Bayesian parameter estimation for the inclusion of uncertainty in progressive damage simulation of composites

Johannes Reiner; Nathaniel Linden; Reza Vaziri; Navid Zobeiry; Boris Kramer

doi:10.1016/j.compstruct.2023.117257

Bayesian parameter estimation for the inclusion of uncertainty in progressive damage simulation of composites

Compos Struct. 2023 Oct 1:321:117257. doi: 10.1016/j.compstruct.2023.117257. Epub 2023 Jun 19.

Authors

Johannes Reiner¹, Nathaniel Linden², Reza Vaziri³, Navid Zobeiry⁴, Boris Kramer²

Affiliations

¹ School of Engineering, Faculty of Science Engineering and Built Environment, Deakin University, Geelong, Australia.
² Department of Mechanical and Aerospace Engineering, University of California San Diego, La Jolla, CA, USA.
³ Composites Research Network, Departments of Civil Engineering and Materials Engineering, The University of British Columbia, Vancouver, Canada.
⁴ Materials Science & Engineering Department, University of Washington, Seattle, USA.

PMID: 38098732
PMCID: PMC10718567 (available on 2024-10-01)
DOI: 10.1016/j.compstruct.2023.117257

Abstract

Despite gradual progress over the past decades, the simulation of progressive damage in composite laminates remains a challenging task, in part due to inherent uncertainties of material properties. This paper combines three computational methods - finite element analysis (FEA), machine learning and Markov Chain Monte Carlo - to estimate the probability density of FEA input parameters while accounting for the variation of mechanical properties. First, 15,000 FEA simulations of open-hole tension tests are carried out with randomly varying input parameters by applying continuum damage mechanics material models. This synthetically-generated data is then used to train and validate a neural network consisting of five hidden layers and 32 nodes per layer to develop a highly efficient surrogate model. With this surrogate model and the incorporation of statistical test data from experiments, the application of Markov Chain Monte Carlo algorithms enables Bayesian parameter estimation to learn the probability density of input parameters for the simulation of progressive damage evolution in fibre reinforced composites. This methodology is validated against various open-hole tension test geometries enabling the determination of virtual design allowables.

Keywords: Finite element analysis; Machine learning; Markov chain Monte Carlo; Open-hole tension.

Grants and funding

T32 EB009380/EB/NIBIB NIH HHS/United States