CIVIL ENGINEERING AND MATERIALS ENGINEERING

COMPOSITES GROUP

MULTI-SCALE MODELING OF STRAND-BASED WOOD COMPOSITES

FPS 65th International ConventionJune 19-21, 2011, Portland, OR, USA

T. Gereke, S. Malekmohammadi, C. Nadot-Martin, C. Dai, F. Ellyin, and R. Vaziri

2

UBC Composites Group

• 2 Departments: Civil Engineering & Materials Engineering

• Group exists since the early 1980‘s

• Projects:

– Processing for Dimensional Control

– Development of an Integrated Process Model for Composite

Structures

– Tool-part interaction - Experiments and modeling

– Viscoelaticity and residual stress generation

– Characterization of damage in impact of composite structures

– Damage and strain-softening characterization

– Observation of fracture in-situ inside an SEM: aerospace and

biomaterial applications

– Multi-scale modelling of wood composite products

www.composites.ubc.ca

3

Outline

• Motivation

• Multi-Scale Approach

• Partial Resin Coverage

• Results

– Mesoscale

– Macroscale

• Conclusions

4

Motivation

• Strand-based wood composites frequently used as

construction materials in residential and other buildings

• Certain requirements on their mechanical properties

such as stiffness and strength

• Realistic modeling as a viable alternative to time

consuming and costly experiments

• Goal: development of a numerical model that can serve

as a tool to control the properties of the constituents in

order to optimize the macroscopic material behavior

5

Multi-Scale Approach

Macroscale

PSL beam

Mesoscale

Resin covered strand

MicroscaleWood cells

x1

x2

x3

Strand

Resin

x3x1

x2

Courtesy of Hass et

al., Wood Sc Tech,

2011

ResinInterfaceWood

Void

6

x3x1

x2

Real Mesostructure

Macroscale

PSL beam

Mesoscale

Resin covered strand

Structure

Idealized Mesostructure

Multi-Scale Approach (cont.)

Resin

Wood

Unit Cell

y3

y1

y2

Macroscopic Element

Effective composite properties

q

7

Multi-Scale Approach (cont.)

Dimensions:

• X1 = 380 mm

• X2 = 39 mm + 6tR

• X3 = 40 mm + 16tR

• Y1 = 600 mm + 2tR

• Y2 = 13 mm + 2tR

• Y3 = 5 mm + 2tR

Macroscale

PSL beam

Mesoscale

Resin covered strand

PSL

x3

x1

x2

X2

X1

X3

Unit Cell

y3

y1

y2

Y2

Y3

Y1

Wood

Resin

tR, resin thickness

8

Multi-Scale Approach (cont.)

Loadq=0°

q=5°

q=10°

q=20°

Macroscale

PSL beam

Mesoscale

Resin covered strand

• Randomly distributing

maximum grain angle

(distribution according

to Clouston, 2007*)

• Calculation of effective

elastic properties by

applying periodic

boundary conditions to

the unit cell

403340

1301

116

0

500

1000

1500

0 5 10 20Maximum grain angle, q (°)

Fre

qu

en

cy

*Clouston, P., Holzforschung 61:394-399, 2007

PSL

Unit Cell

x3

x1

x2

y3

y1

y2

9

Partial Resin Coverage

Why not a full resin coverage?

• In manufacturing process of strand-based composites, strands

are not fully covered by the resin.

• Resin distribution should be considered in the modeling

approach.

• Voids are distributed randomly through a typical wood

composite (PSL).

10 cm × 10 cm 0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.45 0.50 0.55 0.60

Void size (%)

Rela

tive f

req

uen

cy Micro-

voidsMacro-voids

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Partial Resin Coverage (cont.)

Full resin coverage

• Linear relation between resin content (RC) and resin thickness (tR)

• No resin penetration

• No voids in the microstructure

Partial resin coverage

• Resin area coverage (RA) increases as more resin is used in the manufacturing process

• No resin penetration

• Two scenarios considered:

A. RA increases with RC

uniformly at a constant tR

B. Both RA and tR increase with RC (Dai’s model*)

*Dai, C. et al., Wood and Fiber Science 39:56-70, 2007

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0% 2% 4% 6% 8%

0%

20%

40%

60%

80%

100%

Resin content by volume, RC

Resin

area c

overag

e, RA

Resin

th

ickn

ess, t R

(m

m)

11

Partial Resin Coverage (cont.)

