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Volumes 72-83 (2023)
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Volume 83
Pages 1-258 (December 2023)
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Volume 82
Pages 1-204 (November 2023)
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Volume 81
Pages 1-188 (October 2023)
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Volume 80
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Volume 79
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Volume 78
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Volume 77
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Volume 76
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Volume 75
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Volume 74
Pages 1-200 (March 2023)
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Volume 73
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Volume 72
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Volume 83
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Volumes 60-71 (2022)
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Volume 71
Pages 1-108 (December 2022)
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Volume 70
Pages 1-106 (November 2022)
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Volume 69
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Volume 68
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Volume 67
Pages 1-102 (August 2022)
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Volume 66
Pages 1-112 (July 2022)
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Volume 65
Pages 1-138 (June 2022)
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Volume 64
Pages 1-186 (May 2022)
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Volume 63
Pages 1-124 (April 2022)
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Volume 62
Pages 1-104 (March 2022)
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Volume 61
Pages 1-120 (February 2022)
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Volume 60
Pages 1-124 (January 2022)
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Volume 71
- Volumes 54-59 (2021)
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- Volume 11 (2013)
- Volume 10 (2012)
- Volume 9 (2011)
- Volume 8 (2010)
- Volume 7 (2009)
- Volume 6 (2008)
- Volume 5 (2007)
- Volume 4 (2006)
- Volume 3 (2005)
- Volume 2 (2004)
- Volume 1 (2003)
Meso-scale structures existing in the form of particle-rich clusters, streamers or strands in circulating fluidized beds, and of ascending bubble plumes and descending liquid-rich vortices in bubble columns and slurry-bed reactors, as commonly observed, have played an important role in the macro-scale behavior of particle-fluid systems. These meso-scale structures span a wide range of length and time scales, and their origin, evolution and influence are still far from being well understood.
Recent decades have witnessed the emergence of computer simulation of particle-fluid systems based on computational fluid dynamic (CFD) models. However, strictly speaking these models are far from mature and the complex nature of particle-fluid systems arising from the meso-scale structures has been posing great challenges to investigators. The reason may be that the current two-fluid models (TFM) are derived either from continuum mechanics by using different kinds of averaging techniques for the conservation equations of single-phase flow, or from the kinetic theory of gases in which the assumption of molecular chaos is employed, thereby losing sight of the meso-scale heterogeneity at the scale of computational cells and leading to inaccurate calculation of the interaction force between particles and fluids. For example, the overall drag force for particles in a cell is usually calculated from the empirical Wen & Yu/Ergun correlations, which should be suspected since these correlations were originally derived from homogeneous systems.
Schemes to solve this problem for gas-particles systems may be classified into four categories. First, one could capture the detailed meso-scale structure information at the cell scale by employing the so-called direct numerical simulation (DNS) (Hu, 1996), the pseudo-particle modeling (PPM) (Ge & Li, 2003), or the Lattice-Boltzmann method (LBM) to track the interface between gas and particles. Second, refinement of the computational meshes may reduce the heterogeneity to some extent and may be capable of capturing some meso-scale heterogeneity though there still exists some argument about the physical rationality of this approach such as the treatment of particle phase as a continuum while fining the meshes. Third, it is generally agreed that a cascade description, viz. extracting the closure correlations for TFM from microscopic simulations such as PPM and LBM (van der Hoef et al., 2004), can suggest a practical way to explore the multi-scale heterogeneity. Although the above three schemes are logical and fundamental, they are generally difficult to implement at present due to the complexity of the models or the enormous computational cost. The fourth scheme we adopted in this study is the so-called energy-minimization multi-scale (EMMS) model which seems to be a simple yet reasonable approach at the moment.