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

4

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

5

The Models

6

ParScale

The COSI Open-Source Plattform (Euler-Lagrange)

Model Overview

Porto

7

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.

8

I) Particle-Resolved Direct Numerical Simulations

Results: improved models for heat transfer rates

in polydisperse particle beds

9

„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

10

(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

13

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

14

Post-Processing

15

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.

16

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

17

Post-Processing Utility CPPPO

Lagrangian filters and samples are particle based,

i.e., they are performed at user-defined locations

18

Distribution of particle-based

Nusselt number

Post-Processing Utility CPPPO

Parallel scalability

19

Lessons Learned

20

Reduction of a Particle Bed

21

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

22

The „Toy“ Problem: Packed Bed

Early Times Later Times

Biot Number Effects

23

The „Toy“ Problem: Packed Bed

Biot Number Effects

24

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.

25

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

26

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

27

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

29

3-Phase Bubble Columns

30

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)

31

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

32

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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