probing heterogeneities in fluid-particle systems
TRANSCRIPT
1
S. Radl1
1Graz University of Technology
with contributions fromT. Forgber,1 F. Municchi,1 R. Pichler,1 C. Kloss,2
and C. Goniva2
2DCS Computing GmbH, Linz
A. Ozel,3 C. Boyce,3 S. Sundaresan3
3Princeton University, New Jersey, U.S.A.
Probing Heterogeneities in Fluid-Particle Systems with the Computer
2
Motivation
Why modelling & simulation?
Example: Heterogeneous catalyst particles
Spatial-temporal fluctuations1,2 (reactant & active site distribution)
1Ertl, Nobel lecture, 2007. 2Buurmans and Weckhuysen, Nature Chemistry, 2012.
Experimental limitations
• ~20 nm & ~1 µs resolution: average over ~104 active sites & 109 events.
• Do we affect the sample with our synchrotron light beam?
• Are we probing enough particles?
Spiral waves in CO oxidation.1
Ni-complex distribution in Al2O3 support.2
3
Motivation
Why modelling & simulation?
Let us make some predictions…
• Electronic and atomistic models help,3 but limited to < 1 µm & < 1 ns.
• We need to account for heterogeneities, defects, etc. on scales 10 nm … 1 m!
• We need to design (i) the preparation process (impregnation, drying), as well
as (ii) production process.
3Norskokv et al., Nature Chemistry, 2009.
Acetylene (left) and ethylene (right) on NiZn.3
Couple reactions & phase change
with macroscopic transport
phenomena
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Overview
Part I Which models shall we use?
Part II How to analyze the results?
Part III What have we learned?
15 + 5 + 10 = 30 mins
w dt dV
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The Models
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ParScale
The COSI Open-Source Plattform (Euler-Lagrange)
Model Overview
Porto
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I) Particle-Resolved Direct Numerical Simulations
processor boundary
This interpolation cell resides
on a different processor
and it will not be found when
computing the interpolation
points for 𝑃𝑠
Novelty: immersed boundary algorithm for massively-parallel
computations in CFDEM®
4Municchi et al., manuscript in preparation.
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I) Particle-Resolved Direct Numerical Simulations
Results: improved models for heat transfer rates
in polydisperse particle beds
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„Meso“ Scale: Particle Ensemble
„Micro“ Scale: Individual Particle
5Pichler, Master Thesis, TU Graz, 2014
Concentration field on the surface of
reacting particles.5
II) Intra-Particle Transport
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(1) ModelEqn1D(Spherical)
• 1-D discretisation
• fixed number of grid poitns in spherical coordinates
• can be upgraded to cylinder and Cartesian coordinates
(2) ModelEqnShrinkingCore
• 0-D model for reduction (shrinking due to reaction)
of solid core
• can be upgraded to multiple zones (e.g., biomass
combustion: 4 zones)
II) Intra-Particle Transport
Model Categories
11
6Radl et al., PARTICLES Conference,
Barcelona, 2015. 7Forgber et al., PARTICLES Conference,
Barcelona, 2015.
Novelty: modular approach for solving
species and heat transport equations
including reactions
II) Intra-Particle Transport
128Ågren, NIST Diffusion workshop, 2012.
Illustration of oxide layer formation during
the oxidation of iron. 8
II) Intra-Particle Transport
Application: model structure for CLC and
CLR processes
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Novelty: algorithms for robust integration
(stiff coupling!), new models for
momentum, heat & mass transfer
III) Unresolved Euler-Lagrange Model (CFDEM®)
Application: conversion of
porous reactive particles in a
fluidized bed
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Post-Processing
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Filtering
Purpose: accelerate model development
(continuum, 1D, 0D)
The Post-Processing Utility CPPPO
Application: heat transfer from dense
particle bed to fluid
9Municchi et al., CPC (revision submitted), 2016.
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Three sets of operations
Filtering of fluid and particle data, including
variance calculation
Sampling of filtered data and their derivatives with
statistical biasing (e.g., limiters)
Binning of sampled data using running
statistics
The Post-Processing Utility CPPPO
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Post-Processing Utility CPPPO
Lagrangian filters and samples are particle based,
i.e., they are performed at user-defined locations
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Distribution of particle-based
Nusselt number
Post-Processing Utility CPPPO
Parallel scalability
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Lessons Learned
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Reduction of a Particle Bed
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The „Toy“ Problem: Shear Flow
10Forgber et al., manuscript in preparation, 2016.
