Jana Dlouha
email: jana.dlouha@inra.fr

Tancrède Alméras

Jacques Beauchêne

Bruno Clair

Meriem Fournier

| Jacques Beauchêne

| Bruno Clair

Jana Dlouhá

for wood density as a proxy of species strategies is mixed (Moles

HAL is

2. A set of theoretical relationships mechanistically linking wood basic density with some other wood traits is derived from cellular material physics. These theoretical models picture basic physical constraints and thus provide null hypotheses for further ecological studies. Analysis is applied to data from two original datasets and several datasets extracted from the literature.

3. Results emphasize the strong physical constraint behind the link between basic density and maximal storable water on the one hand, and elastic modulus on the other hand. Beyond these basic physical constraints, the developed framework reveals physically less expected trends: the amount of free water available for physiological needs increases in less dense wood of fast-growing species, and the cell wall stiffness decreases with density in temperate hardwoods and is higher in sapling stages in the rainforest understorey where competition for light is associated with high mechanical risk.

4. We emphasize the use of theoretically independent traits derived from models of cellular material physics to investigate the functional variation of wood traits together with their environmental and phylogenetic variations. Although the current study is limited to basic density, green wood lumen saturation and wood specific modulus, we further emphasize the identification of complementary independent wood traits representing other biomechanical functions, nutrient storage, hydraulic conductance and resistance to drought.

K E Y W O R D S

basic density, biomechanical traits, hydraulic traits, wood traits 2018; [START_REF] Russo | Interspecific relationships among growth, mortality and xylem traits of woody species from New Zealand[END_REF]. Indeed, to get relevant functional wood traits, tissue properties must be integrated at the whole plant level taking into account volume and shape of woody organs [START_REF] Fournier | Integrative biomechanics for tree ecology: Beyond wood density and strength[END_REF][START_REF] Lachenbruch | Traits, properties, and performance: How woody plants combine hydraulic and mechanical functions in a cell, tissue, or whole plant[END_REF].

Moreover, as woods with the same density can have different anatomies and cell wall structure, hydraulic and mechanical properties can be poorly linked to wood density [START_REF] Lachenbruch | Traits, properties, and performance: How woody plants combine hydraulic and mechanical functions in a cell, tissue, or whole plant[END_REF][START_REF] Zanne | Angiosperm wood structure: Global patterns in vessel anatomy and their relation to wood density and potential conductivity[END_REF][START_REF] Ziemińska | Broad anatomical variation within a narrow wood density range-A study of twig wood across 69 Australian angiosperms[END_REF]. As developed by [START_REF] Zanne | Angiosperm wood structure: Global patterns in vessel anatomy and their relation to wood density and potential conductivity[END_REF] for hydraulic conductivity, more complex metrics of orthogonal axes of wood traits can be designed beyond the main axis of wood density. Lastly, dealing with wood density or other wood traits, attention must be paid to wood water content.

Basic density, that is, the ratio between dried biomass to wet volume of wood in living trees, is nowadays a widely measured variable, used to estimate biomass and carbon fluxes from wood volumes assessed from non-destructive forest measurements [START_REF] Bastin | Wood specific gravity variations and biomass of central african tree species: The simple choice of the outer wood[END_REF][START_REF] Chave | Towards a worldwide wood economics spectrum[END_REF][START_REF] Nogueira | Normalization of wood density in biomass estimates of Amazon forests[END_REF]. Beyond basic density, green wood density (including water content of wood in the living tree) is functionally relevant-for example, it represents the mechanical load per unit of stem volume-and is statistically well correlated to other wood traits (e.g., [START_REF] Niklas | Worldwide correlations of mechanical properties and green wood density[END_REF]. Beyond this rough correlation, more independent and functionally relevant traits, as water content, can be calculated from basic and green density [START_REF] Gartner | Gas in stems: Abundance and potential consequences for tree biomechanics[END_REF][START_REF] Poorter | The relationships of wood-, gas-and water fractions of tree stems to performance and life history variation in tropical trees[END_REF].

In this paper, we emphasize physical constraints contained in empirical correlations between some wood traits. Indeed, correlation and causation are complex problems for the science of plant traits [START_REF] Westoby | Land-plant ecology on the basis of functional traits[END_REF]. For evolutionary biologists, physical relationships cannot be disentangled from biological ones as evolution operates on the basis of a set of developmental constraints that are "physical" (in the sense that they fulfil mechanical or physiological functions). However, some physical relationships-as, for example, "the higher the density, the lower the porosity in the cellular material"-are biologically meaningless. There is no need of a complex genetic system to insure the co-evolution of density and porosity which is an intrinsic property of cellular materials, biological or artificial [START_REF] Gibson | Cellular solids. Structure and properties[END_REF]. Therefore, such laws of physics (most of them are less intuitive than the link between density and porosity) provide null hypotheses to ecologists. Then, analysing empirical statistical discrepancies from these null hypotheses, so long as they are not the result of measurement error, are meaningful in terms of adaptation to environmental conditions and/or phylogenetic constraints. Note that these meaningful discrepancies can be poor correlations, although they would have been assumed as poor results by empirical science. The mechanistic toolbox we will develop uses a simplified representation of wood as a cellular material [START_REF] Gibson | Cellular solids. Structure and properties[END_REF] to derive a set of physical relationships expected to link wood density measured at different moisture contents with some functional wood properties. We focus on the following properties: the potential capacitance of wood tissue, that is, the maximum amount of water that can be stored in a given wood volume, and the complementary amount of gas stored, already used by [START_REF] Gartner | Gas in stems: Abundance and potential consequences for tree biomechanics[END_REF] or [START_REF] Poorter | The relationships of wood-, gas-and water fractions of tree stems to performance and life history variation in tropical trees[END_REF] as functional traits, and the elastic modulus, that is, the tissue mechanical stiffness, a relevant and basic biomechanical trait [START_REF] Niklas | Mechanical properties of wood disproportionately increase with increasing density[END_REF]. Two original datasets of wood properties will be used. Dataset 1 is part of a large research project (Baraloto, Paine, et al., 2010;[START_REF] Sarmiento | Within-individual variation of trunk and branch xylem density in tropical trees[END_REF], includes a large diversity of species and contains basic density, green density and saturated density data. Dataset 2 provides data about inter-and intraspecific variability of basic density and elastic modulus of wood from trees at sapling stages where mechanical stability is a great constraint.

