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  1. NTU Theses and Dissertations Repository
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請用此 Handle URI 來引用此文件: http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/99500
完整後設資料紀錄
DC 欄位值語言
dc.contributor.advisor盧南佑zh_TW
dc.contributor.advisorNan-You Luen
dc.contributor.author郭鎧霆zh_TW
dc.contributor.authorKai-Ting Kuoen
dc.date.accessioned2025-09-10T16:28:49Z-
dc.date.available2025-09-11-
dc.date.copyright2025-09-10-
dc.date.issued2025-
dc.date.submitted2025-07-18-
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[6] 王義竣. 以深度學習方式預測振顫速度之生成. 碩士論文, 淡江大學, 2021.
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[8] C. Bak et al. Dan-aero mw: Comparisons of airfoil characteristics for two airfoils tested in three different wind tunnels. Torque, 2010:59–70, 2010.
[9] J. Bertrand et al. Experimental evaluation of the critical flutter speed on wings of different aspect ratio. Journal of Applied Fluid Mechanics, 10(6):1509–1514, 2017.
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[11] K. Hayat et al. Flutter performance of bend–twist coupled large-scale wind turbine blades. Journal of Sound and Vibration, 370:149–162, 2016.
[12] P. Pourazarm et al. Perturbation methods for the reliability analysis of wind-turbine blade failure due to flutter. Journal of Wind Engineering and Industrial Aerodynamics, 156:159–171, 2016.
[13] B. Owens et al. Impact of modeling approach on flutter predictions for very largewind turbine blade designs. Technical report, Sandia National Lab.(SNL-NM), Albuquerque, NM (United States), 2013.
[14] S. Larwood. Flutter of variations on a 5 mw swept wind turbine blade. Journal of solar energy engineering, 138(2):024504, 2016.
[15] M. Abdel Hafeez and A. El-Badawy. Flutter limit investigation for a horizontal axis wind turbine blade. Journal of Vibration and Acoustics, 140(4):041014, 2018.
[16] 劉牧宇. 大型風力發電機扇葉之抖振反應分析. 碩士論文, 淡江大學土木工程學系碩士班, 新北, 臺灣, 1 月 2013.
[17] A. Otero and F. Ponta. Structural analysis of wind-turbine blades by a generalized timoshenko beam model. 2010.
[18] D. Hodges and E. Dowell. Nonlinear equations of motion for the elastic bending and torsion of twisted nonuniform rotor blades. Technical report, 1974.
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[20] D. Lobitz. Parameter sensitivities affecting the flutter speed of a mw-sized blade. 2005.
[21] R. Roul and A. Kumar. Fluid-structure interaction of wind turbine blade using four different materials: Numerical investigation. Symmetry, 12(9):1467, 2020.
[22] S. Irani and S. Sazesh. A new flutter speed analysis method using stochastic approach. Journal of Fluids and Structures, 40:105–114, 2013.
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[26] W. Rodden and E. Johnson. User’s Guide of MSC/NASTRAN Aeroelastic Analysis, 1994. MSC/NASTRAN V68.
[27] P.-C. Chen. Damping perturbation method for flutter solution: the g-method. AIAA journal, 38(9):1519–1524, 2000.
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[32] S. Li and L. Caracoglia. Surrogate model monte carlo simulation for stochastic flutter analysis of wind turbine blades. Journal of Wind Engineering and Industrial Aerodynamics, 188:43–60, 2019.
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[36] Y.-C. Fung. An introduction to the theory of aeroelasticity. Courier Dover Publications, 2008.
[37] D. Peters. Two-dimensional incompressible unsteady airfoil theory—an overview. Journal of Fluids and Structures, 24(3):295–312, 2008.
[38] S. Irani and S. Sazesh. A new flutter speed analysis method using stochastic approach. Journal of Fluids and Structures, 40:105–114, 2013.
[39] D. Bellinger. MSC/NASTRAN Aeroelastic Supplement. The MacNeal-Schwendler Corporation, Los Angeles, CA, USA, 1980. Technical Manual.
[40] W. Yuan and X. Zhang. Numerical stabilization for flutter analysis procedure. Aerospace, 10(3):302, 2023.
[41] L. Trefethen. Approximation theory and approximation practice, extended edition. SIAM, 2019.
