ice detection for smart de-icing of wind turbines€¦ · design issues of a prospective ids...

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


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


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


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.


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.


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.


LIDAR in Chalmers Cold Climate Lab

LIDAR

Reflected beams

905nm1550nm

TEST OBJECT

RANGE 10m

The test set-up


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.


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


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


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


FFT for Vertical PolarizationV

H

L

Detection of Ice, thickness of ice and amount of ice


Amplitude of Longitudinal Polarization

Detection of Ice, thickness of ice and amount of ice


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


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


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.


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.


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.


• 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


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.

http://publications.lib.chalmers.se/publication/194762

http://dx.doi.org/10.1117/12.2046362

http://dx.doi.org/10.1117/12.2218791

http://winterwind.se/wp-content/uploads/2015/08/Book-of-abstracts_20161.pdf

http://publications.lib.chalmers.se/records/fulltext/241120/241120.pdf

http://dx.doi.org/10.4236/jamp.2014.210109

http://dx.doi.org/10.4236/jamp.2016.410197


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.


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?


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


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


Thank you for your attention!

Contact: Viktor Berbyuk

[email protected] Mamontov

[email protected] Wickström

[email protected]

ice detection for smart de-icing of wind turbines€¦ · design issues of a prospective ids...

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