Abstract
Abstract Key message The real tissue structure, including local anisotropy directions, is defined from anatomical images of wood. Using this digital representation, thermal/mass diffusivity and mechanical properties (stiffness, large deformation, rupture) are successfully predicted for any anatomical pattern using suitable meshless methods. Introduction Wood, an engineering material of biological origin, presents a huge variability among and within species. Understanding structure/property relationships in wood would allow engineers to control and benefit from this variability. Several decades of studies in this domain have emphasised the need to account simultaneously for the phase properties and the phase morphology in order to be able to predict wood properties from its anatomical features. This work is focused on the possibilities offered by meshless computational methods to perform upscaling in wood using actual tissue morphologies obtained by microscopic images. Methods After a section devoted to the representation step, the digital representation of wood anatomy by image processing and grid generation, the papers focuses on three meshless methods applied to predict different macroscopic properties in the transverse plane of wood (spruce earlywood, spruce latewood and poplar): Lattice Boltzmann Method (LBM) allows thermal conductivity and mass diffusivity to be predicted, Material Point Method (MPM) deals with rigidity and compression at large deformations and peridynamic method is used to predict the fracture pathway in the cellular arrangement. Results This work proves that the macroscopic properties can be predicted with quite good accuracy using only the cellular structure and published data regarding the cell wall properties. A whole set of results is presented and commented, including the anisotropic ratios between radial and tangential directions.
Highlights
Abstract & Key message The real tissue structure, including local anisotropy directions, is defined from anatomical images of wood
As this paper is published in the 50th anniversary issue of the Annals of Forest Science, we reviewed literature starting from the 1960s
This paper presents a comprehensive strategy to predict different wood properties from anatomical images
Summary
Abstract & Key message The real tissue structure, including local anisotropy directions, is defined from anatomical images of wood. When wood is used as a structural material, two properties are of primary matter: longitudinal stiffness and transversal shrinkage This is why most research efforts have focused on these properties. It is not surprising that the first attempts to predict wood properties were in the form of linear or non-linear correlations dependent on density This strategy works nicely for longitudinal stiffness and hardness (Kollmann and Côté 1968; Bosshard 1984). The hidden, and coarse, assumption made in this simple approach is that all phases of wood are in parallel and aligned along the longitudinal direction Using this simple upscaling strategy, the macroscopic property is a weighted average of the microscopic property over all phases of the heterogeneous medium. This means that, for a given sample, the deviation from the general correlation might be large in terms of relative error
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