In this paper, we will check null hypothesis stressed in the physical model of relationships among wood traits. Dataset 1 comes from a zone without a drought constraint so we do not expect any discrepancy from null hypotheses linking wood water content and construction cost, supporting results of Baraloto, Paine, et al., 2010;[START_REF] Baraloto | Decoupled leaf and stem economics in rain forest trees[END_REF] who described one main axis of stem strategy: dense wood vs. high wood water content. In dataset 2, we could expect that to maintain self-support, the lower wood density of fast-growing pioneer species is compensated by higher cell wall stiffness, so that the relationship between wood density and modulus of elasticity differs from the null hypothesis.

| MATERIAL S AND ME THODS

| Plant material

Wood material for dataset 1 was collected in seven permanent 1ha plots in French Guiana between November 2007 and September 2008. Overall, 2234 wood specimens from 601 species (http:// bridge.ecofog.gf/) were collected from adult trees, each species being represented by at least one tree without any within-tree repetition of specimens. A 7-mm-diameter increment borer was used to extract sapwood at 1.3 m above-ground level. Bark and cambium tissues were removed manually from each core to obtain a representative 6-mm-long core containing only sapwood. The specimens were placed in a 2-ml plastic tube, stored at -20°C in a portable freezer in the field and transported to a laboratory at Kourou, French Guiana, for density measurements.

Wood material for dataset 2 was collected at the Paracou Research Station, French Guiana (5°18′ N, 52°55′ W; [START_REF] Gourlet-Fleury | Ecology and management of a neotropical rainforest: Lessons drawn from Paracou, a long-term experimental research site in French Guiana[END_REF], between September 2006 and October 2007. Ten tree species were selected among common angiosperms to cover a wide range of wood density. A total of 528 wood specimens were taken from saplings with diameter at breast height ranging from 40 to 70 mm including several replicates per tree to capture the wood properties' radial variation. Straight as well as leaning stems were included in the study. Specimens (150 × 12 × 2 mm, L × T × R) were cut along the grain from 50-cm-long basal logs within a week after felling the trees at various distances from the pith including the juvenility gradient usually associated with strong radial variation in wood structure and properties [START_REF] Lachenbruch | Radial variation in wood structure and function in woody plants, and hypotheses for its occurrence[END_REF]. In tilted trees, sampling was performed along the diameter with higher eccentricity expecting occurrence of reaction wood [START_REF] Ruelle | Tension wood and opposite wood in 21 tropical rain forest species 2. Comparison of some anatomical and ultrastructural criteria[END_REF]. The objective was to maximize the intraspecific variability in terms of different wood types (juvenile, tension and opposite wood).

Specimens were then wrapped in wet sheets and plastic bags for transport from the field to the laboratory and kept wet immersed in water at low temperatures (4 ± 0.5°C) to avoid degradation [START_REF] Dlouha | Representativeness of wood biomechanical properties measured after storage in different conditions[END_REF]. Both datasets are available in the Dryad Digital Repository [START_REF] Dlouhá | Data from: Biophysical dependences among functional wood traits[END_REF]

| Mass and volume measurements

For both datasets, the mass of specimens was measured in green state, and their fresh volume was estimated by the double weighing method [START_REF] Barnett | Wood quality and its biological basis[END_REF]. For dataset 1, specimens were then saturated in water in vacuum condition during 48-72 hr and finally dried at 103° for 72 h. Specimens of dataset 2 were dried at 60°C for 48 h which results in residual moisture content of 0.75% ± 0.03% [START_REF] Brémaud | Diversity of woods used or usable in musical instruments making[END_REF] but avoids micro-cracks during the drying so that specimens remain suitable for mechanical testing.

Density and moisture content were calculated according to relationships and are summarized in Table 1.

| Elastic modulus

The elastic modulus for dataset 2 was estimated by forced flexural vibration method on specimens in green condition within 1 month after felling the tree. A frequency sweep detects the resonance frequency, from which the specific modulus of elasticity (E /d ) is determined according to the Euler-Bernoulli equation:

where l and h are the specimen length and thickness, f is the resonance frequency of the first mode and m is a constant depending on mode order (m 1 = 4.730). Details on the experimental procedure are given in [START_REF] Brémaud | Characterisation and categorisation of the diversity in viscoelastic vibrational properties between 98 wood types[END_REF].

| Definitions of traits and physical relationships between traits

Definitions of different wood traits are summarized in Table 1.

Physical assumptions and laws, detailed in Supporting information

Appendix S1, give expected relationships among these traits. Results of calculations can be synthesized as follows (see also Table 1):

Above the fibre saturation point (FSP), moisture content w G links basic density d B with green density d G :

The dependence of saturated density d S on basic density d B is linear:

The intercept equal to 1 means that when basic density tends to 0, wood saturated density comes close to the density of water.

E /d = 48π 2 l 4 f 2 m 2 h 2 , (1) w G = d G ∕d B -1. (2) d S = 1 + k d B .
TA B L E 1 Definitions of density in different water condition and related wood traits, models relating them to basic density or saturated density (general and simplified models using k = 1/3 as explained in Supporting information Appendix S1). Some models, indicated by (…), are not detailed here but can be easily computed from relationship with basic density d

d B = m D /v G d B (d S -1)/k d B 3d S -3 Density kg/m 3 Green d G = m G /v G d G d G d G d G Saturated d S = m S /v S k.d B + 1 d S d B /3 + 1 d S Moisture Content % Green w G = (m G -m D )/m D = d G /d B -1 d G /d B -1 (…) d G /d B -1 (…) Saturated w S = (m S -m D )/m D = d S /d B -1 1/d B + (k-1) ((k-1)d S +1)/(d S -1) 1/d B -2/3 (-2d S +3)/(3d S -3) Volumetric Water Content kg/m 3 Green H G = (m G -m D )/v G = d G -d B d G -d B (…) d G -d B (…) Saturated H S = (m S -m D )/v S = d S -d B (k-1)d B + 1 (1-1/k)d S + 1/k -2 d B /3 + 1 -2d S +3 Volumetric Free Water Content kg/m 3 Green F G = H G -w fsp d B d G -(1 + w fsp )d B (…) d G -1.3 d B (…) Saturated F S = H S -w fsp d B (k-1-w fsp )d B + 1 (…) -0.97 d B + 1 (…) Lumen saturation % Green S G = F G /F S (d G -(1 + w fsp )d B )/((k-1-w fsp )d B + 1) (…) (d G -1.3 d B )/(-0.97 d B + 1) (…)

Modulus of elasticity

MPa

Green

Tissue stiffness

E G = E /d •d B (…) - - Saturated Tissue stiffness E S = E G (…) - -
The parameter k is 1/3 according to a simplified model assuming the additivity of volumes. Maximum moisture content w S and basic or saturated density are then linked as follows:

The maximum volumetric water content H S may be expressed as follows:

Partitioning of water contained in wood between the cell wall and cell lumens for wood having different basic density and different saturation state is illustrated in Figure 1. The water bound in the cell wall cannot be used for physiological needs; therefore, free water content F G computed as a difference between volumetric water content H G in green condition and water content bound in the cell wall w FSP is a more appropriate variable as we introduced in Longuetaud et al. ( 2017):

The relative saturation of lumen in green wood specimens (S G ) can be quantified as follows:

where H FSP is volumetric water content at FSP.

