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Title: Polynomial Chaos Characterization of Uncertainty in Multiscale Models and Behavior of Carbon Reinforced Composites

Abstract

Design of non-crimp fabric (NCF) composites entails major challenges pertaining to (1) the complex fine-scale morphology of the constituents, (2) the manufacturing-produced inconsistency of this morphology spatially, and thus (3) the ability to build reliable, robust, and efficient computational surrogate models to account for this complex nature. Traditional approaches to construct computational surrogate models have been to average over the fluctuations of the material properties at different scale lengths. This fails to account for the fine-scale features and fluctuations in morphology, material properties of the constituents, as well as fine-scale phenomena such as damage and cracks. In addition, it fails to accurately predict the scatter in macroscopic properties, which is vital to the design process and behavior prediction. In this work, funded in part by the Department of Energy, we present an approach for addressing these challenges by relying on polynomial chaos representations of both input parameters and material properties at different scales. Moreover, we emphasize the efficiency and robustness of integrating the polynomial chaos expansion with multiscale tools to perform multiscale assimilation, characterization, propagation, and prediction, all of which are necessary to construct the data-driven surrogate models required to design under the uncertainty of composites. These data-driven constructions providemore » an accurate map from parameters (and their uncertainties) at all scales and the system-level behavior relevant for design. While this perspective is quite general and applicable to all multiscale systems, NCF composites present a particular hierarchy of scales that permits the efficient implementation of these concepts.« less

Authors:
 [1];  [1]; ORCiD logo [2];  [2]
  1. University of Southern California
  2. General Motors
Publication Date:
Research Org.:
General Motors LLC
Sponsoring Org.:
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Vehicle Technologies Office (EE-3V)
OSTI Identifier:
1427713
Report Number(s):
DOE-GM-CarbonFiber-ICME
DOE Contract Number:  
EE0006826
Resource Type:
Conference
Resource Relation:
Conference: West Lafayette, IN
Country of Publication:
United States
Language:
English
Subject:
42 ENGINEERING; stochastics, multiscale, composites

Citation Formats

Mehrez, Loujaine, Ghanem, Roger, Aitharaju, Venkat, and Rodgers, William. Polynomial Chaos Characterization of Uncertainty in Multiscale Models and Behavior of Carbon Reinforced Composites. United States: N. p., 2017. Web. doi:10.12783/asc2017/15371.
Mehrez, Loujaine, Ghanem, Roger, Aitharaju, Venkat, & Rodgers, William. Polynomial Chaos Characterization of Uncertainty in Multiscale Models and Behavior of Carbon Reinforced Composites. United States. doi:10.12783/asc2017/15371.
Mehrez, Loujaine, Ghanem, Roger, Aitharaju, Venkat, and Rodgers, William. Mon . "Polynomial Chaos Characterization of Uncertainty in Multiscale Models and Behavior of Carbon Reinforced Composites". United States. doi:10.12783/asc2017/15371. https://www.osti.gov/servlets/purl/1427713.
@article{osti_1427713,
title = {Polynomial Chaos Characterization of Uncertainty in Multiscale Models and Behavior of Carbon Reinforced Composites},
author = {Mehrez, Loujaine and Ghanem, Roger and Aitharaju, Venkat and Rodgers, William},
abstractNote = {Design of non-crimp fabric (NCF) composites entails major challenges pertaining to (1) the complex fine-scale morphology of the constituents, (2) the manufacturing-produced inconsistency of this morphology spatially, and thus (3) the ability to build reliable, robust, and efficient computational surrogate models to account for this complex nature. Traditional approaches to construct computational surrogate models have been to average over the fluctuations of the material properties at different scale lengths. This fails to account for the fine-scale features and fluctuations in morphology, material properties of the constituents, as well as fine-scale phenomena such as damage and cracks. In addition, it fails to accurately predict the scatter in macroscopic properties, which is vital to the design process and behavior prediction. In this work, funded in part by the Department of Energy, we present an approach for addressing these challenges by relying on polynomial chaos representations of both input parameters and material properties at different scales. Moreover, we emphasize the efficiency and robustness of integrating the polynomial chaos expansion with multiscale tools to perform multiscale assimilation, characterization, propagation, and prediction, all of which are necessary to construct the data-driven surrogate models required to design under the uncertainty of composites. These data-driven constructions provide an accurate map from parameters (and their uncertainties) at all scales and the system-level behavior relevant for design. While this perspective is quite general and applicable to all multiscale systems, NCF composites present a particular hierarchy of scales that permits the efficient implementation of these concepts.},
doi = {10.12783/asc2017/15371},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2017},
month = {10}
}

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