 3. Under the "Modules" folder in the "Project - VBAProject" window, double click on

"GlobaIs" and edit the file paths for the first four lines to the current location of the

Excel file.

5.1.1 General Instructions to Operate the Excel Output File

Each output file from NeuroSolutions contains the results ofthe trained neural network as shown in Figure 42 to Figure 44. When opening this file, the user will have to enable the macros. The "Input" worksheet of this file, as shown in Figure 43, contains column headings stating the required input parameters. The initial worksheet contains the input parameters used in the training session. The user can add or delete any of the entries

44 located on this sheet, with the exception of the column headings. All input parameters under the designated column headings are needed for the macros to work. The user should make sure that the "Output" sheet, shown in Figure 44, is empty before going to the next step. The program does not delete data with each analysis; it only overwrites the existing data.

After the records have been entered, go to the "Introduction" worksheet, as shown in Figure 42, and click the "Launch Demonstration" button. The "Custom Solution Wizard

Sample" window, which can also be seen in the figure, will appear. Click on the "Get Network Output" button (the text to the right ofthe button should say "Not tested" before you click on the button and "Check 'Output' worksheet for results" after). The user is taken to the "Output" worksheet as shown in Figure 44. The details of the "Output" worksheet for each Excel file vary depending on the ship types analyzed as explained in the following sections.

5.2.3 Ship Type Recommendations

As a demonstration, a neural network is designed to predict the type of AMV based on four input parameters: length overall, beam, draft, and operational speed. Currently this neural network only classifies catamarans, trimarans, SWATH, SES, and hovercraft type ships. This is due to the fact that there are no complete entries for hydrofoils. The training scheme uses 226 entries consisting mostly of catamaran-type ships. The abundance of catamaran-type ships does not influence the network in favor of this type.

NeuroSolutions has an option to make each class evenly weighted for classification problems. This is called exemplar weighting, in that each class is weighed proportionately according to the number of samples that class contains in the training

45 data set. The result is an unbiased recommendation for various types of ships regardless oftheir sample sizes.

5.2.3.1 User Instructions

The file NN_class_output.xls contains the output application of the trained neural network for ship-type recommendation. The "Input" worksheet in Figure 46 contains the four required inputs: length overall, beam, draft, and operational speed. The user can input different sets of input parameters and determine the corresponding recommended ship types.

Figure 47 shows the "Output" worksheet. The column headings on this worksheet do not show what each output column represents, but it follows the format of the training outputs:

• Exemplar #: Example number set by the number ofinput records.

• Outputl: SES • Output2: Hovercraft

• Output3: Catamaran

• Output4: Trimaran • OutputS: SWATH Each row shows the indication as to how certain the neural network is. The highest value is the designation that the neural network has selected for the given input parameters.

5.2.3.2 Results

To evaluate the performance of the neutral network, the length overall, beam, draft, and operational speed ofeach ship is used as input and the output is compared with the actual ship type. Table 5-14 shows a summary of the analysis. The neural network correctly

46 categorized 204 out of the 226 entries. Since no "new" data could be presented to the network for comparison, the results might be slightly biased. "New" data is defined as data that the neural network has never been introduced to. If the neural network is over trained and the entries memorized, the results would be 100% accurate, but since there is only 90% accuracy it is safe to assume that the neural network learned the problem instead ofjust memorizing the answers.

Table 5-14 Neural Network Results for the Recommendation ofShip Types

# of Shi s NN Recomm. # Correct

Catamaran 197 175 175

Trimaran 6 21 6

SWATH 20 25 20

SES 2 4 2

Hovercraft 1 1

5.2.4 Ship Parameters

Individual neural networks are designed to predict certain key ship parameters given a set ofinputs. The input and output entries for each ship type vary and depend on the number of records. The neural network needs a lot of records to learn. If there are not enough training records, the neural network is less reliable when predicting parameters that it has never been introduced to. A neural network is designed for four ship types: catamarans,

SWATH ships, hydrofoils, and hovercrafts. The other two categories: trimaran and SES does not contain enough entries. The SWATH and hydrofoil categories does not contain a lot of training data so the results for these two neural networks might be somewhat biased towards the existing data.

47 5.2.4.1 General User Instructions

Each individual Excel file contains the results of the trained neural network for the ship parameters for each ship type. The neural network for each ship type is evaluated by a set oftesting data, which contains some data that the neural network has trained with and other data that the neural network has never been introduced to. The testing data is determined by the availability of all the input parameters, as opposed to the training data, which is determined by the availability of all the input and output parameters. The required inputs and outputs for testing are explained in the results section of each ship type.

5.2.4.2 Catamaran Results

The output application for the catamaran neural network can be found in the

NN_cat_output.xls file. The inputs for the preliminary catamaran design are maximum speed and operational speed. The outputs are:

• Exemplar #: Example number set by the number ofinput records.

• Outputl : Length overall in ill

• Output2: Length at waterline in ill

• Output3: Beam in ill

• Output4: Draft in m

• OutputS: Fuel capacity in liter

• Output6: Propulsive power in kW

The number of outputs for this ship type is greater than the other ship types because the catamaran category containes a lot more complete records. The number of records in the testing data is 113, which is more than five times the amount oftraining data. Table 5-15 shows the performance of the neural network in terms of the number and percentage of

48 predicted values that have less than 25% difference, between 25% and 50% difference, and greater than 50% difference from the actual values. Since the testing data set contains incomplete records, the number of comparisons for each parameter is not the same. The results for the missing parameters are not used in the comparison. In general, the performance is quite good with over 50% ofthe predictions fall with 25% of the actual parameters. The results for the fuel capacity are less reliable because there is a large range and scatter in the training data set for this parameter.

Table 5-15 Neural Network Results for the Preliminary Design ofCatamarans Length_ Length_ Beam m Draft m Fuel I Power kW overall water Total cases 113 58 110 101 72 60

<25% 75 32 65 57 30 37

50% >x>25% 21 13 27 19 17 13

>50% 17 13 18 25 25 10

<25% 66% 55% 59% 56% 42% 62%

50%>x > 25% 19% 22% 25% 19% 24% 22%

>50% 15% 22% 16% 25% 35% 17%

5.2.4.3 Hovercraft Results

The output application for the hovercraft neural network can be found in the

NN_Hovercraft_output.xls file. The inputs for the preliminary hovercraft design are maximum and operational speeds. The outputs are:

• Exemplar #: Example number set by the number ofinput records,

• Output! : Length overall in m

• Output2: Beam in m • Output3: Propulsive power in kW

49 The number ofoutputs for this ship type is less than the catamaran category because this category contains much fewer entries. The number of records in the testing data is 37, which is nine records more than the training data set. The results are shown in Table 5-16. The neural network accurately predicted the results for length overall and beam, but it did not perform as well for propulsive power. The propulsive power parameter contains records with a wide range and scatter and that affects the performance of the neural network.

