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208 W.Z. Shen et al. Drag coefficient Drag coefficient 0.5 0.4 0.3 0.2 0.1 0.5 0.4 0.3 0.2 0.1 2D, from [1] AT, from [3] Present 0 0 5 10 15 20 25 30 Angle of attack (degrees) Lift coefficient 2 1.6 1.2 0.8 0.4 2D, from [1] AT, from [3] Present 0 0 5 10 15 20 25 30 Angle of attack (degrees) Fig. 37.2. Drag and lift coefficients at r/R =65.7% 2D, from [1] Average Technique, from [3] Present 0 0 5 10 15 20 25 30 Angle of attack (degrees) Lift coefficient 2 1.6 1.2 0.8 0.4 0 2D, from [1] AT without F, from [3] Present AT with F, from [3] 0 5 10 15 20 25 30 Angle of attack (degrees) Fig. 37.3. Drag and lift coefficients at r/R =94.7% and compared to 2D airfoil characteristics from [1] and the results of AT [2]. Excellent agreement is seen in the linear region up to α = 12. For big AOA (> 15 ◦ ), 2D airfoil data fails due to the Coriolis and centrifugal forces present in the boundary layer of a rotating blade. Next we consider the position closer to the blade tip at r =94.7%R. The drag and lift coefficients are plotted in Fig. 37.3. Since the position is very close to the tip, a tip correction using Prandtl’s tip loss function for AT is also plotted. From the figure, we can see that the present result agrees well with the AT without tip correction. On the other hand, the introduction of a tip correction gives the curve closer to the 2D characteristics. As a summary, the drag and lift coefficients in different radial positions are plotted in Fig. 37.4. From the figure, we can see that drag coefficient increases when the radial position is close to the rotor center whereas lift coefficient remains the same but with a small stall region.
Drag coefficient 0.5 0.4 0.3 0.2 0.1 37 Determination of Angle of Attack (AOA) for Rotating Blades 209 r/R = 31.6% r/R = 45.8% r/R = 65.7% r/R = 84.2% 2D data 0 0 5 10 15 20 25 30 Angle of attack (degrees) 37.4 Conclusion Lift coefficient 2 1.8 1.6 1.4 1.2 1 0.8 0.6 0.4 0.2 r/R = 31.6% r/R = 45.8% r/R = 65.7% r/R = 84.2% 2D data 0 0 5 10 15 20 25 30 Angle of attack (degrees) Fig. 37.4. Drag and lift coefficients in function of AOA We have now developed a general method for determining AOA. Comparisons with the AT shows good agreements. The method can also be used to extract data from experiments (for example, with Pitot tube). Moreover it can be used to study airfoil properties in the region near the tip. References 1. Abbott I.H., von Doenhoff A.E. (1959) Theory of wing sections. Dover, New York 2. Hansen M.O.L., Johansen J. (2004) Tip studies using CFD and computation with tip loss models. Wind Energy 7:343–356 3. Johansen J., Sørensen N.N. (2004) Airfoil characteristics from 3D CFD rotor computations. Wind Energy 7:283–294
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Wind Energy Proceedings of the Euro
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Prof. Dr. Joachim Peinke Dr. Stepha
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VI Preface been presented, spanning
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VIII Contents 3.3 Stochastic Wind F
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X Contents 12.6 Subgrid Scale Turbu
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XII Contents 22 Stochastic Small-Sc
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XIV Contents 32 Handling Systems Dr
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XVI Contents 41 3D Numerical Simula
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XVIII Contents 51 Comparing WAsP an
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XX Contents 58.3 Ultra-High Perform
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XXII List of Contributors Lars Berg
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XXIV List of Contributors Renata Gn
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XXVI List of Contributors A. Kiss D
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XXVIII List of Contributors El˙zbi
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XXX List of Contributors Ivo Sláde
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1 Offshore Wind Power Meteorology B
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1 Offshore Wind Power Meteorology 3
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1 Offshore Wind Power Meteorology 5
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2 Wave Loads on Wind-Power Plants i
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Trubaduren 2 Wave Loads on Wind-Pow
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Base moment (10 6 Nm) 0.4 0.2 −0.
