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Uhm Ms 3812 R.Pdf 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).
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