KEYWORDS: Data modeling, Structural health monitoring, Algorithms, Stochastic processes, Matrices, Systems modeling, System identification, Wind energy, Process modeling, Wind turbine technology
Structural health monitoring (SHM) relies on collection and interrogation of operational data from the monitored
structure. To make this data meaningful, a means of understanding how damage sensitive data features relate to the
physical condition of the structure is required. Model-driven SHM applications achieve this goal through model
updating. This study proposed a novel approach for updating of aero-elastic turbine blade vibrational models for
operational horizontal-axis wind turbines (HAWTs). The proposed approach updates estimates of modal properties for
spinning HAWT blades intended for use in SHM and load estimation of these structures. Spinning structures present
additional challenges for model updating due to spinning effects, dependence of modal properties on rotational velocity,
and gyroscopic effects that lead to complex mode shapes. A cyclo-stationary stochastic-based eigensystem realization
algorithm (ERA) is applied to operational turbine data to identify data-driven modal properties including frequencies and
mode shapes. Model-driven modal properties are derived through modal condensation of spinning finite element models
with variable physical parameters. Complex modes are converted into equivalent real modes through reduction
transformation. Model updating is achieved through use of an adaptive simulated annealing search process, via Modal
Assurance Criterion (MAC) with complex-conjugate modes, to find the physical parameters that best match the
experimentally derived data.
Fiber-reinforced polymers (FRP) composites are widely used in aerospace and civil structures due to its unique material
properties. However, damage can still occur and typically manifests itself from within the composite material that is
invisible to the naked eye. So as to be able to monitor the performance of FRPs, numerous sensing systems have been
proposed for embedment within FRP composites. One such methodology involves the embedment of carbon nanotube-based
thin films within FRP laminates for strain monitoring and potentially even damage detection. Unlike other sensors,
these piezoresistive thin films possess small form factors (and thus do not serve as stress concentration or damage
initiation points) and can be easily integrated during composite manufacturing. In this study, a series of laboratory tests
have been conducted to characterize the static and dynamic strain sensing performance of these nanocomposites for
monitoring glass fiber-reinforced polymer (GFRP) components. Specifically, monotonic uniaxial, cyclic, and fatigue
tests have been conducted, while both time- and frequency-domain measurements have also been obtained. The
characterization results obtained from this study indicates bi-functional strain sensitivity to monotonic loading until
failure, which is found to be reproducible in cyclic dynamic loadings to amplitudes in both functional ranges.
The increased usage of fiber-reinforced polymers (FRP) in recent decades has created a need to monitor the unique
response of these materials to impact and fatigue damage. As most traditional nondestructive evaluation methods are illsuited
to detecting damage in FRPs, new methods must be created without compromising the high strength-to-weight
aspects of FRPs. This paper describes the characterization of carbon nanotube-polyelectrolyte thin films applied to glass
fiber substrates as a means for in situ strain sensing in glass
fiber-reinforced polymers (GFRP). The layer-by-layer
deposition process employed is capable of depositing individual and small bundles of carbon nanotubes within a
polyelectrolyte matrix and directly onto glass fiber matrices. Upon film fabrication, the nanocomposite-coated GFRP
specimens are mounted in a load frame for characterizing their electromechanical performance. This preliminary results
obtained from this study has shown that these thin films exhibit bilinear piezoresistivity. Time- and frequency-domain
techniques are utilized to characterize the nanocomposite strain sensing response. An equivalent circuit is also derived
from electrical impedance spectroscopic analysis of thin film specimens.
Sensors constructed with single-crystal PMN-PT, i.e. Pb(Mg1/3Nb2/3)O3-PbTiO3 or PMN, are developed in this paper for
structural health monitoring of composite plates. To determine the potential of PMN-PT for this application, glass/epoxy
composite specimens were created containing an embedded delamination-starter. Two different piezoelectric materials
were bonded to the surface of each specimen: PMN-PT, the test material, was placed on one side of the specimen, while
a traditional material, PZT-4, was placed on the other. A comparison of the ability of both materials to transmit and
receive an ultrasonic pulse was conducted, with the received signal detected by both a second surface-bonded transducer
constructed of the same material, as well as a laser Doppler vibrometer (LDV) analyzing the same location. The optimal
frequency range of both sets of transducers is discussed and a comparison is presented of the experimental results to
theory. The specimens will be fatigued until failure with further data collected every 3,000 cycles to characterize the
ability of each material to detect the growing delamination in the composite structure. This additional information will be
made available during the conference.
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