Table 5-16 Neural Network Results for the Preliminary Design ofHydrofoils

Length_overall Beam_m Power kW

Total cases 37 36 28

<25% 30 31 12

50% >x > 25% 4 3 7

>50% 3 2 9

<25% 81% 86% 43%

50%>x>25% 11% 8% 25%

>50% 8% 6% 32%

5.2.4.4 SWATH Ship Results

The output application for the SWATH neural network can be found in the NN_SWATH_output.xls file. The inputs for the preliminary SWATH design are maximum and operational speed. The outputs are

• Exemplar #: Example number set by the number ofinput records.

• Output1: Length overall in m

• Output2: Beam in m • Output3: Draft in m

50 The number ofoutputs for this ship type is also less than the catamaran category, because this category contains fewer entries. The SWATH neural network is trained using 13 records, and tested with 15 records. Table 5-17 summarizes the results ofthe evaluation, which shows that all the parameters are within a 25% accuracy at least 85% ofthe time.

The limited number oftraining records and the small difference between the training and testing data is why the neural network output for SWATH ships is so positive.

Table 5-17 Neural Network Results for the Preliminary Design ofSWATH ships

Length_overall Beam m Draft m

Total cases 15 15 13

<25% 13 13 11

50% >x > 25%

> 50%

<25% 87% 87% 85%

50% >x > 25% 7% 7% 8%

>50% 7% 7% 8%

5.2.4.5 Hydrofoil Results

The output application for the hydrofoil neural network can be found in the

NN_Hydrofoil_outpuLxls file. The inputs for the preliminary hydrofoil design are maximum speed, operational speed and range. The outputs are

• Exemplar #: Example number set by the number ofinput records.

• OutputI: Length overall in m

• Output2: Draft ofthe hull in m

• Output3: Draft with the foils in m

• Output4: Maximum displacement in t

• Output5: Propulsive power in kW

51 The hydrofoil category has the smallest training and testing data set, with nine records used for the training and ten for the testing data sets. That is why the results are better than the other ship-type neural network analyses. The results are shown in Table 5-18 and from this table it is shown that the neural network results are within 25% difference from the actual data at least 90% ofthe time for draft and fuel capacity and 100% of the time for length overall, length at waterline and beam.

Table 5-18 Neural Network Results for the Preliminary Design ofHydrofoils

Length Lenglh_water Beam_ffi Draft_m Fuel I overall Total cases 10 9 9 10 10

<25% 10 9 9 9 9

50% > x > 25% 0 0 0 0

>50% 0 0 0 0

<25% 100% 100% 100% 90% 90%

50%> x> 25% 0% 00/0 aD/() 10% 0%

> 50% 0% 0% 00/0 0% 10%

5.3 Comparison of Results

The testing outputs from the neural network and multiple regressIOn analysis are compared with the actual ship parameters. The results of the comparisons are shown in Table 5-19 to Table 5-22 for catamaran, SWATH, hydrofoil, and hovercraft-type ships respectively. In order to have consistent comparisons, the testing parameters use maximum and operational speeds as inputs and the outputs vary depending on the ship type and testing method. The comparison is made only ifthe actual parameter and output parameters for both evaluation methods (i.e. multiple regression and neural network) are available. The best value is defined as the predicted value of either the neural network

(NN) or multiple regression (MR) that is closer to the actual parameter. The percentages 52 of correct answers are shown at the bottom ofthe tables. The total number ofparameters compared is shown in the "Total" row and is calculated by adding the number of best values for the neural network and the multiple regression for each parameter.

Overall the neural network performed better than the multiple regression analysis. In all four ship categories that are evaluated with both analyses, the neural network has higher accuracy with the exception of the length overall parameter in the catamaran ship category. The multiple regression analysis performs reasonably well for the catamaran ship-type and the percentages of best values are within 20% of the neural network predictions for the length overall, waterline length, beam, draft, and fuel capacity. This is due to the linear-like behavior ofthese parameters for catamarans. In many cases, as one parameter changes, the other parameters will change proportionally. The exception is propulsive power, in which the neural network is close to the actual parameter 87% ofthe time. Neural network is more adaptive in following the step-wise increase of propulsive power due to available standard engine sizes. However, propulsive power does not perform as well as other neural network predictions as shown in Table 5-15 to Table

5-18, indicating that more records are needed to capture the highly nonlinear relation. The other three ship categories also favor the neural network results. The neural network is closer to the actual parameters over 75% ofthe time.

Table 5-19 Catamaran: Neural Network verses Multiple Regression

Best Length Length Beam_ffi Draft m Fuel I Power_kW Value overall water Total 113 58 110 101 72 61

NN 54 35 58 54 42 53

MR 59 23 52 47 30 8

NN 48% 60% 53% 53% 58% 87%

MR 52% 40% 47% 47% 42% 13%

53 Table 5-20 SWATH: Neural Network verses Multiple Regression

Best Value Length_overall Beam_m Draft m

Total 15 15 13

NN 12 13 10

MR 3 2 3

NN 80% 87% 77%

MR 20% 13% 23%

Table 5-2 I Hydrofoil: Neural Network verses Multiple Regression Length Draft Draft Displacment_ Best Value Power kW overall hullbome foilbome max Total 10 9 9 10 10

NN 9 7 9 9 9

MR 2 0

NN 90% 78% 100% 90% 90%

MR 10"10 22% 0% 10% 10%

Table 5-22 Hovercraft: Neural Network verses Multiple Regression

Best Value Length_overall Beam_m Draft m

Total 37 36 28

NN 30 31 24

MR 7 5 4

NN 81% 86% 86%

MR 19% 14% 14%

54 6 CONCLUSIONS AND RECOMMENDATIONS

This study has produced a working database of available advanced marine vehicles

(AMV) and their corresponding parameters along with individual ship homeport and route information. This database is used in a geographic information system (GIS) website and the user is able to query, select, and locate AMVs all over the world along with the global wind and wave conditions. The database is also used in multiple regression analyses and to train neural networks to develop preliminary ship design tools.

The four ship types evaluated with both multiple regression and the neural network, with the exception of the catamaran length overall, show better agreement of the neural network results with the actual parameters. This demonstrates the capability ofthe neural network to capture the pattern ofthe data. The neural network is also able to provide ship type recommendations based on certain key ship dimensions; the multiple regression analysis is not able to perform this task. The neural network should be considered over multiple regression analyses for the calculation ofpreliminary ship design parameters.

The current database does not contain enough complete records, especially for the trimaran, SWATH, SES, hovercraft, and hydrofoil ship types. When more complete records become available for the neural network, the accuracy of the output ship parameters will increase. Furthermore, a more complete set of AMV design input parameters that include maximum speed, operational speed, range, and payload can be utilized instead ofthe maximum and operational speeds that are used in the current neural network analyses. The output parameters of displacement, length overall, length at waterline, beam, draft, fuel capacity, and propulsive power also do not contain complete records and many times some ofthe output parameters are not considered for this reason.

