A MULTISCALE FRAMEWORK FOR THE STOCHASTIC ASSIMILATION AND MODELING OF UNCERTAINTY ASSOCIATED NCF COMPOSITE MATERIALS

Mehrez, Loujaine; Ghanem, Roger; Mcauliffe, Colin; Rodgers, William R.; Aitharaju, Venkat

doi:10.7712/100016.2251.12207

Title: A MULTISCALE FRAMEWORK FOR THE STOCHASTIC ASSIMILATION AND MODELING OF UNCERTAINTY ASSOCIATED NCF COMPOSITE MATERIALS

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Abstract

Multiscale framework to construct stochastic macroscopic constitutive material models is proposed. A spectral projection approach, specifically polynomial chaos expansion, has been used to construct explicit functional relationships between the homogenized properties and input parameters from finer scales. A homogenization engine embedded in Multiscale Designer, software for composite materials, has been used for the upscaling process. The framework is demonstrated using non-crimp fabric composite materials by constructing probabilistic models of the homogenized properties of a non-crimp fabric laminate in terms of the input parameters together with the homogenized properties from finer scales.

Authors:: Mehrez, Loujaine; Ghanem, Roger; Mcauliffe, Colin; Rodgers, William R.; Aitharaju, Venkat

Publication Date:: Fri Jan 01 00:00:00 EST 2016

Research Org.:: General Motors

Sponsoring Org.:: USDOE Office of Energy Efficiency and Renewable Energy (EERE), Vehicle Technologies Office (EE-3V)

OSTI Identifier:: 1430233

Report Number(s):: DOE-GM-CarbonFiber-ICME

DOE Contract Number:: EE0006826

Resource Type:: Conference

Resource Relation:: Conference: ECCOMAS Congress 2016 VII European Congress on Computational Methods in Applied Sciences and Engineering

Country of Publication:: United States

Language:: English

Subject:: multiscale homogenization, micro- meso- and macro-scales, stochastic assimilation, uncertainty propagation, NCF composite materials, polynomial chaos expansion

Citation Formats


                    Mehrez, Loujaine, Ghanem, Roger, Mcauliffe, Colin, Rodgers, William R., and Aitharaju, Venkat. A MULTISCALE FRAMEWORK FOR THE STOCHASTIC ASSIMILATION AND MODELING OF UNCERTAINTY ASSOCIATED NCF COMPOSITE MATERIALS.  United States: N. p., 2016. 
        Web.  doi:10.7712/100016.2251.12207.

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                    Mehrez, Loujaine, Ghanem, Roger, Mcauliffe, Colin, Rodgers, William R., & Aitharaju, Venkat. A MULTISCALE FRAMEWORK FOR THE STOCHASTIC ASSIMILATION AND MODELING OF UNCERTAINTY ASSOCIATED NCF COMPOSITE MATERIALS.  United States.  https://doi.org/10.7712/100016.2251.12207

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                    Mehrez, Loujaine, Ghanem, Roger, Mcauliffe, Colin, Rodgers, William R., and Aitharaju, Venkat. 2016.  
        "A MULTISCALE FRAMEWORK FOR THE STOCHASTIC ASSIMILATION AND MODELING OF UNCERTAINTY ASSOCIATED NCF COMPOSITE MATERIALS".  United States.  https://doi.org/10.7712/100016.2251.12207.  https://www.osti.gov/servlets/purl/1430233.

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@article{osti_1430233,

  title        = {A MULTISCALE FRAMEWORK FOR THE STOCHASTIC ASSIMILATION AND MODELING OF UNCERTAINTY ASSOCIATED NCF COMPOSITE MATERIALS},

  author       = {Mehrez, Loujaine and Ghanem, Roger and Mcauliffe, Colin and Rodgers, William R. and Aitharaju, Venkat},

  abstractNote = {Multiscale framework to construct stochastic macroscopic constitutive material models is proposed. A spectral projection approach, specifically polynomial chaos expansion, has been used to construct explicit functional relationships between the homogenized properties and input parameters from finer scales. A homogenization engine embedded in Multiscale Designer, software for composite materials, has been used for the upscaling process. The framework is demonstrated using non-crimp fabric composite materials by constructing probabilistic models of the homogenized properties of a non-crimp fabric laminate in terms of the input parameters together with the homogenized properties from finer scales.},

  doi          = {10.7712/100016.2251.12207},

  url          = {https://www.osti.gov/biblio/1430233},
  journal      = {},
number       = ,

  volume       = ,

  place        = {United States},

  year         = {Fri Jan 01 00:00:00 EST 2016},

  month        = {Fri Jan 01 00:00:00 EST 2016}

}

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