The green and the saturated elastic moduli are the same (E G = E S ).

As for any honeycomb cellular material, wood elastic modulus in the fibre direction is expected to be proportional to its density [START_REF] Gibson | Cellular solids. Structure and properties[END_REF]:

E /d
• is referred to as "wood specific modulus" [START_REF] Gibson | Cellular solids. Structure and properties[END_REF][START_REF] Niklas | Influence of tissue density-specific mechanical properties on the scaling of plant height[END_REF]. We assume here that density reflects the amount of cell wall, by neglecting the contribution of non-structural material such as living cells' content and some extractive molecules located outside the cell wall. As the density of cell wall does not vary significantly among species and among wood types [START_REF] Kellogg | Variation in the cell-wall density of wood[END_REF], the specific modulus directly reflects the stiffness of the cell wall (E cw ) and E /d ≈ E cw /1.5.

It

| Statistical analyses

We investigate three types of relationships from our data: (a) relationships between wood density and water status as expressed by Equations 3-7 (dataset 1); (b) relationships between physical parameters of wood in green condition and basic density (dataset 1) and (c) relationships between elastic modulus and density on dataset 2 (Equation 9). For each examined relationship, linear model was first fitted using the ordinary least square method implemented in "lm" function of the R software (R Core Team, 2017). Further, random effects (species for dataset 1 and nested levels species and individuals for dataset 2) were introduced using the "lmer" function of the "lme4″ package [START_REF] Bates | Fitting linear mixed-effects models using lme4[END_REF]. Mixed-effect models were fitted by the restricted maximum-likelihood (REML) method.

The selection of the best model fitted was based on Akaike's information criterion and likelihood ratio test for nested model ("ANOVA" function in R). For dataset 1, only random effect associated with

(3)

w S = 1∕d B + (k -1) ≈ 1∕d B -2∕3. (4) W S = ((k -1)d S + 1)∕(d S -1) ≈ ( -2⋅d S + 3)∕(3⋅d S -3).
(5) intercept was considered, as small number of specimens per species with limited intraspecific predictor range does not make it suitable for random slope model. Relationship between w S and d B was transformed to a linear form using 1/d B as a variable. For relationships estimating the green wood parameters, d G ~dB , F G ~dB , non-linear forms of model, namely, the second-order polynomial, the logarithmic and the power function for the relationship d G ~dB were tested using the "nlmer" function. In addition, the variance for these two relationships

H S = 1 + (k -1)⋅d B ≈ 1 -2∕3⋅d B (6) or H S = (1 -1∕k)⋅d S + 1∕k ≈ 3 -2d S (7) F G = H G -w FSP ⋅d B (8) S G = (H G -H FSP )∕(H S -H FSP ) (9) E G = E /d ⋅d B F I G U R E 1
was not constant so weighted regression using the "VarExp" function was fitted. To examine whether the model parameter value was significantly different from zero or from the physical model parameter value, confidence intervals of the fixed parameters were investigated using the "confint" function, the level of confidence being set to 0.95.

| RE SULTS

As stated at the end of introduction, the better the physical model fits, the stronger the physical constraint it bears. A poorly fitted model means species or individuals are able to overturn laws of cellular material design, by adjusting the geometry of vessels to porosity, or by changing the cell wall mechanical properties with the amount of cell wall. Parameters of all empirical and physical models are summarized in Table 2.

| Relationship between basic density and saturated density

We found a linear relationship between saturated and basic den-

| Relationship between density and maximal water content

We found a strong inverse relationship between maximum moisture content w S and d B (Figure 3a, EF > 0.99, RMSE = 0.024), as predicted by Equation 4. The w S values as modelled from d S (Equation 5) were overestimated, the model setting the upper bound of experimental values (Figure 3b). Further, we found that

| Relationships between basic density and physical parameters of green wood

Relationship between d G and d B (Figuation 4a) was the best fitted by a logarithmic weighted regression with random effect associated with intercept. We found a negative linear fit with random effect associated with intercept between volumetric water content of green wood H G and d B (Figure 4b). Random effect significantly increased the model efficiency (Table 2), and the confidence interval of the slope parameter did not overlap zero and p-value was highly significant (FSP) to 100% (full saturation), 95% of data ranging between 15% and 88% and the average S G being 52% (Figure 4d). Note that a moisture content w = 50%, a reference value used by [START_REF] Niklas | Worldwide correlations of mechanical properties and green wood density[END_REF], is physically impossible for woods with basic density larger than 0.86 × 10 3 kg/m 3 .

| Relationship between mechanical properties and density

Relationship between elastic modulus E G and d B was best fitted by a linear regression without intercept with nested random effects species/tree associated with the slope (Figure 5a). The form of the statistical fit followed theoretical prediction (Equation 10). Variance components analysis of random effects shows that 47% of the random variance was explained by tree effect and 37% by species effect (Table 2).

Slope of the linear relationship between wood specific modulus E /d and d B was not statistically significant, and its confidence interval overlapped zero for both models, with and without random effects associated with the intercept (p-value equal to 0.18 and 0.08, respectively). Therefore, we fitted y-intercept model with nested random effects species/tree associated with the intercept. As shown in Table 2, tree and species effects explained, respectively, 37% and 26% of the random variance. 2009), Baraloto, Paine, et al., 2010;[START_REF] Baraloto | Decoupled leaf and stem economics in rain forest trees[END_REF][START_REF] Fortunel | Wood specific gravity and anatomy of branches and roots in 113 Amazonian rainforest tree species across environmental gradients[END_REF][START_REF] Fortunel | Wood specific gravity and anatomy of branches and roots in 113 Amazonian rainforest tree species across environmental gradients[END_REF].

| The amount of free water available for physiological needs, namely, F G , slightly increases in light wood species with faster growth

Green wood traits (d G , w G , H G and S G ) are not mechanistically related to basic density because they depend on the relative proportion of gas and water in the lumens. This proportion varies with many parameters such as environmental condition, daytime, season, rain events and ontogeny [START_REF] Barnard | Climate-related trends in sapwood biophysical properties in two conifers: Avoidance of hydraulic dysfunction through coordinated adjustments in xylem efficiency, safety and capacitance[END_REF][START_REF] Hao | Investigating xylem embolism formation, refilling and water storage in tree trunks using frequency domain reflectometry[END_REF][START_REF] Pineda-Garcia | Drought resistance in early and late secondary successional species from a tropical dry forest: The interplay between xylem resistance to embolism, sapwood water storage and leaf shedding[END_REF][START_REF] Scholz | Temporal dynamics of stem expansion and contraction in savanna trees: Withdrawal and recharge of stored water[END_REF]; however, the magnitude of temporal variation does not exceed 10% [START_REF] Hao | Investigating xylem embolism formation, refilling and water storage in tree trunks using frequency domain reflectometry[END_REF].

