Uncertainty quantification for viscoelastic composite materials using time-separated stochastic mechanics

verfasst von: Hendrik Geisler, Philipp Junker
Abstract: With the growing use of composite materials, the need for high-fidelity simulation techniques of the related behavior increases. One important aspect to take into account is the uncertainty of the response due to fluctuations of the material parameters of the constituent materials of the homogenized composite. This inherent randomness leads to stochastic stresses on the microscale and uncertain macroscale response. Until now, the viscoelastic response of the matrix material seriously hindered the application of efficient methods to predict the composite material behavior. In this work, a novel method based on the time-separated stochastic mechanics (TSM) is developed to address this problem. We present how the uncertainty of the microscale stresses of a representative volume element and the homogenized macroscale stresses can be approximated with a low number of deterministic finite element simulations. Quantities of interest are the expectation, standard deviation, and the probability distribution function of the stresses on micro- and macroscale. The results showcase that the TSM is able to approximate the reference results very well at a minimal fraction of the computational cost needed for Monte Carlo simulations.
Organisationseinheit(en): Institut für Kontinuumsmechanik
Internationales GRK 2657: Methoden der Numerischen Mechanik in höheren Dimensionen
Externe Organisation(en): École normale supérieure Paris-Saclay (ENS Paris-Saclay)
Typ: Artikel
Journal: Probabilistic Engineering Mechanics
Band: 76
Anzahl der Seiten: 11
ISSN: 0266-8920
Publikationsdatum: 04.2024
Publikationsstatus: Veröffentlicht
Peer-reviewed: Ja
ASJC Scopus Sachgebiete: Statistische und nichtlineare Physik, Tief- und Ingenieurbau, Kernenergie und Kernkraftwerkstechnik, Physik der kondensierten Materie, Luft- und Raumfahrttechnik, Meerestechnik, Maschinenbau
Elektronische Version(en): https://doi.org/10.1016/j.probengmech.2024.103618 (Zugang: Offen)

BibTeX

@article{dd9fe51e29264de586654bc73e941b33,
title = "Uncertainty quantification for viscoelastic composite materials using time-separated stochastic mechanics",
abstract = "With the growing use of composite materials, the need for high-fidelity simulation techniques of the related behavior increases. One important aspect to take into account is the uncertainty of the response due to fluctuations of the material parameters of the constituent materials of the homogenized composite. This inherent randomness leads to stochastic stresses on the microscale and uncertain macroscale response. Until now, the viscoelastic response of the matrix material seriously hindered the application of efficient methods to predict the composite material behavior. In this work, a novel method based on the time-separated stochastic mechanics (TSM) is developed to address this problem. We present how the uncertainty of the microscale stresses of a representative volume element and the homogenized macroscale stresses can be approximated with a low number of deterministic finite element simulations. Quantities of interest are the expectation, standard deviation, and the probability distribution function of the stresses on micro- and macroscale. The results showcase that the TSM is able to approximate the reference results very well at a minimal fraction of the computational cost needed for Monte Carlo simulations.",
keywords = "Composite materials, Computational homogenization, Time-separated stochastic mechanics, Uncertain material parameters",
author = "Hendrik Geisler and Philipp Junker",
note = "Funding Information: The authors thank J. Nagel for his guidance. This work has been supported by the German Research Foundation (DFG), Germany within the framework of the international research training group IRTG 2657 {\textquoteleft}{\textquoteleft}Computational Mechanics Techniques in High Dimensions{\textquoteright}{\textquoteright} (Reference: GRK 2657/1, Project number 433082294). ",
year = "2024",
month = apr,
doi = "10.1016/j.probengmech.2024.103618",
language = "English",
volume = "76",
journal = "Probabilistic Engineering Mechanics",
issn = "0266-8920",
publisher = "Elsevier Ltd.",
}