Investigation of assumptions and approximations in the virtual fields method for a viscoplastic material model

Journal Article · 26 February 2019 · Strain

DOI: https://doi.org/10.1111/str.12309 · OSTI ID:1501631

Jones, Elizabeth M. C.  ^[1]; Karlson, Kyle N. ^[2]; Reu, Phillip L. ^[1]

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1. Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
2. Sandia National Lab. (SNL-CA), Livermore, CA (United States)

The Virtual Fields Method (VFM) is an inverse technique used for parameter estimation and calibration of constitutive models. Many assumptions and approximations—such as plane stress, incompressible plasticity, and spatial and temporal derivative calculations—are required to use VFM with full-field deformation data, for example, from Digital Image Correlation (DIC). Here, this work presents a comprehensive discussion of the effects of these assumptions and approximations on parameters identified by VFM for a viscoplastic material model for 304L stainless steel. We generated synthetic data from a Finite-Element Analysis (FEA) in order to have a reference solution with a known material model and known model parameters, and we investigated four cases in which successively more assumptions and approximations were included in the data. We found that VFM is tolerant to small deviations from the plane stress condition in a small region of the sample, and that the incompressible plasticity assumption can be used to estimate thickness changes with little error. A local polynomial fit to the displacement data was successfully employed to compute the spatial displacement gradients. The choice of temporal derivative approximation (i.e., backwards difference versus central difference) was found to have a significant influence on the computed rate of deformation and on the VFM results for the rate-dependent model used in this work. Finally, the noise introduced into the displacement data from a stereo-DIC simulator was found to have negligible influence on the VFM results. Evaluating the effects of assumptions and approximations using synthetic data is a critical first step for verifying and validating VFM for specific applications. Finally, the results of this work provide the foundation for confidently using VFM for experimental data.

• Research Organization: Sandia National Lab. (SNL-CA), Livermore, CA (United States); Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
• Sponsoring Organization: USDOE National Nuclear Security Administration (NNSA)
• Grant/Contract Number: AC04-94AL85000; NA0003525
• OSTI ID: 1501631
• Report Number(s): SAND--2019-0930J; 671918
• Journal Information: Strain, Journal Name: Strain Journal Issue: 4 Vol. 55; ISSN 0039-2103
• Publisher: WileyCopyright Statement
• Country of Publication: United States
• Language: English