Development of an embedded thin-film strain-gauge-based SHM network into 3D-woven composite structure for wind turbine blades

Zhou Dongning*, Shafqat Rasool, Michael Forde, Bryan Weafer, Edward Archer, Alistair McIlhagger, James McLaughlin

*Corresponding author for this work

Research output: Contribution to JournalConference Article/Conference Proceedingspeer-review

Abstract

Recently, there has been increasing demand in developing low-cost, effective structure health monitoring system to be embedded into 3D-woven composite wind turbine blades to determine structural integrity and presence of defects. With measuring the strain and temperature inside composites at both in-situ blade resin curing and in-service stages, we are developing a novel scheme to embed a resistive-strain-based thin-metal-film sensory into the blade spar-cap that is made of composite laminates to determine structural integrity and presence of defects. Thus, with fiberglass, epoxy, and a thinmetal- film sensing element, a three-part, low-cost, smart composite laminate is developed. Embedded strain sensory inside composite laminate prototype survived after laminate curing process. The internal strain reading from embedded strain sensor under three-point-bending test standard is comparable. It proves that our proposed method will provide another SHM alternative to reduce sensing costs during the renewable green energy generation.
Original languageEnglish
Number of pages9
JournalProceedings of SPIE - The International Society for Optical Engineering
Volume10171
DOIs
Publication statusPublished - 19 Apr 2017
EventSmart Materials and Nondestructive Evaluation for Energy Systems 2017 - Portland, United States
Duration: 19 Apr 201719 Apr 2017

Keywords

  • Wind turbine blades
  • Strain-gauge-based
  • SHM network
  • 3D-woven composite structure

Fingerprint

Dive into the research topics of 'Development of an embedded thin-film strain-gauge-based SHM network into 3D-woven composite structure for wind turbine blades'. Together they form a unique fingerprint.

Cite this