Week 8 - Simulating Cyclone separator with Discrete Phase Modelling

CYCLONE SEPARATOR SIMULATION USING DPM

1. OBJECTIVE

• To brief about the empirical models used to calculate cyclone separator
• To perform the simulation and calculate the efficiency of cyclone separator by varying particle size from 1 - 5 μm for inlet velocity of 3 m/s.
• To perform the simulation and calculate the efficiency of cyclone separator by varying velocity from 1 - 5 m/s for the particle size of 5 μm.

2. 3D MODEL

Fluid volume of cyclone separator consists of a single inlet along z-axis and two outlets at opposite directions along the y-axis.

Fig.2.1. Cyclone Separator Fluid Volume

3. COMPUTATIONAL MODEL

Cyclone Separator’s efficiency is simulated by using DPM. The empirical models being used for Cyclone Separator Efficiency are:

• Iozia-Leith Model

It’s a modified version of Barth’s model, being developed for force balance. This model assumes that the particle carried by vortex endures both centrifugal force and flow resistance.

• Li-Wang Model

This model introduces particle bounce and turbulent diffusion at the cyclone wall. This model was developed based on

• Non-constant radical particle velocity and radial concentration profile for uncollected particles within the cyclone.
• Boundary conditions based on parameter with the consideration of turbulent diffusion coefficient and particle bounce.
• Tangential velocity related to the radius of the cyclone.

4. MESH DETAILS

Table 4.1: Mesh Details

METRICS	VALUE
MESH SIZE	7.5e-3 m
NODES	43,143
ELEMENTS	2,16,784

Fig.4.1: Fluid Volume Mesh

5. BOUNDARY CONDITIONS

Inlet and wall: Reflect

Outlet (Top): Escape

Outlet (Bottom/dustbin): Trap

6. RESULTS

Solution was calculated for 600 iterations, using standard initialization.

6.1. CASE 1

6.1.1. DIAMETER = 1 μm

A total number of 196 particles has been tracked, where 78 particles escaped and 118 particles are incomplete.

Table 6.1.1.1: Particle Tracked Data for 1μm

METRICS	VALUE
PARTICLES TRACKED	196
PARTICLES ESCAPED	44
PARTICLES TRAPPED	34
PARTICLES INCOMPLETE	118
MASS FLOW AT INLET	0.018375 kg/s
MASS FLOW RATE AT OUTLET 1	0.0148928 kg/s
MASS FLOW RATE AT OUTLET 2	0.00347712 kg/s

6.1.2. DIAMETER = 3 μm

Table 6.1.2.1: Particle Tracked Data for 3μm

METRICS	VALUE
PARTICLES TRACKED	196
PARTICLES ESCAPED	19
PARTICLES TRAPPED	175
PARTICLES INCOMPLETE	2
MASS FLOW AT INLET	0.018375 kg/s
MASS FLOW RATE AT OUTLET 1	0.0148928 kg/s
MASS FLOW RATE AT OUTLET 2	0.00347712 kg/s

6.1.3. DIAMETER = 5 μm

Table 6.1.3.1: Particle Tracked Data for 5μm

METRICS	VALUE
PARTICLES TRACKED	196
PARTICLES ESCAPED	0
PARTICLES TRAPPED	195
PARTICLES INCOMPLETE	1
MASS FLOW AT INLET	0.018375 kg/s
MASS FLOW RATE AT OUTLET 1	0.0148928 kg/s
MASS FLOW RATE AT OUTLET 2	0.00347712 kg/s

6.2. CASE 2

6.2.1. VELOCITY = 1 MPS

Table 6.2.1.1: Particle Tracked Data for 5μm

METRICS	VALUE
PARTICLES TRACKED	196
PARTICLES ESCAPED	7
PARTICLES TRAPPED	10
PARTICLES INCOMPLETE	179
MASS FLOW AT INLET	0.006125 kg/s
MASS FLOW RATE AT OUTLET 1	0.00493269 kg/s
MASS FLOW RATE AT OUTLET 2	0.00119232 kg/s

6.2.2. VELOCITY = 5MPS

Table 6.2.2.1: Particle Tracked Data for 5μm

METRICS	VALUE
PARTICLES TRACKED	196
PARTICLES ESCAPED	0
PARTICLES TRAPPED	156
PARTICLES INCOMPLETE	40
MASS FLOW AT INLET	0.0153125 kg/s
MASS FLOW RATE AT OUTLET 1	0.0123942 kg/s
MASS FLOW RATE AT OUTLET 2	0.00291539 kg/s

6.2.3. VELOCITY = 5 MPS

Table 6.2.3.1: Particle Tracked Data for 5μm

METRICS	VALUE
PARTICLES TRACKED	196
PARTICLES ESCAPED	0
PARTICLES TRAPPED	196
PARTICLES INCOMPLETE	0
MASS FLOW AT INLET	0.030625 kg/s
MASS FLOW RATE AT OUTLET 1	0.0249152 kg/s
MASS FLOW RATE AT OUTLET 2	0.00569919 kg/s

7. CONCLUSION

• The particle flow’s turbulence reduces with increase in velocity and particle size.
• When particle size reduces below 5μm, incomplete particles increases resulting in reduced efficiency.
• When particle velocity reduces below 5 m/s, efficiency reduces due to incomplete particles.