Within the context of Digital Image Correlation (DIC), the optimal treatment of color images is considered. The mathematical bases of a weighted 3-field image correlation are first introduced, which are relevant for RGB encoded images. In this framework, noise characterization methods are developed as noise properties dictate the best suited metric to compare images. Consistent ways to process an image from elementary Bayer matrices are derived. Last, a case study on uncertainty quantification is performed.
A new approximation technique, called Reference Point Method (RPM), is proposed in order to reduce the computational complexity of algebraic operations for constructing reduced-order models in the case of time dependent and/or parametrized nonlinear partial differential equations. Even though model reduction techniques enables one to decrease the dimension of the initial problem in the sense that far fewer degrees of freedom are needed to represent the solution, the complexity of evaluating the nonlinear terms and assembling the low dimensional operator associated with the reduced-order model still scales with the size of the original high-dimensional model. This point can be critical, especially when the reduced-order basis changes throughout the solution strategy as it is the case for model reduction techniques based on Proper Generalized Decomposition (PGD). Based on the concept of spatial, parameter/time reference points and influence patches, the RPM defines a compressed version of the data from which an approximate low-rank separated representation by patch of the operators can be constructed by explicit formulas at low-cost without resorting to SVD-based techniques. An application of the RPM to PGD-based model reduction for a nonlinear parametrized elliptic PDE previously studied by other authors with reduced-basis method and EIM is proposed. It is shown that computational complexity to construct the reduced-order model can be divided in practice by one order of magnitude compared with the classical PGD approach.
Operational Modal Analysis is a method widely used in the wind industry to characterize structures and perform structural health monitoring or numerical model calibration. To reach this goal, optical techniques are becoming appealing because of their non-intrusiveness and their full-field feature in contrast to sensor-based methods. An innovative method is proposed herein for modal measurement with a single camera via Digital Image Correlation. The first resonance frequency of the tower is well captured from images of a shut down turbine.
In the field of structural health monitoring, the use of Digital Image Correlation (DIC) on relevant surfaces offers remarkable advantages. Such a method is presented herein. The statistically stationary nature of usual loads makes it possible to determine, by training with DIC, a reduced kinematic basis (composed of "modes") and a statistical amplitude distribution for each mode. Further, a specific DIC technique is proposed to deal with such particular kinematics by introducing an extractor per mode that operates on images after a filtering step. This per-mode DIC measurement is reduced to a simple scalar product between the image and the corresponding extractor, which allows for very fast and noniterative processing. As an illustration, this methodology is deployed on a test case of fatigue crack propagation.
An extensive code-to-code comparison among DIEGO, DLW and HAWC2 has been performed on a floating wind turbine (modified version of UMaine floater with IEAWIND 15MW wind turbine). In total, 10 cases are compared, and a few key results of this comparison are reported in this paper. From the comparisons, it is clearly seen that the results predicted by the three codes are generally agreed well despite some differences in specific degrees of freedom like roll, sway and yaw for the extreme load case, which requires additional investigations.
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