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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 盧南佑 | zh_TW |
| dc.contributor.advisor | Nan-You Lu | en |
| dc.contributor.author | 伍宸緯 | zh_TW |
| dc.contributor.author | Chen-Wei Wu | en |
| dc.date.accessioned | 2025-09-17T16:12:37Z | - |
| dc.date.available | 2025-09-18 | - |
| dc.date.copyright | 2025-09-17 | - |
| dc.date.issued | 2025 | - |
| dc.date.submitted | 2025-08-07 | - |
| dc.identifier.citation | 行政院國家永續發展委員會. 臺灣 2050 淨零排放路徑及策略總說明, 2022. Retrieved Jul. 10, 2025, from https://ncsd.ndc.gov.tw/.
W. Skamarock et al. A description of the advanced research WRF model version 4. National Center for Atmospheric Research: Boulder, CO, USA, 145(145):550, 2019. N. Booij et al. A third-generation wave model for coastal regions: 1. Model description and validation. Journal of Geophysical Research: Oceans, 104(C4):7649–7666, 1999. K. Hasselmann et al. Measurements of wind-wave growth and swell decay during the Joint North Sea Wave Project (JONSWAP). Ergänzungsheft zur Deutschen Hydrographischen Zeitschrift, 12:1–95, 1973. L.-F. Hsiao et al. Application of WRF 3DVAR to operational typhoon prediction in Taiwan: Impact of outer loop and partial cycling approaches. Weather and Forecasting, 27(5):1249–1263, 2012. L.-F. Hsiao et al. Improvement of the numerical tropical cyclone prediction system at the Central Weather Bureau of Taiwan: TWRF (Typhoon WRF). Atmosphere, 11(6):657, 2020. R. Delfino et al. Sensitivity of simulating Typhoon Haiyan (2013) using WRF: the role of cumulus convection, surface flux parameterizations, spectral nudging, and initial and boundary conditions. Natural Hazards and Earth System Sciences, 22(10):3285–3307, 2022. Z. Di et al. Improving WRF typhoon precipitation and intensity simulation using a surrogate-based automatic parameter optimization method. Atmosphere, 11(1):89, 2020. E. Kim and L. Manuel. Hurricane-induced loads on offshore wind turbines with considerations for nacelle yaw and blade pitch control. Wind Engineering, 38(4):413–423, 2014. Group T. W. The WAM model—A third generation ocean wave prediction model. Journal of physical oceanography, 18(12):1775–1810, 1988. S.-H. Ou et al. Simulating typhoon waves by SWAN wave model in coastal waters of Taiwan. Ocean Engineering, 29(8):947–971, 2002. Z. Yang et al. Modeling analysis of the swell and wind-sea climate in the Salish Sea. Estuarine, Coastal and Shelf Science, 224:289–300, 2019. N. Inagaki et al. Effect of translate speed of typhoon on wind waves. Natural Hazards, 105(1):841–858, 2021. Z. Wu et al. Simulation of extreme waves using coupled atmosphere-wave modeling system over the South China Sea. Ocean Engineering, 221:108531, 2021. O. Mazzaretto et al. A global evaluation of the JONSWAP spectra suitability on coastal areas. Ocean Engineering, 266:112756, 2022. D.-J. Doong et al. Statistical analysis on the long-term observations of typhoon waves in the Taiwan sea. Journal of Marine Science and Technology, 23(6):8, 2015. 林楨琇. 應用於離岸風機設計之波浪參數分析. 國立成功大學水利及海洋工程學系學位論文, pages 1–165, 2022. U.-J. Lee et al. Estimation and analysis of JONSWAP spectrum parameter using observed data around Korean Coast. Journal of Marine Science and Engineering, 10(5):578, 2022. J. Powers et al. The Weather Research and Forecasting Model: Overview, System Efforts, and Future Directions. Bulletin of the American Meteorological Society, 98(8):1717–1737, 2017. A. Arakawa and V. Lamb. Computational design of the basic dynamical processes of the UCLA general circulation model. volume 17, pages 173–265. 