Post on 13-Mar-2018
1 Challenge the future
MSc. Thesis Project Simulation of a Rotary Kiln
MSc. Cand.: Miguel A. Romero Advisor: Dr. Domenico Lahaye
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?
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
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
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?
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
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
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
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)
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.
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
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.
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
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?
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
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
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
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
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
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)
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
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
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?
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
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
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
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