Jaarboek no. 89. 2010/2011 - Koninklijke Maatschappij voor ...
Jaarboek no. 89. 2010/2011 - Koninklijke Maatschappij voor ... Jaarboek no. 89. 2010/2011 - Koninklijke Maatschappij voor ...
Natuurkundige voordrachten I Nieuwe reeks 89 Multi-scale modellering voor gasstromen beladen met deeltjes 64 the particle-particle collisions, which take place on the scale of millimeters or less. Therefore, we have adopted a multi-scale modeling strategy (see Figure 1), with the prime goal to i) obtain a fundamental insight in the complex dynamic behavior of dense gas-particle fluidized suspensions; that is, to gain an understanding based on elementary physical principles such as drag, friction and dissipation ii) based on this insight, develop models with predictive capabilities for dense gas-particle flows encountered in engineering scale equipment. To this end, we consider gas-solid flows at four distinctive levels of modeling (for details see van der Hoef et. al, 2006 and 2008). Discrete Particle Model (DPM) is that it can account for particle-wall and particle-particle interactions in a realistic manner, for system sizes of about O(106 ) particles, which is sufficiently large to allow for a direct comparison with laboratory scale experiments. As a logical consequence of this approach a closure law for the effective momentum exchange has to be specified, which can be obtained from the aforementioned lattice Boltzmann simulations. Note that in chemical engineering, to date mainly empirical relations are used for the friction coefficient β (defined by (5)), such as the Ergun (1952) correlation for porosities: ε < 0.8: βd 2 (1-ε ) 2 (1-ε ) = 150 + 1.75 Re (1) m ε ε At the most detailed level of description the gas flow field is modeled at scales smaller than the size of the solid particles (model 4 and 5 in Figure 1). The interaction of the gas phase with the solid phase is incorporated by imposing ‘stick’ boundary conditions at the surface of the solid particles. This model thus allows us to measure the effective momentum exchange between the two phases, which can be used in the higher scale models. In our approach, the flow field between spherical particles is solved by the lattice Boltzmann model (Succi, 2001 and Ladd and Verberg, 2001) or the Immersed Boundary model. At the intermediate level (model 3 in Figure 1) of description the flow field is modeled at a scale larger than the size of the particles, where a grid cell typically contains O(102 ) – O(103 and the Wen & Yu (1966) equation for porosities ε < 0.8: βd ) particles, which are assumed to be perfect spheres (diameter d). This model consists of two parts: a Lagrangian code for updating the positions and velocities of the solid particles from Newton’s law, and a Eulerian code for updating the local gas density and velocity from the Navier- Stokes equation (Hoomans et al., 1996). The advantage of this 2 3 -2.65 = C dR e (1- ε)ε , m 4 24(1+0.15 Re0.687 )/Re C d = { 0.44 Re103
Nauman (1935) is used. At an even larger scale a continuum description is employed for the solid phase, i.e. the solid phase is not described by individual particles, but by a local density and velocity field (model 2 in Figure 1). Hence, in this model both the gas-phase and the solid phase are treated on an equal footing, and for both phases an Eulerian code is used to describe the time evolution (see Kuipers et al., 1992 and Gidaspow, 1994, amongst others). The information obtained in the two smaller-scale models is then included in the continuum models via the kinetic theory of granular flow. The advantage of this model is that it can predict the flow behavior of gas-solid flows at life-size scales, and these models are therefore widely used in commercial fluid flow simulators of industrial scale equipment. Finally, at the largest scale, the (larger) bubbles that are present in gas-solid fluidized beds are considered as discrete objects, similar to the solid particles in the DPM model. This model is an adapted version of the discrete bubble model for gas-liquid bubble columns (model 1 in Figure 1). We want to stress that this model - as outlined in section 5 - has been developed quite recently, and the results should be considered as very preliminary. In this paper we will give an overview of these four levels of modeling as they are employed in our research group. In the following sections we will describe each of these models in more detail. 2. Lattice Boltzmann Model (LBM) The lattice Boltzmann model (LBM) originates from the lattice-gas cellular automata (LGCA) models (Frisch et al., 1986) for simple fluids. The LGCA model is basically a discrete, simplified version of the molecular dynamics model, which involves propagations and collisions of particles on a lattice. LGCA models have proved a simple and efficient way to simulate a simple fluid at the microscopic level, where it has been demonstrated both numerically and theoretically that the resulting macroscopic flow fields obey the Navier-Stokes equation. The lattice Boltzmann model is the ensemble averaged version of the LGCA model, so that it represents a propagation and collision of the particle distributions instead of the Natuurkundige voordrachten I Nieuwe reeks 89 Multi-scale modellering voor gasstromen beladen met deeltjes actual particles as in the LGCA models (McNamara and Zanetti, 1988). From a