[42] M. Goland. The flutter of a uniform cantilever wing. 1945.
[43] J. Jonkman et al. Definition of a 5-mw reference wind turbine for offshore system development. Technical report, National Renewable Energy Lab.(NREL), Golden, CO (United States), 2009.
[44] A. Sobey et al. Response of a bending–torsion coupled beam to deterministic and random loads. Journal of Sound and Vibration, 195(2):267–283, 1996.
[45] Z. Qin and L. Librescu. Aeroelastic instability of aircraft wings modelled as anisotropic composite thin-walled beams in incompressible flow. Journal of fluids and structures, 18(1):43–61, 2003.
[46] G. Bir. User’s guide to bmodes (software for computing rotating beam-coupled modes). Technical report, National Renewable Energy Lab.(NREL), Golden, CO (United States), 2005.
[47] M. Hansen. Aeroelastic instability problems for wind turbines. Wind Energy: An International Journal for Progress and Applications in Wind Power Conversion Technology, 10(6):551–577, 2007.
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[49] P. Shakya et al. A parametric study of flutter behavior of a composite wind turbine blade with bend-twist coupling. Composite Structures, 207:764–775, 2019.
[50] K. Hayat and S. Ha. Flutter performance of large-scale wind turbine blade with shallow-angled skins. Composite structures, 132:575–583, 2015.
[51] P. Pourazarm et al. A parametric study of coupled-mode flutter for mw-size wind turbine blades. Wind Energy, 19(3):497–514, 2016.
[52] International Electrotechnical Commission. Wind turbines: Part 3: Design requirements for offshore wind turbines, 2009.
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dc.identifier.urihttp://tdr.lib.ntu.edu.tw/jspui/handle/123456789/99500-
dc.description.abstract本研究針對 NREL 5MW 大型風機葉片之顫振特性與失效風險進行系統性探討,分析展弦比變化與參數不確定性對顫振臨界速度與系統可靠度之影響。隨著風機設計朝大型化發展,模態耦合顫振已成為結構設計中不可忽視的關鍵議題。為探究此一現象,本文基於旋轉梁理論建立有限元素模型,結合 P-K 法進行臨界顫振速度分析,並引入模態追蹤技術進行模態識別後處理。隨機分析方面,導入
以 Chebyshev 採樣與插值法構成之蒙地卡羅模擬替代模型,以高效評估不同參數變異條件下之臨界顫振速度機率分布與系統可靠度。為進一步量化系統風險,本研究提出失效機率、安全裕度與臨界展弦比等三項可靠度指標。研究結果顯示,扭轉勁度對臨界顫振轉速影響較升力係數斜率顯著,且當兩者同時存在不確定性時,整體安全裕度相較單變數結果降低約 10–20%,顯示耦合不確定性將提前引發失效風險。進一步比較不同展弦比之分布結果,提高展弦比雖有助於提升風機輸出功率,但同時也降低葉片臨界顫振速度,增加模態耦合與系統不穩定風險。此外,高展弦比葉片在考慮扭轉勁度與升力係數斜率之不確定性時,可能會發生模態跳轉與臨界速度高度集中現象,導致臨界速度分布呈現雙峰、波動與局部極端尖峰等現象。整體而言,本研究深入分析展弦比與參數變異性交互作用對顫振穩定性之重要影響,並建立一套可應用於工程實務之高效率風機葉片顫振風險分析流程,有助於提升未來離岸風電結構之設計與可靠度評估。
zh_TW
dc.description.abstractThis study investigates the flutter characteristics and failure risks of the NREL 5MW large-scale wind turbine blade. The analysis focuses on the influence of aspect ratio variation and parametric uncertainty on the critical flutter speed and system reliability. As wind turbine designs continue to scale up, coupled-mode flutter has emerged as a critical consideration in the structural design of wind turbine blades. To address this, a finite element model based on rotating beam theory was developed, and the P-K method was employed for flutter analysis, integrated with a mode tracking technique for post-processing modal identification.For the stochastic analysis, this study introduces a surrogate model combining Chebyshev sampling and interpolation methods within a Monte Carlo simulation framework to efficiently evaluate the probability distribution of flutter speeds and associated reliability indicators under various uncertainty conditions. Three reliability indexes — failure probability, safety margin, and critical aspect ratio were proposed to quantify the impact of parameter uncertainty on flutter risks.
