time-accurate modeling of liquid mixing and blending: application of the lattice-boltzmann method
TRANSCRIPT
Time-Accurate Modeling of Liquid Mixing and Blending: Application of the Lattice-Boltzmann Method
John Thomas, President, M-Star Simulations
Nicolle Courtemanche, Sr. Application Engineer, SPX Flow, Inc. LLC Lightnin
Richard Kehn, Director R&D, SPX Flow, Inc. LLC LIGHTNIN
Liquid blending (baffled tank) Lack of baffles (problems with suspension)
Mixing governs process yield, efficiency, and repeatability
Fermenter reactor Continuous stirred tank reactor
Static mixer
Mixing is an inherently transient, 3D, and multi-physics problem
Theory
Experiment
Simulation
Process optimization involves theory, experiment, and simulation
Presentation overview
New algorithms and architectures
Test cases and validation
Expanded scope and strategy
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NEW ALGORITHMS AND ARCHITECTURES
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Navier-Stokes and Boltzmann describe same physics
Navier-Stokes
Discrete Boltzmann
Chen et al. (1992). Recovery of Navier-Stokes equations using a lattice-gas Boltzmann method.
Succi, Sauro (2001). The Lattice Boltzmann Equation for Fluid Dynamics and Beyond.
Boltzmann is more computationally attractive
Navier-Stokes Boltzmann
Problem Set-up
Dynamics Typically steady-state Inherently transient
Mesh type/size Variable, ~106
Uniform, ~109
Parallelizability Practical
Trivial
Boltzmann enables superior turbulence models
Conventional RANS Model
(k-e Turbulence Model) Boltzmann LES Model
High resolution means no explicit meshing
Approach has been around for decades…
Nov. 19, 1985
…just needed technology to catch-up.
10-2
10-1
100
101
102
103
104
$US/GB
or
$US/GFLOP
Cloud HPC
GPU-centric
HPC
ASCI White
10 TFLOP, $140 MM
Intel Celeron
10 TFLOP, $500
Key points: CFD
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Boltzmann describes same physics
Superior turbulence models
No user meshing
Approach follows megatrends
TEST CASES AND VALIDATION
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Test configuration: angular offset mount mixing system
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Tank Set-Up
17.5” diameter dish-bottom
18” depth
Water at STP
Impeller Set-Up
5.9” A310 (single and double)
10˚ vertical angle
3” off-bottom
101 RPM to 289 RPM
Validation Goals
Homogenization vs. speed
Vortex formation (single @350 RPM)
Experimental work carried out at
SPX Flow’s Rochester R&D Center
(LIGHTNIN)
RANS CFD Predicts General Features of Flow can also be used to run tracer blend time study
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Set-up time: 10 min
CPU time: 1.25 hours (20 cores)
149 RPM A310 System
LBM CFD Predicts 3D Evolution of Flow Field and Transport
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Resolution=5 mm (4.9 MM vertices)
Timestep=600 μs (113,000 cycles)
Set-up time: 10 min
CPU time: 5.5 hours (20 cores)
101 RPM Double A310 System
Measured and Predicted Blend Times
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149 RPM Neutralization Reaction 149 RPM Salt Homogenization
Predicted versus Measured Blend times
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DMT Predicts Correct Vortex Morphology
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M-Star DMT Input: surface tension, density, viscosity
No correlations or tuning parameters.
350 RPM
Summary and Action
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Experiment, theory and simulation work
together in optimizing reactor design
(there is not one single design tool!)
Major advances in HPC have led to major
advances in process simulation
These improved models provide higher
fidelity data with less effort
Predictions agree very well with
experiment