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Fractal dependence of the packed bed porosity on the particles size distribution

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  • Džiugys, Algis
  • Mahmoudi, Amir Houshang
  • Misiulis, Edgaras
  • Navakas, Robertas
  • Skarbalius, Gediminas

Abstract

Packed beds formed by granular materials are the heart of many engineering and scientific applications. For a better understanding of transport processes occurring in such porous mediums, first the structural characteristics of packed beds should be known. The discrete element method (DEM) has been used widely as a powerful and reliable tool to study packed beds formed by granular materials. In all DEM-based models, the number of particles is a limiting factor as the computational time increases with the number of particles. To overcome this issue, it is common to neglect small particles in the bed. However, due to missed small particles, the porosity of the packed bed is underestimated. This has an impact on the fluid flow and consequently the heat and mass transfer in the bed.

Suggested Citation

  • Džiugys, Algis & Mahmoudi, Amir Houshang & Misiulis, Edgaras & Navakas, Robertas & Skarbalius, Gediminas, 2022. "Fractal dependence of the packed bed porosity on the particles size distribution," Chaos, Solitons & Fractals, Elsevier, vol. 159(C).
    • Handle: RePEc:eee:chsofr:v:159:y:2022:i:c:s096007792200354x
    DOI: 10.1016/j.chaos.2022.112144
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    References listed on IDEAS

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    1. Jeroen Mol & Mina Shahi & Amirhoushang Mahmoudi, 2020. "Numerical Modeling of Thermal Storage Performance of Encapsulated PCM Particles in an Unstructured Packed Bed," Energies, MDPI, vol. 13(23), pages 1-16, December.
    2. Stengler, Jana & Linder, Marc, 2020. "Thermal energy storage combined with a temperature boost: An underestimated feature of thermochemical systems," Applied Energy, Elsevier, vol. 262(C).
    3. Sui, Lili & Yu, Jian & Cang, Dingbang & Miao, Wenjing & Wang, Heyuan & Zhang, Jiwei & Yin, Shuaifeng & Chang, Keliang, 2019. "The fractal description model of rock fracture networks characterization," Chaos, Solitons & Fractals, Elsevier, vol. 129(C), pages 71-76.
    4. Navakas, Robertas & Džiugys, Algis & Peters, Bernhard, 2014. "A community-detection based approach to identification of inhomogeneities in granular matter," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 407(C), pages 312-331.
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