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Title: Validation of finite-element models using full-field experimental data: Levelling finite-element analysis data through a digital image correlation engine

Abstract

Full-field data from digital image correlation (DIC) provide rich information for finite-element analysis (FEA) validation. However, there are several inherent inconsistencies between FEA and DIC data that must be rectified before meaningful, quantitative comparisons can be made, including strain formulations, coordinate systems, data locations, strain calculation algorithms, spatial resolutions and data filtering. As such, in this paper, we investigate two full-field validation approaches: (1) the direct interpolation approach, which addresses the first three inconsistencies by interpolating the quantity of interest from one mesh to the other, and (2) the proposed DIC-levelling approach, which addresses all six inconsistencies simultaneously by processing the FEA data through a stereo-DIC simulator to ‘level’ the FEA data to the DIC data in a regularisation sense. Synthetic ‘experimental’ DIC data were generated based on a reference FEA of an exemplar test specimen. The direct interpolation approach was applied, and significant strain errorswere computed, even though therewas no model form error, because the filtering effect of theDIC enginewas neglected. In contrast, the levelling approach provided accurate validation results, with no strain error when no model form error was present. Next, model form error was purposefully introduced via a mismatch of boundary conditions. With the direct interpolation approach,more » the mismatch in boundary conditions was completely obfuscated, while with the levelling approach, it was clearly observed. Finally, the ‘experimental’ DIC datawere purposefully misaligned slightly fromthe FEA data. Both validation techniques suffered from the misalignment, thus motivating continued efforts to develop a robust alignment process. In summary, direct interpolation is insufficient, and the proposed levelling approach is required to ensure that the FEA and the DIC data have the same spatial resolution and data filtering. Only after the FEA data have been ‘levelled’ to the DIC data can meaningful, quantitative error maps be computed.« less

Authors:
ORCiD logo [1]; ORCiD logo [2];  [1]; ORCiD logo [3]
  1. MatchID NV, Ghent (Belgium)
  2. Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
  3. Univ. of Southampton (United Kingdom)
Publication Date:
Research Org.:
Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Sponsoring Org.:
USDOE National Nuclear Security Administration (NNSA)
OSTI Identifier:
1619229
Report Number(s):
SAND-2020-3553J
Journal ID: ISSN 0039-2103; 685000
Grant/Contract Number:  
AC04-94AL85000; NA0003525
Resource Type:
Accepted Manuscript
Journal Name:
Strain
Additional Journal Information:
Journal Volume: 56; Journal Issue: 4; Journal ID: ISSN 0039-2103
Publisher:
Wiley
Country of Publication:
United States
Language:
English
Subject:
42 ENGINEERING; digital image correlation (DIC); DIC-levelling approach; finite-element analysis (FEA); verification and validation (V&V)

Citation Formats

Lava, Pascal, Jones, Elizabeth M. C., Wittevrongel, Lukas, and Pierron, Fabrice. Validation of finite-element models using full-field experimental data: Levelling finite-element analysis data through a digital image correlation engine. United States: N. p., 2020. Web. doi:10.1111/str.12350.
Lava, Pascal, Jones, Elizabeth M. C., Wittevrongel, Lukas, & Pierron, Fabrice. Validation of finite-element models using full-field experimental data: Levelling finite-element analysis data through a digital image correlation engine. United States. doi:https://doi.org/10.1111/str.12350
Lava, Pascal, Jones, Elizabeth M. C., Wittevrongel, Lukas, and Pierron, Fabrice. Mon . "Validation of finite-element models using full-field experimental data: Levelling finite-element analysis data through a digital image correlation engine". United States. doi:https://doi.org/10.1111/str.12350. https://www.osti.gov/servlets/purl/1619229.
@article{osti_1619229,
title = {Validation of finite-element models using full-field experimental data: Levelling finite-element analysis data through a digital image correlation engine},
author = {Lava, Pascal and Jones, Elizabeth M. C. and Wittevrongel, Lukas and Pierron, Fabrice},
abstractNote = {Full-field data from digital image correlation (DIC) provide rich information for finite-element analysis (FEA) validation. However, there are several inherent inconsistencies between FEA and DIC data that must be rectified before meaningful, quantitative comparisons can be made, including strain formulations, coordinate systems, data locations, strain calculation algorithms, spatial resolutions and data filtering. As such, in this paper, we investigate two full-field validation approaches: (1) the direct interpolation approach, which addresses the first three inconsistencies by interpolating the quantity of interest from one mesh to the other, and (2) the proposed DIC-levelling approach, which addresses all six inconsistencies simultaneously by processing the FEA data through a stereo-DIC simulator to ‘level’ the FEA data to the DIC data in a regularisation sense. Synthetic ‘experimental’ DIC data were generated based on a reference FEA of an exemplar test specimen. The direct interpolation approach was applied, and significant strain errorswere computed, even though therewas no model form error, because the filtering effect of theDIC enginewas neglected. In contrast, the levelling approach provided accurate validation results, with no strain error when no model form error was present. Next, model form error was purposefully introduced via a mismatch of boundary conditions. With the direct interpolation approach, the mismatch in boundary conditions was completely obfuscated, while with the levelling approach, it was clearly observed. Finally, the ‘experimental’ DIC datawere purposefully misaligned slightly fromthe FEA data. Both validation techniques suffered from the misalignment, thus motivating continued efforts to develop a robust alignment process. In summary, direct interpolation is insufficient, and the proposed levelling approach is required to ensure that the FEA and the DIC data have the same spatial resolution and data filtering. Only after the FEA data have been ‘levelled’ to the DIC data can meaningful, quantitative error maps be computed.},
doi = {10.1111/str.12350},
journal = {Strain},
number = 4,
volume = 56,
place = {United States},
year = {2020},
month = {4}
}

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