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REAL-TIME SHAPE ESTIMATION WITH FIBER OPTIC SENSORS DISTRIBUTED IN ROTOR BLADES. Hong-Il Kim 1 , Lae-Hyong Kang 1 , Jae-Hung Han 1* , Hyung-Joon Bang 2 2010.04.23 . 09:00~10:30 1 Department of Aerospace Engineering, KAIST, Republic of Korea - PowerPoint PPT Presentation
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Smart Systems & Structures Lab.
New concepts and Methods : Hardware
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REAL-TIME SHAPE ESTIMATION WITH FIBER OPTIC SENSORS DISTRIBUTED IN ROTOR
BLADES
Hong-Il Kim1, Lae-Hyong Kang1, Jae-Hung Han1*, Hyung-Joon Bang2
2010.04.23. 09:00~10:30
1Department of Aerospace Engineering, KAIST, Republic of Korea2Wind Energy Research Center, KIER, Republic of Korea
Smart Systems & Structures Lab.
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Outline
Experiments
Conclusion
Introduction
Numerical Study
Smart Systems & Structures Lab.
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Introduction - Research Backgrounds (Condition monitoring with Shape estimation) - Why Fiber Bragg Grating sensors? - Shape Estimation based on Measured Strains Using FBG Sensors - Research objectives
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Condition Monitoring for Reliability
Rumsey, 2009, “Condition Monitoring and Wind Turbine Blades,” Wind Turbine Reliability Workshop
Full-scale Testing
O & MData Base
Designed-in
Maintainability
Accurate Loads-Design Requirements
Appropriate Environmental
Conditions
Design
ed-in
Reliab
ility
Reliability
Analysis
High-ReliabilityWT Blade
Conditio
n
Mon
itorin
g
• Strains• Loads• Cracks• Dry-spots• Voids• Operational Dynamics• Temperature gradients• Lightening
Sense What?
Blade shape(Deformation)
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Why blade shapes are important?The “Blades”
The shapes of the “Blades” influence the whole systems’ status
Status Monitoring
Design Validation
Active control for blades
Blade Shape Information - Bending => Flapping motion- Torsion => Pitching motion
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Why blade shapes are important?
The real-time shape estimation techniques based on embeddable sensors
Shape estimation On operation
It is difficult to directly monitor the shape changes on operation.
Marker(DNW)
SPR(Stereo Pattern Recognition)
Optical image processing techniques
PMI (Projection Moire Interferometry)
Pattern (NASA Langley)
Direct Shape Measurement
Smart Systems & Structures Lab.
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• Typical embeddable sensors (Strain gauge, accelerometer..)- Complex electric-wiring (Slip ring) + Significant measurement noise
• FBG (Fiber Bragg Grating) sensor– Small, lightweight, High sensitivity, Electro-magnetic immunity – No hygro-effects and easily installable onto/into host structures.– Multiplexing– Real time strain acquisition
– FBG sensors are already applied to the load monitoring
Why Fiber Bragg grating sensor?
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(1 )Be s f
B G
p T
[1] A fibre Bragg grating sensor system monitors operational load in a wind turbine rotor blade[2] Advanced Wing Turbine Controls Input Based on Real Time Loads Measured with Fibre Optical Sensors embedded in Rotor Blades
[1] [2]
Optical fiberBragg grating
B
I
I
B
I
Input spectrum
Reflected signal Transmitted signal
L 10mmIndex of refraction of
fiber core
zz2z1
ne
Δn = 10-5 to 10-3
2B en
Slip ringOptical Rotary Joint
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Shape Estimation based on Measured Strains Using FBG Sensors – previous works
[DST]Discrete strains
full state vectordisplacement field
Estimation model using• modal approach• FEM data
0200
400600
8000
200400
600
-0.02
-0.01
0
0.01
0.02
0.03
y [mm]x [mm]
disp
lace
men
t [m
m]
real displacement fieldestimated displacement field
4 Laser Sensors
Shaker
16 FBG Sensorsy
x
(0,0)
Distributed FBG sensors
Real time Shape Estimation of a Two-Dimensional Structure
Fr
Kk
C
kx̂
kky ̂ˆ
C’F1ˆ kx
RQ ,2 kP
0x
0P
wState matrixOutput matrix
Weighting matrix
Error covariances
State Space
Integration of the filtering technologies
Real-time shape estimation of the Rotating structures
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Research objectives• Primary objectives
– Development and validation of a real-time shape estimation technique for Wind Tur-bine blades using FBG sensors
• Research steps① Numerical study on the shape estimation method for the rotating beams
• Rotating beam dynamics are simulated. (displacement fields, a few strain data)• Displacement is reconstructed using strains • Shape estimation method is evaluated through the comparison between original dis-
placements and the estimated displacements. • Sensor location is optimized.
② Experimental Demonstration of the real-time shape estimation for the rotating structures• FBG sensors are used to measure multi-point strains of the beam.• Structural deformation shape of the rotating beam is estimated. • The estimated shapes are compared with the directly measured shapes using pho-
togrammety.