55 Additional ship parameter information should be found; a possible source is ship registries of the countries where the ship is in operation. The neural network has some problems predicting the fuel capacity and propulsive power parameters, but with the addition of more complete data, the neural network should be able to accurately predict these parameters. Theoretical relationships among certain parameters can be considered and incorporated into the neural network in the future.

56 II•• I 1l,1!,,; IL,"1 hill... 1"",I/~ "url.I""· I II II,..., II\,I'..J.,"I' l.1 n.lI I 'n..,11 \\';U I.i,,<: ~jl<1\1 1~.llj' lu) ,,-111'1' ll­ '" t." ;_ J I./o.,,,m· \ '11.1 ... Area fwin lIull 11,11 lu' 11).1 ••" I I., \" hili .\, 1\\ \ I Ship SWAfII

Figure 1 Types of Advanced Marine Vehicles (with pennission from Mustafa lnseI)

Coonuy Bouldaties - ClltBmarllll H - Hov81Q;tt HomlipO - I-t(drololl HOfTMlPO

(

__~~:::;:;::::~J,:~=:, • 463_

Figure 2 ENDEAVOR GIS Website: AMV Homeports

57 egan Country Booodanes CllaImllrllO Roo.Ies Hovercrdt Rootes Hvcrotall ROIUS SESRQ/tes SWATH Routes Tnmerlln RQ/t8S

tit.

Figure 3 ENDEAVOR GIS Website: AMV Routes

d I Coonlry Booodanes lIS. SII-'- r"'__ ~H. 0'·05 .08., , '·1.5 18.2 2.1· 2.5 26- 3 31·3.5 36-4 4' .

1~"_!!IfIiii~~~~~~~"'~~--·'~~-':------""'~.la1d

o 1.)-:..:....J~"""""'_1483 •• KS-SIp _W_H0lthtlo_"-.L¥,

Figure 4 ENDEAVOR GIS Website: Significant Wave Height

58 ~:'-"I;",;;;-_"" ••oo.l 2.6-53 54-66 67- 7 6 77- 8,5 88- 9,3 9A.9.9 10.0- 104 10.5- 11 1 11 2- "8 11.9- '3.2 lsld

Figure 5 ENDEAVOR GIS Website: Peak Wave Period

end Counlly BlMClan9S -P..k_O...... (."'~ I 0-45 , 46-00 91- 125 128- 180 , 181.225 226·270 , 271-315 , 316-359

.;;==-<.....,..~~~~~==.. D_IIIIII~bll!'II::]=:--l OP-:-Po.w_Olredio== __"'_lop<

. ------

Figure 6 ENDEAVOR GIS Website: Peak Wave Direction

59 CounllY Bou1d8nes "~IC'. ... ~"_R - SpMIII ...... ~ 2.5-36 37- 4 7 '~~~i!!!!!!!-=JII. 4.8- 58 59-70 71- 81 82.9.2 93-103 10.4- 114 11.5- 126 12.7.137 Land

3 MIl••

Figure 7 ENDEAVOR GIS Website: Wind Speed

nd CounllY Bou1denes D_D WI D~ ..lon , I

Figure 8 ENDEAVOR GIS Website: Wind Direction

60 egena ::::J Counlly Ba.ndaries C;1oI>_~olB"'"",1)' ~"'iS--""'I"''''') -10.654- -10.000 -9.999- -9.000 -8.999 - -8.000 -7.999 ••7.000 -8.999 - -6,000 -5.999 - -5.000 ....999- ....000 -3,999 - -3.000 .2.999 - .2.000 -1.999- -1.000 -999·0 1.1.000 1.001 - 2.000 2.001 • 3.000 3.001 • 4.000 4.001 - 5.000 5.001 • 6.000 6.001 - 7.000 7.001 • 8,000 8.001 - 9.000

Figure 9 ENDEAVOR GIS Website: Topographic and Bathymetry Data

Figure 10 ENDEAVOR GIS Website: Query Setup

61 a,..,·- --...... ______l!!!!!!!!!!!~!:.:.'J_::":",-",!!!!!!!!"",~l!!"::=__~

tlttn ..1 ...... !teH.h

f_,lINllr

• , ... C..."....,..... cln" "".. H' "'.1 iJ...... IUDlrl CuOOOO (

Figure 11 ENDEAVOR GIS Website: Query Output

Figure 12 ENDEAVOR GIS Website: Ship Photo from AMV Database

62 • _ if x i[I '" f.dt ~ ~ f'lI'IllIt lools QIta ~ ~ ~ D ~ iii d i!i a ~ ~ .l(, ~ e,.

3 SEa LGTIi OVERA LGTH WATER BEAM M DRAUGHT II DISPlMT T DEADWGHT T CREW PASSENGERS VEHICLES 4: 1 25,20 13.00 2.70 13.6 2 8 5 2 49.90 22.50 5 90 185.0 6 3 35.90 17.10 3 15 7 446 7 4 20 70 6.60 1 60 3 40 8 5 1510 640 160 3 30 9 6 15 10 620 1 60 2 15 10 7 29 20 243 11.30 4 96 11 8 3932 1560 350 7 410 12 9 1140 6.40 32 13 10 32.30 10.50 230 164.00 166 14 11 3450 32.5 1500 3.50 15 12 23.90 21.0 11.10 2.20 5 75 16 13 :E 50 13.00 270 160.00 6 400 17 14:E6O 13.10 3.50 6 250 16 15 25.00 11.60 170 150: 19 16 19.60 9.70 2.10 52.00 20 17 40.00 16.50 370 :E500 3 430 21 16 32.00 16.60 4.30 160.00 22 19 37.20 18.00 3.40 23 20 25.00 12.00 24 21 20.40 11 28 244 76.00 25 22 15.25 9.15 2.10 64.00 26 23 20.00 10.67 260 6026 4 125 27 24 2764 13.72 3.35 182.60 14 28 25 19.20 9.10 2.10 29 26 21.90 9.40 3.00 70.00 30 17 12.20 5.50 1.35 2400 4 31 28 19.60 11.70 103.00 149 32 29 12.00 5.50 1.30 2400 33 30 35.66 16.00 366 ;M ~ 1200 5.50 2400 ..~L...- 36, . ,t'i ... \swa~h.,JlOrtli$WA'llil?_ctsp.(2jt1:l_O .{?~X?_~~ 1;U1 4 11 ~Jr Rudy

Figure 13 Analyse-It: Tutorial Example

63 ,Qata ~ ~lndow f:!.~ t~ iorL. fier ------~-I F,grm. .• StCtotais. . VaJdation...

Iable... T§lit to CoIunvu'h Co,osoidate... jlfO\.C) Md Outfrle ...... ,""'-'--==-='------== ~ ,ewotTabie and PiwtO\art Report... Get Elltemal Qata •

,Qatasel.. ~aMbIe ...

Figure 14 Analyse-It: Set Dataset Properties Toolbar

Qata ~ ~lndow f:!.~ ~~ Sort.. fier ~------'--' F,grm... SuQtotaIs... VaJdation,.. ======--1 IabIe.·. Tp to CoUnns... Cooso'date., . !al~ Md Ol.tine

,Qataset... ~ariabIe. ..

