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.
The objective of this study was to validate modal analysis, system identification and damage detection of small-scale rotating wind turbine blades in the laboratory and in the field. Here, wind turbine blades were instrumented with accelerometers and strain gages, and data acquisition was achieved using a prototype wireless sensing system. In the first portion of this study conducted in the laboratory, sensors were installed onto metallic structural elements that were fabricated to be representative of an actual wind blade. In order to control the excitation (rotation of the wind blade), a motor was used to spin the blades at controlled angular velocities. The wind turbine was installed on a shaking table for testing under rotation of turbine blades. Data measured by the sensors were recorded while the blade was operated at different speeds. On the other hand, the second part of this study utilized a small-scale wind turbine system mounted on the rooftop of a building. The main difference, as compared to the lab tests, was that the field tests relied on actual wind excitations (as opposed to a controlled motor). The raw data from both tests were analyzed using signal processing and system identification techniques for deriving the model response of the blades. The multivariate singular spectrum analysis (MSSA) and covariance-driven stochastic subspace identification method (SSI-COV) were used to identify the dynamic characteristics of the system. Damage of one turbine blade (loose bolts connection) in the lab test was also conducted. The extracted modal properties for both undamaged and damage cases under different ambient or forced excitations (earthquake loading) were compared. These tests confirmed that dynamic characterization of rotating wind turbines was feasible, and the results will guide future monitoring studies planned for larger-scale systems.
A computational design and analysis of a microtab based aerodynamic loads control system is presented. The microtab consists of a small tab that emerges from a wing approximately perpendicular to its surface in the vicinity of its trailing edge. Tab deployment on the upper side of the wing causes a decrease in the lift generation whereas deployment on the pressure side causes an increase. The computational methods applied in the development of this concept solve the governing Reynolds-averaged Navier-Stokes equations on structured, overset grids. The application of these methods to simulate the flows over lifting surfaces including the tabs has been paramount in the development of these devices. The numerical results demonstrate the effectiveness of the microtab and that it is possible to carry out a sensitivity analysis on the positioning and sizing of the tabs before they are implemented in successfully controlling the aerodynamic loads.