Title: Estimating Material Properties Under Extreme Conditions by Using Bayesian Model Calibration with Functional Outputs

Abstract

Summary 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. 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:

Brown, J. L.; Hund, L. B.

Publication Date:

Sat Mar 24 00:00:00 EDT 2018

Research Org.:

Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

Sponsoring Org.:

USDOE National Nuclear Security Administration (NNSA)

OSTI Identifier:

1958638

Alternate Identifier(s):

OSTI ID: 1429511; OSTI ID: 1482731

Report Number(s):

SAND-2017-1837J
Journal ID: ISSN 0035-9254

Grant/Contract Number:  

NA0003525; DE‐NA0003525; AC04-94AL85000

Resource Type:

Published Article

Journal Name:

Journal of the Royal Statistical Society, Series C: Applied Statistics

Additional Journal Information:

Journal Name: Journal of the Royal Statistical Society, Series C: Applied Statistics Journal Volume: 67 Journal Issue: 4; Journal ID: ISSN 0035-9254

Publisher:

Oxford University Press

Country of Publication:

United Kingdom

Language:

English

Subject:

36 MATERIALS SCIENCE; Calibration; Dynamic material properties; Functional output; Gaussian process; Modularization; Uncertainty quantification