Scenario ARA increases with RC

uniformly at a constant tR

Scenario BBoth, RA and tR increase with resin content

solidsrr

CsA

RMC

RR

12exp1

0%

20%

40%

60%

80%

100%

0% 1% 2% 3% 4% 5% 6% 7% 8%

0.00

0.05

0.10

0.15

0.20

0.25

0.30

Resin content by volume, RC

Resin

area c

overag

e, RA

Resin

th

ickn

ess, t R

(m

m)

0%

20%

40%

60%

80%

100%

0% 1% 2% 3% 4% 5% 6% 7% 8%

0.00

0.05

0.10

0.15

0.20

0.25

0.30

Resin content by volume, RC

Resin

area c

overag

e, RA

Resin

th

ickn

ess, t R

(m

m)

Dai et al. (2007):

12

Partial Resin Coverage (cont.)

• Introducing void elements for partial coverage simulations

Void elements are distributed by replacing some resin

elements in the original full coverage discretized FE

model

RA = 60%.

Discretized Full Coverage FE model

Discretized Partial Coverage FE model

Void Elements

Wood Elements

Resin Elements

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Results (Mesoscale)

Full coverage Partial coverage

E1 = 12.64 GPa

RA = 100%

E1 = 12.41 GPa

RA = 60%

12

3

S23 S23

• Comparison with full coverage case

14

11.40

11.60

11.80

12.00

12.20

12.40

12.60

12.80

13.00

0% 20% 40% 60% 80% 100%

tR = 0.08 mm

tR = 0.28 mm

tR variable

Resin area coverage, RA

E1

(G

Pa)

0%

20%

40%

60%

80%

100%

0% 2% 4% 6% 8%

Resin content by volume, RC

Resin

area c

overag

e, RA

tR = 0.08 mm

tR = 0.28 mm

tR variable

Results (Mesoscale)

Scenario A

Scenario B

n=10 n=1011.40

11.60

11.80

12.00

12.20

12.40

12.60

12.80

13.00

0% 2% 4% 6% 8%

tR = 0.08 mm

tR = 0.28 mm

tR variable

Resin content by volume, RC

E1

(G

Pa)

15

Results (Mesoscale)

• Scenario A

– For a constant resin area coverage, as the resin thickness

decreases, resin content decreases while E1 increases

– E1 increases with resin area coverage

• Scenario B

– By adding more resin, E1 increases until RA ≈ 80% then it

drops, since E of the resin is lower than EL of the wood

• Resin thickness and resin area coverage could

significantly alter the properties of the unit cell.

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Results (Macroscale)

• Prediction of bending MOEScenario B

MOE highly depends on resin thickness and then resin area coverage

as the resin thickness increases.

Scenario A

7

8

9

10

11

0% 2% 4% 6% 8%

Resin content by volume, RC

Ben

din

g M

OE (

GP

a)

tR = 0.08 mm

tR = 0.28 mm

7

8

9

10

11

0% 20% 40% 60% 80% 100%

Resin area coverage, RAB

en

din

g M

OE (

GP

a)

tR = 0.08 mm

tR = 0.28 mm

7

8

9

10

11

0% 20% 40% 60% 80% 100%

Resin area coverage, RAB

en

din

g M

OE (

GP

a)

tR variable

7

8

9

10

11

0% 2% 4% 6% 8%

Resin content by volume, RC

Ben

din

g M

OE (

GP

a)

tR variable

n=250 n=250

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Conclusions

• The concept of resin area coverage has been incorporated into

the multi-scale model.

• A series of codes were developed to distribute void elements

randomly and analyze results both at meso- and macroscale.

• Stochastic simulation shows that MOE could vary between 8 to

10 GPa depending on the resin thickness and resin area

coverage.

• Establishing a realistic relation between RC and RA could help

predicting the macroscopic properties of wood composites

more accurately within a large range of RC.

• Incorporation of resin penetration and strand compaction will

improve the model in the future (microscale)

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Acknowledgements

• Benjamin Tressou, ENSMA, France

• Dr. Carole Nadot-Martin, ENSMA, France

• Sardar Malekmohammadi, UBC

• Dr. Chunping Dai, FPInnovations

• Mr. Gregoire Chateauvieux and Mr. Xavier Mulet,

ENSAM, France

• Financial support: Natural Sciences and Engineering

Research Council of Canada (NSERC)

Questions?