Department of Applied Mechanics Chalmers Industriteknik 1____________________________________________________________________________________________________
Ice detection for smart de-icing of wind turbines
Viktor Berbyuk*, Anders Boström*, Carl-Johan Cederstrand**,Eugen Mamontov***, Siavash Shoja*, Stellan Wickström**
*Department of Applied MechanicsChalmers University of Technology
**WindVector AB***Chalmers Industriteknik
Vindkraftsforkning i focus 20173-4 april 2017, Chalmers, Göteborg
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Agenda
Outlook
Conclusions
ResultsDissemination
Ice detection:Bulk
Acoustic Waves
Ice detection:Guided
Acoustic Waves
Ice detection:LIDAR
Methodology
Aim andObjectives
Introduction
Ice detectionfor smart
de-icing ofwind turbines
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IntroductionIcing problem in wind turbine industries
1. Reduced aerodynamic efficiency.2. Increased loads on the blades.3. Undesired noise, vibrations and turbulence.4. Ice throwing problem.
30%Reduction in
power productionNy Teknik, februari, 2013
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Project Aim and Objectives
Develop theoretical background, methods and algorithms for acoustic waves and laser based technologies for ice detection on rotor blades of wind turbines.
Perform internationally recognizable high quality research and education of doctoral student.
Create and test physical prototypes (demonstrators) of ice detection systems (IDS) based on acoustic waves and laser technologies.
Perform research targeted to be used in developing smart de-icing systems to be able in this way to contribute to increasing cost efficiency of wind turbines operating in cold climate.
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Methodology
Theory: (project publications ref: [1, 2, 3, 5, 7, 8, 10-12])Ice mechanics; Composite mechanics; Modelling of acousticwaves propagation in multi-layer composite structures;Acoustic Equations for Gases, Liquids, and Solids, Including Viscoelastic Media; Models validation; Parameter identification.
Experiment (project publications ref: [1, 2, 5, 7, 13-15])A number of test rigs (demonstrators) are developed based on acoustic waves and laser technologies and experimental study of ice detection on composite strictures has been performed.
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LIDAR for Ice Detection System (IDS)Experimental study and Demostrator
OSCILLATOR
RECEIVER WITH AMPLIFIER
LASER DIODE WITH DRIVER 905 nm
LASER DIODE WITH DRIVER 1550nm
LASER DIODE WITH DRIVER 633nm
SIGNAL PROCESSOR/ OSCILLOSCOPE
COLLIMATING OPTICS
FOCUSSING OPTICS
Block diagram of the LIDAR
A photo of the LIDAR during test.
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LIDAR in Chalmers Cold Climate Lab
LIDAR
Reflected beams
905nm1550nm
TEST OBJECT
RANGE 10m
The test set-up
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Lidar for Ice Detection
Climate temperature: -100C
The signal from the 1550 nm source when illuminated the
test object under various angle of incidence and ice conditions
No particular difference between glace ice and rime
ice in terms of signal strength was noted.
The LIDAR detects early ice growth by measuring the difference in reflectivity of a surface by using two different laser wavelengths.
The limitation of the LIDAR is that it cannot be used in order to determine the amount of ice on the blade, only if there is ice or not.
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Guided Acoustic Waves for IDSSketch of IDS Demonstrator based on AWT
Pulse-like (A), Sinus-like (B), Continuous sinus-like (C) excitationsIcing parameters (icing area, icing area location, thickness of ice, type of ice)
Actuator/sensor placement; CCLab temperature
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GAW DemonstratorTest Object, Actuator & Sensor Placements
Actuators & Sensorsplacement is an importantissue in AWT for ice detection
Test object - a composite plate 20x200x8000 mm3
Piezoelectric transducersof he type IMI608A11
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GAW Demonstrator in CLLab24 Sensors
• Temperature: −24℃; Ice:• Mixed glaze and rime;• Thickness: 10±1 mm • Ice and accelerometers were located on opposite sides of the plate
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FFT for Vertical PolarizationV
H
L
Detection of Ice, thickness of ice and amount of ice
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Amplitude of Longitudinal Polarization
Detection of Ice, thickness of ice and amount of ice
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Guided Acoustic Waves for IDS
Fig. (a): Comparison of signal for different temperatures. Fig. (b): Group velocity versus temperature.
Decreasing the temperature, the amplitude of the signal is decreasing but the group velocity is increasing (Fig. (b). This is because Young’s modulus is highly depended on temperature and lowering the temperature makes the composite more rigid.
Experimental study with GAW demonstrator in CLLab
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Guided Acoustic Waves for IDS
Group velocity versus ice thickness for different excitation frequencies, (Exp.).
Detection of ice thickness.
Comparison between the dispersion curves obtained using experimental data and
numerical and analytical results at room temperature.
