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Reduced-order models for microstructure-sensitive effective thermal conductivity of woven ceramic matrix composites with residual porosity.

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Title: Reduced-order models for microstructure-sensitive effective thermal conductivity of woven ceramic matrix composites with residual porosity.
Authors: Generale, Adam P.1 (AUTHOR), Kalidindi, Surya R.1 (AUTHOR) surya.kalidindi@me.gatech.edu
Source: Composite Structures. Oct2021, Vol. 274, pN.PAG-N.PAG. 1p.
Subjects: Reduced-order models, Thermal conductivity, Kriging, Porosity, Ceramic-matrix composites, Principal components analysis
Abstract: This paper presents a data-driven framework for the development of reduced-order models to predict microstructure-sensitive effective thermal conductivity of woven ceramic matrix composites (CMCs) with residual porosity. The main components of the proposed framework include (i) digital generation of representative volume elements (RVEs), (ii) estimation of the effective thermal conductivities of the RVEs using finite element (FE) models, (iii) low dimensional representation of the microstructure in the RVEs using 2-point spatial correlations and principal component analysis (PCA), and (iv) an active learning strategy based on Gaussian process regression (GPR) that minimizes the size of the training dataset through the selection of microstructures with the highest potential for information gain. The reduced-order models are demonstrated to provide high fidelity predictions on new RVEs. [ABSTRACT FROM AUTHOR]
Copyright of Composite Structures is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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DbLabel: Engineering Source
An: 152099573
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PubTypeId: academicJournal
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  Data: Reduced-order models for microstructure-sensitive effective thermal conductivity of woven ceramic matrix composites with residual porosity.
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  Data: <searchLink fieldCode="AR" term="%22Generale%2C+Adam+P%2E%22">Generale, Adam P.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Kalidindi%2C+Surya+R%2E%22">Kalidindi, Surya R.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> surya.kalidindi@me.gatech.edu</i>
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  Data: <searchLink fieldCode="DE" term="%22Reduced-order+models%22">Reduced-order models</searchLink><br /><searchLink fieldCode="DE" term="%22Thermal+conductivity%22">Thermal conductivity</searchLink><br /><searchLink fieldCode="DE" term="%22Kriging%22">Kriging</searchLink><br /><searchLink fieldCode="DE" term="%22Porosity%22">Porosity</searchLink><br /><searchLink fieldCode="DE" term="%22Ceramic-matrix+composites%22">Ceramic-matrix composites</searchLink><br /><searchLink fieldCode="DE" term="%22Principal+components+analysis%22">Principal components analysis</searchLink>
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  Data: This paper presents a data-driven framework for the development of reduced-order models to predict microstructure-sensitive effective thermal conductivity of woven ceramic matrix composites (CMCs) with residual porosity. The main components of the proposed framework include (i) digital generation of representative volume elements (RVEs), (ii) estimation of the effective thermal conductivities of the RVEs using finite element (FE) models, (iii) low dimensional representation of the microstructure in the RVEs using 2-point spatial correlations and principal component analysis (PCA), and (iv) an active learning strategy based on Gaussian process regression (GPR) that minimizes the size of the training dataset through the selection of microstructures with the highest potential for information gain. The reduced-order models are demonstrated to provide high fidelity predictions on new RVEs. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Composite Structures is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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    Identifiers:
      – Type: doi
        Value: 10.1016/j.compstruct.2021.114399
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      – Code: eng
        Text: English
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      Pagination:
        PageCount: 1
        StartPage: N.PAG
    Subjects:
      – SubjectFull: Reduced-order models
        Type: general
      – SubjectFull: Thermal conductivity
        Type: general
      – SubjectFull: Kriging
        Type: general
      – SubjectFull: Porosity
        Type: general
      – SubjectFull: Ceramic-matrix composites
        Type: general
      – SubjectFull: Principal components analysis
        Type: general
    Titles:
      – TitleFull: Reduced-order models for microstructure-sensitive effective thermal conductivity of woven ceramic matrix composites with residual porosity.
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            NameFull: Generale, Adam P.
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              Text: Oct2021
              Type: published
              Y: 2021
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              Value: 274
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