Stochastic multiscale modeling for quantifying statistical and model errors with application to composite materials

Wang, Zhiheng; Hawi, Philippe; Masri, Sami; Aitharaju, Venkat; Ghanem, Roger

doi:10.1016/j.ress.2023.109213

Stochastic multiscale modeling for quantifying statistical and model errors with application to composite materials

Journal Article · Thu Mar 09 00:00:00 EST 2023 · Reliability Engineering and System Safety

DOI:https://doi.org/10.1016/j.ress.2023.109213· OSTI ID:2422570

^[1]; Hawi, Philippe ^[1]; Masri, Sami ^[1]; ^[2]; ^[1]

University of Southern California, Los Angeles, CA (United States)
General Motors R&D, Warren, MI (United States)

This paper provides a coherent and efficient computational framework for stochastic multiscale analysis of material systems in the presence of parametric uncertainties and modeling errors. Uncertainty in those model parameters that are not deduced as upscaled quantities is attributed to an uncertainty “germ”. While such parameters can appear at any scale, they are predominant at the finest analysis scale. Additional uncertainties stemming from statistical estimation, attributed to lack of data and model error, are associated with each submodel contributing to the multiscale system. Here, a robust and efficient framework based on a generalized extended polynomial chaos expansion (gEPCE) is proposed to simultaneously propagate all these uncertainties in order to provide a probabilistic representation of specific quantities of interest (QoI). We characterize the full probability distribution of the QoI and the uncertainty in the failure probability pertaining to its tails. By combining gEPCE with kernel density estimation (KDE) and directional derivatives, we construct sensitivity measures that connect these statistical metrics of QoI to the various sources of uncertainty to assess their individual and combined impacts. An illustrative problem featuring three-point bending of a composite beam is investigated to demonstrate the presented approach.

View Accepted Manuscript (DOE)

Research Organization:: Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States). FASTMath SciDAC-5 Institute

Sponsoring Organization:: National Science Foundation (NSF); USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR). Scientific Discovery through Advanced Computing (SciDAC)

Grant/Contract Number:: AC02-05CH11231

OSTI ID:: 2422570

Journal Information:: Reliability Engineering and System Safety, Journal Name: Reliability Engineering and System Safety Journal Issue: C Vol. 235; ISSN 0951-8320

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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36 MATERIALS SCIENCE
Engineering
Extended polynomial chaos expansion
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Multiscale modeling
Operations Research & Management Science
Sensitivity
Statistical error
Uncertainty quantification

Stochastic multiscale modeling for quantifying statistical and model errors with application to composite materials

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