Figure 15 Analyse-It: Dataset Properties Dialog Box

64 ••I - 1 ••• -

Measu'emeri scale ------=---­ r NornilaI r Ordnal

Fonnat... I

Figure 16 Analyse-It: Variable Properties Dialog Box

farametric • BNOVA • Hon-parametnc • Rates ± Proportiom •

~relation •

I AeQ,~6"'· .. ~ I ~

Figure 17 Analyse-It: Linear Regression Menu

65 •I

VriabIe X(idepelldellt): OK CREW PASSENGERS 't'EHIQ.ES R.£l_CAP_L PROPPWR..,.KW ., MAX..5PfEI)_ OP_SPEED_K

Variable Y(ciepel del It): IIMICI·m:a;

Ccdldellce iQrval: I

No Irtercept term in model:fJ

Figure 18 Analyse-It: Linear Regression Dialog Box

v.~l,:..d WIt;, ;"l',o,,"4" .. Gtt.f,I~ll to7 tmear rcnression SWATli Mwn Iple fi'E'(l1 e~iOn Anfllysis DRAU

nl 13 (c.....xdochd: Ie du. 10 ....i.' ...... )

R1 0.43 Ad ueted R' 0.32 Sf 0.7584

Term Coeft1cent SE p I 95% CI of Coefflclent Intercept 0.8601 1.04561 4299 -1 4697 to 31699 0. 1 MAX_SPEED- -0.1598 0.12541 02314 -0.4392 to 0.1196 OP_SPEEO_K 0.2710 0.1338! 0.07031 -0.0270 to 0.5691

Source of v rl on SSq I OF MS I F Oueto regre 'on 4.3331 2! 2.1671 0.0603 About regres Ion 5.7521 10j 0.5751 Totel 10.0851 121

Figure 19 Analyse-It: Multiple Regression Analysis Report Table 66 4.5 <> 4 <> 3.5 <> <> ~I I- 3 <> =~ C <> cz:= 2.5 <> CI <> 2 <> <>

1.5 0<> <> 1 1.5 2 2.5 3 3.5 4 PredidedV

2 <> 1.5 <> <> l/I ;; <> ...:I l~ 05 ... 0 ..01 0 '! <> <> .: ·0.5 <> <> II <> ~ <> -1 <> <>

·1.5

-2 Ui 2 2.5 3 3.5 0 5 10 Pr icteodY

Figure 20 Analyse-It: Multiple Regression Analysis Report Graphs

67 4 POlIS of .. ~--,.

Dendrites: Accept inputs

~ r.....------Soma: Process the inputs

Axon: Turn the prooessed inputs into outputs

Synapses: The eleotrochemical contact between neouyons:

Figure 21 Biological Neuron (from Anderson and McNeil, 1992)

Sunmaticn F_on

s"" '", ....x t-iyJIerbo~ Te.ngmt .'" "., Uneer AY

Lemingand Recall Schedule -

i letlrringC'lrde

Figure 22 Neural Network Processing Element (from Anderson and McNeil, 1992)

