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8/3/2019 Textile Modelling Permeability Desplentere Presentation
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1Department of Metallurgy and Materials Engineering of 52
F. Desplentere
Promotors: I. Verpoest, S. LomovAssesors: B. Nicola, D. Vandepitte
29th January 2007
Multiscale modelling of stochastic effects
in mould filling simulations forthermoplastic composites
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2Department of Metallurgy and Materials Engineering of 52
Textile Composites: Definition+production methods
RTM-process: overview+typical problems+Stochastic factors
Viscosity variation
Geometrical scatter within textiles
Random correlated permeability field
Modelling of stochastic mould filling
Conclusions
Overview
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3Department of Metallurgy and Materials Engineering of 52
Textile composites: definition
Matrix
material
Textile
reinforcement
Textile
composite
Thermoset
Thermoplastic
Natural
Biodegradable
Glass fibre
Carbon fibre
Natural fibre
Polymer fibre=+
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4Department of Metallurgy and Materials Engineering of 52
Autoclave
Expensive
Low geometrical complexity
High performance parts
Liquid Composite Moulding
Several variants:Resin Transfer Moulding/Light RTM/VARTM
High geometrical complexity
Expensive tooling
Medium performance: less control on fluid distribution
Recent development: Thermoplastic resin:
e.g. in-situ polymerisation:
Some advantages over thermo set resins
Textile composites: some production techniques
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5Department of Metallurgy and Materials Engineering of 52
Liquid Moulding: Resin Transfer Moulding
Different parts + steps Textile reinforcement
Mould
Resin injection
Curing / Polymerisation
Demoulding
Process description: Darcys law
K = Reinforcement permeability, K: resin viscosity
> @p
Kv
K
&
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6Department of Metallurgy and Materials Engineering of 52
Workflow for current available simulation packages Finite element mesh Material properties
Textile reinforcement: K, I
Viscosity of the resin Proces parameters
Inlet + Vents
Pressure drop
Output Prediction of flow patterns
Pressure distribution
Air entrapments
Simulations for Resin Transfer Moulding
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7Department of Metallurgy and Materials Engineering of 52
RTM is interesting production technique as it allows: Complex part geometries
High rate of automation (labour free)
Good surface quality
But it lacks widely use in industry as:
No rejection rate is allowed in case of high tech expensive
applications No simulation tool exists to predict process stochasticity
Process stochasticity due to:
Large scatter in material properties Possible air entrapments
Viscosity variation
Problem statement
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8Department of Metallurgy and Materials Engineering of 52
Large scatter is experienced in textile reinforcementproperties
Geometrical properties
Flow properties
Scatter for textile properties
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9Department of Metallurgy and Materials Engineering of 52
Possibility for air entrapments
Depends on product shape or mould Large influence of edge effect (imperfect placing of
textile material)
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10Department of Metallurgy and Materials Engineering of 52
RTM with thermoplastic material:
Ring opening of CBT to PBT
Short time window before polymerisation:
< 5minutes before viscosity > 1Pas
Problem statement
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11Department of Metallurgy and Materials Engineering of 52
Different scales in physical phenomena
Meso Macro
Meso
Macro
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12Department of Metallurgy and Materials Engineering of 52
AIM of PhD
Define strategy to characterise stochasticity of the textilereinforcement
Development of models to include stochasticity in RTM
simulation and implementation in software Setting up viscosity measurement technique for in-situ
polymerising thermoplastic material
Validation with experimental data
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13Department of Metallurgy and Materials Engineering of 52
Resin Viscosity
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14Department of Metallurgy and Materials Engineering of 52
In-situ polymerisation reaction Oligomer CBT + Catalyst(0.45w%) o Thermoplastic PBT Advantage: Isothermal processing is possible
Viscosity range Pre-polymer viscosity : 10 mPas< K 1000 Pas
Different types of rheometers necessary
Concentric geometry
Plate plate set-up
Viscosity model
Thermoplastic resin
D
DDK
.
2
20
431
..,
CC
T
C
C
CeCT
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15Department of Metallurgy and Materials Engineering of 52
Oligomer viscosity
ZSK
HDTe34
D
DDK
.
2
20
431
..,CC
T
C
C
CeCT
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16Department of Metallurgy and Materials Engineering of 52
Thermoplastic resin viscosity at 190C
D
DDK
.
