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Nanomalzemelerin Rastgele Dağılımı İçin Algoritma Geliştirilmesi

Year 2021, Issue: 24, 508 - 514, 15.04.2021
https://doi.org/10.31590/ejosat.916108

Abstract

Bu çalışmada, polimer nanokompozitlerin gerçekçi davranışını modellemek için nanomalzemelerin polimer matriks içerisindeki rastgele dağılımını saplayan yeni bir algoritma geliştirilmiştir. Çalışma nanokompozitlerin sonlu elemanlar yöntemi olarak modellenmesinden ziyade bu algoritmanın geliştirilmesine odaklanmıştır. Nanomalzemelerin ve polimer nanokompozitlerin sayısal modellemesine genellikle temsili hacim elemanına sahip çok ölçekli moelleme yöntemi kullanılmaktadır. Takviye malzemesinin etkisi araştırmacılar tarafından araştırılmakta olup takviye mekanizması hem sayısal hem de deneysel olarak tam olarak açıklanmamıştır. Deneysel çalışmalarda takviye mekanizmasının etkisini anlamak için sayısal çalışmaların başarısı da oldukça önemlidir. Bu nedenle, nanomalzemelerin polimer matrisindeki gerçekçi dağılımını modellemek için bir algoritma geliştirilmiş ve sayısal çalışmalara uyarlanmıştır. Algoritma, istenen geometrik boyutlara sahip malzemelerin bir kontrol hacmi içinde rastgele konumlandırılmasını ve birbirleriyle ve kontrol hacmi ile kesişmemesini sağlamaktadır. Algoritma Python programlama dili kullanılarak geliştirilmiş ve nanomalzemelerin konumları komut dosyası dili kullanılarak ABAQUS sonlu eleman programına aktarılmıştır. Nanomalzeme olarak grafen ve polimer matriks olarak epoxy kullanılmıştır. Rastgele dağıtılan RVE modelleri, tek elemanlı RVE modellerinden daha başarılı sonuçlar vermiştir. Deneysel sonuçlarla iyi bir uyum göstermiştir.

References

  • Kim, H., Abdala, A. A. and Macosko, C. W. (2010). Graphene/polymer nanocomposites. Macromolecules 43, 6515–6530.
  • Potts, J. R., Dreyer, D. R., Bielawski, C. W. and Ruoff, R. S. (2011). Graphene-based polymer nanocomposites. Polymer 52, 5–25.
  • Geim, A. K. and Novoselov, K. S. (2007). The rise of graphene. Nat. Mater 6, 183–191.
  • Cho, J., Joshi, M. S. and Sun, C. T. (2006). Effect of inclusion size on mechanical properties of polymeric composites with micro and nano particles. Compos. Sci. Technol. 66, 1941–1952.
  • Tjong, S. C. (2006). Structural and mechanical properties of polymer nanocomposites. Mater. Sci. Eng. R 53, 73–197.
  • Georgantzinos, S. K., Giannopoulos, G. I. and Anifantis, N. K. (2010). Numerical investigation of elastic mechanical properties of graphene structures. Mater Des 31, 4646–54.
  • Shokrieh, M. M. and Rafiee, R. (2010) Prediction of Young’s modulus of graphene sheets and carbon nanotubes using nanoscale continuum mechanics approach. Mater Des 31, 790–5.
  • Tsai, J. L. and Tu, J. F. (2010). Characterizing mechanical properties of graphite using molecular dynamics simulation. Mater Des 31:194–9.
  • Shokrieh, M. M., Shokrieh, Z. and Hashemianzadeh, S. M. (2012). Effective parameters in modeling of graphene sheet Young’s modulus. Modares Mech Eng 12, 147–55.
  • Shen, L., Shen, H. S. and Zhang, C. L. (2010). Temperature-dependent elastic properties of single layer graphene sheets. Mater Des 31, 4445–9.
  • Shokrieh, Z., Seifi, M. and Shokrieh, M. M. (2017). Simulation of stiffness of randomly-distributed-graphene/epoxy nanocomposites using a combined finite element-micromechanics method. Mechanics of Materials 115, 16-21.
  • Manta, A., Gresil, M., and Soutis, C. (2018). Simulated electrical response of randomly distributed and aligned graphene/polymer nanocomposites. Composite Structures 192, 452-459.
  • Dai, G. and Mishnaevsky, Jr., L. (2014). Graphene reinforced nanocomposites: 3D simulation of damage and fracture. Computational Materials Science 95, 684-692.
  • Shokrieh, M. M., Shokrieh, Z. and Hashemianzadeh, S. M. (2014). A novel combined molecular dynamics–micromechanics method for modeling of stiffness of graphene/epoxy nanocomposites with randomly distributed graphene. Materials and Design 64, 96-101.
  • Hadden, C. M., D. R., Klimek-McDonald, Pineda, E. J., King, J. A., Reichanadter, A. M., Miskioglu, I., Gowtham, S. and Odegard, G.M. (2015). Mechanical properties of graphene nanoplatelet/carbon fiber/epoxy hybrid composites: Multiscale modeling and experiments, Carbon 95, 100-112.
  • Heidarhaei, M., Shariati, M. and Eipakchi, H. (2020). Experimental and analytical investigations of the tensilebehavior of graphene-reinforced polymer nanocomposites, Mechanics of Advanced Materials and Structures 27, 2090-2099.
  • Hussein, A., kim, B. (2018). Graphene/polymer nanocomposites: The active role of the matrix in stiffening mechanics, Composite Structures, 202, 170-181.
  • Pontefisso, A., Mishnaevsky Jr., L. (2016). Nanomorphology of graphene and CNT reinforced polymer and its effect on damage: Micromechanical numerical study, Composites Part B, 96, 338-349.