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2 Wave Loads on Wind-Power Plants i
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3 Time Domain Comparison of Simulat
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3 Time Domain Comparison Using Cons
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7.0 6.0 5.0 4.0 3.0 2.0 1.0 0.0 3 T
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4 Mean Wind and Turbulence in the A
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5 Wind Speed Profiles above the Nor
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5 Wind Speed Profiles above the Nor
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Height [m] 100 90 80 70 60 50 40 30
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6 Fundamental Aspects of Fluid Flow
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f Suu(f, z) / σ 2 u(z) 10 0 10 −
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a) c) −0,375 6 Fluid Flow over Co
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7 Models for Computer Simulation of
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y/h 2.5 2 1.5 1 0.5 0 7 Models for
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8 Power Performance via Nacelle Ane
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9 Pollutant Dispersion in Flow Arou
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x1/B 0.3 0.5 0.8 1.0 9 Pollutant Di
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9 Pollutant Dispersion in Flow Arou
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10 On the Atmospheric Flow Modellin
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11 Comparison of Logarithmic Wind P
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Height above ground in m 100 90 80
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12 Turbulence Modelling and Numeric
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13 Gusts in Intermittent Wind Turbu
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Durations (s) 25 20 15 10 5 13 Gust
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14 Report on the Research Project O
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height z (m) 100 80 60 40 20 0 14 R
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14.6 Outlook 14 Report on the Resea
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15 Simulation of Turbulence, Gusts
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S(ƒ)(m/s) 2 /Hz 15 Simulation of T
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15 Simulation of Turbulence, Gusts
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16 Short Time Prediction of Wind Sp
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ms prediction error [m/s] 16 Short
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16 Short Time Prediction of Wind Sp
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17 Wind Extremes and Scales: Multif
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Log10 E(k)*k**(5/3) 6 4 2 0 5/3 cor
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17.3 Discussion and Conclusion 17 W
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18 Boundary-Layer Influence on Extr
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z/H (a) (b) 4 2 18 Boundary-Layer I
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18.6 Concluding Remarks 18 Boundary
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19 The Statistical Distribution of
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19 The Statistical Distribution of
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20 Superposition Model for Atmosphe
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h(u) 0.5 0.3 0.1 20 Superposition M
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21 Extreme Events Under Low-Frequen
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21 Extreme Events Under Low-Frequen
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22 Stochastic Small-Scale Modelling
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p(σ⏐u) 1 0.8 0.6 0.4 0.2 22 Stoc
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22 Stochastic Small-Scale Modelling
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23 Quantitative Estimation of Drift
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24 Scaling Turbulent Atmospheric St
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24 Scaling Turbulent Atmospheric St
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25 Wind Farm Power Fluctuations P.
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25 Wind Farm Power Fluctuations 141
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d rc q rc V 0 q V 25 Wind Farm Powe
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References 25 Wind Farm Power Fluct
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26 Network Perspective of Wind-Powe
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27 Phenomenological Response Theory
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27 Phenomenological Response Theory
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48 Numerical Computations of Wind T
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Z X Y 48 Numerical Computations of
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References 48 Numerical Computation
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49 Modelling Wind Turbine Wakes wit
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U mean / U ref 1 0.9 0.8 0.7 49 Mod
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49.5 Conclusion 49 Modelling Wind T
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50 Prediction of Wind Turbine Noise
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0.8 0.6 0.4 0.2 −0.2 −0.4 −0.
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51 Comparing WAsP and Fluent for Hi
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Table 51.1. Measurements and simula
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51 Comparing WAsP and Fluent for Hi
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52 Fatigue Assessment of Truss Join
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53 Advances in Offshore Wind Techno
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Simulation Measurement pitch angle
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53 Advances in Offshore Wind Techno
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54 Benefits of Fatigue Assessment w
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∆σN [N/mm²] 100 10 54 Benefits
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55 Extension of Life Time of Welded
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Transverse residual stresses [N/mm
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56 Damage Detection on Structures o
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56 Damage Detection on Structures o
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57 Influence of the Type and Size o
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[W/m 2 ] q t [W/m 5000 4000 3000 20
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58 High-cycle Fatigue of “Ultra-H
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(a) (b) 450 Load [kN] 400 350 300 2
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59 A Modular Concept for Integrated
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60 Solutions of Details Regarding F
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SR 1000 800 600 400 200 100 80 60 4
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60 Solutions of Details Regarding F
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61 On the Influence of Low-Level Je
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Relative Power and Loading [%] 61 O
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62 Reliability of Wind Turbines Exp
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62 Reliability of Wind Turbines 331
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