Negative relationships observed between free water content and basic density (Table 3) indicate that light wood species store higher amount of available water per unit of wood volume. We may question the biological reason for this. In angiosperms, light wood is associated with larger vessels (Hietz, Rosner, Hietz-Seifert, & Wright, 2017; Zanne

F I G U R E 2
Relationship between saturated and basic density.

Red line represents empirical fit, and black line corresponds to physical model (Equation 2) et al., 2010) that is not a physical necessity (low density can be easily obtained from a large number of small vessels) but an adaptation of light wood species to fast growth because large vessels enhance hydraulic conductance for a given basic density [START_REF] Lachenbruch | Traits, properties, and performance: How woody plants combine hydraulic and mechanical functions in a cell, tissue, or whole plant[END_REF]. Large vessels can also retain water more easily so the adapted higher conductance of less dense wood is physically associated with a higher capacity to store water although ecological requirements would have suggested that dense wood species, more adapted to drought, should tend to increase their water storage capacity [START_REF] Holbrook | Stem water storage[END_REF].

On the other hand, species with low density wood and, presumably, higher transpiration rates, may more rely on release of stored water to constrain transpiration-induced fluctuation in xylem tension [START_REF] Goldstein | Stem water storage and diurnal patterns of water use in tropical forest canopy trees[END_REF]) compared to species with denser wood that rely primarily on xylem structural features to avoid embolism [START_REF] Meinzer | Xylem hydraulic safety margins in woody plants: Coordination of stomatal control of xylem tension with hydraulic capacitance[END_REF].

| Relative proportion of water and gas in green wood lumens S G represents an axis of wood traits orthogonal to basic density

Wide variety in the use of available porosity for water or gas storage (Figure 4d) suggests that S G is a good candidate trait constituting an axis of functional diversity independent of d B . Our dataset and reanalysed literature data [START_REF] Gartner | Gas in stems: Abundance and potential consequences for tree biomechanics[END_REF][START_REF] Poorter | The relationships of wood-, gas-and water fractions of tree stems to performance and life history variation in tropical trees[END_REF] (Table 3). However, associations between S G and d B can be expected for ecological reasons. For instance, a higher S G would be expected in high density wood to maintain the amount of free water although less available space in lumens; alternatively, if low wood density is an adaptation to produce higher capacity for water storage, then a negative association between d B and S G should be expected. Water storage capacity is definitely an interesting trait to study in the current context of adaptation of forest composition to climate change.

Benefit of low lumen saturation raises the question of the role of gas in the living tree. As stated by [START_REF] Gartner | Gas in stems: Abundance and potential consequences for tree biomechanics[END_REF], oxygen is necessary for the respiration of living cells such as parenchyma and has therefore a physiological role. Correlation between amount of parenchyma and wood gas content has never been examined to our knowledge but could provide some clue. Another potential benefit of replacing lumen water by gas is increasing mechanical stability by decreasing stem weight. [START_REF] Gartner | Gas in stems: Abundance and potential consequences for tree biomechanics[END_REF] showed that this benefit was negligible regarding resistance to wind forces. The benefit regarding self-stability has never been quantified to our knowledge, but is a good candidate since the critical buckling tree height scales with the inverse square root of stem mass [START_REF] Jaouen | How to determine sapling buckling risk with only a few measurements[END_REF], which, for a given basic density, directly depends on the amount of gas it contains. 5) and lower bound corresponding to the FSP. FSP is taken as 30% moisture content. FSP usually ranges between 28% and 32% with exceptionally low values (17%-25%) in wood of high extractives contents [START_REF] Popper | Einfluss des Extraktstoffanteils ausgewählter fremdländischer Holzarten auf deren Gleichgewichtsfeuchte[END_REF]

| Wood mechanical properties: part explained by density and information carried by residual variability related to cell wall variation

Proportional relationship between elastic modulus and density, well established in the wood material scientist community [START_REF] Evans | Rapid prediction of wood stiffness from microfibril angle and density[END_REF][START_REF] Yang | Prediction of MOE of eucalypt wood from microfibril angle and density[END_REF] and validated on large interspecific studies in the context of ecology [START_REF] Chave | Towards a worldwide wood economics spectrum[END_REF][START_REF] Van Gelder | Wood mechanics, allometry, and life-history variation in a tropical rain forest tree community[END_REF][START_REF] Niklas | Worldwide correlations of mechanical properties and green wood density[END_REF] was confirmed also in our study (Figure 5a). High scatter is likely due to variation of parameters affecting cell wall properties such as microfibril angle or chemical composition [START_REF] Salmen | Cell wall features with regard to mechanical performance. A review COST Action E35 2004-2008: Wood machining -micromechanics and fracture[END_REF] known to largely vary with ontogeny [START_REF] Lachenbruch | Radial variation in wood structure and function in woody plants, and hypotheses for its occurrence[END_REF], growth or site condition [START_REF] Medhurst | Intra-specific competition and the radial development of wood density, microfibril angle and modulus of elasticity in plantation-grown Eucalyptus nitens[END_REF][START_REF] Wimmer | Temporal variation of microfibril angle in Eucalyptus nitens grown in different irrigation regimes[END_REF] and reaction wood occurrence [START_REF] Hori | Variation of microfibril angles and chemical composition: Implication for functional properties[END_REF][START_REF] Yoshida | Tensile growth stress and lignin distribution in the cell walls of yellow poplar, Liriodendron tulipifera Linn[END_REF].

Our results together with a compilation of literature data show that green wood specific modulus E /d is highly variable between and within species and independent of d B (Figure 5b, Table 4) except for temperate hardwoods for which weak but significant negative E / d -d B linear relationship (p < 0.01, R 2 = 0.27, Table 4) was observed, revealing that lighter wood has developed stiffer cell walls. Despite 4). Although this difference may by partly attributed to the experimental method (the resonance method is known to slightly overestimate the elastic modulus when compared to static bending test [START_REF] Brancheriau | Natural vibration analysis of clear wooden beams: A theoretical review[END_REF][START_REF] Haines | Determination of Young's modulus for spruce, fir and isotropic materials by the resonance flexure method with comparisons to static flexure and other dynamic methods[END_REF], we assume that higher values of wood specific modulus are not a methodological artefact but originates in the sampling. Actually, other studies do not deal with sapling stage of development in tropical understory, where competition for light foraging is severe, tree trunks extremely slender, easily deformable, and close to their self-supporting limit [START_REF] Jaouen | How to determine sapling buckling risk with only a few measurements[END_REF], so that a higher specific modulus could be a great advantage for both growth and survival as suggested by [START_REF] Mclean | The decreasing radial wood stiffness pattern of some tropical trees growing in the primary forest is reversed and increases when they are grown in a plantation[END_REF].

