Textile Modelling Permeability Desplentere Presentation

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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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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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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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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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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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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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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Large scatter is experienced in textile reinforcementproperties

Geometrical properties

Flow properties

Scatter for textile properties


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Possibility for air entrapments

Depends on product shape or mould Large influence of edge effect (imperfect placing of

textile material)


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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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Different scales in physical phenomena

Meso Macro

Meso

Macro


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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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Resin Viscosity


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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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Oligomer viscosity

ZSK

HDTe34

D

DDK

.

2

20

431

..,CC

T

C

C

CeCT


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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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Meso scale stochasticity


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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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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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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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Gap width distribution (surface scanning)

Average = 510m, CV = 37%

Meso scale textile architecture 2D


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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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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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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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''' 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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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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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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Random assignment

Result is function of sub zone size

Correlation distance 0 is needed


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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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20%[K. Hoes]

ChallengeMacro

Impossible to

measure28 o 74 %Meso

Experimental


Correlation length a


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Determination of correlation length

Based on macro scale information

Gap width along length of 2D woven textile


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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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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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Transformation into correlated permeability

C l i l h f bili


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Correlation length for permeability

I di l i


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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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Stochastic simulation

Monte Carlo: N times (150)

St h ti i l ti diff t t


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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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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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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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Case study 1: Flow patterns:stochastic realisations

Flow fronts at certain times = isochrones

Stochastic results for filling time


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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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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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Link between meso and macro CV



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20%[K. Hoes]5 o 13 %Macro

Impossible to

measure

28 o 74 %Meso

Experimental


Correlation length a

?

Influence of correlation length


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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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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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Case study 2: deterministic case

No problem at all: no air entrapments

Case study 2: stochastic results


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Case study : stoc ast c esu ts

Possible regions

for air entrapment

Conclusions


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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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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

Textile Modelling Permeability Desplentere Presentation

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