1. Distribution of Defects in Wind Turbine Blades & Reliability Assessment of Blades Containing Defects Authors: Henrik Stensgaard Toft, Aalborg University Kim Branner, Risø-DTU Peter Berring, Risø-DTU John Dalsgaard Sørensen, Aalborg University / Risø-DTU

2. Contents Introduction Distribution of Defects in Wind Turbine Blades Influence of Delaminations and Reliability Assessment Non Destructive Inspection Conclusion Future Work

3. Introduction Uncertainties in calculation of the load carrying capacity for wind turbine blades. 1.Material properties Physical uncertainty (Aleatory) Statistical uncertainty (Epistemic) 2.Finite Element calculation Model uncertainty (Epistemic) 3.Failure criteria Model Uncertainty (Epistemic)

4. Introduction Uncertainties – captured by partial safety factors. Local production defects are not taken into account ! Quality control of the production process Non Destructive Inspection (NDI) of the blades Stochastic models for the distribution of defects. The models are set up on an empirical basis and have not yet been calibrated against observations from blade manufacturing and testing.

5. Production Defects Influence parameters: Type of defect Size of defect Position of defect Local production defects: Delaminations  Voids Wrinkles  Defects in glued joints Matrix cracks Delaminations: Areas of poor or no bonding between adjacent plies.

6. Distribution of Defects – Model 1 Defects are completely random distributed in the blade. The blade can be considered as a 2-dimensional planar region A since defects tend to occur in a layer or in the interface between two layers. i)The number of defects in the region A follows a Poisson distribution ii)The distribution of defects is an independent random sample from a uniform distribution

7. Distribution of Defects – Model 2 Defects occur in clusters which are randomly distributed in the blade. i)The “parent” defects follow model 1 ii)Each “parent” defect produces a number of “offsprings” following a Poisson distribution iii)The position of the “offspring” defects relative to their “parents” is independently and identically distributed according to a bivariate probability density function. (Normal distribution)

8. Distribution of Defects Model 1 Completely Random Distribution Model 2 Random Cluster Distribution

9. Load Carrying Capacity of Main Spar Transverse strains in main spar – Failure defined according to: Maximum Strain First Ply Failure

10. Production Defects – Delaminations Size refers to percent of plate width Through thickness position 20% DefectSizeLongitudinal strain (compression) Transverse strain (tension) No defect-1.00 Delamination30%0.630.82* -40%0.470.74* -50%0.32*0.66* * The value is estimated.

11. Influence of Delaminations Delamination at the most critical position in the main spar cap. Size refers to percent of cap width. DefectSizeLoad Carrying Capacity No defect-1.00 Delamination30%0.91 -40%0.86 -50%0.81 Influenced only by strength reduction in the transverse direction. Buckling stability increased by the aerodynamic shell.

12. Reliability Assessment of Blades Containing Defects Reliability of blade containing defects: The limit state function cannot be used directly.  factor characteristic load can be increased before blade failure (characteristic material properties) Influence of specific defects introduced by reduction factor on the load carrying capacity.

13. Reliability Assessment of Blades Containing Defects Reliability of main spar with delamination at the most critical position. Target reliability index in IEC 61400-1,  = 3.09, P F = 10 -3 per year. DefectSizePFPF  No defect- 1.9  10 -3 2.90 Delamination30% 4.0  10 -3 2.65 -40% 6.2  10 -3 2.50 -50% 1.0  10 -2 2.33

14. Non Destructive Inspection Updated probability of failure: Defects are assumed perfect repaired. DefectSizePFPF  No defect- 1.9  10 -3 2.90 Delamination | NDI30% 2.2  10 -3 2.85 -40% 2.2  10 -3 2.84 -50% 2.3  10 -3 2.84 Probability of Detection (PoD) Delamination size without/with NDI

15. Conclusion Probabilistic models for the distribution of defects have been proposed and can be calibrated to observations. Probability of failure increased by large delaminations. (strength reduction in transverse direction is estimated) Updating of P F by Non Destructive Inspection. Future work Quantify uncertainties in stochastic models for reliability. Local defects influence on material properties in fatigue.

16. Distribution of Defects in Wind Turbine Blades & Reliability Assessment of Blades Containing Defects Authors: Henrik Stensgaard Toft, Aalborg University Kim Branner, Risø-DTU Peter Berring, Risø-DTU John Dalsgaard Sørensen, Aalborg University / Risø-DTU