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
2
• 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?
3
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
4
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
uenc
y, %
Software development: Phase Discrimination
6
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
8
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
9
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
10
• 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