68 Output value

1

Transfer function = 1/(1+Exp [-S1lJIl])

~~~--=-~~+--~---~---Input value -1 -0.5 0.5 1

Figure 23 Sigmoid Transfer Function (from Anderson and McNeil, 1992)

INPUT LAVER

HIDDEN LAVER (th~r~ may b~ .~v~r.l h;dd~n 1.y~r.)

OUTPUT LAVER

Figure 24 Artificial Neural Network (from Anderson and McNeil, 1992)

69 Input First Second Output La~r Hidden Hidden La~r La~r La~r

Figure 25 Multilayer Perception Model (from Anderson and McNeil, 1992)

Adjust Weights

- ••• _1100.· . 1 1 0 0 ----1~1 Neural Output 1010 .1010 Network

Repea.ted Pzesenta.tion ofXor Da.ta. Desired 0110 01 10 J

Figure 26 Back-propagation Learning Process (from Anderson and McNeil, 1992)

70 - - I What type of problem do you want to solve? 1 Click the "Help" Il.Jtton en the bottom-left ccrner ofthis panel for Problem Type clescr~tions ard examples ofeach l70blem type. selection r.' ClassifICation Determine a class or group for each input pattern c· Ftn:tion Approximation Determine a contiluous value for each ~ pattern (' Prediction Determine a tine-series value usi'lc;l information from the past (~ Clustering Group or visualize data wlthout knowledge of the desired gr~

If the "BeQImer level" checkbox below is checked, optional panels wi be sIdpped. ~ Begimer level

Help J c.rcel I '\:.l·k I Next> I

Figure 27 NeuroSolutions NeuralExpert Design Tool

... • • Neural Model Generaized Feed Forward ModuIaI Neural Network JOIdanlElman Netwofk IW,kome to tho NoumIIl_. Pri'lcipal Corrc:>onent ~ (PeA) Startirtg with yom date, this tool RBFIGRNNIPNN Network will walk you thzough the SeIf·Organizi'lg Feature Map Network process ofdesignil'lg m:i tJaimrlg Time-Lag Recurrent Network Recurrent Network a nemal Detwork. There are CANAS Network (Fuzzy Logic) many different types ofnemal Sl.4lPO/l Vector Machine Detworks. but lroSt C81\ be classified as beloDPg to one of IMultilayer pelCeptlOllS (MLPs) the major paD:Dgms listed to the are layered feedforwaJd networks left. Each paridigrn will have typically trained with static advantages 8lld disadwntates backpropegation. These Mtworks depelldiDg on yom particular have found their way into epplication. The NeuralBuUier countless epplicatiollS req~ Imakes it easy to try them all! static pattern classifICation. Their v I

Help

Figure 28 NeuroSolutions NeuralBuilder Design Tool

71 Average lISE wlh standard DINIation BoundarIes for 3 Runs

0.35 0.3 I 0.25 -Tr.iDIIlg :I 0.2 - - - - - • 1StHdllrd O.tilltioll i 0.15 ----- • 1StlllldvdDevi__ C•0.1 0.05 ~ 0 1 250 4&8 148 &81 1246 1485 1144 1883 224 2481 Epon

T,MniII,I Tro . !I St"Nt/ AJlRus .. Ehw.,1otI Average of Minimum MSEs 0.008435238 0,001005797 Average of Final "III MSEs 0.008435239 0.001005797

O,si ~'IN1I'A T,~ Auni 2 "I EpochM 2500 MlnimumMSE 0.007854542 - FinalMSE 0.007854542 '

Training MSE

0.35

0.3

0.25 -RuRI1 ~ 0.2 - C/) -Runl2 ~ 0.15·· R..... 13 0.1-

0.051'-'____ .

o I r I , IIIII 1 1 250 433 148 331 1246 1435 1144- 1383 224- 2431 Epoch

Figure 29 NeuroSolutions Training Report Produced in Excel

72 MeuroSolutlons 1l-_Pr_epr_oc_e_ss_D_ata ----J. I0::: DIfference... Analyze Data • R«tdomize______Rows ...JI Tag Dllta • g.+ Sample.,.

Cre8te/Open Network Moving Average,..

Cre8te Datil FlIes Translate Symbolic: Colurms Train Network • Insert Column l~els Test Network Clean Data ..

Apply Production Dataset R\J"I Batch . New Batch... Bat:ch Manager • Gato Active Data slleet Data Sheets...

Goto Active Report Reports...

Open ActWe Network

Help

Figure 30 NeuroSolutions Preprocess Data Menu

73 NeuroSolutions I Preprocess Data

AnalY2e D~a

Teo Date Column(s) As Input

Create/Open Network CoIunn(s) As Desired

Create Data Files CoIurm(s) As Symbol

Trail Network Row(s) As TralnTlg

Test Network Row(s} As Cross ...atldation

Apply Productlon Dataset Row(s} As TestinO New Batch ... Row(s) As Production

Batch Manager All Columns As Input Gato Active Data Sheet All Non-Numeric columns As symbol Data Sheets... All Rows As Training Goto Active Report §l Rows By Percenl:ages .•.

Reports... Clear Tegs...

Open Active Network CIe.ar Column T"0 Help • Clear Symbol Tag Clear Row Tag dear AnTags Select Cross-Section... Refresh Tag Formats

Run Batch...

Figure 31 NeuroSolutions Tag Data Menu

74 t:tewoSOlutlons I Preprocess Data • Analyze Data • Tao Data • I CreatefOpen Network • New... Create Data Fies • Open... Train Network • Close Test Network • ,.. Save Apply ProductIon Dataset Save As ...

New Batch ... Load Be$t Weights Batch Manager • TIle Excel/NS Goto ActIve Data Sheet Run Batch... Data Sheets...

Goto ActIve Report Reports...

Open ActIve Network

Help

Figure 32 NeuroSolutions Create/Open Network Menu

(-. l0- ti () 0 l:= 'is ..,.... lr"I 11M AuoI :~e-a ...... IlInlIon*o• .... lido ... CJ riI iii Q 0 & NIW QMn s- .... :r....e-a -- NSEJHl csv NEJptn T~

For tIIllp, press FI

Figure 33 NeuroSolutions Breadboard

75 tieu'oSoUIons 1 Preprocess Data • Analyze Data • Tao Data • Create/Open Network • Create Data FIes • I Treln Network • Train... Test Network • Tren N Times... Apply ProductIon Dat¥et Vsy A Parameter... New Batch... Train Genetic.•. Batch MeNger • Run Batch ... I-- Goto ActIve Data Sheet Data Sheets...

Goto Active Report Reports ...

Open ActIve Network Help •

Figure 34 NeuroSolutions Train Network Menu

outplt Location Trial Name: Tra1n2