Positive or negative associations between E /d and d B may be expected in some specifically constrained environments or at a particular location inside the tree. As stated by [START_REF] Read | Wood properties and trunk allometry of co-occurring rainforest canopy trees in a cyclone-prone environment[END_REF], Cerberiopsis candelabra trees growing in cyclone-prone environments combine (a) the low density wood associated with fast growth in diameter and then, higher tree bending strength, despite lower wood modulus of rupture (see [START_REF] Larjavaara | Rethinking the value of high wood density[END_REF] for an accurate discussion about why weaker wood of lower density make stronger trees) and (b) high cell wall stiffness E /d to better resist bending wind loads. While the benefit of high E /d is clearly to achieve high stiffness for a given investment in biomass, production of wood with low E /d may result from a trade-off with other mechanical functions. For example, the occurrence of compression wood in gymnosperms is generally associated with large microfibril angle and low cellulose content [START_REF] Donaldson | Within-tree variation in anatomical properties of compression wood in radiata pine[END_REF]). These features are necessary for this wood to perform its function, that is, generate the righting movement that enables a gravitropic correction (1) [START_REF] Poorter | The relationships of wood-, gas-and water fractions of tree stems to performance and life history variation in tropical trees[END_REF] The relationships of wood, gas and water fractions of tree stems to performance and life history variation in tropical trees. [START_REF] Almeras | Biomechanical design and long-term stability of trees: Morphological and wood traits involved in the balance between weight increase and the gravitropic reaction[END_REF], but they also induce a lower specific modulus. Here, the positioning along the E /d axis reveals a trade-off between two mechanical functions of wood: providing the stem with high stiffness or providing it with a motor system. Other typical examples of occurrence of wood with low specific modulus are juvenile wood [START_REF] Lachenbruch | Radial variation in wood structure and function in woody plants, and hypotheses for its occurrence[END_REF] found in young stem or in the core of older stems, and so-called flexure wood [START_REF] Telewski | Structure and function of flexure wood in Abies fraseri[END_REF] sometimes found in trees growing in windy conditions. Both these woods have a large microfibril angle, reducing their stiffness, but also probably increasing their deformability, that is, their capacity to undergo large deformations without damage. A large wood deformability increases the overall flexibility of the stem and allows for the reduction winds loads (or other external loads such as falling trees) by geometric reconfiguration. In this instance, the positioning along the E /d axis expresses a trade-off between mechanical stress-resistant and stressavoidant strategies. Data that we have about mechanical properties of wood are mostly limited to elastic modulus, and very little is known about the green wood behaviour in rupture although this parameter is essential for the tree survival to bending loads. If a low value of wood specific modulus may be useful in small trees or large trees relying on the stress-avoidant strategy against wind, high modulus of rupture is presumably always useful for woody plants.

| Ecological significance of mechanistic and statistical independences among wood traits: advocating the use of physically independent traits

Based on physical relationships between basic density and other wood traits, we advocate the use of three mechanistically independent traits (d B , S G and E /d ) as relevant wood traits to investigate the spectrum of life strategies and functional diversity of trees.

The method we used to define E /d from E G and d B from mechanistic models of cellular material could be extended to any mechanical or hydraulic property. In cellular solids, density is a basic trait, and theoretical scaling laws of tissue properties vs. density can be derived from material physics [START_REF] Gibson | Cellular solids. Structure and properties[END_REF]. The residual variation from these theoretical scaling laws observed in nature along ecological gradients bring relevant information about species adaptation even if no direct data about tissue anatomy or cell wall ultrastructure are available. For example, as the resistance to cavitation P50 has become a common hydraulic trait measured with quite high throughput and organized in large databases [START_REF] Choat | Global convergence in the vulnerability of forests to drought[END_REF], it would be interesting to design a robust physical model of this trait combining approaches of [START_REF] Hacke | Trends in wood density and structure are linked to prevention of xylem implosion by negative pressure[END_REF] and current developments at the cell wall level [START_REF] Tixier | Modelling the mechanical behaviour of pit membranes in bordered pits with respect to cavitation resistance in angiosperms[END_REF] to infer theoretically what is the underlying information born by the residual variation of the moderate correlation observed between P50 and d B [START_REF] Westoby | Land-plant ecology on the basis of functional traits[END_REF].

Many recent works in wood ecology take inspiration from previous works of wood anatomists [START_REF] Carlquist | Comparative wood anatomy: Systematic, ecological, and evolutionary aspects of dicotyledon wood[END_REF] to interpret variations of wood structural traits as functional adaptations [START_REF] Beeckman | Wood anatomy and trait-based ecology[END_REF].

Environmental and phylogenetic variations of vessel frequencies and size [START_REF] Lachenbruch | Traits, properties, and performance: How woody plants combine hydraulic and mechanical functions in a cell, tissue, or whole plant[END_REF][START_REF] Zanne | Angiosperm wood structure: Global patterns in vessel anatomy and their relation to wood density and potential conductivity[END_REF], axial and radial parenchyma [START_REF] Zheng | Wood anatomical correlates with theoretical conductivity and wood density across China: Evolutionary evidence of the functional differentiation of axial and radial parenchyma[END_REF], microfibril angle [START_REF] Read | Wood properties and trunk allometry of co-occurring rainforest canopy trees in a cyclone-prone environment[END_REF] are then discussed. Generalizing the way physiologists use prediction of maximum conductance by the fourth power of conduit diameters, using biophysical framework to design metrics of functionally significant and theoretically independent

TAB L E 4 Wood specific modulus (E /d ) and its correlation with basic density (d B ) computed for dataset 2 and different literature sources (1),( 2),( 3),( 4) traits, instead of analysing roughly the whole set of traits measured, is likely to contribute to identification of growth strategies among the large functional diversity of woods.

  d B and saturated density d S . FSP: fibre saturation point, m: mass, v: volume, index D, G, S stand for dry, green and saturated state, respectively

  Illustration of theoretical variations of green density d G and moisture content w G within the range of volume available for water and air. As shown in Figure 1, the volume of voids (H S ) decreases when basic density d B increases, can be filled only by air at the theoretical fibre saturation point and replaced by water at in the saturated situation. Three situations are explored: a very low basic density d B = 0.1 with plenty of space available for water and air, a very high one d B = 0.8 where voids for air or water are very limited and an intermediary common one of d B = 0

  sity (Figure 2). Confidence interval of the slope parameter k does not overlap the slope value of simplified physical model (0.33) but overlaps 0.36, the k value of a more complete model without the simplified assumption of volume additivity between water and cell wall material (see Supporting information Appendix S1). Slight overestimation of d S as predicted from d B by the physical model likely also comes from this simplification.