2
20
431
..,
CC
T
C
C
CeCT
4
2
R
eT
ZSK
s
.constant
dt
d 100120
D
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17Department of Metallurgy and Materials Engineering of 52
Meso scale stochasticity
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18Department of Metallurgy and Materials Engineering of 52
Meso scale geometrical variation
Width of yarns w
Spacing of yarns s Gap between yarns g
Transformation into Variation for permeability on meso
scale Permeability governed by gap dimension
Meso scale textile architecture
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19Department of Metallurgy and Materials Engineering of 52
Validation of measurement techniquesSurface scanning / Optical microscopy / X-ray micro CT
Surface measurement for 2D
X-ray technique for 3D
Meso scale textile architecture
3D reconstruction of X-ray images
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20Department of Metallurgy and Materials Engineering of 52
Gap width distribution (X-ray CT)Spacing Yarn width Gap width
3568m(2.4%) 3023m(3.6%) 539m(17.5%)
Normal Normal Lognormal
Maximum values
Width CV = 15%,
Spacing CV = 5%
Meso scale textile architecture 3D
=-
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21Department of Metallurgy and Materials Engineering of 52
Gap width distribution (surface scanning)
Average = 510m, CV = 37%
Meso scale textile architecture 2D
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22Department of Metallurgy and Materials Engineering of 52
3D textile architecture
CV for width of yarn: 15%
CV for yarn spacing: 5%
Distribution type for gap width: 55%, lognormal 2D textile architecture
CV for gap width: 37%
Meso scale textile architecture variation
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23Department of Metallurgy and Materials Engineering of 52
Transformation into meso scale permeability
Geometrical information into permeability information Monte Carlo modelling of Lattice Boltzmann:
N times (Textile model WiseTex o FlowTex ) o CVK
Analytical relation:
RH= Hydraulic radius
RK CVCV
RCK
2
2
|
pxvK XX '' K
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24Department of Metallurgy and Materials Engineering of 52
Transformation into macro scale permeability
20%[K. Hoes]
ChallengeMacro
Impossible to
measureMeso
Experimental
CVCalculated CVPermeability
28%
Monte
Carlo
p74%
Analytic
relation
p
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25Department of Metallurgy and Materials Engineering of 52
''' 2
1222 1
yxa
expxV V
0VxV
xR'
'
Properties of a random field
Average value Standard deviation
Distribution type: normal, lognormal,
Correlation Dimensions, textile properties should be continuous, no
sudden changes allowed
Example of simple variance function
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26Department of Metallurgy and Materials Engineering of 52
Assigning permeability values
Which meso scale level CV for K is neededto obtain experimental scatter on macro scale?
28 or 74 %
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27Department of Metallurgy and Materials Engineering of 52
Definition of master zone
Subdivision of master zone into sub zones (~ 1 unit cell)
Master zone Sub zone
Macro scale Meso scale
Uncorrelated assignment
Assigning permeability values
= problem
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28Department of Metallurgy and Materials Engineering of 52
Random assignment
Result is function of sub zone size
Correlation distance 0 is needed
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29Department of Metallurgy and Materials Engineering of 52
a = 0.1m a = 0.3m
Generation of correlated random field
Calculation of correlation matrix R
Calculation of covariation matrix V
Cholesky decomposition V = L.LT
Generate random column Z, average 0, V = 1
Calculate product Y=L.Z Add average value
''
a
xexpxV
2V
'' a
xexpxR
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30Department of Metallurgy and Materials Engineering of 52
Transformation into macro scale permeability
20%[K. Hoes]
ChallengeMacro
Impossible to
measure28 o 74 %Meso
Experimental
CVCalculated CVPermeability
Correlation length a
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31Department of Metallurgy and Materials Engineering of 52
Determination of correlation length
Based on macro scale information
Gap width along length of 2D woven textile
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32Department of Metallurgy and Materials Engineering of 52
Determination of correlation length a
xaexpxVxR ''' 2V Fitting of correlation data
Estimation of Variance + Correlation
> @ > @XzizXXzXM
ziV jziM
j
j '' '
1
1 0VziV
ziR ''
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33Department of Metallurgy and Materials Engineering of 52
Transformation into correlated permeability
Generation of correlated gap width values Coefficient of variation 37% (for 2D gap width)
Number of models: 100
Total length 500mm
Correlation length a =10mm
Building 100 WiseTex models
Calculating for each unit cell the permeability
Correlation of gap width
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34Department of Metallurgy and Materials Engineering of 52
Transformation into correlated permeability
C l i l h f bili
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35Department of Metallurgy and Materials Engineering of 52
Correlation length for permeability
I di l i
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36Department of Metallurgy and Materials Engineering of 52
