Predictive multiscale modeling for Unidirectional Carbon Fiber Reinforced Polymers

Jiaying Gao; Modesar Shakoor; Gino Domel; Matthias Merzkirch; Guowei Zhou; Danielle Zeng; Xuming Su; Wing Kam Liu

doi:10.1016/j.compscitech.2019.107922

Predictive multiscale modeling for Unidirectional Carbon Fiber Reinforced Polymers

Jiaying Gao, Modesar Shakoor, Gino Domel, Matthias Merzkirch, Guowei Zhou, Danielle Zeng, Xuming Su, Wing Kam Liu^*

^*Corresponding author for this work

Mechanical Engineering

Research output: Contribution to journal › Article › peer-review

15 Scopus citations

Abstract

This paper presents a predictive multiscale modeling scheme for Unidirectional (UD) Carbon Fiber Reinforced Polymers (CFRP). A bottom-up modeling procedure is discussed for predicting the performance of UD structures. UD material responses are computed from high fidelity Representative Volume Elements (RVEs). A data-driven Reduced Order Modeling approach compresses RVEs into Reduced Order Models so material responses can be computed in a concurrent fashion along with the structural level simulation. The approach presented in this paper is validated against experimental data and is believed to provide design guidance for future fiber reinforced polymers development.

Original language	English (US)
Article number	107922
Journal	Composites Science and Technology
Volume	186
DOIs	https://doi.org/10.1016/j.compscitech.2019.107922
State	Published - Jan 20 2020

Keywords

Carbon fiber reinforced polymers
Data-driven
Integrated Computational Material and Engineering (ICME)
Multiscale modeling
Structural performance prediction

ASJC Scopus subject areas

Ceramics and Composites
Engineering(all)

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10.1016/j.compscitech.2019.107922

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title = "Predictive multiscale modeling for Unidirectional Carbon Fiber Reinforced Polymers",

abstract = "This paper presents a predictive multiscale modeling scheme for Unidirectional (UD) Carbon Fiber Reinforced Polymers (CFRP). A bottom-up modeling procedure is discussed for predicting the performance of UD structures. UD material responses are computed from high fidelity Representative Volume Elements (RVEs). A data-driven Reduced Order Modeling approach compresses RVEs into Reduced Order Models so material responses can be computed in a concurrent fashion along with the structural level simulation. The approach presented in this paper is validated against experimental data and is believed to provide design guidance for future fiber reinforced polymers development.",

keywords = "Carbon fiber reinforced polymers, Data-driven, Integrated Computational Material and Engineering (ICME), Multiscale modeling, Structural performance prediction",

author = "Jiaying Gao and Modesar Shakoor and Gino Domel and Matthias Merzkirch and Guowei Zhou and Danielle Zeng and Xuming Su and Liu, {Wing Kam}",

note = "Funding Information: The financial support for this project was provided by the U.S. Department of Energy{\textquoteright}s Office of Energy Efficiency and Renewable Energy (EERE) , under Award Number DE-EE0006867 . W.K.L acknowledges the support of the National Science Foundation, United States under Grant No. MOMS/CMMI-1762035 . Appendix A Funding Information: The financial support for this project was provided by the U.S. Department of Energy's Office of Energy Efficiency and Renewable Energy (EERE), under Award Number DE-EE0006867. W.K.L acknowledges the support of the National Science Foundation, United States under Grant No. MOMS/CMMI-1762035. Publisher Copyright: {\textcopyright} 2019 Elsevier Ltd",

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doi = "10.1016/j.compscitech.2019.107922",

language = "English (US)",

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AU - Gao, Jiaying

AU - Shakoor, Modesar

AU - Domel, Gino

AU - Merzkirch, Matthias

AU - Zhou, Guowei

AU - Zeng, Danielle

AU - Su, Xuming

AU - Liu, Wing Kam

N1 - Funding Information: The financial support for this project was provided by the U.S. Department of Energy’s Office of Energy Efficiency and Renewable Energy (EERE) , under Award Number DE-EE0006867 . W.K.L acknowledges the support of the National Science Foundation, United States under Grant No. MOMS/CMMI-1762035 . Appendix A Funding Information: The financial support for this project was provided by the U.S. Department of Energy's Office of Energy Efficiency and Renewable Energy (EERE), under Award Number DE-EE0006867. W.K.L acknowledges the support of the National Science Foundation, United States under Grant No. MOMS/CMMI-1762035. Publisher Copyright: © 2019 Elsevier Ltd

PY - 2020/1/20

Y1 - 2020/1/20

N2 - This paper presents a predictive multiscale modeling scheme for Unidirectional (UD) Carbon Fiber Reinforced Polymers (CFRP). A bottom-up modeling procedure is discussed for predicting the performance of UD structures. UD material responses are computed from high fidelity Representative Volume Elements (RVEs). A data-driven Reduced Order Modeling approach compresses RVEs into Reduced Order Models so material responses can be computed in a concurrent fashion along with the structural level simulation. The approach presented in this paper is validated against experimental data and is believed to provide design guidance for future fiber reinforced polymers development.

AB - This paper presents a predictive multiscale modeling scheme for Unidirectional (UD) Carbon Fiber Reinforced Polymers (CFRP). A bottom-up modeling procedure is discussed for predicting the performance of UD structures. UD material responses are computed from high fidelity Representative Volume Elements (RVEs). A data-driven Reduced Order Modeling approach compresses RVEs into Reduced Order Models so material responses can be computed in a concurrent fashion along with the structural level simulation. The approach presented in this paper is validated against experimental data and is believed to provide design guidance for future fiber reinforced polymers development.

KW - Carbon fiber reinforced polymers

KW - Data-driven

KW - Integrated Computational Material and Engineering (ICME)

KW - Multiscale modeling

KW - Structural performance prediction

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Predictive multiscale modeling for Unidirectional Carbon Fiber Reinforced Polymers

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Keywords

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