Theory, Experiment and Model Validation
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Bulk Acoustic WavesPassive sensing approach
1. Joseph L. Rose, The Upcoming Revolution in Ultrasonic Guided Waves, SPIE2011
The bulk waves cover only a small localized section ofa structure. Scanning isnecessary to complete an inspection of a test part.
The ultrasonic guided wavefloods a large area froma single probe position.
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BAW for IDS for smart de-icing of wind turbines
The smart de-icing is understood by us in the sense thatthe following ice parameters are available:
• thickness h ; • mass density ρ ;• porosity φ ; • bulk and shear moduli K and G ;
• stress-relaxation time θ.
The latter three parameters also provide volume and shear viscosities: η = K θ, μ = G θ
A deicing must be smart. This means that it:(A) prevents under-heating and over-heating of the blade shell (BS);(B) detects the AI on the BS under the strongly non-equilibrium,
operational load (OL)-caused conditions;(C) operates in the real-time mode;(D) is inexpensive and consumes as little energy as possible.
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Bulk Acoustic Waves for IDSPassive sensing approach
Design issues of a prospective IDS motivated by smart de-icing and based on the bulk acoustic wave sensing are presented in detail in:
E. Mamontov and V. Berbyuk, “The third-order viscoelastic acoustic model enablesan ice-detection system for a smart deicing of wind-turbine blade shells,” J. AppliedMathematics and Physics, vol. 4, no. 10, pp. 1949-1976, October 2016.
The identification algorithm is based on the third-order spatiotemporal viscoelasticacoustic model of the Zener type for the acoustic stress in the BS/AI-layer system.
The algorithm uses the time-dependent acoustic OSs measured by each of theForce Sensing Resistors (FSRs) and can at, each time point, identify parameters ofthe AI including the following: thickness; speed of the bulk acoustic waves; stress-relaxation time; volumetric mass density; bulk modulus; and porosity.
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• thickness: 0.203 mm• length: 25.4 mm• width: 14 mm• sensing-area diameter: 9.53 mm• three versions: for the force intervals
from 0N to 4.4N, 111N, 445N, respectively• linearity: within ±3 %• operating temperature: from -40 °C to
+60 °C• force reading change per degree of
temperature change: 0.36 % / ºC
An example of FSRs: The Flexiforce A301 sensorhttps://www.tekscan.com/products-solutions/force-sensors/a301?tab=description
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Project Results1. Berbyuk, V., Peterson, B., Möller, J. (2014): Towards early ice detection on wind turbine blades using acoustic waves. Proc. of SPIE,
Nondestructive Characterization for Composite Materials, Aerospace Engineering, Civil Infrastructure, and Homeland Security 2014, H. Felix Wu; Tzu-Yang Yu; Andrew L. Gyekenyesi; Aaron A. Diaz; Peter J. Shull, San Diego, California, USA, March 09, 2014, 9063 pp. 90630F-1 -90630F-11, http://dx.doi.org/10.1117/12.2046362
2. Shoja, S., Berbyuk, V., and A. Boström, (2015): Investigating the application of guided wave propagation for ice detection on composite materials, In Proc. of the International Conference on Engineering Vibrations, Ljubljana, 7 - 10 September ; [editors Miha Boltežar, JankoSlavič, Marian Wiercigroch]. - EBook. - Ljubljana: Faculty for Mechanical Engineering, 2015, p. 152-161.
3. Shoja, S., Berbyuk, V., and A. Boström, (2015): Ultrasonic guided waves approach for ice detection on wind turbines, in WinterwindInternational Wind Energy Conference 2015, Piteå, Book of Abstract, page 17.
4. Shoja, S., Berbyuk, V., and A. Boström, (2015): Towards application of ultrasonic guided waves in ice detection on wind turbines, In International Conference on Advances in Vibrations, Porto, Portugal, March 30-April 1, 2015, Book of Abstract, page 18.
5. Shoja, S., Berbyuk, V., and A. Boström, (2016): Effect of temperature variatios on guided waves propagating in composite structures, Proc. SPIE 9806, Smart Materials and Nondestructive Evaluation for Energy Systems, Norbert G. Meyendorf, Ed., Las Vegas, Nevada, United States, 20-24 April, 2016; 12 pages, http://dx.doi.org/10.1117/12.2218791
6. Shoja, S., Berbyuk, V., and A. Boström, (2016): An approach in using guided waves for ice detection on wind turbines, in WinterwindInternational Wind Energy Conference 2016, Åre, Book of Abstract, http://winterwind.se/wp-content/uploads/2015/08/Book-of-abstracts_20161.pdf, page 61.
7. Shoja, S., (2016), Guided Wave Propagation in Composite Structures: Application to ice detection on wind turbine blades, Lic. Eng. thesis, 2016:14, ISSN 1652-8565, Department of Applied Mechanics, Chalmers University of Technology, Göteborg, Sweden, http://publications.lib.chalmers.se/records/fulltext/241120/241120.pdf
8. Shoja, S., Berbyuk, V., and A. Boström, (2016): Application of guided waves for ice detection on composite structures, Cold Regions Science and Technology, (Submitted for publication).