Developing Algorithm for Random Distribution of Nanomaterials

Year 2021, Issue: 24, 508 - 514, 15.04.2021
https://doi.org/10.31590/ejosat.916108

Abstract

In this study, a new algorithm was developed for the random distribution of the nanomaterials in the polymer matrix to model realistic behavior of polymer nanocomposites. The study focused on the development of this algorithm rather than the modeling of nanocomposites as a finite element method. The multi-scale method with a representative volume element (RVE) is generally used for numerical modeling of nanomaterials and polymer nanocomposites. The researchers investigate the effect of the reinforcement material and the reinforcement mechanism has not been fully explained both numerically and experimentally. The success of numerical studies is also very important to specify the effect of reinforcement mechanism in experimental studies. For this reason, an algorithm was developed to model the realistic distribution of nanomaterials in the polymer matrix and adapted to numerical studies. The algorithm provided that materials of desired geometric dimensions were randomly positioned within a control volume and did not intersect with each other and the control volume. The algorithm was developed using the Python programming language and the positions of the nanomaterials were transferred to the ABAQUS finite element program using scripting language. Graphene was used as a nanomaterial and epoxy was used as a polymer matrix. Randomly distributed RVE models gave more successful results than single element RVE models. It shows a good agreement with experimental results.

References

  • Kim, H., Abdala, A. A. and Macosko, C. W. (2010). Graphene/polymer nanocomposites. Macromolecules 43, 6515–6530.
  • Potts, J. R., Dreyer, D. R., Bielawski, C. W. and Ruoff, R. S. (2011). Graphene-based polymer nanocomposites. Polymer 52, 5–25.
  • Geim, A. K. and Novoselov, K. S. (2007). The rise of graphene. Nat. Mater 6, 183–191.
  • Cho, J., Joshi, M. S. and Sun, C. T. (2006). Effect of inclusion size on mechanical properties of polymeric composites with micro and nano particles. Compos. Sci. Technol. 66, 1941–1952.
  • Tjong, S. C. (2006). Structural and mechanical properties of polymer nanocomposites. Mater. Sci. Eng. R 53, 73–197.
  • Georgantzinos, S. K., Giannopoulos, G. I. and Anifantis, N. K. (2010). Numerical investigation of elastic mechanical properties of graphene structures. Mater Des 31, 4646–54.
  • Shokrieh, M. M. and Rafiee, R. (2010) Prediction of Young’s modulus of graphene sheets and carbon nanotubes using nanoscale continuum mechanics approach. Mater Des 31, 790–5.
  • Tsai, J. L. and Tu, J. F. (2010). Characterizing mechanical properties of graphite using molecular dynamics simulation. Mater Des 31:194–9.
  • Shokrieh, M. M., Shokrieh, Z. and Hashemianzadeh, S. M. (2012). Effective parameters in modeling of graphene sheet Young’s modulus. Modares Mech Eng 12, 147–55.
  • Shen, L., Shen, H. S. and Zhang, C. L. (2010). Temperature-dependent elastic properties of single layer graphene sheets. Mater Des 31, 4445–9.
  • Shokrieh, Z., Seifi, M. and Shokrieh, M. M. (2017). Simulation of stiffness of randomly-distributed-graphene/epoxy nanocomposites using a combined finite element-micromechanics method. Mechanics of Materials 115, 16-21.
  • Manta, A., Gresil, M., and Soutis, C. (2018). Simulated electrical response of randomly distributed and aligned graphene/polymer nanocomposites. Composite Structures 192, 452-459.
  • Dai, G. and Mishnaevsky, Jr., L. (2014). Graphene reinforced nanocomposites: 3D simulation of damage and fracture. Computational Materials Science 95, 684-692.
  • Shokrieh, M. M., Shokrieh, Z. and Hashemianzadeh, S. M. (2014). A novel combined molecular dynamics–micromechanics method for modeling of stiffness of graphene/epoxy nanocomposites with randomly distributed graphene. Materials and Design 64, 96-101.
  • Hadden, C. M., D. R., Klimek-McDonald, Pineda, E. J., King, J. A., Reichanadter, A. M., Miskioglu, I., Gowtham, S. and Odegard, G.M. (2015). Mechanical properties of graphene nanoplatelet/carbon fiber/epoxy hybrid composites: Multiscale modeling and experiments, Carbon 95, 100-112.
  • Heidarhaei, M., Shariati, M. and Eipakchi, H. (2020). Experimental and analytical investigations of the tensilebehavior of graphene-reinforced polymer nanocomposites, Mechanics of Advanced Materials and Structures 27, 2090-2099.
  • Hussein, A., kim, B. (2018). Graphene/polymer nanocomposites: The active role of the matrix in stiffening mechanics, Composite Structures, 202, 170-181.
  • Pontefisso, A., Mishnaevsky Jr., L. (2016). Nanomorphology of graphene and CNT reinforced polymer and its effect on damage: Micromechanical numerical study, Composites Part B, 96, 338-349.
There are 18 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Articles
Authors

Umut Caliskan 0000-0002-8043-2799

Publication Date April 15, 2021
Published in Issue Year 2021 Issue: 24

Cite

APA Caliskan, U. (2021). Developing Algorithm for Random Distribution of Nanomaterials. Avrupa Bilim Ve Teknoloji Dergisi(24), 508-514. https://doi.org/10.31590/ejosat.916108