My research activities

Gaetano Sardina

Division day, 29th August 2018

Agenda•  Challengeswithmul1phaseflows

•  Bubbles

•  Droplets

•  Par1cles

•  Somenewideas/possiblecollabora1ons

Challenges•  Mul1-physics:fluiddynamics-chemistry-heattransfer-masstransfer-phasechange-fluid/structureinterac1ons

•  Mul1-scale:turbulentregimeinindustrialprocessesàManytemporalandlengthscalesfromthelargescalespropor1onaltotheapparatuslengthdowntoKolmogorovdissipa1vescales

•  Highvolumefrac1ons:opaquesystemswheretheclassiclaser-basedexperimentaltechniquesfail

Bubbledynamics

Whichtechniquetouse?

10 1010101010 1−1−2−3−4 0

Scale [m]

MRI

X−ray

PIV/CTA/LDS(very dilute systems)

FR−DNS

EE/EL−LES

EL−DNS

exper

imen

tsC

FD

models

models an

d co

mpariso

ns

EE−RANS/STOCHASTICAPPROACH

IDEAL SIMULATION−−>YEAR 2070−−>SIZE GAP

FullyresolvedDNS

∆

dp

•  VOF•  OpenSourceCode-GERRIS

•  EvaluatedragandliQcoefficient

•  Detaileddynamicsaroundthesinglepar1cles

•  Limit:DNSlowReynoldsnumber/smallnumberofpar1clesevolved

•  NiklasPh.d.project

FullyresolvedDNS

∆

dp

•  VOF•  OpenSourceCode-GERRIS

•  EvaluatedragandliQcoefficient

•  Detaileddynamicsaroundthesinglepar1cles

•  Limit:DNSlowReynoldsnumber/smallnumberofpar1clesevolved

•  NiklasPh.d.project

Eulerian-LagrangianvsEulerian-Eulerian

∆

dp

Eulerian-LagrangianvsEulerian-Eulerian

EL Filtered EL EE-OpenFoam (developerKlas Jareteg FCC)

Dropletcondensa1on/evapora1on

Macroscopic effects of droplet distribution

•  Climate/Weatherpredic1onmodelsareessen1allyRANS

•  Asmallscalevaria1oncandeeplyinfluencelargerscales

Clouds are highly turbulentRe 106

Dropletcondensa1on/evapora1on

•  StateoftheartDNS(1024^3+109lagrangiandroplets)

•  Originalstochas1cLESmodelfordropletphasechanges(Sardinaetal,PRL,2015)

•  Largeeffectsduetoturbulence•  Novelpredic1onforthedropletradiusgrowth•  Thenewtheoryhasbeenverifiedbyexperimentalmeasures(Chandrakaraetal,PNAS,2016)

Comparisons with DNS results ● The model

approximates the largest simulation

● Smallest simulations are influenced by viscous effects of the smallest scales

●  In general the stochastic model tends to overestimate

t[s]

σR

2[µ

m2]

100

101

102

103

10-2

10-1

100

101

A

A

B

B

C

C

D

D

t1/2

t[s]

<s’

r2’>

[µm

2]

101

102

10310

-9

10-8

10-7

10-6

10-5

Par1cletransportinturbulentflows712 A. Nowbahar, G. Sardina, F. Picano and L. Brandt

–3

–2

–1

0

1

2

3

0 2 4 6–3

–2

–1

0

1

2

3

0 2 4 6

(a) (b)

FIGURE 2. (Colour online) Snapshot of an x–z plane in the buffer layer at y+ = 15 for(a) Newtonian case and (b) polymeric flow at Wi = 5. Black dots represent particles in theslab 10 6 y+ 6 20, and colours indicate the magnitude of the streamwise velocity component,lighter tones being highest and darker lowest.

the Newtonian and viscoelastic cases and comparing the data with the unconditionedfluid velocity p.d.f.s.