Trainir'lg Options . tbnber of Epochs: 1·1000

r

r For dassflcatlon problems, make classes evenly ~

Help Io...-OK_I C-.eel

Figure 35 NeuroSolutions Train Menu

76 Output Loc«ion

Trial Name!

Training OptIons

Nunber of Epochs: 1000

NLmber of Runs: 3 r

r I I

- For Oassiflc&lon problems, IMke classes evenly wek;tted

OK Cancel

Figure 36 NeuroSolutions Train N Times Function

77 Oltput Location Trlal~: rTraln2

Tro!llnlno Options Number of Epodhs: 1000

Number of RlJ"lS: 3 r-

PMameter Options Component .Action: hidden1Axon. setRows

start: YMJe: Increment: II of V.yjo!ltions: 2 I 1 I " Descr~lve Name: Hidden 1 PEs

Help __OK__I__C_anceI__

Figure 37 NeuroSolutions Vary A Parameter Function

78 ~oSoIutlOns I Preprocess Date • Analyze Data • TllQ Data • create/Open Network • Create Deta FIes • Train Network ~ • Test Network. • Test ... Apply ProO.tction Dataset 5ensltlvty About the Mean... New Batch ... R\I'l Betch... Batch MMager • ---- Goto Active Data Sheet

Data Sheets...

Gota Active Report

Reports...

Open ActIVo Network Help .,

Figure 38 NeuroSolutions Test Network Menu

Figure 39 NeuroSolutions Apply Production Dataset Function

79 l!J '" tdt ~ /rdert flrmIt 10015 QIta Vbldow tteIP ~ ~ [9.~. ~ ~~ i~ ~ D !iii d '!1 &; .It e· <1 k'). t. L • 0 100"4 • G) • ~ .~g f;~ ~ Ariel • 10 • C!.ITJ y ¥ :;; m $ % J :08 tlf • • A. •• ~ 105

1 • 7 41.25 11 e.3 1M 5QOO .u»o .ro 36 8 2e 24"e e 1 2500 700 18 15 oo "., 29 26סס2e.2 25 11.2 1.18 1 9 oo 3UO 37 33סס 1.8 10.8 « 42.1 ,10 oo 48U .ro 35סס1 1.8 10.8 « 42.1 11 oo 2e10 28 25סס1 1.1 12 32.1 35.5 12 13 25.1 23.4 1.6 1.3 7000 22U 35 32 14 2e.25 28.4 1.38 3.85 5000 .u»o 50 45-1 15 25.5 2U I 1.1 8000 2000 3) 28 16 38.1 10.51 1.35 5500 148 237 22.5 oo 2110 32 29סס 1.6 15.6 '31.435 41.17 17 18 : 13.1e 11.6 SA 0.6 1100 532 28 24 19 91.3 11.1 28 3.1 475110 21320 50 43 20; 31.2 33 10.8 1.1 1000 2500 35 33 21 ' 31.6 31.4 15.8 2.6 .u»o N60 3) 27 22' 34.1 21.4 U5 1 "30 3350 41 35 23' 18 1/) 24, 22 18 25 ::25 175 26 23 19 27 23 20 28 23 20 29 237 225 3) 25 20 31 ; 25 24 32, 25 2'5 33' 253 25 34· 27 22 35 27 23 36 27 25 37 27 25 38. 28 24 39, 28 24 .ro' 28 24 41 28 25 ·1 I~ ~ • H /.. Tranl TrrM3E /.. Trali13 Repat /.. Trai'13 AVf1IIX /.. Trai'13 TrrM3E At\ I~ I r·r Ready ,.....

Figure 40 NeuroSolutions Input for the Production Dataset

80 • _ /II x !DEle ~ :tIeW In-t fIrmIt lools ~ ~ ~ ~ D ~liiId~ ~~~ :J,~e,.<1 .I:·HH .D.fl. 100% • 11) • ~. • 10 ·[!011 ==li'lim 1Il % J :08 .~ ~~ • A. •• If, 24 4653472!Ul39 I J

1 7 ".25 31 SI.J 1." 5DOO 4000 40 :J) B 2e 24.0 1 2500 100 1B 15 9 N.2 25 11.2" 1.78 10000 2aO 29 26 to 4U 311 10.8 U l1OOO JUG 37 33 11 4U 311 10.8 U 10000 46fO 40 35 12 35.5 32., 12 1.1 10000 21110 2B 25 13 25.1 23.4 1.8 f.3 7000 nu 35 32 14 211.25 2M '.38 3.85 5000 4000 50 45 15 25,5 21.11 , 1.1 8000 2000 :IJ 2B 16 38.1 35 10.5 1.35 5500 74' 23.7 22.5 17 40.17 31.4 15,8 U l1OOO 2110 32 29 1B 13.1" 11.6 5.4 0.8 1100 532 2B 24 19 11.3 11.7 26 3.7 47500 2,m 50 43 20 37.2 33 10.8 1.7 IfJOO 2500 35 33 21 31.8 31.4 15,8 2.8 UOO 2«IG :IJ 'Zl 22 34.1 21.4 1.75 1 "30 3350 41 35 23 24.0 23.78 8.72 1.01 30J4 320 18 15 24 24.50 24.42 7.60 0.'0 2fl62 413 22 18 25 24.4t. 24.t.0 7.43 0.77 2(132 421 22 '5 175 26 24.5' 24.72 7.25 0.74 2(1511 463 23 19: 'Zl 24.86 24.83 7.42 0.77 3073 500 23 20 28 24.86 24.83 7.42 0.77 3073 500 23 20 29 27.75 25.75 9.97 1.31 4356 863 237 22 '5 :f) 24.67 25.41 6.4' 0.63 2(112 573 2'"..,. 20 31 30.48 27.21 11.25 1.65 5601 1371 26 24 32 33.10 27.74 14.71 2.4(1 7000 1725 26 25 33 3VJ5 27.14 14.15 2.37 6(173 1735 263 25 34 25.66 26.55 6.1(1 0.5(1 3211 135 27 22 35 27.30 26.96 7.21 0.7!> 3(1J4 1046 27 23 :E 32.60 28.0(1 12.83 2.05 66(17 1759 27 25 ~ 32.60 28.0(1 12.83 2.05 6697 1759 27 25 38 29.28 27.85 11.17 0.94 4816 1396 28 24 39 29.2' 27.85 8.17 0.94 4816 1396 28 24 40 29.2' 27.85 8.17 U4 4816 1396 28 24 41 32.07 28.43 11.06 1.62 629' 1781 28 25 . H 4 • H l. TraliliTrrH;E l. T;in3"Report l. -;:riiJ .AVtI'EC l T;«*l3 TrrH;E->':~ 14 I T~r Rea6r N..fll

Figure 41 NeuroSolutions Output for the Production Dataset.

81 I!J '" ~ ~ p.t I'IrIIlIt look QU ~ WIndow ~ D ~ 'I d!1 "[l ~ .l/, ~ If"". - <1 ",.:, t; 1: • U i~ .b -8 100% • (7) • ArIII • 10 • B l' !l 1i',.:aI m III %, :.8 .,/).8 ~ ~lf • ~ • A. •• A18 • It ri'AIB'.Ci D : E F G H ,J K L .. 1 : Microsoft ExcelProjectShell 2 i(Recall Network) 3' 4 :this shell demonstrates the slrJ1)lIc1ty of Int~atlngthe generated 5 .!naral network DU. with Excel data tfrot.dl the use ofVisual Basic 6 !for Applications. 7 BcThe Input values from the original NelroSolutlons breachoard 9 ,have been loaded Into the 'Ir1xJt' worksheet. this data can 10 jbe used to generate outputs from the neural network DLL 11 i !? iThe 'Output' worksheet will receive the output values generated by theDLL 1~ :Press the 7..atneh Demon6trBtIon' button below to ru1 the demonstration. 14: lS:ro view the Visual Basic code used for this demonstration, click f~~ Ma:ro -> VIIius/BssIcEdltrJr' from the Excel 'TooIs'menu. 18 I I 19 Ulunch 20: ?f: Demonstration 22 PI_ the bWIorc 10 M'llha ~ hrocliaN 23' 24 ; .. I IRecai NelWOlk 0"'. 25": 26: 27 1 281 I Nallelleli

291 ._~. -ill '...__ .__ _ ._ ._._. __ . .. __ •. ••__• J 31 ' ~~-i 33; '34-: 3ifl 14' 4 • "'I~t~'L,.(~ lO!4!u.t./ I~ I RMdy /.

Figure 42 Excel User Application: Introduction Page

