Experimental%20and%20modeling%20investigations%20of%20multiphase%20(turbulent%20and%20laminar)%20%20%20%20reacting%20flows

1
Experimental and modeling investigations of
multiphase (turbulent and laminar)
reacting flows
Daniele L. Marchisio
Dipartimento di Scienza dei Materiali e
Ingegneria Chimica Politecnico di
Torino daniele.marchisio_at_polito.it
2
Politecnico di Torino
3
Politecnico di Torino
4
Politecnico di Torino
Turin, Piedmont
5
Politecnico di Torino
Turin, Piedmont
6
Politecnico di Torino

• 26.000 students enrolled in 120 programs (39
  Batchelor programs 35 Master programs 30
  Doctorate Courses).
• There are about 900 faculty and 800
  administrative staff members.
• 6 Colleges (4 of Engineering and 2 of
  Architecture)
• 1 PhD School
• 18 Departments
• 223 millions for 2005

7
Politecnico di Torino

• 26.000 students enrolled in 120 programs (39
  Batchelor programs 35 Master programs 30
  Doctorate Courses).
• There are about 900 faculty and 800
  administrative staff members.
• 6 Colleges (4 of Engineering and 2 of
  Architecture)
• 1 PhD School
• 18 Departments
• 223 millions for 2005

8
Politecnico di Torino

• 26.000 students enrolled in 120 programs (39
  Batchelor programs 35 Master programs 30
  Doctorate Courses).
• There are about 900 faculty and 800
  administrative staff members.
• 6 Colleges (4 of Engineering and 2 of
  Architecture)
• 1 PhD School
• 18 Departments
• 223 millions for 2005

9
Politecnico di Torino

• 26.000 students enrolled in 120 programs (39
  Batchelor programs 35 Master programs 30
  Doctorate Courses).
• There are about 900 faculty and 800
  administrative staff members.
• 6 Colleges (4 of Engineering and 2 of
  Architecture)
• 1 PhD School
• 18 Departments
• 223 millions for 2005

10
Politecnico di Torino

• 26.000 students enrolled in 120 programs (39
  Batchelor programs 35 Master programs 30
  Doctorate Courses).
• There are about 900 faculty and 800
  administrative staff members.
• 6 Colleges (4 of Engineering and 2 of
  Architecture)
• 1 PhD School
• 18 Departments
• 223 millions for 2005

11
Politecnico di Torino

• 26.000 students enrolled in 120 programs (39
  Batchelor programs 35 Master programs 30
  Doctorate Courses).
• There are about 900 faculty and 800
  administrative staff members.
• 6 Colleges (4 of Engineering and 2 of
  Architecture)
• 1 PhD School
• 18 Departments
• 223 millions for 2005

12
Politecnico di Torino

• Department of Material Science and Chemical
  Engineering
• Faculty members and staff number over 60
• Research areas and PhD projects

• Scale up of sol-gel processes for production of
  TiO2 nano-particles (Federica Omegna, PhD
  Student)
• Production of polymeric nano-particles for
  pharmaceutical applications (Federica Lince, PhD
  Student)
• CFD modeling of a freeze-drying chamber (Valeria
  Rasetto, PhD Student)
• CFD modeling of gas-liquid stirred tanks (Miriam
  Petitti, post-doc Andrea Nasuti, Ms Student)

• CFD modeling of soot formation in flames
  (Federica Furcas, PhD Student)
• CFD modeling of soot traps for automotive
  applications (Samir Bensaid, PhD Student, in
  collaboration with Guido Saracco and Debora Fino)
• CFD modeling of nano-particles precipitation in
  micro-reactors (Emmanuela Gavi, PhD Student
  Franco di Giacobbe, Ms Student)

13
Outline

• Introduction
• Aggregation and breakage of solid particles
• Gas-liquid stirrer tanks
• Liquid-liquid turbulent dispersions
• Nano-particle precipitation
• Soot particle formation in combustion processes
• Soot filtration systems
• Conclusions

