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Numerical Study on Heat Transfer Performance in Packed Bed

Author

Listed:
  • Shicheng Wang

    (School of Energy and Power engineering, Huazhong University of Science and Technology, Wuhan 430074, China)

  • Chenyi Xu

    (School of Energy and Power engineering, Huazhong University of Science and Technology, Wuhan 430074, China)

  • Wei Liu

    (School of Energy and Power engineering, Huazhong University of Science and Technology, Wuhan 430074, China)

  • Zhichun Liu

    (School of Energy and Power engineering, Huazhong University of Science and Technology, Wuhan 430074, China)

Abstract

Packed beds are widely used in industries and it is of great significance to enhance the heat transfer between gas and solid states inside the bed. In this paper, numerical simulation method is adopted to investigate the heat transfer principle in the bed at particle scale, and to develop the direct enhanced heat transfer methods in packed beds. The gas is treated as continuous phase and solved by Computational Fluid Dynamics (CFD), while the particles are treated as discrete phase and solved by the Discrete Element Method (DEM); taking entransy dissipation to evaluate the heat transfer process. Considering the overall performance and entransy dissipation, the results show that, compared with the uniform particle size distribution, radial distribution of multiparticle size can effectively improve the heat transfer performance because it optimizes the velocity and temperature field, reduces the equivalent thermal resistance of convection heat transfer process, and the temperature of outlet gas increases significantly, which indicates the heat quality of the gas has been greatly improved. The increase in distribution thickness obviously enhances heat transfer performance without reducing the equivalent thermal resistance in the bed. The result is of great importance for guiding practical engineering applications.

Suggested Citation

  • Shicheng Wang & Chenyi Xu & Wei Liu & Zhichun Liu, 2019. "Numerical Study on Heat Transfer Performance in Packed Bed," Energies, MDPI, vol. 12(3), pages 1-22, January.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:3:p:414-:d:201499
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    References listed on IDEAS

    as
    1. Sun, Henan & Ge, Ya & Liu, Wei & Liu, Zhichun, 2019. "Geometric optimization of two-stage thermoelectric generator using genetic algorithms and thermodynamic analysis," Energy, Elsevier, vol. 171(C), pages 37-48.
    2. Ge, Ya & Liu, Zhichun & Sun, Henan & Liu, Wei, 2018. "Optimal design of a segmented thermoelectric generator based on three-dimensional numerical simulation and multi-objective genetic algorithm," Energy, Elsevier, vol. 147(C), pages 1060-1069.
    3. Chen, Qun & Wang, Moran & Pan, Ning & Guo, Zeng-Yuan, 2009. "Optimization principles for convective heat transfer," Energy, Elsevier, vol. 34(9), pages 1199-1206.
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    Cited by:

    1. Xing Tian & Jian Yang & Zhigang Guo & Qiuwang Wang & Bengt Sunden, 2020. "Numerical Study of Heat Transfer in Gravity-Driven Particle Flow around Tubes with Different Shapes," Energies, MDPI, vol. 13(8), pages 1-15, April.
    2. Francesco Calise & Maria Vicidomini & Mário Costa & Qiuwang Wang & Poul Alberg Østergaard & Neven Duić, 2019. "Toward an Efficient and Sustainable Use of Energy in Industries and Cities," Energies, MDPI, vol. 12(16), pages 1-28, August.
    3. Jian Yang & Yingxue Hu & Qiuwang Wang, 2019. "Investigation of Effective Thermal Conductivity for Ordered and Randomly Packed Bed with Thermal Resistance Network Method," Energies, MDPI, vol. 12(9), pages 1-14, May.

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