Bayesian calibration of strength model parameters from Taylor impact data

Rivera, David; Bernstein, Jason; Schmidt, Kathleen; Muyskens, Amanda; Nelms, Matthew; Barton, Nathan; Kupresanin, Ana; Florando, Jeff

doi:10.1016/j.commatsci.2021.110999

Bayesian calibration of strength model parameters from Taylor impact data

Journal Article · Thu Nov 11 23:00:00 EST 2021 · Computational Materials Science

DOI:https://doi.org/10.1016/j.commatsci.2021.110999· OSTI ID:1887013

^[1]; Bernstein, Jason ^[1]; Schmidt, Kathleen ^[1]; ^[1]; ^[1]; Barton, Nathan ^[1]; Kupresanin, Ana ^[1]; Florando, Jeff ^[1]

Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)

Materials strength plays a key role in determining the mechanical response of engineered structures. As such, accurate strength models are crucial in simulations involving complex loading conditions, particularly when deformation in the plastic regime is deemed important. In this work, a Gaussian process based surrogate for a finite element simulation of a Taylor impact test is developed and used for Bayesian calibration of the Preston–Tonks–Wallace strength model. Here, the surrogate model is shown to closely approximate the salient features of the Taylor cylinder deformation and is validated against simulation output before being used in the strength model calibration routine. The results show that Taylor impact test data can be used in the calibration of constitutive equations through the use of a combination of data science techniques, namely Gaussian processes and Bayesian inference.

View Accepted Manuscript (DOE)

Research Organization:: Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)

Sponsoring Organization:: USDOE National Nuclear Security Administration (NNSA); USDOE Laboratory Directed Research and Development (LDRD) Program

Grant/Contract Number:: AC52-07NA27344

OSTI ID:: 1887013

Alternate ID(s):: OSTI ID: 1868383

Report Number(s):: LLNL-JRNL-820901; 1024559

Journal Information:: Computational Materials Science, Journal Name: Computational Materials Science Journal Issue: 7 Vol. 210; ISSN 0927-0256

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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