14
Introduction

• Multiphase systems constituted by a continuous
  primary phase and a poly-dispersed secondary
  phase (particles, bubbles and droplets)
• Boltzmann equation (granular flows)
• Williams equation (evaporating spray)
• Population Balance Equation (crystallization)
• Particle Dynamics Equation (aerosol)
• Generalized Population Balance Equation!
• From this equation it is possible to derive
• Mass balance equation, momentum balance equations
  and more
• Closure problem QMOM and DQMOM
• Full spatial resolution finite-volume codes ?
  Computational Fluid Dynamics
• Ability to describe one-way coupling, two-way
  coupling, four-way coupling

15
Introduction

• Multiphase systems constituted by a continuous
  primary phase and a poly-dispersed secondary
  phase (particles, bubbles and droplets)
• Boltzmann equation (granular flows)
• Williams equation (evaporating spray)
• Population Balance Equation (crystallization)
• Particle Dynamics Equation (aerosol)
• Generalized Population Balance Equation!
• From this equation it is possible to derive
• Mass balance equation, momentum balance equations
  and more
• Closure problem QMOM and DQMOM
• Full spatial resolution finite-volume codes ?
  Computational Fluid Dynamics
• Ability to describe one-way coupling, two-way
  coupling, four-way coupling

16
Introduction

• Multiphase systems constituted by a continuous
  primary phase and a poly-dispersed secondary
  phase (particles, bubbles and droplets)
• Boltzmann equation (granular flows)
• Williams equation (evaporating spray)
• Population Balance Equation (crystallization)
• Particle Dynamics Equation (aerosol)
• Generalized Population Balance Equation!
• From this equation it is possible to derive
• Mass balance equation, momentum balance equations
  and more
• Closure problem QMOM and DQMOM
• Full spatial resolution finite-volume codes ?
  Computational Fluid Dynamics
• Ability to describe one-way coupling, two-way
  coupling, four-way coupling

17
Introduction

• Multiphase systems constituted by a continuous
  primary phase and a poly-dispersed secondary
  phase (particles, bubbles and droplets)
• Boltzmann equation (granular flows)
• Williams equation (evaporating spray)
• Population Balance Equation (crystallization)
• Particle Dynamics Equation (aerosol)
• Generalized Population Balance Equation!
• From this equation it is possible to derive
• Mass balance equation, momentum balance equations
  and more
• Closure problem QMOM and DQMOM
• Full spatial resolution finite-volume codes ?
  Computational Fluid Dynamics
• Ability to describe one-way coupling, two-way
  coupling, four-way coupling

18
Introduction

• Multiphase systems constituted by a continuous
  primary phase and a poly-dispersed secondary
  phase (particles, bubbles and droplets)
• Boltzmann equation (granular flows)
• Williams equation (evaporating spray)
• Population Balance Equation (crystallization)
• Particle Dynamics Equation (aerosol)
• Generalized Population Balance Equation!
• From this equation it is possible to derive
• Mass balance equation, momentum balance equations
  and more
• Closure problem QMOM and DQMOM
• Full spatial resolution finite-volume codes ?
  Computational Fluid Dynamics
• Ability to describe one-way coupling, two-way
  coupling, four-way coupling

19
Introduction

• Multiphase systems constituted by a continuous
  primary phase and a poly-dispersed secondary
  phase (particles, bubbles and droplets)
• Boltzmann equation (granular flows)
• Williams equation (evaporating spray)
• Population Balance Equation (crystallization)
• Particle Dynamics Equation (aerosol)
• Generalized Population Balance Equation!
• From this equation it is possible to derive
• Mass balance equation, momentum balance equations
  and more
• Closure problem QMOM and DQMOM
• Full spatial resolution finite-volume codes ?
  Computational Fluid Dynamics
• Ability to describe one-way coupling, two-way
  coupling, four-way coupling

20
Introduction

• Multiphase systems constituted by a continuous
  primary phase and a poly-dispersed secondary
  phase (particles, bubbles and droplets)
• Boltzmann equation (granular flows)
• Williams equation (evaporating spray)
• Population Balance Equation (crystallization)
• Particle Dynamics Equation (aerosol)
• Generalized Population Balance Equation!
• From this equation it is possible to derive
• Mass balance equation, momentum balance equations
  and more
• Closure problem QMOM and DQMOM
• Full spatial resolution finite-volume codes ?
  Computational Fluid Dynamics
• Ability to describe one-way coupling, two-way
  coupling, four-way coupling

