Presented at World Congress of Chemical Engineering describing a new approach to simulate industrial scale by adding information collected at the microscale.

1. Droghetti H.1, Asinari P.1, Carbone P.2,
Pagonabarraga I.3, Marchisio D. L.1
Politecnico di Torino, Torino, Italy
University of Manchester, Manchester, UK
University of Barcelona, Barcelona, Spain
@hermesalive
hermesdroghetti

2. Agenda
3’ 3’ 5’ 3’ 9’
Big Picture Idea Theory Modelling
and
Simulation
Results &
Conclusion
2

3. 3

4.  Structured fluids: Water, Surfactants, and organic phases.
 Detergents, Personal Care, Drug Delivery, Cleaning, and more
applications.
 Manufacturing requires mixing different solutions.
4

5. 5
 Different Microstructures are
obtained
 These phases give a different
rheological behavior
 Shear affects the structures
End – user experience

6. 6
 Are we able to predict rheological curves for structured
fluids based on variation in the microscopic structure?
 Can we enhance the current models used in computational
fluid dynamics using mesoscale models?

7. 7

8. 8
 CFD models do not take into
account how microstructures
evolve during mixing
 Models based only on
experimental evidences

9. 9
 Solving Two-way coupling
problem
 Shear affects microstructures
& microstructures defines
viscosity
Adding microscopic
resolution to CFD
CFD
Viscosity
Shear
DPD

10. 10
Macroscale
Local
Shear
Microscale
Local
Viscosity
Updated
velocity field
 Macroscopic
description can be
coupled to microscopic
phases.
 Computational Fluid
Dynamics And
Dissipative Particle
Dynamics.

11. 11

12. 12
 Different Techniques: Full Atom Molecular Dynamics (MD),
Coarse Grained Models (CG)
MD:
 Atomistic resolution
 Conservative forces
 Smaller timescales
CG:
 Beads describe clusters of
Interacting atoms
 Different forces
 Wider timescale

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 Beads interact via Conservative, Dissipative and Stochastic
interactions. Momentum is conserved.
Water
Bead
MD DPD
𝐹𝑖𝑗 =
𝑖≠𝑗
(𝐹𝑖𝑗
𝐶
+ 𝐹𝑖𝑗
𝐷
+ 𝐹𝑖𝑗
𝑆
)
𝐹𝑖𝑗
𝐶
=
𝑎𝑖𝑗(1 − 𝑟𝑖𝑗) 𝒓𝒊𝒋, 𝑟𝑖𝑗 < 𝑟𝑐
0, 𝑟𝑖𝑗 ≥ 𝑟𝑐
𝐹𝑖𝑗
𝐷
= − 𝛾 𝑤 𝐷(𝑟𝑖𝑗) 𝒓𝒊𝒋 ∙ 𝒗𝒊𝒋 𝒓𝒊𝒋
𝐹𝑖𝑗
𝑆
= 𝜎 𝑤 𝑆 𝑟𝑖𝑗 𝜁𝑖𝑗 Δ𝑡−1/2 𝒓𝒊𝒋

14. 14
𝐹𝑖𝑗
𝐶
=
𝑎𝑖𝑗(1 − 𝑟𝑖𝑗) 𝒓𝒊𝒋, 𝑟𝑖𝑗 < 𝑟𝑐
0, 𝑟𝑖𝑗 ≥ 𝑟𝑐
Accessible Timescale closer to CFD
𝑝 𝐷𝑃𝐷 = 𝜌𝑘 𝑏 𝑇 + 𝛼𝑎𝑖𝑗 𝜌2
ideal
Soft interactions
Overlap is possible

