msc. thesis project - applied...
TRANSCRIPT
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1 Challenge the future
MSc. Thesis Project Simulation of a Rotary Kiln
MSc. Cand.: Miguel A. Romero Advisor: Dr. Domenico Lahaye
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2 Challenge the future
Problem Description
• A Rotary Kiln is a pyroprocessing device used to raise materials to high temperatures in a continuous process.
What is a Rotary Kiln?
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3 Challenge the future
Problem Objectives
• Accurately calculate the Temperature Profile of the Granular bed of the Rotary Kiln. This will lead to an accurate analysis on where hot spots could appear and a sensibility analysis in conjunction with M. Pisaroni’s work by varying parameters, such as G/Air ratio, inclination and RPM, in order to homogenise the profile and reduce hot spots.
• If reaction kinetics are known, a more accurate description of the process can be made and concentration profiles can be incorporated into the Simulation.
Abstract
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4 Challenge the future
Simulation Set-up
• The Problem can be divided into two sub problems: • Simulation of the Combusting Gases
• Work done by M. Pisaroni
• Simulation of the Granular Bed • To be the focus of the present project
• The the simulation of the Granular Bed will use data from the Combusting gases as input
Rotary Kiln simulation
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5 Challenge the future
Granular Bed Simulation
• “Granular material is a collection of solid particles or grains, such that most of the particles are in contact with at least some of their neighboring particles. Examples: sand, gravel, food grains, seeds, sugar coal and cement,” (Kesava & Prabhu, 2008)
• We call granular flow to the displacement of granular material
• Granular materials exhibit characteristics similar to both solids and liquids
What is Granular Flow?
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6 Challenge the future
Granular Bed Simulation
• There are two typical ways of modelling granular flow: • Discrete Method: Euler-Lagrange approach (Coupled DEM)
• Treat the material as a collection of particles. Newton’s laws of
motion are applied to each particle
• Continuum Models: Euler-Euler approach (Two fluid modeling) • Particles are modeled by a continious medium where all the
quantities are assumed to be smooth functions of position and time
(local averaging)
Modeling Approaches
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7 Challenge the future
Granular Bed Simulation
• Consists of an ODE system: • Particle Motion / Particle Tracking
• With contact forces using the soft-sphere approach (suitable for multiple contacts), spring and dampener model.
• Then we solve a “new” ODE system with linear or non-linear “spring”.
Euler-Lagrange: Discrete Element Method
dxidt
= up,idup,idt
= 1mp
Fp
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8 Challenge the future
Granular Bed Simulation
• Advantages • Relatively simple model, easy to understand physics • Easy to implement, there are also a number of Commercial and
Open Source software implementations: Star CCM+, OpenFOAM, LIGGGHTS/LAMMPS, MFIX.
• Implementations are in parallel/parallelizable
• Disadvantages • May still need some empirical adjustments because of the non-
sphericity of particles. Still needs validation of certain parameters.
• Very computationally expensive -> in 3-D one needs for particle motion 6 ODEs per particle, in our problem we have ~1.5 billion particles
Euler-Lagrange: Discrete Element Method
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9 Challenge the future
Granular Bed Simulation Euler-Lagrange: Discrete Element Method
• Experiments were done with LIGGGHTS in order to investigate feasibility because of the size of the problem
• What is LIGGGHTS? • Open Source discrete element method particle simulation
software based on LAMMPS (molecular dynamics simulator from Sandia National Laboratories from the US DoE)
• “Highly scalable parallel DEM Simulator” (uses MPI)
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10 Challenge the future
Granular Bed Simulation Euler-Lagrange: Discrete Element Method
• Experiment Setup
• Simulation of a rotating cylinder • Diameter: 2.1 m • Number of particles: ~15,000 - 200,000 • Cylinder Length: 0.1 m • Simulation time: 3 s • Timestep: 0.00001 s • 1 core • 2 RPM • 5% loading by volume
*Test for visualization. NOT an experiment run.
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11 Challenge the future
Granular Bed Simulation Euler-Lagrange: Discrete Element Method
0
10
20
30
40
50
60
70
80
0 20000 40000 60000 80000 100000 120000 140000 160000 180000 200000
Np vs t
Np vs t
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12 Challenge the future
Granular Bed Simulation Euler-Lagrange: Discrete Element Method
• Notes about the simulation • There was overhead because of writing of data every 1000 time
steps • Not yet parallelized • Only 3 s of simulation time • There is maybe a cheaper way of incorporating the rotation of
the cylinder • No Heat Transfer or Chemical Reactions were incorporated
• By taking the packing limit of 0.5 and a loading of 5% with particles of 2.5 mm, one gets ~1.1 billion particles.
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13 Challenge the future
Granular Bed Simulation Euler-Lagrange: Discrete Element Method
• Possible set up for the Simulation • Using fixed temperature profile/radiation from the flame data
already available • Having a Coupled simulation of the Combustion and Particle flow
using the model already available
• Data needed: • Mass and Energy balances for set up and validation • Reaction kinetics or simplified kinetics in order to calculate
accurately the T profile of the particle bed
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14 Challenge the future
Granular Bed Simulation Euler-Lagrange: Discrete Element Method
• Open Questions
• Mass and Energy balance data • Reaction Kinetics • Questions on implementation of solid-solid reactions with respect
to the Discrete Element Method (opposed to a much easier implementation of solid-fluid reactions)
• How will the performance be affected by the Heat Transfer/Chemical Reactions and Parallelization on the simulation?