21
Introduction

• Multiphase systems constituted by a continuous
  primary phase and a poly-dispersed secondary
  phase (particles, bubbles and droplets)
• Boltzmann equation (granular flows)
• Williams equation (evaporating spray)
• Population Balance Equation (crystallization)
• Particle Dynamics Equation (aerosol)
• Generalized Population Balance Equation!
• From this equation it is possible to derive
• Mass balance equation, momentum balance equations
  and more
• Closure problem QMOM and DQMOM
• Full spatial resolution finite-volume codes ?
  Computational Fluid Dynamics
• Ability to describe one-way coupling, two-way
  coupling, four-way coupling

22
Introduction

• Multiphase systems constituted by a continuous
  primary phase and a poly-dispersed secondary
  phase (particles, bubbles and droplets)
• Boltzmann equation (granular flows)
• Williams equation (evaporating spray)
• Population Balance Equation (crystallization)
• Particle Dynamics Equation (aerosol)
• Generalized Population Balance Equation!
• From this equation it is possible to derive
• Mass balance equation, momentum balance equations
  and more
• Closure problem QMOM and DQMOM
• Full spatial resolution finite-volume codes ?
  Computational Fluid Dynamics
• Ability to describe one-way coupling, two-way
  coupling, four-way coupling

23
Introduction

• Multiphase systems constituted by a continuous
  primary phase and a poly-dispersed secondary
  phase (particles, bubbles and droplets)
• Boltzmann equation (granular flows)
• Williams equation (evaporating spray)
• Population Balance Equation (crystallization)
• Particle Dynamics Equation (aerosol)
• Generalized Population Balance Equation!
• From this equation it is possible to derive
• Mass balance equation, momentum balance equations
  and more
• Closure problem QMOM and DQMOM
• Full spatial resolution finite-volume codes ?
  Computational Fluid Dynamics
• Ability to describe one-way coupling, two-way
  coupling, four-way coupling

24
Introduction

• Multiphase systems constituted by a continuous
  primary phase and a poly-dispersed secondary
  phase (particles, bubbles and droplets)
• Boltzmann equation (granular flows)
• Williams equation (evaporating spray)
• Population Balance Equation (crystallization)
• Particle Dynamics Equation (aerosol)
• Generalized Population Balance Equation!
• From this equation it is possible to derive
• Mass balance equation, momentum balance equations
  and more
• Closure problem QMOM and DQMOM
• Full spatial resolution finite-volume codes ?
  Computational Fluid Dynamics
• Ability to describe one-way coupling, two-way
  coupling, four-way coupling

25
Validation mono-variate PBE

• QMOM/DQMOM has been validated in various
  operating conditions against rigorous solution of
  PBE
• Aggregation/breakage problems (N 3) ? 6 moments

QMOM/ DQMOM
Rigorous solution
MARCHISIO D. L., VIGIL R.D., R.O. FOX. (2003).
JOURNAL OF COLLOID AND INTERFACE SCIENCE. 258,
322-334.
26
Validation mono-variate PBE

• QMOM/DQMOM has been validated in various
  operating conditions against rigorous solution of
  PBE
• Aggregation/breakage problems (N 3) ? 6 moments

PSD at steady-state
QMOM/ DQMOM
n(L)
Rigorous solution
L
MARCHISIO D. L., VIGIL R.D., R.O. FOX. (2003).
JOURNAL OF COLLOID AND INTERFACE SCIENCE. 258,
322-334.
27
Bi-variate PBE particle volume and surface area
28
Validation of DQMOM

• Aggregation and sintering
• Predictions with DQMOM N2 for global order two

Zucca, A., Marchisio, D.L., Vanni, M., Barresi,
A.A., 2007. Validation of the bivariate DQMOM for
nano-particle processes simulation, A.I.Ch.E
Journal 53, 918-931.
29
Aggregation and breakage of solid particles

• In many polymer production processes after
  polymerization particles are suspended in a fluid
  and their characteristic size is about 100-200 nm
• Particles are too small to be handled therefore a
  coagulation step is necessary

30
Aggregation and breakage of solid particles

• In many polymer production processes after
  polymerization particles are suspended in a fluid
  and their characteristic size is about 100-200 nm
• Particles are too small to be handled therefore a
  coagulation step is necessary