1977. R. Laprise. The Euler equations of motion with hydrostatic pressure as an independent variable. Monthly Weather Review, 120(1):197–207, 1992. H. Tolman. A third-generation model for wind waves on slowly varying, unsteady, and inhomogeneous depths and currents. Journal of Physical Oceanography, 21(6):782–797, 1991. W. Pierson and L. Moskowitz. A proposed spectral form for fully developed wind seas based on the similarity theory of S. A. Kitaigorodskii. Journal of Geophysical Research, 69(24):5181–5190, 1964. 柯旻佑. 應用WRF與LES耦合模型分析真實颱風邊界層中之風場特性. 國立臺灣大學機械工程學系學位論文, pages 1–77, 2023. C.-H. Luo et al. Evaluation of the effect of WRF physical parameterizations on typhoon and wave simulation in the Taiwan strait. Water, 15(8):1526, 2023. 曹恂如. 以 WRF-SWAN 模擬臺灣離岸風場於颱風下之近岸波場. 國立臺灣大學機械工程學系學位論文, pages 1–66, 2024. O. Madsen et al. Spectral wave attenuation by bottom friction: Theory. In Coastal Engineering, pages 492-504. 1988. 中央氣象署. Retrieved Jun. 5, 2025, from https://www.cwa.gov.tw/V8/C/K/Encyclopedia/typhoon/index.html. | - |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/99632 | - |
| dc.description.abstract | 臺灣海峽具有良好風能資源，但每年夏秋兩季受颱風頻繁侵襲，極端風浪事件對離岸風機結構安全性與營運維護造成重大挑戰。本研究以2001年至2023年間36起侵臺颱風作為研究案例，利用天氣研究和預報WRF (Weather Research and Forecasting)模式與第三代風浪模擬模型SWAN (Simulating WAves Nearshore)耦合模擬，並透過浮標資料驗證。模擬結果顯示，WRF能有效重現颱風風場之變化趨勢，但當颱風結構受地形干擾時，模擬結果會產生誤差。第3類路徑及第7類路徑之風速模擬誤差較大，第3類路徑風速均方根誤差平均達6.21 m/s，第7類路徑相關係數平均僅0.24，原因主要來自颱風中心接近陸地與地形遮蔽效應所致。波場方面，SWAN能有效模擬示性波高、平均週期與波向之整體變化趨勢。然而，波高模擬之準確性高度依賴WRF提供之風場，因此WRF模擬誤差會進一步放大SWAN波高模擬誤差。第2類路徑波高均方根誤差平均為1.04公尺，第7類路徑波高相關係數平均僅0.16，兩類路徑誤差較大。第6類路徑模擬波高均方根誤差平均僅0.35公尺，相關係數平均為0.83，模擬誤差小。本研究亦將新竹浮標處之一維波譜以JONSWAP波譜擬合，整體波譜平均飛利浦常數與尖峯集中因子分別為0.0081與1.11。分析另外五座離岸風場極端波況下之波譜差異。結果顯示，近岸之允能風場波浪能量分散，尖峯集中因子較低，平均為1.09。彰芳西島風場則具有最高之尖峯集中因子平均值1.29，波能較高且集中，極端波況下波浪對風力發電機影響最大。整體而言，WRF耦合SWAN模擬能重現颱風侵襲時臺灣鄰近海域之風浪，並進一步提供不同風場面臨極端波況之JONSWAP波譜參數，作為離岸風機結構設計與運維之參考依據。 | zh_TW |
| dc.description.abstract | The Taiwan Strait possesses abundant wind energy resources. However, frequent typhoon occurrences in summer and autumn pose significant challenges to the structural safety and operational maintenance of offshore wind turbines. This study analyzed 36 typhoon cases between 2001 and 2023, utilizing a coupled numerical model integrating the Weather Research and Forecasting (WRF) model with the third-generation wave simulation model, Simulating WAves Nearshore (SWAN), and validated the simulation results against observational data. Simulation results demonstrated that WRF could capture the trends of wind fields of typhoon. Notable discrepancies arose when typhoons were disrupted by complex terrain interactions. The largest simulation errors occurred in the typhoon paths classified as category 3 and category 7. The average root mean square error (RMSE) for wind speed simulations under category 3 reached 6.21 m/s, while the average correlation coefficient for category 7 was only 0.24. These errors primarily resulted from proximity of typhoon centers to land and terrain-induced shielding effects. Wave simulations indicated that SWAN effectively reproduced the trends of wave fields. However, the accuracy of simulated wave heights heavily depended on the accuracy of wind fields generated by WRF. The average RMSE for simulated wave heights in category 2 was 1.04 m, with an average correlation coefficient of merely 0.16 for category 