9. Mamontov, E. and V. Berbyuk, (2014): A Scalar Acoustic Equation for Gases, Liquids, and Solids, Including Viscoelastic Media, Journal of Applied Mathematics and Physics, Vol. 2, p. 960-970, http://dx.doi.org/10.4236/jamp.2014.210109
10. Mamontov, E. and V. Berbyuk, (2015): Passive acoustic signal sensing appapproach to detection of ice on the rotor blades of wind turbines, In Proc. of IWAIS2015 16th International Workshop on Atmospheric Icing of Structures, Uppsala, 28 June-3 July, 2015, ISBN 978-91-637-8552-8, 6 pages.
11. Mamontov, E. and V. Berbyuk, (2015): Identification of Material Parameters of Thin Curvilinear Viscoelastic Solid Layers in Ships and Ocean Structures by Sensing the Bulk Acoustic Signals. In VI International Conference on Computational Methods in Marine Engineering, MARINE 2015, Rome, Italy, June 15-17, 2015, F. Salvatore, R. Broglia, and R. Muscari (Eds), ISBN 978-84-943928-6-3, pages: 502-513.
12. Mamontov, E., and V. Berbyuk, (2016): The third-order viscoelastic acoustic model enables an ice-detection system for a smart deicing of wind-turbine blade shells”, Journal of Applied Mathematics and Physics, 2016, 4, 1949-1976, http://dx.doi.org/10.4236/jamp.2016.410197
13. Wickström, S., and C.-J. Cederstrand, (2016): An ice detection LIDAR for wind turbine application, WindVector AB, Report.14. Klasen, L., (2014): Lidar systems for wind energy applications. In Proceeding of Swedish Society of Automated Image Analysis, Symposium
on Image Analysis, Luleå, 11–12 March 2014, pages 97-100.15. Wickström, S., (2013): Method and device for detecting accumulation of ice and/or snow on a blade of a wind turbine, International Publication
Number WO 2013/149811 A1.
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Dissemination• Wind Power Research in Focus 2013, Göteborg.• SPIE2014, Nondestructive Characterization for Composite Materials, San Diego,
California, USA.• Swedish Society of Automated Image Analysis, Symposium on Image Analysis,
Luleå, 2014.• The International Conference on Engineering Vibrations, Ljubljana, 2015.• Winterwind 2015 International Wind Energy Conference, Piteå.• International Conference on Advances in Vibrations, Porto, Portugal, 2015.• Vindkraftsforskning i fokus konferens 2015, Uppsala.• IWAIS2015 16th International Workshop on Atmospheric Icing of Structures,
Uppsala, 2015.• VI International Conference on Computational Methods in Marine Engineering,
MARINE 2015, Rome, Italy, 2015.• SPIE2016, Smart Materials and Nondestructive Evaluation for Energy Systems,
Las Vegas, Nevada, United States, 2016.• Winterwind 2016 International Wind Energy Conference, Åre, Sweden.• Föreningen för Oförstörande Provning, FOP:s temadagar 2016, Göteborg.
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Conclusions
• Acoustic waves technological solutions (AWTS) of ice detection for smart de-icing of wind turbines blades are promising.
• The AWTS are multifunctional ones and can also be used for Structural Health Monitoring of wind turbines.
• The LIDAR can be used to detect early ice growth by measuring the difference in reflectivity of a surface by using two different laser wavelengths.
• Ice detection for smart de-icing of a wind turbine operating in cold region is a bigchallenge and fully acceptable solution is not available yet.
What you can take with you after my talk?
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Outlook of future research
Advanced Modelling of Acoustic wave propagationsin multi-layer anisotropic structures for ice detectionto enable smart de-icing system development.
Validation of the advanced computational models.
Virtual prototype of smart de-icing systemof a wind turbine.
Implementation of Acoustic Wave Technologyin Ice Detection enable smart de-icing ofWind Turbines.
Chalmers Hönö wind turbine blades
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The project was funded by the Swedish Energy Agency, which is gratefully acknowledged.
Lennart Thålin, Håkan Johansson, Carl-Johan Lindholm, DIAB International AB
David Thorsson, Triventus Service AB
Jan Möller and Bo Peterson, CHALMERS
Reference group at Chalmers PhD project " "Ice detection for smart de-icing of wind turbines":
Andreas Forsberg, DIAB/CCG DIAB International ABCarl-Johan Lindholm, DIAB/CCG DIAB International ABAnders Wickström, AWind ABKen Petersson, Triventus Service AB
Acknowledgments
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Thank you for your attention!
Contact: Viktor Berbyuk
[email protected] Mamontov
[email protected] Wickström