31
Aggregation and breakage of solid particles

• In many polymer production processes after
  polymerization particles are suspended in a fluid
  and their characteristic size is about 100-200 nm
• Particles are too small to be handled therefore a
  coagulation step is necessary

Dispersion of stable primary particles
m
32
Aggregation and breakage of solid particles

• In many polymer production processes after
  polymerization particles are suspended in a fluid
  and their characteristic size is about 100-200 nm
• Particles are too small to be handled therefore a
  coagulation step is necessary

Dispersion of stable primary particles
destabilization
m
33
Aggregation and breakage of solid particles

• In many polymer production processes after
  polymerization particles are suspended in a fluid
  and their characteristic size is about 100-200 nm
• Particles are too small to be handled therefore a
  coagulation step is necessary

Dispersion of stable primary particles
Aggregates / Granules
destabilization
34
Aggregation and breakage of solid particles
Mean particle size
MARCHISIO D. L., MIROSLAV SOOS, JAN SEFCIK,
MASSIMO MORBIDELLI, ANTONELLO A. BARRESI,
GIANCARLO BALDI. (2006). Effect of fluid dynamics
on particle size distribution in particulate
processes. CHEMICAL ENGINEERING TECHNOLOGY.
vol. 29 (2), pp. 1-9
35
Aggregation and breakage of solid particles
Mean particle size
MARCHISIO D. L., MIROSLAV SOOS, JAN SEFCIK,
MASSIMO MORBIDELLI, ANTONELLO A. BARRESI,
GIANCARLO BALDI. (2006). Effect of fluid dynamics
on particle size distribution in particulate
processes. CHEMICAL ENGINEERING TECHNOLOGY.
vol. 29 (2), pp. 1-9
36
Aggregation and breakage of solid particles
Aggregation and breakage time scales
MARCHISIO D. L., SOOS M., SEFCIK J., MORBIDELLI
M. (2006). AICHE JOURNAL. 52, 158-173.
Mixing time scales
37
Aggregation and breakage of solid particles
Aggregation and breakage time scales
MARCHISIO D. L., SOOS M., SEFCIK J., MORBIDELLI
M. (2006). AICHE JOURNAL. 52, 158-173.
Volume-averaged homogeneous model
Mixing time scales
38
Aggregation and breakage of solid particles
Aggregation and breakage time scales
MARCHISIO D. L., SOOS M., SEFCIK J., MORBIDELLI
M. (2006). AICHE JOURNAL. 52, 158-173.
Volume-averaged homogeneous model
Full GPBE one-way coupling
Mixing time scales
39
Aggregation and breakage of solid particles
Aggregation and breakage time scales
MARCHISIO D. L., SOOS M., SEFCIK J., MORBIDELLI
M. (2006). AICHE JOURNAL. 52, 158-173.
Two-way coupling and turbulent fluctuations
Volume-averaged homogeneous model
Full GPBE one-way coupling
Mixing time scales
40
Aggregation and breakage of solid particles
Aggregation and breakage time scales
MARCHISIO D. L., SOOS M., SEFCIK J., MORBIDELLI
M. (2006). AICHE JOURNAL. 52, 158-173.
Two-way coupling and turbulent fluctuations
Full GPBE one-way coupling
fs 1?10-4
Mixing time scales
41
Aggregation and breakage of solid particles
Aggregation and breakage time scales
MARCHISIO D. L., SOOS M., SEFCIK J., MORBIDELLI
M. (2006). AICHE JOURNAL. 52, 158-173.
Two-way coupling and turbulent fluctuations
Volume-averaged homogeneous model
Full GPBE one-way coupling
fs 1?10-3
fs 1?10-4
Mixing time scales
42
Aggregation and breakage of solid particles
Aggregation and breakage time scales
MARCHISIO D. L., SOOS M., SEFCIK J., MORBIDELLI
M. (2006). AICHE JOURNAL. 52, 158-173.
Two-way coupling and turbulent fluctuations
Volume-averaged homogeneous model
Full GPBE one-way coupling
fs 1?10-1
fs 1?10-3
fs 1?10-4
Mixing time scales
43
Gas-liquid stirred tanks
Wu Patterson (1989) Standard Rushton
turbine Baffles and blade 0.44 cm D shaft 1.46
cm D disk 6.02 cm Disk tickness 0.47 cm
Gas sparger with 46 holes with d 0.5 mm
44
Gas-liquid stirred tanks
Wu Patterson (1989) Standard Rushton
turbine Baffles and blade 0.44 cm D shaft 1.46
cm D disk 6.02 cm Disk tickness 0.47 cm
Computational grid about 150.000 cells for ½ of
the domain (multi-reference frame approach)
Gas sparger with 46 holes with d 0.5 mm
45
Gas-liquid stirred tanks
Comparison with experimental data of velocity
profiles obtained with different drag
relationships (bubble diameter 2 mm!)
Miriam Petitti, Micaela Caramellino, Daniele L.
Marchisio and Marco Vanni (2007) Two-scale
simulation of mass transfer in an agitated
gas-liquid tank, 6th International Conference on
Multiphase Flow, ICMF 2007, Leipzig, Germany,
July 9 - 13, 2007
46
Gas-liquid stirred tanks
Bubbles enter the reactor with size equal to 6 mm
and near the impeller they are broken up
47
Gas-liquid stirred tanks
Bubbles enter the reactor with size equal to 6 mm
and near the impeller they are broken up
48
Gas-liquid stirred tanks
Bubbles enter the reactor with size equal to 6 mm
and near the impeller they are broken
up Including the Population Balance Equation both
velocity profiles and global hold up are in good
agreement with experimental data
49
Gas-liquid stirred tanks
Bubbles enter the reactor with size equal to 6 mm
and near the impeller they are broken
up Including the Population Balance Equation both
velocity profiles and global hold up are in good
agreement with experimental data
50
Liquid-liquid turbulent dispersions