  (p < 0.001). Only 23 points out of 2,234 lie beyond the theoretical bounds set up by Equations 6 and 7 corresponding to FSP and saturated state, respectively. Free volumetric water content F G scales with d B as predicted by Equation 8 (Figure4c), and the relationship was best fitted by a second-order polynomial weighted regression with null linear term. The slope of the S G -d B linear model was not significant (confidence interval of the slope parameter overlapped zero, p-value = 0.74) so random y-intercept model was fitted. Lumen saturation degree S G covers all the potential range from close to 0%

  figure legend for details) and shows wide range of variation of E /d (from 20 to 30 GPa.m 3 /kg).

  come from a zone without a drought constraint which is consistent with very weak or non-significant correlation found between S G and d B F I G U R E 3 Relationships between (a) maximum moisture content and basic density; (b) maximum moisture content and saturated density, (c) maximum volumetric water content and basic density and (d) maximum volumetric water content and saturated density. Red curve represents empirical fit, and black curve corresponds to physical model (Equations 3, 6-8)

F

  I G U R E 4 Relationships between (a) green and basic density; (b) green volumetric water content and basic density; (c) free volumetric water content and basic density and (d) degree of lumen saturation and basic density. In all graphs, red lines represent empirical fit. Black lines in Figure 4b correspond to the physical relationship expected at fibre saturation point (FSP) and in fully saturated condition. Black lines in Figure 4d correspond to the FSP and full saturation condition, and dashed red line corresponds to moisture content of 50%. Solid black lines indicate the theoretical upper bound corresponding to the saturated state (Equation

  differences in geographical origin and phylogenic classification, average values for all datasets are very close except for the present study and the study by McLean, Arnould, Beauchene, and Clair (2012) (Table

F

  I G U R E 5 Relationship between (a) elastic modulus and basic density; (b) wood specific modulus and basic density for our study (average values per species) and different literature sources (1) (Forest Product Laboratory 2010); (2) (Niklas & Spatz, 2010); (3) (Chudnoff 1979); (4) database of Brazilian wood properties available online (http://www.remade.com.br) and McLean et al. (2012). Vm: Virola michelii Heckel; Dg: Dicorynia guianensis Amsh.; Eg: Eperua grandiflora (Aubl.) Benth.; Lp: Lecythis persistens Sagot; La: Licania alba (Bernoulli) Cuatrec.; Oa: Oxandra asbeckii (Pulle) R.E. Fries; Gg: Goupia glabra Aubl.; Tm: Tachigali melionii (Harms) Zarucchi & Herend.; Gh: Gustavia hexapetala (Aubl.) J.E. Smith; Ps: Pogonophora schomburgkiana Miers ex Benth.; temp: temperate; trop: tropical; HW: hardwoods; SW: softwoods; OW: opposite wood; TW: tension wood. Red line corresponds to empirical fit TA B L E 3 Hydraulic properties and their correlation with basic density (d B ) computed for dataset 1 and literature sources (1),(2) . For dataset 1, eight data with aberrant (negative) gas content values were removed from the analysis water content, F G : free volumetric water content, S G : degree of lumen saturation of green wood, Hardw.: hardwood (angiosperm) species; Softw.: softwood (gymnosperm) species; Sapw.: sapwood samples; Heartw.: heartwood samples. n: number of specimens (Dataset 1) or species (1 and 2). Range: range of values (a) for 95% of data; (b) for all data.

  is important to note that E G , although being a property of green wood, is physically linked to basic density d B rather than to green density d G because d G is addition of mass of the mechanically efficient cell wall material and of the mechanically inert free water in lumen.[START_REF] Niklas | Worldwide correlations of mechanical properties and green wood density[END_REF] have found that d G measured at 50% moisture content was a good predictor of mechanical properties which is not surprising because d 50 is a multiple of d B : Equation2gives d 50 = 1.5 d B . However, 50% of moisture content has no specific meaning regarding wood status in the living tree and conditioning wood at 50% moisture content is technically challenging. The proper mechanistic predictor of E G is d B since it represents the amount of supporting tissue in the unit of volume.

  volumetric water content H S scales linearly with both d B and d S Goodness of fit of statistical and biophysical models and variances explained by random effects. EF is model efficiency, RMSE is root-mean-square error of the statistical fit, RMSE BM is root-mean-square error of the biophysical model. EF and RMSE for marginal predictions are also reported. For hydraulic parameters, random variances of species effect associated with intercept are shown. For E

	Residual variance	2.26E-04	6.79E-04	2.32E-04	1.24E-03	2.15E-03	4.03E-03	5.54E-04	2.40E-02	Residual variance	3.47	7.93
	Variance/random intercept	4.71E-05	2.27E-04	4.46E-05	6.88E-04	1.95E-03	2.51E-03	1.58E-03	1.38E-02	Variance/Species	5.70	5.71
	RMSE BM	0.017	0.032	0.017	0.052				Variance/	Trees	8.01	8.11
	Marginal RMSE	0.016	0.029	0.016	0.043	0.077	0.078	0.079	0.192	Marginal RMSE	2.983	4.577
	Marginal EF	0.88	0.99	0.96	0.70	0.64	0.05	0.34	0.00	Marginal EF	0.67	0.00
	Relative RMSE	1.2%	2.4%	2.5%	5.6%	5.7%	14.7%	29.8%	27.4%	Relative RMSE	10.4%	10.3%
	RMSE	0.014	0.024	0.015	0.032	0.058	0.058	0.061	0.14	RMSE	1.761	2.658
	EF	0.91	1.00	0.97	0.83	0.80	0.48	0.59	0.45	EF	0.88	0.66
	Average	1.20	0.99	0.58	0.58	1.02	0.39	0.21	0.52	Average	16.95	25.69	as predicted by Equations 6 and 7 (Figure 3c,d). Note that relative
												B )
		d S (d B )	w S (d B )	H S (d B )	H S (d S )	d G (d B )	H G (d B )	F G (d B )	S G (d B )		E G (d B )	E G/dB (d

