Publications

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Understanding the final uncertainty in position, displacement and strain for digital image correlation (DIC) is a difficult or impossible enterprise when done analytically. In contrast, this paper will present a new approach using a pseudo-experimental method for estimating the 2D matching uncertainty, a Monte Carlo approach for understanding the calibration uncertainty, and the propagation of these error contributions to calculate a final 3D uncertainty. The methodology of calculating the errors will be presented using a sample measurement case. Additionally, the sensitivity of the position and motion errors to the various DIC parameters will be discussed. © The Society for Experimental Mechanics, Inc. 2013.

Understanding the final uncertainty in position, displacement and strain for digital image correlation (DIC) is a difficult or impossible enterprise when done analytically. In contrast, this paper will present a new approach using a pseudo-experimental method for estimating the 2D matching uncertainty, a Monte Carlo approach for understanding the calibration uncertainty, and the propagation of these error contributions to calculate a final 3D uncertainty. The methodology of calculating the errors will be presented using a sample measurement case. Additionally, the sensitivity of the position and motion errors to the various DIC parameters will be discussed. © The Society for Experimental Mechanics, Inc. 2013.

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The accuracy of digital in-line holography to detect particle position and size within a 3D domain is evaluated with particular focus placed on detection of nonspherical particles. Dimensionless models are proposed for simulation of holograms from single particles, and these models are used to evaluate the uncertainty of existing particle detection methods. From the lessons learned, a new hybrid method is proposed. This method features automatic determination of optimum thresholds, and simulations indicate improved accuracy compared to alternative methods. To validate this, experiments are performed using quasi-stationary, 3D particle fields with imposed translations. For the spherical particles considered in experiments, the proposed hybrid method resolves mean particle concentration and size to within 4% of the actual value, while the standard deviation of particle depth is less than two particle diameters. Initial experimental results for nonspherical particles reveal similar performance.

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Uncertainty quantification (UQ) equations have been derived for predicting matching uncertainty in two-dimensional image correlation a priori. These equations include terms that represent the image noise and image contrast. Researchers at the University of South Carolina have extended previous 1D work to calculate matching errors in 2D. These 2D equations have been coded into a Sandia National Laboratories UQ software package to predict the uncertainty for DIC images. This paper presents those equations and the resulting error surfaces for trial speckle images. Comparison of the UQ results with experimentally subpixel-shifted images is also discussed. ©2010 Society for Experimental Mechanics Inc.

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The ability to see what is happening during an experiment is often critical to human understanding. High and ultra-high speed cameras have for decades allowed scientists to see these extremely short time-scale events; starting with film cameras and now with digital versions of these cameras. The move to digital cameras has invited the use of computer analysis of the images for obtaining quantitative information well beyond the qualitative usefulness of merely being able to see the event. Digital image correlation (DIC) is one of these powerful and popular quantitative techniques, but by no means the only possible image analysis method. All of these analysis techniques ask more of the camera technology than simply providing images. They require highquality images that are amenable to analysis and do not introduce error sources that compromise the data. Possible error sources include image noise, image distortions, synchronization and spatial sampling issues. As a minimal starting point, the introduced errors must be well understood in order to put error bounds on the results. This is because in many experiments some result is better than no result; with the caveat that the error sources and the relative confidence of the data are understood. The concepts will be framed in relation to ongoing ultra-high speed work being done at Sandia. A call and challenge will be given to begin thinking in more detail about how to successfully turn these cameras into diagnostic instruments. © (2011) Trans tech publications Switzerland.

The ability to see what is happening during an experiment is often critical to human understanding. High and ultra-high speed cameras have for decades allowed scientists to see these extremely short time-scale events; starting with film cameras and now with digital versions of these cameras. The move to digital cameras has invited the use of computer analysis of the images for obtaining quantitative information well beyond the qualitative usefulness of merely being able to see the event. Digital image correlation (DIC) is one of these powerful and popular quantitative techniques, but by no means the only possible image analysis method. All of these analysis techniques ask more of the camera technology than simply providing images. They require highquality images that are amenable to analysis and do not introduce error sources that compromise the data. Possible error sources include image noise, image distortions, synchronization and spatial sampling issues. As a minimal starting point, the introduced errors must be well understood in order to put error bounds on the results. This is because in many experiments some result is better than no result; with the caveat that the error sources and the relative confidence of the data are understood. The concepts will be framed in relation to ongoing ultra-high speed work being done at Sandia. A call and challenge will be given to begin thinking in more detail about how to successfully turn these cameras into diagnostic instruments. © (2011) Trans tech publications Switzerland.

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