1. MULTI-SCALE MODELING OF
STRAND-BASED WOOD COMPOSITES
FPS 65th International Convention
June 19-21, 2011, Portland, OR, USA
T. Gereke, S. Malekmohammadi, C. Nadot-Martin,
C. Dai, F. Ellyin, and R. Vaziri
CIVIL ENGINEERING AND MATERIALS ENGINEERING
COMPOSITES GROUP

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
2

3. Outline
• Motivation
• Multi-Scale Approach
• Partial Resin Coverage
• Results
– Mesoscale
– Macroscale
• Conclusions
3

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
4

5. Multi-Scale Approach
Macroscale x3
PSL beam x2
x1
Strand
Void
Mesoscale Resin
x2
Resin covered
strand
x1 x3
Resin
Interface
Microscale Wood
Wood cells
Courtesy of Hass et
al., Wood Sc Tech,
2011
5

6. Multi-Scale Approach (cont.)
Macroscopic Element Unit Cell
y2
Macroscale
y1
Wood
y3
PSL beam
Structure
q Resin
Effective composite properties
Mesoscale
Resin covered
strand
x2
x1 x3
Real Mesostructure Idealized Mesostructure
6

7. Multi-Scale Approach (cont.)
PSL Dimensions:
Macroscale X2
• X1 = 380 mm
PSL beam • X2 = 39 mm + 6tR
X1 x2
• X3 = 40 mm + 16tR
x1
X3 x3
Mesoscale Unit Cell • Y1 = 600 mm + 2tR
Resin covered Resin • Y2 = 13 mm + 2tR
Wood Y2
strand • Y3 = 5 mm + 2tR
Y1 y
2
y1
Y3 y3 tR, resin thickness
7

8. Multi-Scale Approach (cont.)
PSL • Randomly distributing
q=0°
Macroscale q=5°
Load maximum grain angle
(distribution according
PSL beam q=10°
q=20° to Clouston, 2007*)
x2 1500
1301
Frequency
x1 1000
x3 340 403
500 116
0
0 5 10 20
Maximum grain angle, q (°)
Mesoscale Unit Cell
• Calculation of effective
Resin covered elastic properties by
strand applying periodic
boundary conditions to
the unit cell
y2
y1
y3 *Clouston, P., Holzforschung
61:394-399, 2007
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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).
0.6
0.5
Micro- Macro-
Relative frequency 0.4
voids voids
0.3
0.2
0.1
10 cm × 10 cm 0.0
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 (%)
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10. Partial Resin Coverage (cont.)
Full resin coverage Partial resin coverage
• Linear relation between • Resin area coverage (RA)
resin content (RC) and resin increases as more resin is
thickness (tR) used in the manufacturing
• No resin penetration process
• No voids in the • No resin penetration
microstructure • Two scenarios considered:
0.30 100%
A. RA increases with RC
Resin thickness, tR (mm)
Resin area coverage, RA
0.25
80%
uniformly at a constant tR
0.20
60%
B. Both RA and tR increase
with RC (Dai’s model*)
0.15
40%
0.10
20%
0.05
0.00 0%
0% 2% 4% 6% 8%
Resin content by volume, RC
*Dai, C. et al., Wood and Fiber Science
39:56-70, 2007
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11. Partial Resin Coverage (cont.)
Scenario A Scenario B
RA increases with RC Both, RA and tR increase
uniformly at a constant tR with resin content
100% 0.30 100% 0.30
Resin thickness, tR (mm)
0.25 0.25
Resin area coverage, RA
Resin thickness, tR (mm)
Resin area coverage, RA
80% 80%
0.20 0.20
60% 60%
0.15 0.15
40% 40%
0.10 0.10
20% 20%
0.05 0.05
0% 0.00 0% 0.00
0% 1% 2% 3% 4% 5% 6% 7% 8% 0% 1% 2% 3% 4% 5% 6% 7% 8%
Resin content by volume, RC Resin content by volume, RC
Dai et al. (2007):
  s RC 
RA  1  exp 
 21  MC   R 

 r r solids 
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12. Partial Resin Coverage (cont.)
• Introducing void elements for partial coverage
simulations
Resin Void elements are distributed
Elements by replacing some resin
elements in the original full
coverage discretized FE
model
Wood
Elements RA = 60%.
Discretized Full
Coverage FE model
Discretized Partial Void
Coverage FE model Elements
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13. Results (Mesoscale)
• Comparison with full coverage case
S23 S23
3
2
1
Full coverage Partial coverage
E1 = 12.64 GPa E1 = 12.41 GPa
RA = 100% RA = 60%
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14. Results (Mesoscale)
13.00 13.00
tR variable tR variable
12.80 12.80
12.60 tR = 0.08 mm 12.60
tR = 0.08 mm
E1 (GPa)
E1 (GPa)
12.40 12.40
12.20 tR = 0.28 mm 12.20
tR = 0.28 mm
12.00 12.00
11.80 11.80
11.60 n=10 11.60
n=10
11.40 11.40
0% 2% 4% 6% 8% 0% 20% 40% 60% 80% 100%
Resin content by volume, RC Resin area coverage, RA
100%
tR variable
Resin area coverage, RA
80%
tR = 0.08 mm
60%
tR = 0.28 mm
40%
20%
Scenario A
0% Scenario B
0% 2% 4% 6% 8%
Resin content by volume, RC
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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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16. Results (Macroscale)
Scenario A
• Prediction of bending MOE
Scenario B
11 11
tR = 0.08 mm
tR variable tRtR variable
= 0.08 mm
10
Bending MOE (GPa)
10
Bending MOE (GPa)
tR = 0.28 mm tR = 0.28 mm
9 9
8 8
n=250 n=250
7 7
0% 2% 4% 6% 8% 0% 20% 40% 60% 80% 100%
Resin content by volume, RC Resin area coverage, RA
MOE highly depends on resin thickness and then resin area coverage
as the resin thickness increases.
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17. 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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18. 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)
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19. Questions?