GPUCorrel: A GPU accelerated Digital Image Correlation software written in Python
Victor Couty,
Jean-François Witz,
Pauline Lecomte-Grosbras,
Julien Berthe,
Eric Deletombe,
Mathias Brieu
Affiliations
Victor Couty
Univ. Lille, CNRS, Centrale Lille, UMR 9013 - LaMcube - Laboratoire de Mécanique, Multiphysique, Multi-échelle, F-59000 Lille, France; Corresponding author.
Jean-François Witz
Univ. Lille, CNRS, Centrale Lille, UMR 9013 - LaMcube - Laboratoire de Mécanique, Multiphysique, Multi-échelle, F-59000 Lille, France
Pauline Lecomte-Grosbras
Univ. Lille, CNRS, Centrale Lille, UMR 9013 - LaMcube - Laboratoire de Mécanique, Multiphysique, Multi-échelle, F-59000 Lille, France
Julien Berthe
ONERA, The French Aerospace Lab, Lille, France; Univ. Lille, CNRS, Centrale Lille, UMR 9013 - LaMcube - Laboratoire de Mécanique, Multiphysique, Multi-échelle, F-59000 Lille, France
Eric Deletombe
ONERA, The French Aerospace Lab, Lille, France; Univ. Lille, CNRS, Centrale Lille, UMR 9013 - LaMcube - Laboratoire de Mécanique, Multiphysique, Multi-échelle, F-59000 Lille, France
Mathias Brieu
Mechanical Engineering Dpt, College ECST, California State University, Los Angeles, USA
This article presents an open-source Integrated Digital Image Correlation (I-DIC) software written in Python using CUDA-enabled GPUs designed to run at high (1–100 Hz) frequency. The field computation is performed using a global approach and the result is a projection of the real field in a user-defined base of fields. This software can be used in many applications and one use in experimental mechanics is demonstrated by driving a bi-axial tensile test on a cruciform specimen.
