DISCRETE PHASE MODELLING for efficiency calculation of Cyclone Separator (ANSYS FLUENT)

Aim: To perform analysis on cyclone separator and calculate the separation efficiency and pressure drop.

Objective:

1. To write a few words about any four empirical models used to calculate the cyclone separator efficiency.
2. To perform an analysis on a given cyclone separator model by varying the particle diameter from 1 μm to 5 μm and calculate the separation efficiency in each case. Discuss the results. [Use both the velocity's as 3m/sec.]
3. To perform an analysis on a given cyclone separator model by varying the particle velocity from 1 m/sec to 5 m/sec and calculate the separation efficiency and pressure drop in each case. Discuss the results. [Use particle diameter size as 5 μm for all cases & keep flow velocity same as particle velocity]

THEORY:

CYCLONE SEPERATORS:

Cyclone separators provide a method of removing particulate matter from air or other gas streams at low cost and low maintenance. Cyclones are somewhat more complicated in design than simple gravity settling systems, and their removal efficiency is much better than that of settling chamber. Cyclones are basically centrifugal separators, consists of an upper cylindrical part referred to as the barrel and a lower conical part referred to as cone. They simply transform the inertia force of gas particle flows to a centrifugal force by means of a vortex generated in the cyclone body. The particle laden air stream enters tangentially at the top of the barrel and travels downward into the cone forming an outer vortex. The increasing air velocity in the outer vortex results in a centrifugal force on the particles separating them from the air stream. When the air reaches the bottom of the cone, it begins to flow radially inwards and out the top as clean air/gas while the particulates fall into the dust collection chamber attached to the bottom of the cyclone.

The collection efficiency of cyclones varies as a function of density, particle size and cyclone design. Cyclone efficiency will generally increase with increases in particle size and/or density; inlet duct velocity; cyclone body length; number of gas revolutions in the cyclone; ratio of cyclone body diameter to gas exit diameter; inlet dust loading; smoothness of the cyclone inner wall.Similarly, cyclone efficiency will decrease with increases in the parameters such as gas viscosity; cyclone body diameter; gas exit diameter; gas inlet duct area; gas density; leakage of air into the dust outlet.
The efficiency of a cyclone collector is related to the pressure drop across the collector. This is an indirect measure of the energy required to move the gas through the system. The pressure drop is a function of the inlet velocity and cyclone diameter. Form the above discussion it is clear that small cyclones are more efficient than large cyclones. Small cyclones, however, have a higher pressure drop and are limited with respect to volumetric flow rates.

CYCLONE SEPERATOR EMPERICAL MODELS

1. Iozia and Leith Model
   Iozia and Leith [4] logistic model is a modified version of Barth Model, which is developed based on force balance.The model assumes that a particle carried by the vortex endures the influence of two forces: a centrifugal force, Z and a flow resistance, W . The collection efficiency η of particle diameter d can be calculated from
   
   ηi=11 + (dpc/dpi)β
   
   
   β is an expression for slope parameter
   
   
2. Koch and Licht Model
   Koch and Licht collection theory recognized the inherently turbulent nature of cyclones and the distribution of gas residence times within the cyclone. Koch and Licht described particle motion in the entry and collection regions with the additional following assumptions:
   • The tangential velocity of a particle is equal to the tangential velocity of the gas flow, i.e. there is no slip in the tangential direction between the particle and the gas.
   •The tangential velocity is related to the radius of cyclone by: u Rn= constant.
   A force balance and an equation on the particles collection yields
   the grade efficiency
   ηi= 1 − exp{−2[GτiQ/D3*(n + 1)]^(0.5/(n+1))}
   
   

   G =8Kc/[(Ka^2)*(Kb^2)]

   
   
   
3. Lapple Model
   Lapple model was developed based on force balance without considering the flow resistance. Lapple assumed that a particle entering the cyclone is evenly distributed across the inlet opening. The particle that travels from inlet half width to the wall in the cyclone is collected with 50% efficiency
   ηi=1/[1 + (dpc/(dpi^2))]
   
   
4. Li and Wang Model
   The Li and Wang [3] model includes particle bounce or re-entrainment and turbulent diffusion at the cyclone wall. A two-dimensional analytical expression of particle distribution in the cyclone is obtained. Li and Wang model was developed based on the following assumptions:
   • The radial particle velocity and the radial concentration profile are not constant for uncollected particles within the cyclone.
   • Boundary conditions with the consideration of turbulent diffusion coefficient and particle bounce re-entrainment on the cyclone wall.
   • The tangential velocity is related to the radius of cyclone by: u Rn= constant.
   The resultant expression of the collection efficiency for particle of any size is given as
   ηi= 1 − exp{−λθ1}
   where
   θ1= 2π (S + L)/a
   
