Numerical and experimental approaches to characterize the mass transfer process in wood elements

Abstract

The scope of this paper is an experimental characterization of diffusion parameters for wood material. Based on a nonlinear mass transfer algorithm, the present study focuses on the need to capture experimental moisture profiles in the sample, along with its evolution in weighting during both the desorption and adsorption phases, especially when the moisture content of the samples is far from the equilibrium state inducing a great gradient between heart and exchange surfaces of specimen. These moisture profiles are derived by means of a gammadensimetry laboratory method based on the water adsorption of gamma rays. The determination of the diffusion parameters obtained through optimizing a simulation by means of implementing the mass transfer kinetics into a finite difference method. Both the diffusion coefficient and convective exchange coefficient are deduced by considering a Nelder-Mead simplex inversion method. This work highlights the efficiency of the approach dedicated to uncoupling nonlinear diffusion in the cross-sections from boundary conditions in terms of convective

Enhancing understanding of moisture diffusion in wood: numerical approach and diffusion parameter optimization

“…The diffusion rate is governed by moisture diffusion coefficients, contingent upon both the wood's material composition and its prevailing MC. In the context of scientific investigation, inverse methods are employed [5][6][7], employing optimization algorithms to align numerical models with experimental data -a technique widely embraced within research endeavors. Among these methods, the gravimetric approach is a prevalent choice for monitoring temporal shifts in average MC.…”

Hybrid numerical method for the ultrasonic wave propagation velocity in orthotropic materials

“…which can be associated with inverse methods using complementary parallel modeling. Let's cite as an example the image analysis method like correlation or tracking markers (Silva et al, 2022) and resistive measurements (Nguyen et al, 2017), dielectric or electromagnetic methods (Matsunaga et al, 2022), which are more sensitive to humidity.…”