Experimental damage detection of wind turbine blade using thin film sensor array

dc.contributor.author Downey, Austin
dc.contributor.author Laflamme, Simon
dc.contributor.author Ubertini, Filippo
dc.contributor.author Sarkar, Partha
dc.contributor.author Laflamme, Simon
dc.contributor.department Mechanical Engineering
dc.contributor.department Civil, Construction and Environmental Engineering
dc.contributor.department Electrical and Computer Engineering
dc.contributor.department Center for Nondestructive Evaluation (CNDE)
dc.date 2018-03-05T16:32:01.000
dc.date.accessioned 2020-06-30T01:11:31Z
dc.date.available 2020-06-30T01:11:31Z
dc.date.copyright Sun Jan 01 00:00:00 UTC 2017
dc.date.embargo 2018-02-21
dc.date.issued 2017-04-12
dc.description.abstract <p>Damage detection of wind turbine blades is difficult due to their large sizes and complex geometries. Additionally, economic restraints limit the viability of high-cost monitoring methods. While it is possible to monitor certain global signatures through modal analysis, obtaining useful measurements over a blade's surface using off-the-shelf sensing technologies is difficult and typically not economical. A solution is to deploy dedicated sensor networks fabricated from inexpensive materials and electronics. The authors have recently developed a novel large-area electronic sensor measuring strain over very large surfaces. The sensing system is analogous to a biological skin, where local strain can be monitored over a global area. In this paper, we propose the utilization of a hybrid dense sensor network of soft elastomeric capacitors to detect, localize, and quantify damage, and resistive strain gauges to augment such dense sensor network with high accuracy data at key locations. The proposed hybrid dense sensor network is installed inside a wind turbine blade model and tested in a wind tunnel to simulate an operational environment. Damage in the form of changing boundary conditions is introduced into the monitored section of the blade. Results demonstrate the ability of the hybrid dense sensor network, and associated algorithms, to detect, localize, and quantify damage.</p>
dc.description.comments <p>This proceeding is published as Austin Downey, Simon Laflamme, Filippo Ubertini, Partha Sarkar, "Experimental damage detection of wind turbine blade using thin film sensor array", Proc. SPIE 10168, Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2017, 1016815 (12 April 2017); doi: <a href="http://dx.doi.org/10.1117/12.2261531" target="_blank">10.1117/12.2261531</a>. Posted with permission.</p>
dc.format.mimetype application/pdf
dc.identifier archive/lib.dr.iastate.edu/ccee_conf/57/
dc.identifier.articleid 1063
dc.identifier.contextkey 11611182
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath ccee_conf/57
dc.identifier.uri https://dr.lib.iastate.edu/handle/20.500.12876/13681
dc.language.iso en
dc.source.bitstream archive/lib.dr.iastate.edu/ccee_conf/57/2017_Laflamme_ExperimentalDamage.pdf|||Sat Jan 15 00:59:04 UTC 2022
dc.source.uri 10.1117/12.2261531
dc.subject.disciplines Civil Engineering
dc.subject.disciplines Structural Engineering
dc.subject.disciplines VLSI and Circuits, Embedded and Hardware Systems
dc.subject.keywords structural health monitoring
dc.subject.keywords capacitive-based sensor
dc.subject.keywords soft elastomeric capacitor
dc.subject.keywords exible membrane sensor
dc.subject.keywords sensor network
dc.subject.keywords signal decomposition
dc.subject.keywords damage detection
dc.subject.keywords damage localization
dc.title Experimental damage detection of wind turbine blade using thin film sensor array
dc.type article
dc.type.genre conference
dspace.entity.type Publication
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