• Very often liquid-liquid turbulent dispersions
  are created using static mixers

51
Liquid-liquid turbulent dispersions

• Very often liquid-liquid turbulent dispersions
  are created using static mixers

52
Liquid-liquid turbulent dispersions

• Very often liquid-liquid turbulent dispersions
  are created using static mixers

53
Liquid-liquid turbulent dispersions

• Very often liquid-liquid turbulent dispersions
  are created using static mixers

54
Liquid-liquid turbulent dispersions

• Very often liquid-liquid turbulent dispersions
  are created using static mixers

55
Liquid-liquid turbulent dispersions

• Very often liquid-liquid turbulent dispersions
  are created using static mixers
• Our goal is to develop a fully predictive model
  based on the Generalized Population Balance
  Equation (full three-dimensional simulations!)

56
Liquid-liquid turbulent dispersions


Drop size distribution at Re12 000 Drop size distribution at Re18 000




Drop size distribution at Re15 000 Drop size distribution at Re21 000
Z. JAWORSKI, P. PIANKO-OPRYCH, MARCHISIO D. L.,
A.W. NIENOW. (2007). CFD modelling of turbulent
drop breakage in a Kenics static mixer and
comparison with experimental data. CHEMICAL
ENGINEERING RESEARCH DESIGN, in press.
57
Nano-particle precipitation

• Very small particles with narrow PSD are obtained
  when working under very rapid mixing conditions
• Several micro-mixers can be used

58
Nano-particle precipitation

• Very small particles with narrow PSD are obtained
  when working under very rapid mixing conditions
• Several micro-mixers can be used

59
Nano-particle precipitation

• Very small particles with narrow PSD are obtained
  when working under very rapid mixing conditions
• Several micro-mixers can be used

Confined Impinging Jet Reactor
4 mm
60
Nano-particle precipitation

• Very small particles with narrow PSD are obtained
  when working under very rapid mixing conditions
• Several micro-mixers can be used

Confined Impinging Jet Reactor
Vortex Reactor
4 mm
61
Nano-particle precipitation
Water
Acetone and PCL
Acetone and PCL
Water
Mixing influences the particle size
distribution!!!!!
62
Nano-particle precipitation
Acetone and PCL
Water
d43 280 nm
63
Nano-particle precipitation
64
Nano-particle precipitation
microPIV measurements (in collaboration with Iowa
State Univesity)
65
Nano-particle precipitation

• cAo 100 mol/m3 - cBo 800 mol/m3

E. GAVI, RIVAUTELLA L., MARCHISIO D. L., VANNI
M., BARRESI A., BALDI G. (2007). For CFD
modelling of nano-particle precipitation in
Confined Impinging Jet Reactors. CHEMICAL
ENGINEERING RESEARCH DESIGN, in press.
66
Nano-particle precipitation
Super-saturation Nucleation rate

