Two-fluid modeling of Geldart A particles in gas-fluidized beds

We have investigated the effect of cohesion and drag models on the bed hydrodynamics of Geldart A particles based on the two-fluid （TF） model. For a high gas velocity U0 = 0.03 m/s, we found a transition from the homogeneous fluidization to bubbling fluidization with an increase of the coefficient C1, which is used to account for the contribution of cohesion to the excess compressibility. Thus cohesion can play a role in the bed expansion of Geldart A particles. Apart from cohesion, we have also investigated the influence of the drag models. When using the Wen and Yu drag correlation with an exponent n = 4.65, we find an under-prediction of the bed expansion at low gas velocities （U0 = 0.009 m/s）. When using a larger exponent （n = 9.6）, as reported in experimental studies of gas-fluidization, a much better agreement with the experimental bed expansion is obtained. These findings suggest that at low gas velocity, a scale-down of the commonly used drag model is required. On the other hand, a scale-up of the commonly used drag model is necessary at high gas velocity （U0 = 0.2 and 0.06 m/s）. We therefore conclude that scaling the drag force represent only an ad hoc way of repairing the deficiencies of the TF model, and that a far more detailed study is required into the origin of the failure of the TF model for simulating fluidized beds of fine powders.

FLOW STRUCTURE FORMATION AND EVOLUTION IN CIRCULATING GAS-FLUIDIZED BEDS

The occurrence of heterogeneous flow structures in gas-particle flows seriously affects the gas-solid contacting and transport processes in high-velocity gas-fluidized beds. Particles do not disperse uniformly in the flow but pass through the bed in a swarm of clusters. The so-called 揷ore-annulus?structure in the radial direction and 揝?shaped axial distribution of solids concentration characterize the typical flow structure in the system. A computational study, using the discrete particle approach based on molecular dynamics techniques, has been carried out to explore the mechanisms underlying formation of the clusters and the core-annulus structure. Based on energy budget analysis including work done by the drag force, kinetic energy, rotational energy, potential energy, and energy dissipation due to particle-particle and particle-wall collisions, the role of gas-solid interaction and inelastic collisions between the particles are elucidated. It is concluded that the competition between gas-solid interaction and particle-particle interaction determines the pattern formation in high-velocity gas-solid flows: if the gas-solid interaction (under elevated pressure) dominates, most of particle energy obtained by drag from the gas phase is partitioned such that particle potential energy is raised, leading to a uniform flow structure. Otherwise, a heterogeneous pattern exists, which could be induced by both particle-particle collisions and gas-solid interaction. Although both factors could cause the flow instability, the non-linear drag force is demonstrated to be the necessary condition to trigger heterogeneous flow structure formation. As gas velocity increases and goes beyond a critical value, the fluid-particle interaction suppresses particle collisional dissipation, and as a consequence a more homogeneous flow regime is formed.

A natural gas hydrate-oil-gas system in the Qilian Mountain permafrost area, northeast of Qinghai-Tibet Plateau

Natural gas hydrate,oil and gas were all found together in the Qilian Mountain permafrost area,northeast of Qinghai-Tibet Plateau,China.They are closely associated with each other in space,but whether they are in any genetic relations are unknown yet.In this paper,a hydrocarbon gas-generation series,gas-fluid migration series and hydrocarbon gas-accumulation series are analyzed to probe the spatial,temporal and genetic relationships among natural natural gas hydrate,oil and gas.The subsequent results show that natural gas hydrate,oil and gas actually form a natural gas hydrate-oil-gas system.Based on the Middle Jurassic and the Upper Triassic hydrocarbon gas-generation series,it is divided into four major sub-systems in the study area:(1)A conventional Upper Triassic gas-bearing sub-system with peak hydrocarbon gas-generation in the late Middle Jurassic;(2)a conventional Middle Jurassic oil-bearing sub-system with low to mature hydrocarbon gas-generation in the late Middle Jurassic;(3)a natural gas hydrate sub-system with main gas source from the Upper Triassic gas-bearing sub-system and minor gas source from the Middle Jurassic oil-bearing sub-system as well as little gas source from the Middle Jurassic coal-bed gas and the microbial gas;(4)a shallower gas sub-system with microbial alteration of the main gas source from the Upper Triassic gas-bearing sub-system.This natural gas hydrate-oil-gas system and its sub-systems are not only theoretical but also practical,and thus they will play an important role in the further exploration of natural gas hydrate,oil and gas,even other energy resources in the study area.

Validation of a discrete element model using magnetic resonance measurements

The discrete element model （DEM） is a very promising modelling strategy for two-phase granular systems. However, owing to a lack of experimental measurements, validation of numerical simulations of two-phase granular systems is still an important issue. In this study, a small two-dimensional gas- fluidized bed was simulated using a discrete element model. The dimensions of the simulated bed were 44mm × 10mm × 120 mm and the fluidized particles had a diameter dp = 1.2 mm and density ρp = 1000 kg/m^3. The comparison between DEM simulations and experiments are performed on the basis of time-averaged voidage maps. The drag-law of Beetstra et al. [Beetstra, R., van der Hoef, M.A., ＆ Kuipers,J. A. M. （2007b）. Drag force of intermediate Reynolds number flow past mono- and bidispersed arrays of spheres. AIChE Journal, 53,489-501 ] seems to give the best results. The simulations are fairly insensitive to the coefficient of restitution and the coefficient of friction as long as some route of energy dissipation during particle-particle and particle-wall contact is provided. Changing the boundary condition of the gas phase at the side-walls from zero-slip to full-slip does not affect the simulation results. Care is to be taken that the cell sizes are chosen so that a reasonable number of particles can be found in a fluid cell.

COMPUTATIONAL FLUID DYNAMICS FOR DENSE GAS-SOLID FLUIDIZED BEDS: A MULTI-SCALE MODELING STRATEGY

Dense gas-particle flows are encountered in a variety of industrially important processes for large scale production of fuels, fertilizers and base chemicals. The scale-up of these processes is often problematic and is related to the intrinsic complexities of these flows which are unfortunately not yet fully understood despite significant efforts made in both academic and industrial research laboratories. In dense gas-particle flows both (effective) fluid-particle and (dissi-pative) particle-particle interactions need to be accounted for because these phenomena to a large extent govern the prevailing flow phenomena, i.e. the formation and evolution of heterogeneous structures. These structures have significant impact on the quality of the gas-solid contact and as a direct consequence thereof strongly affect the performance of the process. Due to the inherent complexity of dense gas-particles flows, we have adopted a multi-scale modeling approach in which both fluid-particle and particle-particle interactions can be properly accounted for. The idea is essentially that fundamental models, taking into account the relevant details of fluid-particle (lattice Boltzmann model) and particle-particle (discrete particle model) interactions, are used to develop closure laws to feed continuum models which can be used to compute the flow structures on a much larger (industrial) scale. Our multi-scale approach (see Fig. 1) involves the lattice Boltzmann model, the discrete particle model, the continuum model based on the kinetic theory of granular flow, and the discrete bubble model. In this paper we give an overview of the multi-scale modeling strategy, accompanied by illustrative computational results for bubble formation. In addition, areas which need substantial further attention will be highlighted.