Performing CFD Analysis On A Cyclone Separator To Track Motion Of Particles With Varied Sizes Using ANSYS Fluent

Abstract:- Cyclone separators are very useful equipment for particle removal from gaseous streams. Their simple design and low capital and maintenance costs have made cyclones one of the most widely used gas−solid separators. Their dynamic behavior includes several phenomena, such as vortex breakdown, a reversal of flow, and high turbulence intensity. Prediction of pressure drop and collection efficiency over a temperature range of several hundred degrees with a high solids loading flow and different cyclone dimensions is very difficult with simplified models. In this work, a model that is based on computational fluid dynamics (CFD) techniques are used to verify the performance of cyclone separators. The model is solved for multiple cases using the finite volume method FVM).  

                                Case1:- To perform an analysis on a given cyclone separator model with a Fine mesh & particle diameter of $5$$\mu m,$traveling at 3m/sec.

                                 Case2:- To perform an analysis on a given cyclone separator model with a Fine mesh & particle diameter of 4 $\mu m,$traveling at 3m/sec.

                                 Case3:- To perform an analysis on a given cyclone separator model with a Fine mesh & particle diameter of 3 $\mu m,$traveling at 3m/sec.

                                 Case4:- To perform an analysis on a given cyclone separator model with a Fine mesh & particle diameter of 2 $\mu m,$traveling at 3m/sec.

                                Case5:- To perform an analysis on a given cyclone separator model with a Fine Mesh & particle diameter of 1 $\mu m,$traveling at 3m/sec.

                                Case6:- To perform an analysis on a given cyclone separator model with a Coarse Mesh & particle diameter of 5 $\mu m,$traveling at 1m/sec. 

                                Case7:- To perform an analysis on a given cyclone separator model with a Coarse Mesh & particle diameter of 5 $\mu m,$traveling at 5m/sec.  

Theory_________________________________________________________________________

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 pre-cleaners for more expensive final control devices such as 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 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 inaccuracy in the efficiency prediction may result in an inefficient design of the cyclone separator. CFD has a great potential to predict the 45 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 modeling the cyclone pressure drop. 

     CYCLONE EFFICIENCY EMPIRICAL MODELS

                    A. Iozia and Leith Mode

Iozia and Leith's logistic model is a modified version of the Barth Model, which is developed based on force balance. The model assumes that a particle carried by the vortex endures 165 the influence of two forces: a centrifugal force, Z and a flow resistance, W . The collection efficiency ηi of particle diameter dpi can be calculated from                                                                                             `ηi= 1/((1 + (d_(pc) / d_pi))^beta.`                                                                                                                                                                                        β is an expression for slope parameter derived based on the statistical analysis of experimental data of a cyclone with D = diameter of the cyclone separator.

Geometry_______________________________________________________________________

 We start by converting the unit system to "m" & then import the pre-modeled Exhaust Port geometry to Spaceclaim, where we will be editing our geometry according to our simulation requirements. Our geometry is made up of two volumes, the solid volume where heat will be transferred & the fluid volume though which fluid flow will occur. We use the "Volume Extraction" tool to create two separate regions (solid & volume region) as shown in Fig 1.1. The Cyclone separator has an inlet duct accompanied by 2 outlets, one located at the bottom, the other located at the top. Lighter particles get ejected from the system through the top outlet whereas the heavier particles exit the domain through the bottom outlet.

Meshing Module_______________________________________________________________

The process of discretizing our domain of interest i.e. the geometry into smaller pieces is called mesh generation. Ansys Fluent uses a technique called the "bounding box" method to generate a body-fitted mesh. This is done by creating the smallest size hypothetical box possible that can be used to fit the entire geometry into it. In the meshing module interface, we start by assigning names to our geometry. For our geometry, we have 1 inlet, 2 outlets & the rest of the unmarked walls will automatically be assigned as adiabatic walls by Ansys fluent as shown in Fig 2.1. The next part of the meshing module involves the meshing strategies used for both cases.  We will be running the simulation for 7 cases with varying mesh sizes. Case 1,2,3,4,5 will have a fine mesh with an element size of 15 mm equally distributed throughout the geometry & a "body sizing" mesh of 5 mm near the inlet region while Case 6 & 7 will have a coarse mesh with an element size of 20 mm.