82 Arlal • ID • BIll $%, A21 • ~ • A B C G H J 1 MAX_SPEEIOP_SPEED_K 2 1~ ,- 3 2~ 10 4 22... 17 ~ 5 ~3 19 6 23 20 7 23 20 8 237 225 9 25 20 10 26 24 11 26 25 12 26.3 25 13 27 22 14 17 23 15 ."-, 25 16 27 25 17 28 24 18 28 24 19 28 24 :!I 28 211 I 22 23 24 25 26 II 28 29 3J 31 32 33 34 35 3) 37

Figure 43 Excel User Application: Input Page

83 l!) ~ tilt l!IIW P8t Fg'mIt 1oo1s ~ WIrldow ~ ftIlIoSaUlans ;, . r,·,· ,... ,' ~ ~Y ~ ~ t~ ~.e D III d ti e [l. e· <1 l(') ••',. ",:r:. H 100% • C?) • AriaI • 10 •B I Y 1f.:II m $ % J :68.;Oj \~ ~ • ,~, • A. •• A21 ~ A I BCD, EFG H r J 1 :Exemplar ill Output1 Output2 Output3 Output4 Output5 OutputS 2 ' . 1 24047 23 7B B.72 1.01 3J34 . 320 3 : 2 24.50 2442 760 0.00 2962 413 4 ! 3 24.45 2450 743 0.77 2932 421 5 i 4 24.56 24.72 7.25 0.74 2959 463 ..~ .J 5 24.$ 24 63 7 42 0.77 :JJ73 500 7 : 6 24.$ 24.63 7.42 0.77 3173 500 8 : 7 27.75 25.75 997 1.31 4356 ffi3 9 B 24.67 25.41 648 0.63 2912 573 10: 9 3l.48 27.21 11.25 1.65 5601 1371 1.1 i 10 33.10 27.74 14.71 2.49 7090 1725 12, 11 32.95 2764 14.15 2.37 6973 1735 1~ I 12 25.66 26.55 619 0.59 3211' 83s 14 ; 13 27.:Jl 26.96 721 075 393C 1046 -151 14 32.60 2B.09 1:2.63 2.05 6697 1759 16 i 15 32.60 2B 09 1263 205 6697 1759 17: 16 29.26 27.65 B.17 094 4816 1396 tal 17 29.26 2765 B.17 094 4816 1396 ~ ---~19 18 29.26 27 65 8 17 0.94 4816 1396 20: 19 32.07 2B 43 1106 1.62 629B 1766 1t1 I 221 '23 i 241 :is 1 261 271 28: 291 3li 31 : 32: 331 34; iii '36 ! 37: Ml 14 4 • .." _~~!ll!1- .J..JrlM. }.~/ J-,

Figure 44 Excel User Application: Output Page

84 ------.:'-M,c,o,of! £,c..1 - RD_output.~ls -- ~I~ [lBe Edt YitJw Insert IVmlIt Iools [lata w;ldow ~ nsert rurrbef' •_ e x A B C 0 E r G H I K L M J ~ 1 Th. 0U1p1ll .ra.....' with th. AdJ.....d II' d_III 1.00 lIlould .....d. Openll'o DISplace Max_spa nal.spee Range,n Payload ment_ma Displacem length_oyer length_wate Power_k 2 eat.,n",.n ed k d k 101 T .10 ent mIn m all m rllne "' Beam m O,aft m Fuel I ", 3 3!j -a lW not used nla nla 37.!IO :lIl5ll 1097 173 lI62ll 2li64 ~ Adjusted R' 019 012 011 002 O.W 0111 5 6 35 29 nol u.ed not u8ed 11352 !I073 3695 36016 1105 167 10672 ElIXI 7 Adjusted R' -{l,10 -{lU 020 022 013 015 -{lD' O~ 8 9 10 Op".Uo !Uox, pe na•.sp•• Dlopl.c. leng1~."" 11 SWAlIt .d k d k R...... P...I.ed mant m ...11 10 a.am m D..ft 10 Fu.1 t Power kw 12 2:> :Ill not usad not usad 76 21 9 l :;I!Hl »il 13 'Adjusted R' DCB 0.11 0.30 032 D~ 020 14 15 16 Opera.'o Ma.,.pe natapee Displace Lengt~,"" D,aft_~ullbor Draft.oR_cu. P....',k - 17 SES ed k d k Ran•• P...toad ml,,1 m .r,lI m 8eam m n. m hlon 10 fu.1 I w 18 4!> :Ill lW not usod nla 3095 ,63 ~,~ nI. llIe nla 19 Adjusted R' Dgg 092 100 20 21 25 nol used 100 not used nil 2423 846 2CB 140 -1222 :lIll 22 Adjusted R' Dill 063 030 O~I 096 0.66 23 ~ 25 20 nol uI.d not used nil 01679 1157 285 nle nla 6 25 Adjusted R' 0111 091 072 0.'.2 26 27 :lIl Ope,allo Dlsptace Ma".pe nal,spee ment_ma lengt~,"" D,aft,~ullbo, O,aftJolibom Pow.',k 29 Hvd,.loli ed k d k Ran•• Pavloed .10 erall m Belm m Foil wid.~ 10 ne 10 em Fuel I ", lJ 25 20 100 not used 1756 3756 528 1756 313 In nla -468 31 Adjusled R' 016 042 -076 -028 030 030 0.31 32 33 25 2D not used 15 25S411 5304 rli ria 624 061 rle 6812 34 Adjusled R' oa; 089 096 087 oa; 35 36 25 2D nol used not used 556 3302 619 830 072 157 ria ·1194 37 Adiusted R' OOS 017 05ll 001 OlJ 032 037 38 39 40 Ope,etle Obl'lec. laa••_1fl net poe Ranga_n P-r'0ad menl_ma leng1l>,"" D,aft,~ullbo, D,aft,lollbo,n Powe',k 41 HOV1trcr.ft .d k d k ml T .10 Brall m Beam m ne 10 em Fu.1 I w 42 : 25 aJ lW 1S" n!tJ 1700 540 nfe nle rio rio 43 Adjusted R' 077 098 44 45 25 2D not used 15 rll 1894 512 nil nla ria 110 46 Adjusted R' oa; 098 092 47 48 25 20 100 not used ria 20 79 478 395 nil ria -185 49 Adjusted R' 034 066 -{l56 027 50 51 25 2D nol used not used ria 1289 2!1O 138 Dill ·36'2 -390 52 Adjusted R' 026 058 024 -{l18 076 044

Figure 45 Multiple Regression Analysis Output

85 . D Micn~soft £Xcel- ND_c1ass_output.xls GJ(Q)~ .. _ f!I ![JEte tdIt ~ 1f\Sert F2l'mat 10015 Qata WIndow l:IeIP x [j~liiJo1ti Q [l.~. d/,~6·<1 on. '" . '& ~ • ~~ H to .e 100% .13). .. ArtaI • 10 .. B I ~~==m $ % +~ t~ i~ -:.'. & .. A • Y J :08 : _. E1 ... f- A B C D H- F G ~ ...... 1 LGTH_OVERA BEAM_M DRAUGHT_M OP_SPEED_K 2 23.90 11.10 2.20 12.00 3 97.00 22.50 3.20 12.00 4 49.90 22.50 5.!:Il 14.00 5 26.00 9.00 1.00 15.00 6 15.10 6.20 1.60 16.00 7 40.00 16.50 3.70 16.00 8 15.10 6.40 1.60 17.00 9 18.40 5.00 1.20 17.50 10 12.00 5.50 1.30 18.00 11 45.00 14.70 1.Ell 18.00 12 25.20 13.00 2.70 18.00 13 17.50 5.50 O.EIl 20.00 14 30.50. 7.00 1.30 20.00 15 23.00 8.30 1.40 20.00 16 33.60 10.EIl 1.95 20.00 17 45.00 13.00 2.00 20.00 16 15.25 9.15 2.10 20.00 19 19.80 9.70 2.10 20.00 20 21.90 9.40 3.00 20.00 21 34.50 15.00 3.50 21.60 22 30.20 6.60 O.EIl 22.00 23 3160 7.00 1.30 22.00 24 30.00 11.20 2.51 22.00 25 25.00 8.40 1.30 22.50 26 36.60 10.50 1.35 22.50 27 32.00 9.00 1.30 23.00 28 27.60 8.!:Il 1.60 23.00 29 25.00 11.60 1.70 23.00 30 29.10 6.10 2.05 23.00 31 20.40 11.28 2.44 23.00 32 13.19 5.40 0.60 24.00 33 16.35 5.40 0.60 24.00 34 25.00 7.20 O.!:Il 24.00 35 30.33: 8.30 1.30 24.00 36 33.00 10.00 1.40 24.00 .1 H • • .1 \ Intrcrl.ctm_-).,InPUti outPut/ I ~ j ..Ir Ready N.M

Figure 46 Neural Network User Application: Ship Type Recommendation Input