Intermediate conclusions
Link between correlation length for geometry andpermeability
Method to implement correlated random field for
permeability is developed
Next steps Comparison of simulation results with experiments for only
available stochastic data on macro scale
Application on real part geometry
typermeabiligeometry aa 2
St h ti i l ti
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37Department of Metallurgy and Materials Engineering of 52
Stochastic simulation
Monte Carlo: N times (150)
St h ti i l ti diff t t
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38Department of Metallurgy and Materials Engineering of 52
Stochastic simulation: different steps
Reading externally created FE model
Assigning boundary conditions
Assigning stochastic parameters
Average/standard deviation/correlation length for each masterzone
Viscosity as function of time or time and temperature
Solving the N (Monte Carlo) files
Generation of results Average filling time
Standard deviation for filling time
Number of times an element is not filled Macro scale permeability
D i ti f i t l lt
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39Department of Metallurgy and Materials Engineering of 52
Description of experimental results
> 80 measurements for same textile reinforcement(4 layers of textile)
Highly automated setup to find in plane permeability
Result is averaged all over the mould with different sensors
Macro scale
CV = 20%
Case study 1:simulations
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40Department of Metallurgy and Materials Engineering of 52
Case study 1:simulations
Central injection setup similar to the experiment
1 master zone = 1 set of average data
Subdivision into sub zones
Correlated permeability field in 2 directions
no correlation between different directions
Case study 1: Flow patterns:stochastic realisations
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41Department of Metallurgy and Materials Engineering of 52
Case study 1: Flow patterns:stochastic realisations
Flow fronts at certain times = isochrones
Stochastic results for filling time
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42Department of Metallurgy and Materials Engineering of 52
Stochastic results for filling time
Standard deviation Coefficient of variation(Absolute information) (Relative information)
25%
67%
104%
Case study 1: simulation results
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43Department of Metallurgy and Materials Engineering of 52
Case study 1: simulation results
Post-processing of simulation results to find macro scale K
Sensor strategy to find average macro scale K values
Fitting of ellipses onto the different flow fronts as function of time
No difference between both techniques
Link between meso and macro CV
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44Department of Metallurgy and Materials Engineering of 52
Link between meso and macro CV
Transformation into macro scale permeability
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45Department of Metallurgy and Materials Engineering of 52
Transformation into macro scale permeability
20%[K. Hoes]5 o 13 %Macro
Impossible to
measure
28 o 74 %Meso
Experimental
CVCalculated CVPermeability
Correlation length a
?
Influence of correlation length
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46Department of Metallurgy and Materials Engineering of 52
Influence of correlation length
High meso scale CV needed to end up with reasonable macroscale CV
In modelling, only one layer of reinforcement considered
Stochastic modelling can be used for any mould filling problem!
120
%
Application to real problem: Case study 2
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47Department of Metallurgy and Materials Engineering of 52
Application to real problem: Case study 2
RTM with race tracking: 2 master zones
Race tracking channel
Case study 2: deterministic case
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48Department of Metallurgy and Materials Engineering of 52
Case study 2: deterministic case
No problem at all: no air entrapments
Case study 2: stochastic results
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49Department of Metallurgy and Materials Engineering of 52
Case study : stoc ast c esu ts
Possible regions
for air entrapment
Conclusions
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50Department of Metallurgy and Materials Engineering of 52
Measurement technique is developed to measure viscosity of
highly reactive resins
Use of random correlated field is proposed and validated forpermeability values
Technique is developed to find scatter + correlation for permeability
2 Permeability CV = Geometrical CV
Stochastics are implemented as add-on for PAM-RTM software
High meso scale CV needed to end up with reasonable macro scale CV
Stochastic modelling approaches the scatter level observed in experiments
In modelling, only one layer of reinforcement considered
Stochastic simulation reveals the sensitivity of the process to therace tracking, not seen in a deterministic simulation
In future, additional investigation of the correlation function andlength is needed together with the distribution type for K.
Acknowledgements
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51Department of Metallurgy and Materials Engineering of 52
g
KHBO for funding the whole PhD study Amiterm project partners for supplying the
thermoplastic material
Colleagues in Ostend
Colleagues in Leuven
Technicians
Family and friends