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Title: Estimating material properties under extreme conditions by using Bayesian model calibration with functional outputs

Abstract

Dynamic material properties experiments provide access to the most extreme temperatures and pressures attainable in a laboratory setting; the data from these experiments are often used to improve our understanding of material models at these extreme conditions. We apply Bayesian model calibration to dynamic material property applications where the experimental output is a function: velocity over time. This framework can accommodate more uncertainties and facilitate analysis of new types of experiments relative to techniques traditionally used to analyse dynamic material experiments. However, implementation of Bayesian model calibration requires more sophisticated statistical techniques, because of the functional nature of the output as well as parameter and model discrepancy identifiability. We propose a novel Bayesian model calibration process to simplify and improve the estimation of the material property calibration parameters. Specifically, we propose scaling the likelihood function by an effective sample size rather than modelling the auto–correlation function to accommodate the functional output. Additionally, we propose sensitivity analyses by using the notion of 'modularization' to assess the effect of experiment–specific nuisance input parameters on estimates of the physical parameters. Furthermore, the Bayesian model calibration framework proposed is applied to dynamic compression of tantalum to extreme pressures, and we conclude that the proceduremore » results in simple, fast and valid inferences on the material properties for tantalum.« less

Authors:
 [1];  [1]
  1. Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Publication Date:
Research Org.:
Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Sponsoring Org.:
USDOE National Nuclear Security Administration (NNSA)
OSTI Identifier:
1482731
Alternate Identifier(s):
OSTI ID: 1429511
Report Number(s):
SAND-2017-1837J
Journal ID: ISSN 0035-9254; 654073
Grant/Contract Number:  
AC04-94AL85000; NA0003525
Resource Type:
Accepted Manuscript
Journal Name:
Journal of the Royal Statistical Society, Series C: Applied Statistics
Additional Journal Information:
Journal Volume: 67; Journal Issue: 4; Journal ID: ISSN 0035-9254
Publisher:
Wiley
Country of Publication:
United States
Language:
English
Subject:
36 MATERIALS SCIENCE; Calibration; Dynamic material properties; Functional output; Gaussian process; Modularization; Uncertainty quantification

Citation Formats

Brown, Justin L., and Hund, Lauren B. Estimating material properties under extreme conditions by using Bayesian model calibration with functional outputs. United States: N. p., 2018. Web. doi:10.1111/rssc.12273.
Brown, Justin L., & Hund, Lauren B. Estimating material properties under extreme conditions by using Bayesian model calibration with functional outputs. United States. doi:10.1111/rssc.12273.
Brown, Justin L., and Hund, Lauren B. Sat . "Estimating material properties under extreme conditions by using Bayesian model calibration with functional outputs". United States. doi:10.1111/rssc.12273. https://www.osti.gov/servlets/purl/1482731.
@article{osti_1482731,
title = {Estimating material properties under extreme conditions by using Bayesian model calibration with functional outputs},
author = {Brown, Justin L. and Hund, Lauren B.},
abstractNote = {Dynamic material properties experiments provide access to the most extreme temperatures and pressures attainable in a laboratory setting; the data from these experiments are often used to improve our understanding of material models at these extreme conditions. We apply Bayesian model calibration to dynamic material property applications where the experimental output is a function: velocity over time. This framework can accommodate more uncertainties and facilitate analysis of new types of experiments relative to techniques traditionally used to analyse dynamic material experiments. However, implementation of Bayesian model calibration requires more sophisticated statistical techniques, because of the functional nature of the output as well as parameter and model discrepancy identifiability. We propose a novel Bayesian model calibration process to simplify and improve the estimation of the material property calibration parameters. Specifically, we propose scaling the likelihood function by an effective sample size rather than modelling the auto–correlation function to accommodate the functional output. Additionally, we propose sensitivity analyses by using the notion of 'modularization' to assess the effect of experiment–specific nuisance input parameters on estimates of the physical parameters. Furthermore, the Bayesian model calibration framework proposed is applied to dynamic compression of tantalum to extreme pressures, and we conclude that the procedure results in simple, fast and valid inferences on the material properties for tantalum.},
doi = {10.1111/rssc.12273},
journal = {Journal of the Royal Statistical Society, Series C: Applied Statistics},
number = 4,
volume = 67,
place = {United States},
year = {2018},
month = {3}
}

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Cited by: 1 work
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Figures / Tables:

Fig. 1. Fig. 1. : One-dimensional configuration used to simulate the experiments: the time-dependent magnetic field boundary condition results in a time-dependent stress wave propagating through the system; the simulated velocity at the interface of the Ta sample and LiF window can be compared directly with the experimental measurement; the uncertainties associatedmore » with the configuration considered here include the Ta material properties, scaling of the magnetic field, thickness of the Al and Ta materials and errors in the measured velocity including a constant time offset from the boundary condition time base« less

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