Towards High Performance Digital Volume Correlation
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We develop speed and efficiency improvements to a three-dimensional (3D) digital volume correlation (DVC) algorithm, which measures displacement and strain fields throughout the interior of a material. Our goal is to perform DVC with resolution comparable to that achieved in 2D digital image correlation, in time that is commensurate with the image acquisition time. This would represent a significant improvement over the current state-of-the-art available in the literature. Using an X-ray micro-CT scanner, we can resolve features at the 5 micron scale, generating 3D images with up to 36 billion voxels. We compute twelve degrees-of-freedom at each correlation point and utilize tricubic spline interpolation to achieve high accuracy. We improve the algorithm’s speed and robustness through an improved coarse search, efficient implementation of spline interpolation, and using smoothing splines to address noisy image data. For DVC, the volume of data, number of correlation points, and work to solve each correlation point grow cubically. We therefore employ parallel computing to handle this tremendous increase in computational and memory requirements. We demonstrate the application of DVC using simulated deformations of 3D micro-CT scans of polymer samples with embedded particles forming an internal pattern.
KeywordsDigital volume correlation X-ray tomography Strain measurement Parallel computing Smoothing splines
We are grateful to Charles Mark Bee and the Imaging Technology Group at the Beckman Institute for the use of the Skyscan MicroCT scanner, Prof. Nancy Sottos and Brett Beiermann of the Autonomic Materials Research Group at the Beckman Institute for help in preparing PDMS samples, and Prof. Michael Sutton for helpful discussions. We gratefully acknowledge the use of the Turing cluster maintained and operated by the Computational Science and Engineering Program at the University of Illinois. This work was supported by the Center for Simulation of Advanced Rockets under contract number B523819 funded by the U.S. Department of Energy, by the Institute for Advanced Computing Applications and Technologies, and by award number 09084 from the University of Illinois Campus Research Board.
- 17.Haldrup K, Nielsen S, Mishnaevsky Jr L, Beckman F, Wert J (2006) 3-dimensional strain fields from tomographic measurements. In: Proceedings of SPIE 6318, 63,181BGoogle Scholar
- 19.Limodin N, Réthoré J, Adrien J, Buffière JY, Hild F, Roux S (2010) Analysis and artifact correction for volume correlation measurements using tomographic images from a laboratory x-ray source. Exp Mech. doi: 10.1007/s11340-010-9397-4
- 24.MathWorks, Inc (2009) MATLAB. Natick, MAGoogle Scholar
- 27.Dierckx P (1993) Curve and surface fitting with splines. Oxford University PressGoogle Scholar
- 28.de Boor C (1978) A practical guide to splines. Springer-VerlagGoogle Scholar
- 30.McCalpin JD (1995) Memory bandwidth and machine balance in current high performance computers. IEEE Computer Society Technical Committee on Computer Architecture (TCCA) NewsletterGoogle Scholar
- 31.Bartels RH, Beatty JC, Barsky BA (1995) An introduction to splines for use in computer graphics and geometric modeling. Morgan KaufmannGoogle Scholar
- 32.Anderson E et al (1999) LAPACK users’ guide, 3rd edn. SIAMGoogle Scholar
- 34.MPI Forum (2009) MPI: a message-passing interface standard. http://www.mpi-forum.org/
- 35.Goodier JN (1933) Concentration of stress around spherical and cylindrical inclusions and flaws. J Appl Mech 55(7):39–44Google Scholar