• cAo 100 mol/m3 - cBo 800 mol/m3

E. GAVI, RIVAUTELLA L., MARCHISIO D. L., VANNI
M., BARRESI A., BALDI G. (2007). For CFD
modelling of nano-particle precipitation in
Confined Impinging Jet Reactors. CHEMICAL
ENGINEERING RESEARCH DESIGN, in press.
67
(No Transcript)
68
Soot particle formation and evolution
69
Soot particle formation and evolution
Population Balance Equation
70
Soot particle formation and evolution
ZUCCA A., MARCHISIO D. L., BARRESI A.A., FOX R.O.
(2006). Implementation of the population balance
equation in CFD codes for modelling soot
formation in flames. CHEMICAL ENGINEERING
SCIENCE. vol. 61, pp. 87-95
71
Soot particle formation radiation
No radiation
72
Soot particle filtration
73
Soot particle filtration
2 mm
200 nm
74
Soot particle filtration
2 mm
S. Bensaid, D. Marchisio, D. Fino, G. Saracco, V.
Specchia (2007) Numerical Simulation of Soot
Filtration in Wall-Flow Diesel Particulate Traps
via Computational Fluid Dynamics XXX Meeting on
Combustion, June 20 23, 2007, Ischia Porto,
Italy
200 nm
75
Summary and conclusions

• The evolution of poly-disperse multi-phase
  systems is governed by the Generalized Population
  Balance Equation
• This equation can be solved by using the method
  of moments (QMOM and DQMOM)
• The method is computationally very efficient and
  accurate
• It allows for real three dimensional simulations
  (CFD)
• One-, two- and four-way coupling
• Some theoretical issues are still to be
  addressed
• but first results are very promising (i.e., lot
  of exciting work to do)

76
Summary and conclusions

• The evolution of poly-disperse multi-phase
  systems is governed by the Generalized Population
  Balance Equation
• This equation can be solved by using the method
  of moments (QMOM and DQMOM)
• The method is computationally very efficient and
  accurate
• It allows for real three dimensional simulations
  (CFD)
• One-, two- and four-way coupling
• Some theoretical issues are still to be
  addressed
• but first results are very promising (i.e., lot
  of exciting work to do)

77
Summary and conclusions

• The evolution of poly-disperse multi-phase
  systems is governed by the Generalized Population
  Balance Equation
• This equation can be solved by using the method
  of moments (QMOM and DQMOM)
• The method is computationally very efficient and
  accurate
• It allows for real three dimensional simulations
  (CFD)
• One-, two- and four-way coupling
• Some theoretical issues are still to be
  addressed
• but first results are very promising (i.e., lot
  of exciting work to do)

78
Summary and conclusions

• The evolution of poly-disperse multi-phase
  systems is governed by the Generalized Population
  Balance Equation
• This equation can be solved by using the method
  of moments (QMOM and DQMOM)
• The method is computationally very efficient and
  accurate
• It allows for real three dimensional simulations
  (CFD)
• One-, two- and four-way coupling
• Some theoretical issues are still to be
  addressed
• but first results are very promising (i.e., lot
  of exciting work to do)

79
Summary and conclusions

• The evolution of poly-disperse multi-phase
  systems is governed by the Generalized Population
  Balance Equation
• This equation can be solved by using the method
  of moments (QMOM and DQMOM)
• The method is computationally very efficient and
  accurate
• It allows for real three dimensional simulations
  (CFD)
• One-, two- and four-way coupling
• Some theoretical issues are still to be
  addressed
• but first results are very promising (i.e., lot
  of exciting work to do)

80
Summary and conclusions

• The evolution of poly-disperse multi-phase
  systems is governed by the Generalized Population
  Balance Equation
• This equation can be solved by using the method
  of moments (QMOM and DQMOM)
• The method is computationally very efficient and
  accurate
• It allows for real three dimensional simulations
  (CFD)
• One-, two- and four-way coupling
• Some theoretical issues are still to be
  addressed
• but first results are very promising (i.e., lot
  of exciting work to do)