The turbophoretic drift induces a mean particle migration towards the wall thatis quantified by the mean particle concentration c/c0, defined as the ratio of thetime-averaged particle number per unit volume normalized by the bulk concentration.Figure 3 shows the concentration profile c/c0 against the wall-normal direction y+.For the Newtonian and polymer flows we observe high concentrations of particlesat the wall (turbophoresis) as compared with the bulk. For St+ = 1 (figure 3a), theparticles are dispersed throughout the channel with only slight accumulation at thewall. Most interestingly, the addition of polymers leads to a decrease in concentrationin the near-wall region, which correspond to higher concentrations in the bulk, forall Stokes numbers investigated. Larger the Weissenberg number Wi, more accentuatedis the effect. The decrease in the wall concentration for the polymeric flows is mostpronounced for St+ = 10 (figure 3b), where the concentration decreases by more thantwo times for Wi = 5 and more than five times for Wi = 10.

As the particle wall accumulation is linked to the particle preferential samplingof slow fluid and ejection events, we report the p.d.f. of the fluid velocity sampledby particles in figure 4 for the Newtonian and polymeric flows and differentparticle populations in the steady-state regime. We assume that positive values ofthe fluid velocity sampled by the particles indicate velocity directed towards the walls.Considering first the Newtonian case (figure 4a), it is apparent that accumulatingparticles (St+ = 10, 20) tend to avoid intense fluid motions directed towards thewall, u0+

y > 0, showing values lower than the fluid on the positive side of thep.d.f. The particles tend to oversample the slow departing motions, small negativevalues, as displayed in the inset of the figure. The situation drastically changes forthe viscoelastic cases (figure 4b,c). The preferential sampling of the slow departingmotions is not evident anymore especially for the case Wi = 10, although also inthese cases the particles with St+ = 10, 20 tend to filter strong approaching fluidmotions. The qualitative behaviour observed in the instantaneous snapshots of figure 2

474 B. Milici, M. De Marchis, G. Sardina and E. Napoli

20

16

12

8

4

0

02

46

810

12 0

2

4

02

46

8

1012

X1

X20

2

4

–4

0

4

8

12

(a)

(b)

FIGURE 5. (Colour online) Wall-normal slices of an instantaneous configuration of thestreamwise velocity field (contours, indicating the non-dimensional instantaneous value) andparticles (black particles have positive vertical velocity and move upward, light grey particleshave negative vertical velocity and move downward) with St+ = 25 for the flat (a) and therough (b) cases.

channel centre, characterized by fast streamwise velocity, and avoid the slow andhighly vortical flow regions close to the walls.

The combined effect of the reduction of the particle concentration close to thewall and its augmentation in the channel bulk region deeply affects the total particlemass flux. In order to quantify this flux, we introduce a new physical quantity namedparticle bulk Reynolds number Rep

b = Vpb�/⌫, where Vp

b is the particle bulk velocity,defined as the averaged particle velocity in the streamwise direction (Vp

b = PNpi=1Vp

x1/Np,

where Vpx1

is the pth particle instantaneous streamwise velocity and Np is the totalnumber of particles). Figure 6 shows the behaviour of the steady-state particle bulkReynolds number for the six particle populations, relating to the flat (solid line)and the rough (dashed line) simulations. For St+ = 0, the Lagrangian tracer limitis recovered and the particle bulk Reynolds number is equal to the bulk Reynoldsnumber of the carrier phase (Rep

b = 1720 and Repb = 2880 for the rough and the flat

case, respectively). Thus, the mass flux for the flat-wall case is larger than that ofthe rough-wall case, because of the roughness function. In the flat-wall case, thelarger the particle inertia (by increasing the Stokes number) the lower the particlemass flux, assuming its minimum value for the most accumulating particles (St+ = 25).

Possiblecollabora1ons

Particle transportin an urban environment

PM (10/2.5) preferentialaccumulation

•  Stablestra1fiedboundarylayer•  Codeforcomplexgeometry

Possiblecollabora1ons

Developing a new multiphase(VOF/diffuse interface)compressible code

•  Bubbleimplosion/dropletphasechanges•  Compressibleschemes(TVD,WENO)tocaptureshockwaves/density-viscosityjumpsacrosstheinterface

Dragreduc1onsformarineapplica1ons

•  DNSofturbulentchannelflows/boundarylayer•  Polymericflows(Fene-pmodel)•  Microbubbleinjec1ons•  Superhydrophobicsurfaces