MSc. Thesis Project - Applied...

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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

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?

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

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

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?

• 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

• 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

= up,idup,idt

• 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

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)

•  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.

0 20000 40000 60000 80000 100000 120000 140000 160000 180000 200000

Np vs t

• 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.

•  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

• 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?

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

• 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 = −ε s∇Pg − Fg

vs −vg( ) + R0v

• 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

•  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

• 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

• 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)

• 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

•  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

• 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?

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

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

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

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

MSc. Thesis Project - Applied...

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