86 I!) EtIe ~ ~ Insert FQrmal: 10ds ~ ~ t1eIP •- tJ x Cl ~ III d ti if [9. ~ Jb ~ e· <1 kl. " & L • ~~ u a.fl 100% • f1) • Anal • 10 • B I Y §::3:::§ m $ % J ~08.~ i~ ~~ ~. ~ • ~ •• G1 • " ABC 0 E F 1 [Exemplar # Output1 Output2 Output3 Output4 Output5 2 1 0,07 0.03 -0.05 -0.00 1.05 3 2 -0.00 0.01 -0.02 0.95 -0.02 4 3 -0.01 -0.06 -0.02 -0.06 0.98 5 4 -0.00 0.16 0.94 -0.05 0.14 6 5 -0.03 -0.01 -0.04 -0.06 1.02 7 6 0.00 -0.05 -0.03 -0.06 1.01 8 7 -0.03 -0.03 -0.03 -0.06 1.02 9 8 -0.00 0.02 0.99 -0.06 0.14 10 9 -0.05 -0.01 0.13 -0.06 0.89 11 10 -0.00 0.04 0.73 -0.05 0.05 12 11 0.02 -0.05 -0.04 -0.00 1.03 13 12 -0.00 0.03 1.02 -0.00 0.05 14 13 -0.00 -0.04 1.01 -0.05 -0.02 15 14 -0.00 -0.02 0.96 -0.06 0.20 16 15 -0.00 -0.05 0.78 -0.06 0.34 17 16' -0.00 -0.04 0.92 -0.05 -0.01 18 17 0.01 -0.05 -0.04 -0.06 1.02 19 18 0.01 -0.05 -0.04 -0.06 1.02 20 19 -001 -0.06 -0.02 -0.06 0.99 21 20 -0.01 -0.06 -0.02 -0.00 0.99 22 21 -0.00 -0.04 0.94 0.04 -0.05 23 22 -0.00 -0.05 1.05 -0.05 -0.05 24 23 -0.01 -0.00 -0.02 -0.06 0.99 25 24 -0.00 -0.05 1.04 -0.00 0.01 26 25 -0.00 -0.04 0.98 -0.05 -0.04 27 26 -0.00 -0.05 1.04 -0.05 -0.04 28 27 -0.06 -0.05 1.03 -0.06 0.03 29 28 -0.05 -0.05 0.14 -0.06 0.85 30 29 -0.05 -0.00 0.56 -0.00 0.51 31 3l 0.00 -O.lE -0.03 -0.00 1.00 32 31 -0 05 0.27 0.83 -0.05 -0.01 33 32 -0.05 0.25 0.88 -0.05 -0.03 34 33 -0.00 -0.04 1.05 -0.05 -0.04 35 34 -0.00 -0.05 1.05 -0.06 -0.05 36 35 -0.00 -0.05 1.05 -0.06 -0.04 • I~= H 4 • ., \ 11r'lPUt 'AOutPut/ A r; 1 NUM

Figure 47 Neural Network User Application: Ship Type Recommendation Output

87 REFERENCES

Anderson, D. and McNeil, G. (1992). Artificial Neural Networks Technology, Contract Number F30602-89-C-0082. Prepared for Rome Laboratory, Griffiss Business Park, Rome, NY. Battelle Memorial Institute (1997). What is an Artificial Neural Network? http://www.emsl.pnl.gov:2080/projlneuronlneural/what.html. Butler, E.A. (1985). The surface effect ship. Naval Engineers Journal, 97(2), 200-258. Chen, H.S., Burroughs, L.D., and Tolman, H.L. (1999). Ocean surface waves. Technical Procedures Bulletin No. 453, NOAA National Weather Service, Silver Spring, MD. Eames, M.C. (1985). Future naval surface ships. Naval Engineers Journal, 97(2), 56-62. Eggington, W.J. and Kobitz, N. (1975). The domain of the surface-effect ship. Transaction, Society oJNaval Architects andMarine Engineers, 83, 268-298. Gabrielli G. and von Karman, T. (1950). What price speed. Mechanical Engineering, 72(10),775-781. Greig, A.R and Bucknall, RW.G. (1998). Marine engineering the trimaran hull form ­ opporunities and constraints. Transactions, Institute oj Marine Engineers, 110(3), 181-193. Jeffrey, K. and Kanter, C.E. (1998). Sailor's Muitihull Guide. 2nd Edition, Avalon House Publishing, Belfast, Prince Edward Island, 464p. Johnston, R.J. (1985). Hydrofoils. Naval Engineers Journal, 97(2), 142-199. Kennell C. (1998). Design trends in high-speed transport. Marine Technology, 35(3), 127-134. Lavis, DR (1985). Air cushion craft. Naval Engineers Journal, 97(2), 259-316. Lavis, DR and Forstell, B.G. (1999). The cost-benefit of emerging technologies using physics based ship-design synthesis. FAST'99, Seattle WA. Lavis, D.R, Rogalski, Jr., W.W., and Spaulding, K.P. (1990). The promise of advanced naval vehicles for NATO. Marine Technology, 27(2), 65-93. Lavis, D.R. and Spaulding, K.B. (1991). Surface effect ships (SES) developments worldwide. Naval Engineers Journal, 103(5),39-83. McCullough, B.D. (1998). Assessing the reliability of statistical software: Part I. The American Statistician, 52(4), 358-366. McCullough, B.D. (1999). Assessing the reliability of statistical software: Part II. The American Statistician, 53(2),149-159. McCullough, B.D. and Wilson, B., (1999). On the accuracy of statistical procedures in Microsoft Excel 97. Computational Statistics and Data Analysis, 31(1), 27-37.

88 McCullough, B.D. and Wilson, 8., (2002). On the accuracy of statistical procedures in Microsoft Excel 2000 and Excel XP. Computational Statistics and Data Analysis, 40(4),713-721. McKesson, C.B. (1996). High speed cargo ship trends. Presentation at the International Association ofPorts and Harbours, Algeciras, Spain. Newell, J. (1999). A review ofAnalyse-It. Maths & Stats, 10(1). Phillips, SJ. ed. (1996). Jane's High-Speed Marine Transportation. 29th ed., Abeking & Rasmussen, Lemwerder, Germany, 486 p. Phillips, SJ. ed. (2002). Jane's High-Speed Marine Transportation. 35th ed., Abeking & Rasmussen, Lemwerder, Germany, 567p. Rumelhart, D., Hinton, G., and Williams, R. (1986). Learning internal representations by error propagation. Parallel Distributed Processing, MIT Press, Cambridge, MA, 318-362. Sanders, D.H., Murph, A.F., and Eng, RJ. (1976). Statistics A Fresh Approach. McGraw-Hill, New York, 367p. Savitsky, D. (1985). Planing craft. Naval Engineers Journal, 97(2),113-141. Schiller, T. R., Daidola, lC., KIoetzli, J.M., and Pfister, l (2000). Portfolio of ship designs - early stage design tools. Marine Technology, 38(2),71-91. Seidl, L.H., Clifford, W.F., and Cummings, J.P. (1993). Design and operational experience ofthe SWATH ship Navatek I. Marine Technology, 30(3), 153-171. Skinner, J. (2001). Swarm the littorals! Proceedings ofthe Us. Naval Institute, 127(3), 88-91. Wright, C. (1990). Operation and cost of high-speed craft. Marine Technology, 27(2), 104-113.

89