Simulating Cyclone separator with Discrete Phase Modelling
Simulating Cyclone separator with Discrete Phase Modelling
Aim:- To perform analysis on cyclone separator and calculate the separation efficiency and pressure drop
Objective:-
- To write a few words about any four empirical models used to calculate the cyclone separator efficiency.
- 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.]
- 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 separator:
Cyclone separators are separation devices that use the principle of inertia to remove particulate matter from flue gases. Cyclone separator is one of many air pollution control devices known as pre-cleaners since they generally remove larger pieces of particulate matter. This prevents finer filtration methods from having to deal with large, more abrasive particles later on. In addition, several cyclone separators can operate in parallel, and this system is known as a multi-cyclone.
The cyclone separator consists of 1 inlet and two outlets. At inlet, dusty gas is inputted at a high tangential velocity and due to swirling motion, outer vortex generation takes place. Due to inertial effect, the particulate matter don't follow the exact vortex motion and momentum losses of particulates occurs due to Collison with the walls of cyclone separator. The heavy particulate matter gets collected at the bottom of the cyclone separator. The axial velocity of fluid in the conical section, gets reversed and the fluid forms inner vortex and cleaned gas exits from the top outlet.
Factors affecting cyclone separation efficiency -
- Particle density - As the particulate density increases, the separation efficiency increases.
- Particle size - As the particulate size increases, the separation efficiency increases.
- Flow rate - As the flow rate increases, the separation efficiency increases.
- Cone and body length - The separation efficiency increases with increase in the body length and decrease in cone, body diameter.
- Pressure drop - The efficiency of collector depends of pressure drop, as it indirectly estimated the amount of energy required to move the gas through the separator. The pressure drop depends on various parameters such as inlet velocity, cyclone diameter, roughness of inner surface wall of cyclone separator, viscosity of fluid etc. Pressure drop = Pinlet - Poutlet
Empirical models -
1) Iozia and Leith model - This model was developed based on force balance. In this model, since the particle undergoes vortex flow, two main forces are considered - i.e, centrifugal force and flow resistance.
The collection efficiency(ηi) of particle diameter(dpi) is given as -
Where,
2) Li and Wang model - This model includes particle bounce or re-entrainment and turbulent diffusion at the cyclone wall. In this model, 2D analytical expression of particle distribution in the cyclone is given as -
The resultant expression of the collection efficiency for particle of any size is given as-
3) Koch and Licht model - This model includes the inherently turbulent nature of cyclones and the distribution of gas residence times within the cyclone.
A force balance and an equation on the particles collection yields the grade efficiency (ηi) as -
4) Lapple model - This 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 efficiency of collection of any size of particle is given by -
Process:
In this project we are running two cases
1. first we are taking both particle and fluid velocity as 3m/s and changing particle size from 3 to 5 micrones.
2. In this case particle size is 5micrones in each case but changing the velocity of both particle and fluid from 1m/s to 5m/s.
Before generating cases we are arranging the geometry, mesh and setup.
Geometry:
First go to prepare-volume extract-edge select and select the edges and click on green tick and the uncheck the cyclone separator volume and supress for physics.Here we have extracted the inside fluid volume.
Now close the geometry and open meshing.
Meshing:
Take element size 9mm and generate mesh and keep the names of the inlet, outlet_1 and outlet_2 by named selection.
Now update mesh and close.
Set up:
Solver-pressure based(as mach number<0.3)
velocity-Absolute, Time-steady,enable gravity
Physics-viscous-k-epsilon,RNG,swirl Dominated Flow
Discrete phase-
Since, particulate matter don't obey the continuity law, discrete phase modeling is performed with coupled analysis.
In tracking parameters, maximum no.of steps are considered as 50,000 by which, the stucked particles in the recirculation zone are not tracked.
The injection particulate, Anthracite material is considered which is inert type as shown in the fig. The injection type is surface injection with velocity of 3m/s and particulate diameter of 5 microns.