81
Summary and conclusions

• The evolution of poly-disperse multi-phase
  systems is governed by the Generalized Population
  Balance Equation
• This equation can be solved by using the method
  of moments (QMOM and DQMOM)
• The method is computationally very efficient and
  accurate
• It allows for real three dimensional simulations
  (CFD)
• One-, two- and four-way coupling
• Some theoretical issues are still to be
  addressed
• but first results are very promising (i.e., lot
  of exciting work to do)

82
Acknowledgements

• ETH - Zurich
• Massimo Morbidelli
• Miroslav Soos
• Jan Sefcik

• Politecnico di Torino
• Giancarlo Baldi
• Antonello A. Barresi
• Marco Vanni
• Guido Saracco
• Debora Fino

• Iowa State University
• Rodney O. Fox
• R. Dennis Vigil

• Fluent Inc.
• Jay Sanyal
• Kumar Dhanasekharan
• Yann Sommer

• University of Birmingham
• Alvin Nienow

• Financial contributions from
• Ministry of Higher Education and Scientific
  Research
• European Commission
• ENI Tecnologie / ENI
• Fluent Inc.

• Technical University of Szczecin
• Paulina Pianko-Oprych
• Zdzislaw Jaworski

83
Validation mono-variate PBE

• Validation for aggregation/breakage processes
  against Hounslows and Kumar and Ramkrishnas
  methods

mean particle size
Kumar Ramsrishna (120 classes)
N 3
N 2
QMOM/ DQMOM
N 4
N 5
Hounslow (30 classes)
MARCHISIO D. L., SOOS M., SEFCIK J., MORBIDELLI
M. (2006). AICHE JOURNAL. 52, 158-173.
84
Validation mono-variate PBE
Complex NDF sometimes are tough to describe!
N 3
N 2
N 5
N 4
85
Multi-variate PBE

• There are cases where one internal coordinate is
  not enough
• Example crystallization

86
Validation mono-variate PBE

• Validation for aggregation only against Monte
  Carlo simulations

QMOM/DQMOM (N 3)
Monte Carlo
MARCHISIO D. L., PIKTURNA J.T., FOX R.O., VIGIL
D.R., A.A. BARRESI. (2003). AICHE JOURNAL. 49,
1266-1279.
87
Validation of DQMOM

• Aggregation and sintering (N2) Error for Iagg
  0.99

OK
88
Validation of DQMOM

• Aggregation and sintering (N3) Error for Iagg
  0.99

OK
89
Gas-liquid stirred tanks
By using a quadrature approximation with three
nodes it is possible to couple the population
balance model with a multi-fluid model with three
dispersed phases
?1
a3 aTOT
a2
90
Gas-liquid stirred tanks
L3, m
L1, m
L2, m
91
Gas-liquid stirred tanks
Laakkonen et al. (2003, 2006) Coalescence and
breakup
d32, m
d32, m
CD terminal velocity
CD Tomiyama
92
Nano-particle precipitation
Velocity magnitude, m/s
Turbulent dissipation rate, m2/s3
Turbulent kinetic energy, m2/s2
Rej 362
Rej 2696
Emmanuela Gavi, Daniele L. Marchisio, Antonello
A. Barresi (2007) CFD modelling and scale-up of
Con?ned Impinging Jet Reactors, Chemical
Engineering Science 62, 2228 2241.
93
Reactor scale-up
Mixing time macro meso micro-mixing time
Mixing time, s
small reactor
big reactor
Marchisio, D.L., Rivautella, L. and Barresi,
A.A., 2006, Design and scale-up of chemical
reactors for nano-particle precipitation, AIChE
J, 52 18771887.
94
Reactor scale-up
small reactor
big reactor
from CFD
95
Soot particle formation nucleation
Moss inception law (1995)
Primary particles number density (m1)
Fairweather inception law (1992)
4,111019
J1 Cmin2.7e5
J1 Cmin9e4
J2
0.00
F. Furcas, A. Zucca, D. L. Marchisio, A. A.
Barresi (2007) Mathematical modelling of soot
nanoparticles formation and evolution in
turbulent flames XXX Meeting on Combustion, June
20 23, 2007, Ischia Porto, Italy
96
Soot particle formation oxidation
Radius of gyration
80
Radius of gyration
40


experimental
Complete model
Without oxidation
0

primary particles diameter
primary particles diameter


dimensioneless axis coordinate (z/D)