   

DISCRETE PHASE MODELLING :

A discrete phase model (DPM) is used when the aim is to investigate the behavior of the particles from a Lagrangian view and a discrete perspective. In fact, the difference between the Lagrangian and the Eulerian view is that fluid behavior in Lagrangian view is examined on the basis of a particle tracking of a particle of fluid flow; Whereas fluid behavior is considered in Eulerian view based on the assumption of a finite volume element in the fluid flow path.
If the behavior of discrete particles in the presence of a continuous flow field is affected by continuous flow, the interaction with the continuous phase must be activated. Also, if the particle tracking is relatively unsteady over time, the unsteady particle tracking option must be activated. The discrete phase can be applied in various physical forms, the type of which depends on the problem model. continuous- consists of fluid flowing the fluid volume; discrete- smaller particles association with the fluid
Also, the injection process for the mixed flow is defined in the input section of the model. In this way, in the input section of the model, the desired material enters the interior space of the model with the same discrete state or particle by particle. This particle injection as a discrete state can occur in different states. For example, the combusting mode is used when the material is injected into the model’s interior to perform the combustion process.

CASE SETUP:

GEOMETRY :

Open space claim and import the geometry, we need to extract the volume to carry out the simulation in, use the volume extract tool using edge selection for this. below is an image of the extracted volume.

MESHING:

In the meshing interface we need to make sure we are performing meshing using a cutcell meshing approach as the normal approach will not be able to generate a mesh due to overlapping regions. We also need to name the required sections ie inlet and the outlets.

FLUENT:

This simulation is run with the following general and viscous model settings.The RNG based turbulence model provides with both accuracy and efficiency in the modeling of turbulent flows. This model follows the two equation turbulence modeling framework and has been derived from the original governing equations for fluid flow using mathematical techniques called Renormalization Group (RNG).The RNG model provides a more general and fundamental model and is expected to yield improved predictions of near wall flows, separated flows, flows in curved geometries and flows that are strained by effects such as impingement or stagnation. Time dependent flows with large-scale motions, as in turbulent vortex shedding are well predicted by RNG turbulence model.

Turn on the discrete phase model.The following discrete phase model settings was used, setting the injection properties as shown in the image. The particles are injected from the inlet surface with a size between 1e-6 m and 5e-6 m for different cases.

Next we need to input the boundary conditions as mentioned below.

Inlet and wall:- Reflect
Outlet(Top):- Escape
Outlet(Bottom/dustbin) - Trap

Once the boundary conditions have been set we need to initialise the problem, for this use standard initialisation with the following options.The z velocity will be the same as the inlet velocity.

Run the simulation for 600 iterations, then export the particle data. It will appear like so in the console.

Open CFD post and import the particle tracking file and plot the required figures.

SIMULATION RESULTS:

case 1:particle size 1e-6; particle velocity - 3m/s; inlet velocity -3m/s

case 2:particle size 3e-6; particle velocity - 3m/s; inlet velocity -3m/s

case 3:particle size 5e-6; particle velocity - 3m/s; inlet velocity -3m/s

case 4:particle size 5e-6; particle velocity - 1m/s; inlet velocity -1m/s

case 5:particle size 5e-6; particle velocity - 5m/s; inlet velocity -5m/s

CALCULATION:

Cyclone efficiency =(number of particles trapped/(number of particles injected - number of particles incomplete))*100

given below is the table with the cyclone efficiency calculated for each case

Case serial number	Particle size (M)	Particle Velocity (M/s)	Inlet velocity(M/s)	Cyclone efficiency
1	1e-6	3	3	40.8
2	3e-6	3	3	73.8
3	5e-6	3	3	100
4	5e-6	1	1	34.69
5	5e-6	5	5	100

CONCLUSIONS:

• We can see that cyclone efiiciency depends on both particle size and the inlet velocity.
• If the particle size is larger and the sectional area of entry into the cyclone is smaller, the efficiency of the cyclone is higher.
• Cyclone efficienct is generally higher if the particle size is larger.
• If the inlet velocity is higher the cyclone efficiency is higher.