Turbulent Dispersion - Discrete Random Walk model is selected as it can take turbulent velocity fluctuations into accont.
Boundaries:
1. Inlet type - Velocity, magnitude = 3m/s and reflect type
2. Outlet_1 type - Pressure, magnitude = zero gauge and escape type
3. Outlet2 type - Pressure, magnitude = zero gauge and trap type
4. Wall type - no slip conditions, magnitude = zero gauge and reflect type
Solution:
2nd order upwind scheme produces more accurate spatial discretization schemes.
Initialize the model with standard method and compute from inlet.
patch the z-velocity
Now we are taking 500 iterations.
After completion of iteration track the particles at graphics-particle track
After that export data by Export-particle History data-Brows to select location file - write data.
Case-1:
one micrones particle size with velocity 3m/s:
console:
Residuals:
Anthrasite particle time:
Here time of the particles to be trapped is show by the contour . The contour is created by importing the data and editing the fluent pT for Anthrasite - its colour as variable and variable-Anthrasite particle time.Here 140 particle are tracked but 25 particles are shown.
Pressure:
changing the variable name at Fluent PT for Anthrasite to pressure, we got the pressure difference.
Animation:
Cyclone separator efficiency=(number of particles tracked)/(number of particles trapped) =22/140 =15.71%
Pressure Difference= 15.53-(-5.24) = 20.77 pa
Three micrones particle size with velocity 3m/s:
console:
Residuals:
Anthrasite particle time:
Pressure:
Animation:
Cyclone separator efficiency=(number of particles tracked)/(number of particles trapped) = 87/140 = 62.14%
pressure Difference= 20.77 pa
Five micrones particle size with velocity 3m/s:
console:
Residuals:
Anthrasite particle time:
Pressure:
Animation:
Cyclone separator efficiency=(number of particles tracked)/(number of particles trapped) = 131/140 =93.57%
Pressure Difference = 20.77 pa
Case-2
Five micrones particle size with velocity 1m/s:
console:
Residuals:
Anthrasite particle time:
Pressure:
Animation:
_1666152786.gif)
Cyclone separator efficiency=(number of particles tracked)/(number of particles trapped)=86/142 =60.56%
Pressure Difference = 1.42-(-.43) = 3.30 pa
Five micrones particle size with velocity 3m/s:
console:
Residuals:
Anthrasite particle time:
Pressure:
Animation:
Cyclone separator efficiency=(number of particles tracked)/(number of particles trapped) = 131/140 =93.57%
Pressure Difference = 21.77 pa
Five micrones particle size with velocity 5m/s:
console:
Residuals:
Anthrasite particle time:
Pressure:
Animation:
Cyclone separator efficiency=(number of particles tracked)/(number of particles trapped) =192/205 =93.65%
Pressure Difference = 60.08 pa
Data Table:
C.E. = collector Efficiency, P.D. = Pressure Difference
| 1 micrones | 3 micrones | 5 micrones | |
| 1m/s | C.E.= 60.56% P.D. = 3.30 pa | ||
| 3m/s | C.E. = 15.71% P.D. = 20.77 pa | C.E.=62.14% P.D. =20.77 pa | C.E.=93.57% P.D.= 20.77 pa |
| 5m/s | C.E.=93.65% P.D. = 60.08 pa |
Conclusion:
It is concluded that, the cyclone efficiency increases when particle inlet velocity is increased and the particle diameter size is increased. The pressure drop inside the cyclone separator increases when the inlet velocity is increased and the particle size doesn't affect the pressure drop inside the cyclone separator.
Here turbulent dispersion- random walk model is used so velocity fluctuations for each particles is taken into account and the time required is increased , if its not included time required will be less because of taking average velocity. If random walk model is not included the mesh should be refined at the inlet section so as to get the desired result.
References:
https://www.sciencedirect.com/science/article/pii/S0307904X06000291
Comments
Post a Comment