Week 8 - Simulating Cyclone separator with Discrete Phase Modelling
AIM
To perform analysis on cyclone separator and calculate the separation efficiency and pressure drop.
OBJECTIVES
- 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
Cyclones are devices that employ a centrifugal force generated by a spinning gas stream to separate particles from the carrier gas. Their simple design, low capital cost and nearly maintenance-free operation make them ideal for use as precleaners for more expensive final control devices such as baghouses or electrostatic precipitators. Cyclones are particularly well suited for high temperature and pressure conditions because of their rugged design and flexible component materials. Cyclone collection efficiencies can reach 99% for particles bigger than 5 µm, and can be operated at very high dust loading. Cyclones are used for the removal of large particles for both air pollution control and process use. Application in extreme conditions includes the removal of coal dust in a power plant and the use as a spray dryer or gasification reactor. Engineers are generally interested in two parameters in order to carry out an assessment of the design and performance of a cyclone. These parameters are the collection efficiency of particle and pressure drop through the cyclone. An accurate prediction of cyclone efficiency is very important because an inaccuracy in the efficiency prediction may result in an inefficient design of the cyclone separator. CFD has a great potential to predict the flow field characteristics and particle trajectories inside the cyclone as well as the pressure drop. The complicated swirling turbulent flow in a cyclone places great demands on the numerical techniques and the turbulence models employed in the CFD codes when modelling the cyclone pressure drop.
Cyclone separators work much like a centrifuge, but with a continuous feed of dirty air. In a cyclone separator, dirty flue gas is fed into a chamber. The inside of the chamber creates a spiral vortex, similar to a tornado. The lighter components of this gas have less inertia, so it is easier for them to be influenced by the vortex and travel up it. Contrarily, larger components of particulate matter have more inertia and are not as easily influenced by the vortex.
Since these larger particles have difficulty following the high-speed spiral motion of the gas and the vortex, the particles hit the inside walls of the container and drop down into a collection hopper. These chambers are shaped like an upside-down cone to promote the collection of these particles at the bottom of the container. The cleaned flue gas escapes out the top of the chamber.
Most cyclones are built to control and remove particulate matter that is larger than 10 micrometers in diameter. However, there do exist high efficiency cyclones that are designed to be effective on particles as small as 2.5 micrometers. As well, these separators are not effective on extremely large particulate matter. For particulates around 200 micrometers in size, gravity settling chambers or momentum separators are a better option.
Cyclone pressure drop essentially depends on the cyclone geometry, surface roughness and operating conditions including gas temperature, inlet velocity, and solid loading. The pressure drop can be divided into two parts: Local losses and frictional losses in the cyclone body.
CYCLONE EFFICIENCY EMPIRICAL MODELS
Iozia and Leith Model
Iozia and Leith 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 of particle diameter dpi can be calculated from
β is an expression for slope parameter derived based on the statistical analysis of experimental data of a cyclone
Li and Wang Model
The Li and Wang model includes particle bounce or reentrainment 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 reentrainment on the cyclone wall
The concentration distribution in a cyclone is given as
The resultant expression of the collection efficiency for particle of any size is given as
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`R^n` = constant.
A force balance and an equation on the particles collection yields the grade efficiency ηi
G is a factor related to the configuration of the cyclone, n is related to the vortex and `tau` is the relaxation term.
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. The semi empirical relationship developed by Lapple to calculate a 50% cut diameter, dpc, is
The efficiency of collection of any size of particle is given by
SIMULATION PROCEDURE
Open Ansys Workbench, select fluid flow(fluent) and open spaceclaim. Import the file containing cyclone separator into spaceclaim.
Remove the extra edges
Extract the fluid volume using volume extract option.
After creating geometry, close spacelaim then the next step is to create the mesh. Before meshing name the boundaries.
Meshing is complete. Double click on setup to open CFD solver.
Element size = 5.5mm
Number of elements = 455388
CFD Case Setup
- Pressure based solver
- Steady state simulation is performed
- Gravity is enabled in -ve Y axis.
- Viscous model - K epsilon RNG with swirl dominated flow
- Discrete phase modelling is turned on (interaction with continous phase).
-
Boundary Conditions:-
Inlet and wall:- Reflect
Outlet(Top):- Escape
Outlet(Bottom/dustbin) - Trap
- Initialize the solution using standard initialization.
- Run the calculation.
RESULTS
Perform an analysis on a given cyclone separator model by varying the particle diameter from 1 μm to 5 μm (velocity constant = 3m/sec)
Case 1a
Particle diameter = 5`mum`
Velocity = 3m/s
Residual plot
Particle time
The animation video is provided in the below link :
https://drive.google.com/file/d/1TnK0tB7gX48UZPb9qAx60RXOCLdyJH7W/view?usp=sharing
Separation efficiency = (No. of particles trapped / No. of particles tracked)*100
= (346 / 346)*100 = 100%
Case 1b
Particle diameter = 3`mum`
Velocity = 3m/s
Residual plot
Particle time
The animation video is provided in the below link :
https://drive.google.com/file/d/19cz4TdEBcH91INm_uzRImPBqCsW7nVuy/view?usp=sharing
Separation efficiency = (No. of particles trapped / No. of particles tracked)*100
= (333 / 346)*100 = 96.24%
Case 1c
Particle diameter = 1`mum`
Velocity = 3m/s
Residual plot
Particle time
The animation video is provided in the below link :
https://drive.google.com/file/d/1Pm30DpZ5kemkl5YpcYqA2Jq0H3L-B5ny/view?usp=sharing
Separation efficiency = (No. of particles trapped / No. of particles tracked)*100
= (267 / 346)*100 = 77.16%
Perform an analysis by varying the particle velocity from 1 m/sec to 5 m/sec (particle diameter constant = 5`mum`)
Case 2a
Particle diameter = 5`mum`
Velocity = 1m/s
Residual plot
Pressure at inlet
Pressure at outlet
Particle time
The animation video is provided in the below link :
https://drive.google.com/file/d/1ZGtJTOgwJ2AOvWQUBwXGBcVIHvMqRse3/view?usp=sharing
Separation efficiency = (No. of particles trapped / No. of particles tracked)*100
= (284 / 346)*100 = 82.08%
Pressure drop = 1.997Pa
Case 2b
Particle diameter = 5`mum`
Velocity = 3m/s
Residual plot
Pressure at outlet
Pressure at inlet
Particle time
The animation video is provided in the below link :
https://drive.google.com/file/d/10sR0MlDBHeXzm6vHGy6WMnnf9tQZCzWw/view?usp=sharing
Separation efficiency = (No. of particles trapped / No. of particles tracked)*100
= (346 / 346)*100 = 100%
Pressure drop =24.519 Pa
Case 2c
Particle diameter = 5`mum`
Velocity = 5m/s
Residual plot
Pressure at outlet
Pressure at inlet
Particle time
The animation video is provided in the below link :
https://drive.google.com/file/d/1IIwRHIsRbuxTPiN8EVxu-FgoQzFaDY_Z/view?usp=sharing
Separation efficiency = (No. of particles trapped / No. of particles tracked)*100
= (346 / 346)*100 = 100%
Pressure drop = 70.829Pa
CONCLUSION
- An analysis was performed on a cyclone separator to caclulate the separation efficiency and pressure drop. The simulation was carried out by varying the particle velocity and particle diameter.
- Separation efficiency increases with increase in the particle size.
- Pressure drop across the cyclone is of much importance in a cyclone separator. Pressure drop occurs due to swirl and energy dissipation. Pressure drop increases with increase in the particle velocity. As pressure drop increases separation efficiency also increases.