The results indicate that torsional stiffness has a more significant impact on the critical flutter speed than the slope of lift coefficient. When both parameters exhibit uncertainty, the safety margin is reduced by approximately 10–20% compared to single-variable cases, revealing the interactive effects of multiple uncertainties. Moreover, increasing the aspect ratio improves power output but also lowers the critical flutter speed and increases the likelihood of mode coupling and dynamic instability. High-aspect-ratio blades subjected to uncertainty in both torsional stiffness and slope of lift coefficient exhibit phenomena such as mode switching and concentrated critical speeds, leading to double-peaked, fluctuating, and locally spiked distributions of flutter speed.
In summary, this study presents a comprehensive investigation of the effects of aspect ratio and parametric uncertainty on flutter stability, and proposes an efficient flutter risk evaluation framework applicable to engineering practice. The proposed method offers useful insights for future offshore wind turbine blade design and reliability assessment.
en
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dc.description.provenanceMade available in DSpace on 2025-09-10T16:28:49Z (GMT). No. of bitstreams: 0en
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 xiii
縮寫表 xiv
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 7
第二章 分析方法與模型建構 10
2.1 運動方程式 10
2.2 空氣動力函數 13
2.3 顫振特徵值問題 14
2.4 有限元素法 16
2.5 P-K 方法 20
2.6 模態追蹤法 21
2.7 隨機分析模型 23
2.8 葉片模型簡介 25
2.9 整體分析模擬流程 26
第三章 葉片結構之動態分析 38
3.1 等翼面 Goland 機翼 38
3.1.1 自然頻率與模態收斂性 38
3.1.2 顫振分析 39
3.2 NREL 5MW 風機葉片 40
3.2.1 自然頻率與模態收斂性 40
3.2.2 顫振分析 41
第四章 顫振臨界速度隨機分析 55
4.1 敏感性分析 55
4.2 單隨機變數臨界顫振速度機率密度 57
4.2.1 扭轉勁度不確定性之臨界顫振速度機率分布 57
4.2.2 升力係數斜率不確定性之臨界顫振速度機率分布 59
4.3 多隨機變數臨界顫振速度機率密度 60
第五章 不同展弦比之顫振失效機率比較 76
5.1 扭轉勁度隨機情況下之展弦比影響分析 76
5.2 升力係數斜率隨機情況下之展弦比影響分析 79
5.3 扭轉勁度與升力係數斜率耦合隨機情況下之展弦比影響分析 82
第六章 結論與建議 108
6.1 結果與討論 108
6.2 未來展望 110
參考文獻 111
附錄 A — 有限元素矩陣 117
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dc.language.isozh_TW-
dc.subject模態耦合顫振zh_TW
dc.subject風機葉片zh_TW
dc.subject展弦比zh_TW
dc.subject隨機分析zh_TW
dc.subject蒙地卡羅模擬zh_TW
dc.subjectMonte Carlo simulationen
dc.subjectwind turbine bladeen
dc.subjectaspect ratioen
dc.subjectstochastic analysisen
dc.subjectcoupled-mode flutteen
dc.title展弦比與隨機性對風機葉片顫振失效機率之影響分析zh_TW
dc.titleEffects of Aspect Ratio and Stochastic Parameters on Flutter Failure Probability of Wind Turbine Bladesen
dc.typeThesis-
dc.date.schoolyear113-2-
dc.description.degree碩士-
dc.contributor.oralexamcommittee吳亦莊;王建凱;林宗岳zh_TW
dc.contributor.oralexamcommitteeYi-Zhuang Wu;CHIEN-KAI WANG;Tsung-Yueh Linen
dc.subject.keyword模態耦合顫振,風機葉片,展弦比,隨機分析,蒙地卡羅模擬,zh_TW
dc.subject.keywordcoupled-mode flutte,wind turbine blade,aspect ratio,stochastic analysis,Monte Carlo simulation,en
dc.relation.page120-
dc.identifier.doi10.6342/NTU202501823-
dc.rights.note同意授權(限校園內公開)-
dc.date.accepted2025-07-21-
dc.contributor.author-college工學院-
dc.contributor.author-dept機械工程學系-
dc.date.embargo-lift2030-07-18-
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