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
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
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
8
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 (%)
9
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
10
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
21 MC R
r r solids
11
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
12
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%
13
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
14
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.
15
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.
16
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)
17
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)
18