RMSE is lower when d B is used as a predictor for H S (RMSE = 2.5% and 5.6% when d S is used). This is likely related to the weight of experimental error. Indeed, based on error propagation analysis, it can be shown that the prediction error for H S in response to measurement error on density is five times higher when based on d S rather than on d B . TA B L E 2 G (d B ) relationship, variance components shown correspond to species/tree nested random effects associated with slope. For E G /d B (d B ) relationship, the variances components shown correspond to species/nested random effects associated with intercept

  Method of measurement is SB: static bending; R: resonance measurement; ST: quasi-static tensile test on a DMA device. OW: opposite wood; TW: tension wood; N: number of species; SD: standard deviation. (1) Forest Product Laboratory (2010) Wood handbook -Wood as an engineering material. General Technical Report FPL-GTR-190 (ed. W.U.S. Madison), pp. 508.; (2) Niklas, K.J. & Spatz, H.-C. (2010) Worldwide correlations of mechanical properties and green wood density. American Journal of Botany, 97, 1587-1594.; (3) Chudnoff, M. (1979) Tropical timbers of the world.; (4) database of Brazilian wood properties available online (http://www.remade.com.br) and McLean McLean, J.P., Arnould, O., Beauchene, J. & Clair, B. (2012) The effect of the G-layer on the viscoelastic properties of tropical hardwoods. Annals of Forest Science, 69, 399-408. Statistical significance of correlations is indicated by ns. (p > 0.05), *(p < 0.05) and **(p < 0.01).

						Correlation with d B
		N	Method	E /d	SD	R 2	Sign
	Temperate softwoods (1)	69	SB	20.9	3.28	0.048	ns
	Temperate softwoods (2)	37	SB	18.6	2.66	0.002	ns
	Temperate hardwoods (1)	71	SB	17.4	2.71	0.223	**
	Temp. + trop. hardwoods (2)	138	SB	17.9	3.09	0.000	ns
	Tropical hardwoods (1)	67	SB	19.5	3.62	0.000	ns
	Tropical hardwoods (3)	103	SB	19.5	3.68	0.010	ns
	Tropical hardwoods (4)	20	SB	20.2	3.46	0.010	ns
	Dataset 2	10	R	25.8	2.58	0.130	ns
	McLean_OW	6	ST	19.9	6.37	0.264	ns
	McLean_TW	6	ST	24.2	7.78	0.023	ns

ACKNOWLEDGEMENTS

This work was supported by a grant overseen by the French National Research Agency to the Woodiversity (ANR-05-BDIV-0012-04) and Bridge (ANR-06-BDIV-0004) projects. The UMR1092 and EcoFoG are supported by a grant overseen by the French National Research Agency (ANR) as part of the "Investissements d'Avenir" program (ANR-11-LABX-0002-01, Lab of Excellence ARBRE) and (CEBA, ref. ANR-10-LABX-25-01), respectively. Authors would also like to thank the late A. Thibaut for help with experimental measurements performed in the framework of Bridge project.

Funding information

French National Research Agency to the Woodiversity, Grant/Award Number: ANR-05-BDIV-0012-04; Bridge, Grant/ Award Number: ANR-06-BDIV-0004; French National Research Agency (ANR), Grant/Award Number: ANR-11-LABX-0002-01 and ANR-10-LABX-25-01 1 | INTRODUC TION

DATA ACCESSIBILIT Y

Data available from the Dryad Digital Repository: https://doi. org/10.5061/dryad.t775334 [START_REF] Dlouhá | Data from: Biophysical dependences among functional wood traits[END_REF].

ORCID

Jana Dlouhá

http://orcid.org/0000-0002-9282-639X


Florentin Berthet

Pierre Devos

DEMONSTRATING THE NEED OF TAKING INTO ACCOUNT THE COMPATIBILITY TACKIFIER/RESIN, AND PROCESSING CONDITIONS TO CONTROL MECHANICAL PROPERTIES OF RTM CFRP

teaching and research institutions in France or abroad, or from public or private research centers.

Demonstrating the need of taking into account the compatibility tackifier/resin, and processing conditions to control mechanical properties of RTM CFRP

Introduction

Use of advanced composite materials in defense and aeronautics increased dramatically in the last 30 years because these sectors could bear extra costs of production provided there was a reduction in the component weight. The challenge which now has to be faced by composites materials producers is to reduce their costs. For this reason the Resin Transfer Molding Process (R.T.M.) nowadays provides new interest for the aeronautics industry.

Numerous studies have been performed to simulate flow of resin through preforms with applications in mold design. A new requirement has arisen: the identification of the relationship between process parameters and mechanical properties of the molded component.

Therefore a study had been conducted experimentally to fill a part of the gap. Mechanical properties such as open hole compressive strength (OHCS) and interlaminar shear strenth (ILSS) have been evaluated.

Background

Taguchi method

Experimental design is well adapted to evaluate influence of processing conditions on mechanical properties of RTM CFRP because the potential parameters are numerous. Experimental design make it possible to simultaneously minimize the number of trials necessary and maximize the quality of results.

A test on assumed equality of variance between samples and in samples leads to a partition of factors in three categories: very significant (VS), significant (S), not significant (NS). The greater the variance between two levels, the more significant the concerned factor.

An effect is associated with a factor i and a level j. It represents what should theoretically be added to the average value if the component has been molded at level j of the factor i. That is to say the effect Eij of having molded at level j instead of at average level. This effect is defined as Eij=average at level j-general average (1) More details can be found in M.Pillet's book (6).

The main difficulty in this kind of problem is to establish a relationship between what happens microscopically and macroscopically. It is all the more complicated because the involved mechanisms are multiple. Moreover the expected effects are small at room temperature, thus one has had the idea of working at higher temperatures to facilitate the differentiation of materials as functions of their production conditions.

RTM

RTM is a process in which a dry fibrous preform is impregnated by a thermoset to produce composite. In a prior stage called preforming the fibre layup is made to the shape of the component to produce a preform. The role of the tackifier is to keep the shape of the component and to make it stiff enough to be handled prior to injection.

It is now widely accepted, that in RTM resin flow is governed by Darcy's law: v K grad(p) (2) v: front velocity in porous media, : viscosity, porosity (volume let free by fibers divided by total volume), p: applied pressure K: permeability tensor.

In RTM porous media for impregnation are heterogeneous. In a component there are two levels of heterogeneity, microscopic (fiber level) and macroscopic (fabric level). This implies that there are two types of flow (around and in bundle) and permeability.

Available studies of the influence of process parameters on mechanical properties are rare. Nevertheless Hayward and Harris(1) Dockum and Shell (2), Steenkamer(3), Young(4) and Michaeli(5) have done some work in this area. But their conclusions are contradictory.