Biot Number EffectsSl
ow
sh
ear
Fast
sh
ear
Slow cooling Fast cooling
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The „Toy“ Problem: Packed Bed
Early Times Later Times
Biot Number Effects
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The „Toy“ Problem: Packed Bed
Biot Number Effects
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Optimal Metal Loading
Numerical model for optimal metal oxide loading
for the reduction of hematite
4 2 3 3 4 2 2 24CH 27Fe O 18Fe O 2CO 2CO 3H O 5H
4 3 4 2 2 23CH 8Fe O 24FeO 2CO CO 3H O 3H
4CH ,exp /in
t i i i A iX w X y k E T
R. 1
R. 2
Conversion Rate
2 3
2 3
2 3
Fe O
Fe O ,
Fe O
1i t is X
MW
Molar Reaction Rate
Parameter Value Parameter Value
ε 0.5 yCH4 0.2
τ 1.5 T 1089 [K]
dp 1 [mm] p 1 [bar]
(R.1) treact 60 [s] Bi ∞
sFe2O3 0.11 [kmol/m³/s] RR1 4.10-3 [kmol/m³/s]
Parameters for
reduction of
hematite.
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Normalized concentration profiles of gas (blue dots) and Fe2O3 (red circles; t = 10, 30, 50 [s]
from top to bottom; Left: εs / εs,max = 0.90; Right: εs / εs,max = 0.96).
• Sharp hematite concentration front at r/R = 0.3 (right panel)
o due to relatively high Thiele modulus diffusion limitation
• Sharp front vanishes for moderate loading (smaller Thiele modulus)
Moderate metal loading Very high metal loading
Optimal Metal Loading
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Normalized metal consumption as a function of the
relative metal loading and pore size of the support.
, ,0ress t s s resc c X t,0 /s s s sc MW
Optimal solids loading
with (tres =100 s)
Optimal solids loading
close to maximum solids
loading
Optimum depends on pore
size
≈ 84 % for 20 nm
≈ 90 % for 50 nm
≈ 95 % for 200 nm
Optimal Metal Loading
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Sedimenting Suspensions - Wet
11Boyce et al. & Ozel et al., 2015 AIChE meeting. 12Girardi et al. CES, 2016.
Particles with
Z > 3
• Measure liquid spreading rate, drag
• Challenge: domain size
Agglomerate
(initial)
Droplet in a fluidized bed (Ca = 0.1;
𝑑𝑎𝑔𝑔/𝑑𝑝 = 13.8; 𝜙𝑝 = 0.25).11
Clustering of a
wet fluidized
bed.12
Liquid Dispersion in Fluidized Beds
2813Gruber et al., CES (in press), 2016.
3-Phase Bubble Columns
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3-Phase Bubble Columns
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Conclusions
Realistic simulators for the development of meso-scale
models relevant for (reactive) fluid-particle systems near
to reach.
Challenge: meso-scale models for (i) reactions / heat
transfer, (ii) liquid-particle-bubble suspensions, and
(iii) polydisperse fluid-particle systems.
Wet fluidized beds come with additional challenges
(more from Maryam Askarishahi in the afternoon).
Domain size often too small performance.
Validation often limiting factor (lack of direct simulation,
or sophisticated experiment)
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S. Radl1
1Graz University of Technology
with contributions fromT. Forgber,1 F. Municchi,1 R. Pichler,1 C. Kloss,2
and C. Goniva2
2DCS Computing GmbH, Linz
A. Ozel,3 C. Boyce,3 S. Sundaresan3
3Princeton University, New Jersey, U.S.A.
Probing Heterogeneities in Fluid-Particle Systems with the Computer
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Acknowledgement and Disclaimer
Parts of the “ParScale” and “CPPPO” code were developed in the frame of the “NanoSim” project
funded by the European Commission through FP7 Grant agreement no. 604656.
http://www.sintef.no/projectweb/nanosim/
©2016 by TU Graz, DCS Computing GmbH, and Princeton University. All rights reserved. No part of
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