Characteristics of Inertial Fibre Suspensions in a Turbulent Pipe Flow

Stella Dearing*, Cristian Marchioli, Alfredo Soldati

Dipartimento di Energetica e Macchine,

Università di Udine, Italy

13rd SIG43 Workshop: Udine, Italy, 2011

3rd SIG43 Workshop: Dynamics of non-spherical particles in fluid turbulence,

Udine, Italy, April 6th - 8th, 2011

Applications: Which industries benefit

Inertial fibres suspended in turbulent flows are commonly encountered industry

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• Pulp and paper processing• Controlling rheological behaviour and fibre orientation distribution

crucial to optimise operations• Furniture Industry

• Pneumatic transport of fibres

• Fibres as an alternative to polymers as a DRA?• Examples include the TAPs, medical application, firehouse• Fibres provide more modest reductions but improved shear

degradation and filterability

• Nappy Fabrication• Fluff, cellulose fibres, make up 60% of nappy material. Uniform

distribution is highly desirable for increased absorption

Experimental data is required to validate simulation assumptions, develop and improve current models, provide industry with practical correlations for sizing of industry equipment (aim not to oversize)

3rd SIG43 Workshop: Udine, Italy, 2011

Motivation: Why Study?

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Fibre suspensions have complicated rheological properties that are quite different than carrier fluid even at low mass fractions. The physical properties f(fibre spatial distribution & orientation)

1. Fibres rotate/align when subject to velocity gradient –hydrodynamic drag of fibres is f(rotational motion) controls translational motion

2. Strong turbulence tends to randomise orientation (BERNSTEIN &SHAPIRO)

3. BUT coherent structures interact with fibres causing segregation and accumulation (MARCHIOLI et al. PHYS. FLUIDS. 2010)

y

z

3rd SIG43 Workshop: Udine, Italy, 2011

DNS one way coupling no fibre-fibre interactionsReτ =150; Re = 9000Benchmarking required!

Set-up: Pipe Circuit measurements

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Pipe length 9m 31m

Pipe material glass Galvanised steel

Pipe diameter ϕ/ 0.022m 0.1m

Re 6,000-20,000 25,000-250,000

3rd SIG43 Workshop: Udine, Italy, 2011

Laser

Cameraz

x

Mirror

ND Yag laser 1000mJ

PCO sensicam 1280 x1024F = 8hz

x

z Flow Direction

ϕ/7

ϕ/5

Experimental Parameters

Fibre type Mass fraction, ϕ, %

nL3

Nylon 0.01 0.018

0.02 0.035

0.05 0.089

0.1 0.177

0.5 0.890

1 1.78

Additive Length Diameter Specific Density

Nylon ~0.3mm 24μm 1.14

3rd SIG43 Workshop: Udine, Italy, 2011

Freq

uen

cy,

%

Software development: Phase Discrimination

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Adjust intensity Dilate Remove

noiseErode to orig. size

3rd SIG43 Workshop: Udine, Italy, 2011

zi Mean orientations

Normalised number density

1. Preprocessing2. Object Identification3. Discriminate object

type based on length & aspect ratio

4. Ellipse fitting5. Calculate statistics

Software development: Statistics calculation and validation

73rd SIG43 Workshop: Udine, Italy, 2011

Results mean orientationGreen triangles DNS data (Marchioli et al., 2010) at Re = 9000,Red squares experimental data Re = 8043

Artificial images

Projected fibre onto plane random distribution tends to 0.64 vs 0.5

Results: Mean orientations

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Fibres strongly align to the streamwise direction close to wall Alignment decreases with distance from wall but does not become

completely random Alignment reduction increases with Re for mass fraction of 0.01% Alignment reductions tends to same value for all Re at mass

fraction of 0.02%.

3rd SIG43 Workshop: Udine, Italy, 2011

DNSReτ =150; Re = 9000

Results: Normalised mean number density

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Near wall peak; reduction away from wall Less fibres at near wall cf at higher Re cf to Re=8043 At 0.02% less fibres close to wall, increase in fibres away from

wall

3rd SIG43 Workshop: Udine, Italy, 2011

DNSReτ =150; Re = 9000

Re = 8043

Conclusions

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• Phase discrimination technique and validation has been presented. – Limitations and errors have been quantified. – Phase discrimination technique is appears suitable for the problem( 3D set-up)

• First set of experimental data of orientation/distribution close to wall

• Mean orientation and number density data qualitatively agrees with DNS data promising.

• High Re number tests: sensitive to concentrations. – Deposition at higher Re tests. Why?

• More information required• Information on decomposed velocity flow field is required.• Study for instantaneous images with high frequency camera • We are in the process of doing error analysis of phase discrimination PIV

algorithm using a DNS flow field

3rd SIG43 Workshop: Udine, Italy, 2011