Bayesian calibration of strength model parameters from Taylor impact data
Rivera, David 1Bernstein, Jason 1Schmidt, Kathleen 1Muyskens, Amanda 1Nelms, Matthew 1Barton, Nathan 1Kupresanin, Ana 1Florando, Jeff1
扫码查看
点击上方二维码区域，可以放大扫码查看
作者信息
1. Lawrence Livermore Natl Lab
折叠
Abstract
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. 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.
NSTL
Elsevier