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Systems are simulated using DPD units.
Equilibrium DPD units into Real units:
𝑚 𝑏𝑒𝑎𝑑 = 𝜌𝑁𝑐 𝑚 𝑚𝑜𝑙𝑒𝑐𝑢𝑙𝑒,
𝑟𝑐𝑢𝑡 = (𝜌𝑉 𝑚𝑜𝑙𝑒𝑐𝑢𝑙𝑒 𝑁 𝑚 )1/3,
𝑘 𝐵 𝑇 = 1,
Other units can be retrieved using the aforementioned rules.
𝑉𝑒𝑙𝑜𝑐𝑖𝑡𝑦! Different possibilities, an example:
Thermal Velocity  𝑣 =
3𝑘 𝐵 𝑇
𝑚
,
 …But for Non Equilibrium problems, this procedure leads to unphysical results
 (e.g. shear values in the order of magnitude of 1011 s-1)
𝑁 𝑚
𝜌𝑟𝑐

16. Couette flow
DPD scale
Lees-Edwards Boundary Conditions

17. 17

18.  We developed coupled
CFD/DPD Code
 Single phase, Non-Newtonian
Fluids using DPD viscosity
 Macro: Shear & Concentration
for each cell, Micro: Pressure
tensor & microscopic details
 Serial or Parallel (MPI)
 Look-up tables
 Post processing, Cluster analysis

19. Macroscale
Computed from CFD
Local values:
• Shear Rate
• Concentration
Mesoscale
Pressure tensor
DPD Simulations:
• Viscosity
• Microscopic
deformations
C
e
l
l
s
CFD Domain
DPD Domain
𝝁 𝑫𝑷𝑫 = −
𝑷 𝒙𝒚
𝜸
CFD Time
81 000 particles
4 – 5 hrs per simulation
10 procs

20. • Well known Surfactant, triblock copolymer: ABA
• Polyethylene Oxide (EO), Polypropylene (PO) Oxide,
Polyethylene Oxide(EO).
• Hydrophobic core (PO), Hydrophilic tails (EO)
• Different products based on the number of EO/PO
groups.
• Drug delivery, cosmetic, detergent products…
L64 in Water:
• Liquid, paste, flake form according to
the number of EO/PO groups.
• L64 is a liquid compound, MW
around 2900 , with a ratio
hydrophilic/hydrophobic = 0.40 wt)
• A13B30 A13
• Experimental Phase Diagram @
Equilibrium
• Non Ionic

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22. 22
Φ = 0.05
Φ = 0.15
Φ = 0.25 Φ = 0.90
Φ = 0.70
Φ = 0.50
Different concentration of Pluronic L64 in water at equilibrium.
Micellar, Mixed and Lamellar phases a recognized

23. 23
Φ = 0.05
Φ = 0.15
Φ = 0.25 Φ = 0.90
Φ = 0.70
Φ = 0.50
SCALABILITY of the parameters?

24. 𝛾 𝐷𝑃𝐷
Low shear region
Thermal velocity competes
with shear
Optimal shear region
Shear is dominant,
temperature bonded
High shear region
Very high temperature
Drag coeff. affects viscosity
10-3
100
Finding the Operative Range
Detecting Unphysical Behaviour
DPD Viscosity of the water

25. Non-Newtonian behaviour at
high concentration
Looking for experimental data
for comparison (collaboration
with Rossana Pasquino @
UniNA)
Validating rheological curves
and obtaining a scaling factor
for real units

26. Eq
NEq
Φ = 0.25 Φ = 0.50 Φ = 0.70

27. Water Only, DPD Newtonian
Water/Surfactant, DPD Shear Thinning
Low Shear
High Shear
Conversion coefficient missing
High Shear
Low Shear

28.  We simulated a structured fluid at equilibrium to obtain microphases.
 We applied shear to detect modification of the structures and highlight non-
Newtonian behaviour.
 We wrote a CFD/DPD computational code, able to couple two different levels on
resolution.
We are working on the scalability of the parameters for different species of surfactant
and trying to derive a scaling factor to convert the DPD into CFD viscosity.
I thank HPC@polito for the collaboration

29. 29
@hermesalive

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