distribution of defects in wind turbine blades & reliability assessment of blades containing...
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Distribution of Defects in Wind Turbine Blades &
Reliability Assessment of Blades Containing Defects
Authors:
Henrik Stensgaard Toft, Aalborg UniversityKim Branner, Risø-DTUPeter Berring, Risø-DTUJohn Dalsgaard Sørensen, Aalborg University / Risø-
DTU
Contents
• Introduction
• Distribution of Defects in Wind Turbine Blades
• Influence of Delaminations and Reliability Assessment
• Non Destructive Inspection
• Conclusion
• Future Work
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)
Leading edge
M ain spar(load carrying box)
Upw ind side
Downwind side
Towards tip
Tra iling edge
Aerodynam icshell
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.
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.
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
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)
Distribution of Defects
Model 1Completely Random Distribution
Model 2Random Cluster Distribution
Load Carrying Capacity of Main Spar
Transverse strains in main spar – Failure defined according to:
• Maximum Strain• First Ply Failure
0 0.2 0.4 0.6 0.8 1 1.2-1
0
1
2
3
4
Normalized load [-]
Nor
mal
ized
str
ain
[-]
Transverse xxLongitudinal zzFirst Ply Failure
Production Defects – Delaminations
• Size refers to percent of plate width
• Through thickness position 20%
Defect Size Longitudinal strain
(compression)
Transverse strain
(tension)
No defect - 1.00 1.00
Delamination
30% 0.63 0.82*
- 40% 0.47 0.74*
- 50% 0.32* 0.66** The value is estimated.
Influence of Delaminations
Delamination at the most critical position in the main spar cap.
Size refers to percent of cap width.
Defect Size Load Carrying Capacity
No defect - 1.00
Delamination
30% 0.91
- 40% 0.86
- 50% 0.81
Influenced only by strength reduction in the transverse direction.
Buckling stability increased by the aerodynamic shell.
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.
, , ,R Lg X R X L max maxσ ε E D
intact
, ,R Lg X R X L
max maxσ ε E
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, PF = 10-3 per year.
Defect Size PF
No defect - 1.910-3 2.90
Delamination 30% 4.010-3 2.65
- 40% 6.210-3 2.50
- 50% 1.010-2 2.33
1FP
Non Destructive Inspection
Updated probability of failure:
Defects are assumed perfect repaired.
0 0.2 0.4 0.6 0.8 10
0.2
0.4
0.6
0.8
1
Delamination size s [m]
Cum
ulat
ive
Val
ue [
-]
Without NDIWith NDI
0 0.2 0.4 0.6 0.8 10
0.2
0.4
0.6
0.8
1
Delamination size s [m]
Cum
ulat
ive
Val
ue [
-]
PoD-curve
Defect Size PF
No defect - 1.910-3 2.90
Delamination | NDI
30% 2.210-3 2.85
- 40% 2.210-3 2.84
- 50% 2.310-3 2.84
Probability of Detection (PoD)
Delamination size without/with NDI
| , intact
defect
0 |
0 1 |
F defect NDIP P g P NDI
P g P NDI
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 PF by Non Destructive Inspection.
Future work
• Quantify uncertainties in stochastic models for reliability.
• Local defects influence on material properties in fatigue.
Distribution of Defects in Wind Turbine Blades &
Reliability Assessment of Blades Containing Defects
Authors:
Henrik Stensgaard Toft, Aalborg UniversityKim Branner, Risø-DTUPeter Berring, Risø-DTUJohn Dalsgaard Sørensen, Aalborg University / Risø-
DTU
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