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Offshore transport and installation simulation for process planning : 일정 계획 지원을 위한 해상 구조물의 이송 및 설치 지원 시뮬레이션

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dc.contributor.advisor김태완-
dc.contributor.author김보람-
dc.date.accessioned2017-07-13T06:07:33Z-
dc.date.available2017-07-13T06:07:33Z-
dc.date.issued2016-08-
dc.identifier.other000000136850-
dc.identifier.urihttps://hdl.handle.net/10371/118289-
dc.description학위논문 (박사)-- 서울대학교 대학원 : 산업·조선공학부, 2016. 8. 김태완.-
dc.description.abstractThe ocean covers approximately 72% of the planets surface, and 80% of Earths species sustain their lives in the ocean. Ocean supplies not living resources alone but non-living resources: fossil, renewable energy and mineral resources. In this reason, the future prosperity of humanity depends on marine development.
Today, about 30% of crude oil is produced from the offshore oil field. The offshore share is increasing gradually because of the advances in exploration technology. Furthermore, the ocean is a huge renewable energy supplier, such as wind, tidal, wave and thermal difference energy.
The development of renewable energy has a drawback of low energy efficiency. Nevertheless, related technology is facilitated under the series of the global climatic change convention such as United Nations Framework Convention on Climate Change (UNFCCC), Kyoto Protocol and Paris Agreement.
Fixed or floating typed offshore platform is necessary to develop ocean resources. Such platforms are constructed at the coast shipyard and towed to the installation site. The offshore platform requires extra processes such as the construction of the foundation, offshore transportation, installation of the mooring line and submarine cable. This transportation and installation (T&I) process charges 10 to 20% of total project cost and can affect the design and construction process. The T&I process has the risk to delay the entire project schedule in two ways. First, the design and construction schedule may be affected by the T&I planning. Second, the delay of T&I process can affect the project schedule directly. Therefore, effective and safe T&I planning in the early stage is important for the successful project.
However, todays T&I planning is relying upon the experience of the related industry. And the planning has following restrictions:
(1) Weather data acquisition points are limited as a start point, midpoint, and endpoint.
(2) Weather forecasting data is not applied.
(3) The physical response of tow is not applied.
In this thesis, an offshore T&I simulation system is suggested to overcome these restrictions. The implemented system provides four functions:
(1) Transportation planning simulation using a long-range weather history.
(2) Transportation route optimization using a medium-range weather forecast.
(3) Persistence analysis for installation using a long-range weather history.
(4) Installation planning simulation using a medium-range weather forecast.
The implemented system is applied to the T&I planning of semi-submersible offshore structure. The transportation route is assumed as the route from Rongcheng, China to the Jeju Island, Korea. The installation site is assumed as the west of Jeju Island where the transportation finishes. The application shows suggest the 1) right timing for transportation, 2) optimal transportation route, 3) persistence analysis result for installation, 4) weather windows for installation.
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dc.description.tableofcontents1. Introduction 1
1.1. Background 1
1.2. Research objective and methods 9
1.3. Contributions of this thesis 13
1.4. Outline 14

2. Theoretical backgrounds 16
2.1. Weather window and persistence analysis 16
2.2. Weather routing 20
2.3. Genetic algorithm 23
2.4. Monte Carlo simulation 27
2.5. ISO 15016 29

3. Related works 31
3.1. Persistence analysis 31
3.2. Weather routing 36
3.2.1. Weather routing using isochrones method 36
3.2.2. Weather routing using dynamic programming 39
3.2.3. Weather routing using genetic algorithm 40
3.2.4. Weather routing using A* algorithm 42
3.2.5. Comparison of related works with this research 43

4. Method of acquiring information 47
4.1. Target offshore structure 47
4.2. Topography data 50
4.3. Metocean data 52
4.3.1. Global Forecasting System (GFS) 53
4.3.2. WAVEWATCH III 55
4.3.3. Ocean Surface Current Analysis Real time (OSCAR) 58
4.3.4. Real-time Ocean Forecast System (RTOFS) 60

5. Ship behavior estimation 63
5.1. Geometry modeling 65
5.2. Motion response and mean drift force 67
5.2.1. Analysis input and procedure 67
5.2.2. Motion response 70
5.2.3. Mean drift force 97
5.3. Verification of motion response 103
5.3.1. Motion response verification for following seas 103
5.3.2. Motion response verification for stern quartering seas 107
5.4. Resistance 111
5.4.1. Resistance in calm water 111
5.4.2. Resistance increase due to the wind 114
5.4.3. Resistance increase due to wave 116
5.5. Speed 118
5.6. Example of ship behavior estimation 119

6. System architecture and implementation 122
6.1. Grid data implementation 122
6.2. Grid data interpolation 126
6.3. System technical architecture 127
6.4. Class diagrams 130

7. Persistence analysis and weather windows simulation 138
7.1. Persistence analysis for wave 139
7.1.1. Raw data and monthly statistics 139
7.1.2. Monthly statistics 141
7.1.3. Persistence of significant wave height versus threshold 142
7.1.4. Persistence of significant wave height versus duration 149
7.1.5. Persistence of mean wave period versus threshold 158
7.1.6. Persistence of mean wave period versus duration 165
7.2. Persistence analysis for the wind 171
7.2.1. Raw data and monthly statistics 171
7.2.2. Persistence of wind speed vs. threshold 173
7.2.3. Persistence of wind speed versus duration 180
7.2.4. Rose of wind speeds versus direction 186
7.3. Persistence analysis for current 187
7.3.1. Raw data and monthly statistics 187
7.3.2. Rose of current speed versus direction 189
7.4. Application of persistence analysis and weather window 190
7.4.1. Persistence for installation 191
7.4.2. Weather windows simulation for installation 193

8. Monte Carlo simulation 196
8.1. Problem definition 196
8.2. Assumptions for simulation 200
8.3. Implementation of genetic algorithm to find great circle route 201
8.4. Implementation of Monte Carlo simulation to offshore transportation 205
8.5. Simulation result 209
8.5.1. Raw results 209
8.5.2. Monthly statistics 219

9. Route optimization 223
9.1. Problem definition 223
9.2. Assumptions for optimization 227
9.3. Implementation for optimization 228
9.4. Optimization results 231
9.4.1. Case 1: clear route 231
9.4.2. Case 2: indirect route 238

10. Conclusion and future work 245

References 246

Appendixes 250
A. Map projection and geodesic problem 250
B. Linear interpolation 258
C. Beaufort scale 260
D. Global Forecasting System (GFS) 263
E. Motion response 270
F. Mean drift force 342

초록 354
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dc.formatapplication/pdf-
dc.format.extent33950508 bytes-
dc.format.mediumapplication/pdf-
dc.language.isoen-
dc.publisher서울대학교 대학원-
dc.subjectoffshore transportation and installation-
dc.subjectweather routing-
dc.subjectmodeling and simulation-
dc.subjectpersistence analysis-
dc.subjectweather window-
dc.subjectISO 15016-
dc.subject.ddc623-
dc.titleOffshore transport and installation simulation for process planning-
dc.title.alternative일정 계획 지원을 위한 해상 구조물의 이송 및 설치 지원 시뮬레이션-
dc.typeThesis-
dc.contributor.AlternativeAuthorKim, Boram-
dc.description.degreeDoctor-
dc.citation.pages356-
dc.contributor.affiliation공과대학 산업·조선공학부-
dc.date.awarded2016-08-
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