Description of equipment and process

Our equipment consisted of A square steel mold allowing fabrication of plates 400*400mm in different thicknesses.With this mold we can do peripherical injection (as indicated on figure 1) or directional injection.

An heated 20T hydraulic press

An RTM machine (pressurized pot) allowing injection up to 4 bars A vaccum pump A data recorder (pressures, temperatures, vaccum)

The typical resin transfer molding cycle is as follows 1) cut out of reinforcements 2) assembly of reinforcements 3) preforming 5 min at 120°C under 2 bars (cohesion of reinforcements is achieved by a tackifier that is a thermoplastic or a re activable uncatalized thermoset). 4) mold closure, vacuum application, resin degasing, and injection until mold is filled 5) holding pressure until no more material enters the mold 6) cure 75 min at 160°C 5) mold opening 6) post cure in autoclave following a programmed cycle (120 min at 180°C)

Description of experiment parameters

Parameters chosen for this study are: mold temperature, injection pressure, holding pressure, fiber bundle size, tackifier level, injection type and interaction between mold temperature and injection pressure. These parameters are varied between two values according to L8 taguchi table (Table 1).

We discount the effects of cure and post cure because these two steps are not characteristics of RTM.

What is specific to RTM is the fiber/matrix interface formation with defect creation during injection caused by unknown mechanisms. From this one assumes that processing influences fiber/matrix bonding and thus the mechanical properties of the composite.

materials

Four fabrics were used, the fabric style was the same, but fabrics are woven from fiber bunbles of different size (3K or 6K), and contained two possible levels of tackifier (5g/face/m2 or 15g/face/m2). When different fiber bundle size are used, different thickness had to be used to achieve comparable fiber volume fraction (Kvf) (Table 2).

The injected resin is a special RTM single component epoxy resin .

Presentation of data and results

Results are summarized in table 3. For each of the processing conditions, five samples are cut from the plate. Average and standard deviation of evaluated properties are put into this table.

Interpretation of datas

ILSS at 120°C

Fiber bundle size, injection pressure, interaction, mold temperature and tackifier level (in increasing order of importance) have a significant (S) effect on ILSS at 120°C. Inversely, injection type and holding pressure have no significant effect on ILSS at 120°C (Table 4).

Our results, confirm conclusions given by Kittelson and al.( 7), on importance of resin/tackifier compatibility. The new aspect is the effect of this incompatibility on mechanical properties. Presence of tackifier in the resin reduces the glass transition temperature significantally by 30°C. This loss is all the more important if the tackifier level is greater. At higher temperatures, materials with lower glass transition temperature, have reduced mechanical properties. ILSS at 120°C is lower for an injection pressure of 2bars and a 100°C mold temperature as can be seen in figure 2. We assume that temperature and pressure conditions influence partition of resin flow between micro and macroscopic levels. A hot preform will make it easier for the resin to flow closer to fibers because of the associated viscosity reduction (equation 2), thus flowing microscopically. Inversely, for a low mold temperature and higher pressure (2 bars), flow will be preferentially macroscopic, in macrospaces left free between two plies. This accounts for some weakness of this resin rich area in shear.

OHCS at 120°C

In increasing order of importance, interaction, mold temperature, injection pressure and tackifier level influence significantly open hole compressive strength at 120°C (table 5).

Open hole compression at 120°C is weaker for high injection pressure at 140°C mold temperature as can be seen on figure 3. High temperature will favor microscopic flow and higher pressure disrupts fibers at both micro and macro scale. Compression is known to be sensitive to fiber misalignment (buckling).

conclusion

Experimental design made it possible to demonstrate the effect of some processing factors on mechanical properties of RTM CFRP, especially for hot ILSS and hot OHCS. We established the (limited) effect of temperature and pressure conditions during injection on final quality of composites. Moreover, the problem of resin/tackifier compatibility has to be considered to guarantee the conservation of mechanical properties with heat. Quantitatively, loss of mechanical properties is more significant at higher tackifier levels . The micro/macro competition explains why OHCS and ILSS are differently influenced by processing conditions, and why studies on the subject disagree.

Bibliography

(1)J.S.Hayward and B.Harris, 'Effects of process variables on the quality of RTM moldings' ,SAMPE journal,vol.26 n°3 may june1990 (2) J.F.Jr.Dockum and P.L. Schell,'fiber directed preform reinforcement: factor that may influencemechanical properties in liquid composite molding', proceeding of the ASM/EDconference, (1990),pp.393-406 (3)D.A.Steenkamer,'the influence of preform design and manufacturing issues on the processing and performance of the resin transfer molded composites', thesis of the university of delaware, 1994. ( 4) W.B.Young,C.W.Tseng,'Study on the preheated temperatures and injection pressures of the RTM process', J.of reinf.plast.and comp.,vol.13-May1994,pp.467-482.

(5)W.Michaeli,J.Dickhoff,'Production of fibre reinforced components with high surface quality using a modified RTM technique ',SAMPE 39,April 1994,pp.145-153. (6)

  Figure 1: Top view of half mold with preform

Figure 2 :Figure3:

 2 Figure 2: Effects graph of temperature-pressure interaction for ILSS at 120°C

Table 2 :

 2 Layup used for different fiber bundle size

	N°	hot ILSS	standard deviation	hot open hole	standard deviation
		(MPa)		compression (MPa)
	1	43,54	1,16	261,25	4,81
	2	38,92	1,97	226,8	6,11
	3	42,9	1,47	254,2	6,21
	4	33,94	1,31	226,2	18,82
	5	38,125	1,62	226,8	4,35
	6	44,62	1,58	255,4	6,34
	7	37,98	1,6	198,2	2,48
	8	45,02	1,72	236,8	7,83

Table 3 :

 3 Results of experimentation (averages and std dev. are calculated on 5 specimens)

		Effect (MPa)	Fexp	Fthco95	Fthco99
	mold temperature	0,8	8,63	4,17	7,56	VS
	injection pressure	-0,67	5,98	4,17	7,56	S
	holding pressure	-0,006	0	4,17	7,56	NS
	tackifier level	-3,39	152,78	4,17	7,56	VS
	injection type	-0,47	2,99	4,17	7,56	NS
	fiber bundle size	0,61	4,95	4,17	7,56	S
	interaction P-T	0,73	7,17	4,17	7,56	S
		Table 4: variance analysis table for ILSS at 120°C

Table 5 :

 5 Variance analysis table for open hole compression at 120°C

	interaction P-T
	42
	41
	40
		100	°C
	39	140 °C
	38
	37
	36
	1 bar	2 bars
	injection pressure (MPa)