                                                                                                                     Fig 2.1:- Assigning appropriate names to the geometry

                                                     Fig 2.2:- Mesh for case 1 & 2                                                                                            Fig 2.3:- Mesh for case 2 & 3

Setting Up The Flow Physics____________________________________________________

 In Ansys Fluent after creating the mesh & assigning the boundaries, the next stage of the process is to set up the simulation parameters. This step involves specifying certain properties to capture the physics of the problem perfectly, such as materials used, simulation time parameters, solver parameters, initial & boundary conditions to solve the Navier Stokes equation which are PDEs, defining body forces, type of fluid flow & species involved.

We start by setting up the Domain, here we will be using a pressure-based steady-state solver with absolute velocity formation. We can justify using a pressure-based solver since the velocity or Mach number at the inlet for all 4 cases will be very low (V= 3 m/s, 1 m/s & 5 m/s). We also enable the Gravitational force with magnitude 9.81 along the negative Y-axis. Along with the standard PDEs, we will also be solving the Energy equation. For materials, our choice of fluid (gas) will be air and our solid material will be aluminum. 

Next, we use "Discrete Phase Model" feature, where we will be setting up the parameters required for Discrete Phase Modelling. We enable "interact with continuous phase" & "update DPM sources every flow iterations". For tracking data, our maximum number of timesteps will be 50,000 & step length factor of 5. For injection, we are going to inject a material known as "anthracite" & it will be injected through the "inlet surface" at a velocity of 3 m/s or 1 m/s or 5 m/s depending on the case setup.

The next parameter that we need to set up is the Flow Physics, here we will be navigating to the viscous feature & use an upgraded version of the original K-epsilon (k-ε) turbulence model i.e. the RNG k-epsilon (k-ε) turbulence with standard wall function in which the magnitudes of two turbulence quantities, the turbulence kinetic energy ‘k’ and its dissipation rate ‘ϵ’, is calculated from transport equations solved simultaneously with those governing equations for capturing better flow physics, we also enable "swirl dominated flow" as we can expect swirling inside the cyclone separator. 

We then navigate to the boundary zone, these are user-defined parameters where we assign the inlet velocity & DPM boundary condition. All 4 cases will have a varied velocity magnitude ranging from 1 m/s to 5m/s & we define the pressure near the outlet area to be 1pa or gauge pressure of 0pa with the Top Outlet having "Escape" boundary condition & Bottom Outlet having "Trap" boundary condition for DPM. The final step includes performing a hybrid initialization & running the simulation for 400-500 iterations until the solutions converge.

Results &  Observations_________________________________________________________

     A. Residual Plots

     B. Particle Information 

     C. Particle Motion Contour

     D. Particle Tracking Animation

Conclusion______________________________________________________________________

For Case 1 to Case 5, where we had a mesh with an element size of 15 mm with a 5 mm "body sizing" mesh at the inlet, generated a total cell count of 50940 & Case 6 & 7 also had a mesh with an element size of 15 mm but with no "body sizing" mesh at the inlet, hence a lower cell count of 46392 was observed. Due to larger cell count for cases 1 to 5, a total of 408 particles of anthracite were generated for the simulation & for cases 6 & 7, a total of just 48 particles of anthracite were generated. Hence, we can conclude that a finer mesh at the inlet creates additional cells near the inlet allows more particles of the  "injected material" to enter the computational domain. The size (diameter) of the injected element also plays a vital role as it is evident from Cases 1 to 5 simulations. Our injected material, anthracite for case 1 was the largest particle with an element size of 5 micrometers, we can observe that all of the particles were able to escape the domain, with a majority of them escaping from the bottom outlet (Trap) but as we decrease the element size to 1 micrometer, we observe the majority of the particles are constrained in the recirculation zone even after the solutions have converged. Thus, it's evident that size discrepancy of the injected material plays an important role in Cyclone separators. As for Case 6 & 7, we varied the inlet velocity of the particles keeping the diameter constant & observed that at low velocities majority of the particles are stuck in the recirculation zone while for a higher inlet velocity for the same particle diameter, all the particles are able to exit the domain. To conclude, mesh size at the inlet, injected element size (diameter) & velocity of the injected material along with the design of the cyclone separator plays an important role for the efficient working of a cyclone separator in the industry.

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