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15 Challenge the future
Granular Bed Simulation Euler-Euler: Two Fluid approach
• Two-phase hydrodynamic models treat the fluid and the solids as two interpenetrating continua.
• One uses an averaging approach where equations are derived by space, time or ensemble averaging of the local, instantaneous balances of each of the phases.
• Basically a multiphase RANS code; implemented in almost any CFD software such as: Fluent, Star CCM+, OpenFOAM and MFIX.
• Extensive use for simulating Fluidised Beds and Slurry flows
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16 Challenge the future
Granular Bed Simulation Euler-Euler: Two Fluid approach
• Conservation of mass and momentum
• The interaction force (momentum transfer) between phases can be modeled in the same way as in the Euler-Lagrange approach, having Drag, Buoyancy and Mass Transfer.
∂∂t
εgρg( ) +∇ i εgρg vg( ) = Rg
∂∂t
ε sρs( ) +∇ i ε sρs vs( ) = Rs
∂∂t
εgρg vg( ) +∇ i εgρg vg
vg( ) = ∇ i Sg + εgρg g
− Ig
∂∂t
ε sρs vs( ) +∇ i ε sρs vs
vs( ) = ∇ i Ss + ε sρs g
+ Ig
Ig = −ε s∇Pg − Fg
vs −vg( ) + R0v
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17 Challenge the future
Granular Bed Simulation Euler-Euler: Two Fluid approach
• The most difficult and interesting part is the modeling and definition of the Stress Tensors.
• For the fluid phase it takes the usual form:
• With the Pressure and the Newtonian Viscous Stress Tensor
Sg = −PgI +τ g
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18 Challenge the future
Granular Bed Simulation Euler-Euler: Two Fluid approach
• For the solids, we can observe that granular flows can be classified with two distinct flow regimes • Viscous flow which is rapidly shearing, where stresses arise
because of collisions (momentum transfer) • Plastic flow which is slowly shearing, where stresses arise
because of enduring contact (coulomb friction)
• We then have two models for the Stress tensor in our Solids Momentum transfer • Viscous flow is based on Kinetic theory of gases • Plastic flow by an empirical power law depending on material
properties
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19 Challenge the future
Granular Bed Simulation Euler-Euler: Two Fluid approach
• Advantages • Less computational cost • Chemical Reactions are easy to include (modeled as a PFR on
the bed “=“ as a series of CSTRs on the volumes along the axis of the bed)
• Easier integration with previous work
• Disadvantages • Much more modeling required, more validation needed and not
so easy to understand • Never has been used for a 3-D rotary drum (at least not
reported) but there are reported results on a 2-D rotary drum • Boundary conditions are tricky; Rotating walls, inflow velocity
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20 Challenge the future
Granular Bed Simulation Euler-Euler: Two Fluid approach
• OpenFOAM was used to do a 2-D rotating cylinder full of particles in order to learn about the possible caveats on an euler-euler simulation for a rotary drum.
• Tutorials on two phase euler simulations for fluidised beds was followed with modifications in order to adapt it to my specific problem
• Arbitrary material properties were chosen and a kinetic theory description was used for the stress tensor of the solid phase (viscous flow)
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21 Challenge the future
Granular Bed Simulation Euler-Euler: Two Fluid approach
• There was some difficulty to get a stable solution, especially because the system is near the packing limit of the particles
• Steady state conditions not met; initial conditions are tricky • An angle of repose can be seen but correct recirculation
zones are not observed
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22 Challenge the future
Granular Bed Simulation Euler-Euler: Two Fluid approach
• Possible set up for the Simulation • Define a flow rate on the particle bed on the direction of the axis
of the kiln and make a coupled two phase simulation with chemical reactions included
• Data needed • Mass and Energy balances for set up and validation • Reaction kinetics • Residence time of the particles due to inclination and rotation
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23 Challenge the future
Granular Bed Simulation Euler-Euler: Two Fluid approach
• Open Questions
• Mass and Energy balance data • Reaction Kinetics • Residence Time of particles with respect to current or possible
configurations (inclination and rotational speed)
• How to create a good mesh for the calculations? • Exactly how fast can it be?
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24 Challenge the future
Simulation Set-up Granular Bed Simulation
• Now What? • The DEM approach can be almost readily set-up for use with
Star CCM+ and sent to a computational cluster • A Two-Fluid approach needs to be further investigated although
first results look quite promising • Further reading in Reaction Kinetics needs to be done in order to
have a correct Temperature Profile
• An Euler-Lagrangian simulation will be set up and sent to a computational cluster with particle heat transfer
• Meanwhile the Euler-Euler approach will be investigated
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25 Challenge the future
Granular Bed Simulation Validation of the Simulation
• There are various papers by Boateng that describe the “hydrodynamics” of the particle flow on a rotary kiln, these are to be used to validate the flow patterns and the angle of repose of the simulations
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26 Challenge the future
Granular Bed Simulation Validation of the Simulation
• Mass Balances and Energy Balances of the actual Rotary kiln can be used to validate the Heat Transfer / Temperature Profile and the Concentration Profile if done
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27 Challenge the future
Conclusions
• Each simulation approach can be used for different goals
• Discrete Element Modelling: • Particle Mean Residence time depending on angle and RPM • Accurate Temperature profile to look at hot spots
• Two Fluid Approach: • Because of the averaging nature of the approach, temperature
profile is not as accurate • Concentration profiles are easily incorporated if reaction kinetics
are known
From literature study