Self-Sensing Wind Blades

Project title: Integrated Wind Turbine Blade and Tower Health Monitoring and Failure Prognosis

Source of support: National Science Foundation

Wind turbines represent an important investment in sustainable energy production. Large and geographically remote wind farm facilities require robust and reliable information regarding the condition of individual turbine structures to assure efficient and safe operation. Thus, the overarching goal of this research was to derive a probabilistic structural health monitoring and failure prognosis methodology for wind turbine structures. Specifically, the research activities will validate an in situ sensing technology for damage detection in composite materials, utilize experimental data for updating numerical models, and characterize structural demand for failure prognosis of critical elements within wind turbine structures. This study began with embedding thin film sensors  in fiber-reinforced composites for detecting and localizing damage occurring at critical hotspots within the composite blade structure. Then, scaled wind turbine blades were fabricated, tested in the lab under static and dynamic load configurations, and subjected to dynamic testing in a large-scale laboratory environment (in collaboration with National Taiwan University). Damage estimates were used to update the resistance model of the structure based on the finite element method. Finally, failure prognosis was performed as a risk assessment step in which global vibrations of the structure were used to update aero-elastic analysis models and then used for estimating structural demand. 

One of the key material developments led by the ARMOR Lab is the work on multi-walled carbon nanotube (MWCNT)-latex structural coatings. First, MWCNT-latex ink solutions were formulated, and spray coating (airbrushing) was employed for depositing homogeneous thin films onto various structural surfaces. The ink solution and fabrication technique was carefully designed in that MWCNTs remained dispersed and formed a percolated nanostructure after film deposition and drying. Extensive laboratory tests verified that the film's electrical properties vary linearly with applied strains. Second, MWCNT-latex thin films were successfully applied onto structural surfaces and embedded within glass fiber-reinforced polymer (GFRP) composite panels. Then, an electrical impedance tomography (EIT) algorithm was implemented and coupled with MWCNT-latex films for achieving spatial damage detection and localization. In short, EIT uses limited boundary excitation-and-measurement data for back-calculating and estimating the entire material's spatial distribution of electrical properties. Since the film's electrical properties are pre-calibrated to strain, EIT would reveal highly localized changes in material conductivity due to structural damage. The MWCNT-latex thin films and EIT have been validated for detecting the severity and location of impact damage (Figs. 1 and 2), drilled holes, cracks, and applied strains/deformation. 

Collaborators:

• Prof. Valeria La Saponara, UC Davis

• Prof. Chin-Hsiung Loh, National Taiwan University

• Dr. Bryan R. Loyola, Sandia National Laboratories

• Prof. Nicola Pugno, University of Trento

• Prof. R. Andrew Swartz, Michigan Technological University

Peer-Reviewed Publications:

1. L. Wang, K. J. Loh, L. Brely, F. Bosia, and N. M. Pugno, 2016 “An Experimental and Numerical Study on the Mechanical Properties of Carbon Nanotube-Latex Thin Films,” Journal of the European Ceramics Society (0955-2219), Elsevier, 36(9): 2255-2262. DOI: 10.1016/j.jeurceramsoc.2015.12.052

2. C-H. Loh, K. J. Loh, Y-S. Yang, W-Y. Hsiung, and Y-T. Huang, 2016, "Vibration-based System Identification and Damage Detection of Wind Turbine Blades," Journal of Structural Control and Health Monitoring (1545-2263), Wiley, Online. DOI: 10.1002/stc.1876

3. L. Wang and K. J. Loh, 2015, “Spray-coated Carbon Nanotube-Latex Strain Sensors,” Science Letters Journal (2278-3393), Cognizure, 5(234): 1-10.

4. B. Pinto, S. Kern, J. J. Ku-Herrera, J. Yasui, V. La Saponara, and K. J. Loh, 2015, “A Comparative Study of a Self-monitoring CNT Film and Carbon Fibers under Flexural Loading by Electrical Resistance Changes,” Journal of Physics: Conference Series (1742-6596), IOP, 628(012098): 1-7. DOI: 10.1088/1742-6596/628/1/012098

5. L. P. Mortensen, D. Ryu, Y. Zhao, and K. J. Loh, 2013, “Rapid Assembly of Multifunctional Thin Film Sensors for Wind Turbine Blade Monitoring,” Key Engineering Materials Journal (1013-9826), Trans Tech, 569-570: 515-522. DOI: 10.4028/www.scientific.net/KEM.569-570.515 (DAMAS Best Paper Award)

6. B. R. Loyola, V. La Saponara, K. J. Loh, T. M. Briggs, G. O’Bryan, and J. L. Skinner, 2013, “Spatial Sensing using Electrical Impedance Tomography,” IEEE Sensors (1530-437X), IEEE, 13(6): 2357-2367. DOI: 10.1109/JSEN.2013.2253456

7. B. R. Loyola, T. M. Briggs, L. Arronche, K. J. Loh, V. La Saponara, G. O’Bryan, and J. L. Skinner, 2013, “Detection of Spatially Distributed Damage in Fiber-Reinforced Polymer Composites,” Structural Health Monitoring (1475-9217), Sage, 12(3): 225-240. DOI: 10.1177/1475921713479642

8. B. R. Loyola, Y. Zhao, K. J. Loh, and V. La Saponara, 2013, “The Electrical Response of Carbon Nanotube-based Thin Films Subjected to Mechanical and Environmental Effects,” Smart Materials and Structures (0964-1726), IOP, 22(025010): 1-11. DOI: 10.1088/0964-1726/22/2/025010