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International Journal for Multiscale Computational Engineering

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ISSN Druckformat: 1543-1649

ISSN Online: 1940-4352

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LARGE-SCALE COMPUTATIONS OF EFFECTIVE ELASTIC PROPERTIES OF RUBBER WITH CARBON BLACK FILLERS

Volumen 9, Ausgabe 3, 2011, pp. 271-303
DOI: 10.1615/IntJMultCompEng.v9.i3.30
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ABSTRAKT

A general method, based on a multiscale approach, is proposed to derive the effective elastic shear modulus of a rubber with 14% carbon black fillers from finite element and fast Fourier transform methods. The complex multiscale microstructure of such material was generated numerically from a mathematical model of its morphology that was identified from statistical moments out of transmission electron microscopy images. For finite element computations, the simulated microstructures were meshed from three-dimensional reconstruction of the isosurface using the marching cubes algorithm with special attention to the quality of the topology and the geometry of the mesh. To compute the shear modulus and to determine the representative volume element, homogeneous boundary conditions were prescribed on meshes and combined with a domain decomposition method. Regarding parallel computing, specific difficulties related to the highly heterogeneous microstructures and complex geometry are pointed out. The experimental shear modulus (1.8 MPa) obtained from dynamic mechanical analysis was estimated by the Hashin-Shtrikman lower bound ( 1.4 MPa) and the computations on simulated microstructures ( 2.4 MPa). The shear modulus was determined for two materials with the same volume fraction but different distribution of fillers. The current model of microstructures is capable of estimating the relative effect of the mixing time in processing associated with change in morphology on the elastic behavior. The computations also provide the local fields of stress/strain in the elastomeric matrix.

REFERENZIERT VON
  1. Jeulin Dominique, Morphology and effective properties of multi-scale random sets: A review, Comptes Rendus Mécanique, 340, 4-5, 2012. Crossref

  2. Wu C.T., Guo Y., Askari E., Numerical modeling of composite solids using an immersed meshfree Galerkin method, Composites Part B: Engineering, 45, 1, 2013. Crossref

  3. Dirrenberger J., Forest S., Jeulin D., Towards gigantic RVE sizes for 3D stochastic fibrous networks, International Journal of Solids and Structures, 51, 2, 2014. Crossref

  4. Willot François, Abdallah Bassam, Pellegrini Yves-Patrick, Fourier-based schemes with modified Green operator for computing the electrical response of heterogeneous media with accurate local fields, International Journal for Numerical Methods in Engineering, 98, 7, 2014. Crossref

  5. Cantournet Sabine, Layouni Khaled, Laiarinandrasana Lucien, Piques Roland, Experimental investigation and modelling of compressibility induced by damage in carbon black-reinforced natural rubber, Comptes Rendus Mécanique, 342, 5, 2014. Crossref

  6. Benedetti I., Barbe F., Modelling Polycrystalline Materials: An Overview of Three-Dimensional Grain-Scale Mechanical Models, Journal of Multiscale Modelling, 05, 01, 2013. Crossref

  7. Yang Ping, Zhang Liqiang, Tang Yunqing, Gong Jie, Zhao Yanfang, Yang Jianming, An atomic-continuum multiscale modeling approach for interfacial thermal behavior between materials, Applied Mathematical Modelling, 38, 14, 2014. Crossref

  8. Tang Yunqing, Zhang Liqiang, Yang Haiying, Guo Juan, Liao Ningbo, Yang Ping, Numerical simulation of thermal properties at Cu/Al interfaces based on hybrid model, Engineering Computations, 32, 3, 2015. Crossref

  9. Maire E., Withers P. J., Quantitative X-ray tomography, International Materials Reviews, 59, 1, 2014. Crossref

  10. Gundlach Norman, Hentschke Reinhard, Modelling Filler Dispersion in Elastomers: Relating Filler Morphology to Interface Free Energies via SAXS and TEM Simulation Studies, Polymers, 10, 4, 2018. Crossref

  11. Al Habis Nuha, El Moumen Ahmed, Tarfaoui Mostapha, Lafdi Khalid, Mechanical properties of carbon black/poly (ε-caprolactone)-based tissue scaffolds, Arabian Journal of Chemistry, 13, 1, 2020. Crossref

  12. Sokołowski D., Kamiński M., Computational Homogenization of Anisotropic Carbon/Rubber Composites With Stochastic Interface Defects, in Carbon-Based Nanofillers and Their Rubber Nanocomposites, 2019. Crossref

  13. Yang S., Dirrenberger J., Monteiro E., Ranc N., Representative volume element size determination for viscoplastic properties in polycrystalline materials, International Journal of Solids and Structures, 158, 2019. Crossref

  14. Anoukou K., Brenner R., Hong F., Pellerin M., Danas K., Random distribution of polydisperse ellipsoidal inclusions and homogenization estimates for porous elastic materials, Computers & Structures, 210, 2018. Crossref

  15. Dirrenberger Justin, Forest Samuel, Jeulin Dominique, Computational Homogenization of Architectured Materials, in Architectured Materials in Nature and Engineering, 282, 2019. Crossref

  16. Jeulin Dominique, Multi Scale Random Sets: From Morphology to Effective Behaviour, in Progress in Industrial Mathematics at ECMI 2010, 17, 2012. Crossref

  17. Nikpour M., Mazzeo B. A., Wheeler D. R., A Model for Investigating Sources of Li-Ion Battery Electrode Heterogeneity: Part II. Active Material Size, Shape, Orientation, and Stiffness, Journal of The Electrochemical Society, 168, 12, 2021. Crossref

  18. Masurel Robin J., Cantournet Sabine, Dequidt Alain, Long Didier R., Montes Hélène, Lequeux François, Role of Dynamical Heterogeneities on the Viscoelastic Spectrum of Polymers: A Stochastic Continuum Mechanics Model, Macromolecules, 48, 18, 2015. Crossref

  19. Belashov A.V., Beltukov Y.M., Moskalyuk O.A., Semenova I.V., Relative variations of nonlinear elastic moduli in polystyrene-based nanocomposites, Polymer Testing, 95, 2021. Crossref

  20. Schneider Matti, A review of nonlinear FFT-based computational homogenization methods, Acta Mechanica, 232, 6, 2021. Crossref

  21. Viktorova Mariia, Hentschke Reinhard, Fleck Frank, Prange Corinna, Karimi-Varzaneh Hossein Ali, Mesoscopic Model for the Simulation of Dynamic Mechanical Properties of Filled Elastomers: Model Construction and Parameterization, ACS Applied Polymer Materials, 2, 12, 2020. Crossref

  22. Brändel Matthias, Brands Dominik, Maike Simon, Rheinbach Oliver, Schröder Jörg, Schwarz Alexander, Stoyan Dietrich, Effective hyperelastic material parameters from microstructures constructed using the planar Boolean model, Computational Mechanics, 69, 6, 2022. Crossref

  23. Viktorova Mariia, Hentschke Reinhard, Fleck Frank, Taherian Fereshte, Karimi-Varzaneh Hossein Ali, A mesoscopic model for the simulation of dynamic mechanical properties of filled elastomers: Filled binary polymer blends, Computational Materials Science, 212, 2022. Crossref

  24. Shen Lijun, Qian Quan, A virtual sample generation algorithm supporting machine learning with a small-sample dataset: A case study for rubber materials, Computational Materials Science, 211, 2022. Crossref

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