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Numerical Study - Simulation Steps - Simulation Results - Optimization of Sensor locations
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FMD
Virtual experiments – simulation steps
DST
N MT
( )ey t( )s t
Beam model
Mode shapes
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Rotating beam motions are simulated - Full-field Displacement & strain
( )y t( )x t
M : # of sensors, N : # of disp. Points, n : # of used modes
Discrete strainsDST matrix constructed Shape estimation
Sensor locationOptimization
Full-field Strain & Displacement
Sensorlocation
Evaluation
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• Rotating beam dynamics are simulated– Full-field displacement & distributed strain
Simulation Results
Full-field Displacement Discrete strains
Estimated Deflection Strains at a few points are used for reconstruction of full-field displacement via DST matrix.
Comparison
Directly Simulated Deflection
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Simulation ResultsRotating beam displacement at the Tip of the beam(Numerical simulation vs. Shape estimation results)
- Shape estimation using simulated strains are performed- Full-field displacement from numerical simulation are compared with Estimated shape using
strains
Numerical simulation
Shape estimation
Reconstructed fromstrains
Directly Simulated
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Optimization of Sensor locations
Initial Seed Sensor 1: 0~4cm Sensor 2: 5~16cm Sensor 3: 19~31cm Sensor 4: 33~38cm
Condition Number of DSTSensor position CN=19, (4.0,15.0,21,33)
ˆ( ) ( )DSTy t T tEstimated displacement Measured strain
DST matrix (Displacement Strain Transformation)
1C T T Condition number
- Used as the objective function for sensor location optimization - Small condition number indicates good information conservation during matrix operations
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Experiments –rotating beam - Rotating beam test setup - Test measurand/DST matrix - Results
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Test setup – Demonstration of the rotating beam
fbg1fbg2
fbg3
fbg4Reconstructed shape (DST)Photo-grammetry
Images taken by High-speed camera
Optical rotary joint
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Test measurand
• Measurand– Four Strains (FBG sensors)– 13 Marker positions (Photogrammety)– Angular position
Strain by FBG
Rotating angle60RPM case
In. Volt. Ang. Vel
Case 1 0.1V 15 RPM
Case 2 0.2V 30 RPM
Case 3 0.3V 45 RPM
Case 4 0.4V 60 RPM
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DST matrix
fbg1
fbg2
fbg3
fbg4
point pt1 pt2 pt3 pt4 pt5 pt6 pt7 pt8 pt9 pt10 pt11 pt12 pt13[mm] 0 40 80 120 160 200 240 280 320 360 400 440 500
Marker position
FBG1 FBG2 FBG3 FBG4wavelength
[nm] 1533 1541 1547 1556[mm] 40 150 210 310
FBG position
DSTmatrix
13 4
TDSTT
-1.20 -4.14 -7.94 -11.99 -16.04 -20.06 -24.16 -28.36 -32.59 -36.73 -40.72 -46.54
0.66 1.35 0.58 -2.18 -6.38 -10.90 -14.81 -17.95 -20.89 -24.40 -28.84 -36.56
-0.38 -0.79 -0.51 0.35 0.90 0.01 -2.89 -7.35 -12.16 -16.12 -18.74 -21.15
0.07 0.15 0.11 -0.02 -0.12 -0.22 -0.61 -1.90 -4.77 -9.52 -15.91 -26.94
- Acrylic beam (500mm×20mm×1.9mm) was used for denstrating large deflection in low speed
Optimized sensor locations
Marker positions
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Results – qualitative aspects
30 RPM rotation 60 RPM rotation
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Results -Shape comparison between DST vs. Images
Directly Measured(High Speed Cam-era)
Estimated(from strains using FBG)
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Results – quantitative aspects
Pole effect
15RPM 30RPM 45RPM 60RPM
MAC (median) 0.993 0.997 0.999 0.998Skewed distribu-
tion
Time [s]
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Conclusion
• Development of the shape estimation technique for a rotating structure– A real-time deflection of the rotating beam is successfully estimated based displacement -strain
transformation - Sensor location optimization is executed.
- From the test results, it is clear that beam shape estimation of the rotating beam is successfully performed based on DST method and strain data obtained by FBG sensors.
– FBG (Fiber Bragg grating) sensor is selected as a strain sensor because of many inherent advan-tages of fiber optic sensors and multiplexing capability.
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Smart Systems & Structures Lab.
New concepts and Methods : Hardware
Hong-Il Kim ([email protected]) Ph. D. candidateAerospace Engineering, KAIST
Jae-Hung Han ([email protected]) Associate Prof.Aerospace Engineering, KAISTSmart Systems and Structures Lab. : Design & Control
Visit our website: http://sss.kaist.ac.kr
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THANK YOU!
Acknowledgments
This work was supported by the Korea Institute of Energy Research through the research project (grant No. NT2009-0008).