7. Conversely, category 6 exhibited smaller errors, with an average RMSE of 0.35 m and an average correlation coefficient of 0.83. Additionally, the research fitted the one-dimensional wave spectrum with the JONSWAP spectrum, determining an overall average Phillips constant of 0.0081 and a peak enhancement factor of 1.11 at Hsinchu buoy. The study also analyzed the wave spectra under extreme conditions at five offshore wind farm sites around Taiwan. Results indicated that nearshore wind farms such as the Yunlin site exhibited lower peak enhancement factors averaging 1.09. Situated in Taiwan’s central offshore region, the Changfang and Xidao wind farms exhibited the highest mean peak-enhancement factor of 1.29, signifying more intense and concentrated wave energy. Accordingly, extreme sea states are expected to impose the greatest wave loading on turbines at these sites. In conclusion, the coupled WRF-SWAN simulation approach employed in this study reliably models wind and wave conditions in Taiwan during typhoon events. Furthermore, it provides detailed JONSWAP spectrum parameters for extreme wave conditions at various offshore wind farms, serving as critical references for offshore wind turbine design and operational planning. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2025-09-17T16:12:37Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2025-09-17T16:12:37Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | 口試委員審定書 i
誌謝 ii 摘要 iii Abstract iv 目次 vi 圖次 ix 表次 xi 縮寫表 xii 第一章 緒論 1 1.1 研究背景與動機 1 1.2 文獻回顧 3 1.3 論文架構 5 第二章 模擬方法與流程 7 2.1 WRF 模式 7 2.1.1 WRF 背景概述 7 2.1.2 WRF 理論 8 2.2 SWAN 模式 10 2.2.1 SWAN 背景概述 10 2.2.2 SWAN 理論 10 2.2.3 JONSWAP 波譜 12 2.3 浮標與離岸風場介紹 13 2.3.1 浮標介紹 13 2.3.2 離岸風場介紹 14 2.4 颱風選擇及分類 15 2.5 模型參數設定 15 2.5.1 WRF 參數設定 15 2.5.2 SWAN 參數設定 16 2.6 研究流程 17 第三章 風浪模擬結果 24 3.1 風場模擬結果 24 3.1.1 WRF 模擬與實測資料之歷時比對 24 3.1.2 WRF 模式模擬結果 26 3.1.3 誤差統計分析 27 3.2 波場模擬結果 28 3.2.1 波場模擬與實測資料之歷時比對 28 3.2.2 SWAN 模擬結果 30 3.2.3 誤差統計分析 31 第四章 波譜擬合結果 46 4.1 方向波譜 46 4.2 一維波譜 47 4.2.1 各類路徑颱風一維波譜模擬結果 47 4.2.2 JONSWAP 波譜擬合結果 49 第五章 離岸風場場址波譜推估 59 5.1 運轉風場波譜參數分析 59 5.2 離岸風場潛力場址波譜參數分析 61 5.3 蒲福氏風級浪級關係與 JONSWAP 波譜之比較 64 第六章 結論與建議 71 6.1 成果與討論 71 6.2 未來展望 72 參考文獻 73 | - |
| dc.language.iso | zh_TW | - |
| dc.subject | SWAN | zh_TW |
| dc.subject | WRF | zh_TW |
| dc.subject | 離岸風場 | zh_TW |
| dc.subject | JONSWAP波譜參數 | zh_TW |
| dc.subject | 颱風 | zh_TW |
| dc.subject | typhoon | en |
| dc.subject | JONSWAP spectrum parameters | en |
| dc.subject | offshore wind farm | en |
| dc.subject | SWAN | en |
| dc.subject | WRF | en |
| dc.title | 基於WRF–SWAN耦合與JONSWAP波譜之離岸風電場極端風浪特性分析 | zh_TW |
| dc.title | Analysis of Extreme Wave in Offshore Wind Farms Based on WRF–SWAN Model and JONSWAP Spectrum | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 113-2 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.oralexamcommittee | 吳偉成;黃琮暉;劉定朋 | zh_TW |
| dc.contributor.oralexamcommittee | Wei-Cheng Wu;Tsung-Hui Huang;Ding-Peng Liu | en |
| dc.subject.keyword | WRF,SWAN,颱風,JONSWAP波譜參數,離岸風場, | zh_TW |
| dc.subject.keyword | WRF,SWAN,typhoon,JONSWAP spectrum parameters,offshore wind farm, | en |
| dc.relation.page | 76 | - |
| dc.identifier.doi | 10.6342/NTU202504247 | - |
| dc.rights.note | 同意授權(限校園內公開) | - |
| dc.date.accepted | 2025-08-13 | - |
| dc.contributor.author-college | 工學院 | - |
| dc.contributor.author-dept | 機械工程學系 | - |
| dc.date.embargo-lift | 2030-08-07 | - |
| 顯示於系所單位： | 機械工程學系 | |
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