macroscopic point of view, the LB model can be regarded as a finite difference scheme that solves the Boltzmann equation, the fundamental equation in the kinetic theory which underlies of the equations of hydrodynamics. In its most simple form the finite difference scheme reads: ƒ(v,r+vδ t,t+δ t)-ƒ(v,r,t)δ t (ƒ(v,r,t)-ƒ eq t (v,r,t)) (3) where ƒ is the single particle distribution function, which is equivalent to the fluid density in the 6 dimensional velocity-coordinate space, and ƒeq represents the equilibrium distribution. In (3), the position r and velocity v are discrete, i.e. the possible positions are restricted to the sites of a lattice, and thus the possible velocities are vectors connecting nearest neighbor sites of this lattice. Note that Eq. (3) represents a propagation, followed by a ‘collision’ (relaxation to the equilibrium distribution). From the single particle distribution function, the hydrodynamic variables of interest - the local gas density r and velocity v - are obtained by summing out over all possible velocities: r (r,t) = Σ ƒ (v,r,t), r (r,t) u = Σ vƒ (v,r,t) (4) v v It can be shown that the flow fields obtained from the LB model are - to order δ t 2 - equivalent to those obtained from the Navier-Stokes equation, where the viscosity is set by the relaxation time t. One of the advantages of the LB model over other finite difference models for fluid flow, is that boundary conditions can be modeled in a very simple way. This makes the method particularly suited to simulate large moving particles suspended in a fluid phase. An obvious choice of the boundary condition is where the gas next to the solid particle moves with the local velocity of the surface of the solid particle, i.e. the so-called ‘stick’ boundary condition. For a spherical particle suspended in an infinite threedimensional system, moving with velocity v, this condition will give rise to a frictional force on the particle F = 3πmdv, at least in the limit of low particle Reynolds numbers Re = rdε v /m , where d is the hydrodynamic diameter of the particle, and m is the 65
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Natuurkundige <strong>voor</strong>drachten I Nieuwe reeks 89<br />
Multi-scale modellering <strong>voor</strong> gasstromen beladen met deeltjes<br />
64<br />
the particle-particle collisions, which take place on<br />
the scale of millimeters or less. Therefore, we have<br />
adopted a multi-scale modeling strategy (see Figure<br />
1), with the prime goal to i) obtain a fundamental<br />
insight in the complex dynamic behavior of dense<br />
gas-particle fluidized suspensions; that is, to gain an<br />
understanding based on elementary physical principles<br />
such as drag, friction and dissipation ii) based<br />
on this insight, develop models with predictive<br />
capabilities for dense gas-particle flows encountered<br />
in engineering scale equipment. To this end, we<br />
consider gas-solid flows at four distinctive levels of<br />
modeling (for details see van der Hoef et. al, 2006<br />
and 2008).<br />
Discrete Particle Model (DPM) is that it can account<br />
for particle-wall and particle-particle interactions in<br />
a realistic manner, for system sizes of about O(106 )<br />
particles, which is sufficiently large to allow for a<br />
direct comparison with laboratory scale experiments.<br />
As a logical consequence of this approach a<br />
closure law for the effective momentum exchange<br />
has to be specified, which can be obtained from<br />
the aforementioned lattice Boltzmann simulations.<br />
Note that in chemical engineering, to date mainly<br />
empirical relations are used for the friction coefficient<br />
β (defined by (5)), such as the Ergun (1952) correlation<br />
for porosities: ε < 0.8:<br />
βd 2 (1-ε ) 2 (1-ε )<br />
= 150 + 1.75 Re (1)<br />
m ε ε<br />
At the most detailed level of description the gas flow<br />
field is modeled at scales smaller than the size of the<br />
solid particles (model 4 and 5 in Figure 1). The interaction<br />
of the gas phase with the solid phase is incorporated<br />
by imposing ‘stick’ boundary conditions at the<br />
surface of the solid particles. This model thus allows<br />
us to measure the effective momentum exchange<br />
between the two phases, which can be used in the<br />
higher scale models. In our approach, the flow field<br />
between spherical particles is solved by the lattice<br />
Boltzmann model (Succi, 2001<br />
and Ladd and Verberg, 2001) or<br />
the Immersed Boundary model.<br />
At the intermediate level (model<br />
3 in Figure 1) of description the<br />
flow field is modeled at a scale<br />
larger than the size of the particles,<br />
where a grid cell typically<br />
contains O(102 ) – O(103 and the Wen & Yu (1966) equation for porosities<br />
ε < 0.8:<br />
βd<br />
) particles,<br />
which are assumed to be<br />
perfect spheres (diameter d).<br />
This model consists of two parts:<br />
a Lagrangian code for updating<br />
the positions and velocities of<br />
the solid particles from Newton’s<br />
law, and a Eulerian code for<br />
updating the local gas density<br />
and velocity from the Navier-<br />
Stokes equation (Hoomans et<br />
al., 1996). The advantage of this<br />
2 3 -2.65<br />
= C dR e (1- ε)ε ,<br />
m 4<br />
24(1+0.15 Re0.687 )/Re<br />
C d = { 0.44<br />
Re103