The optimization in the simulation time of non-isothermal filling process without losing effectiveness remains a challenge in the resin transfer moulding process simulation. We are interested in this work on developing an improved computational approach based on finite element method coupled with control volume approach. Simulations can predict the position of the front of resin flow, pressure and temperature distribution at each time step. Our optimization approach is first based on the modification of conventional control volume/finite element method, then on the adaptation of the iterative algorithm of conjugate gradient to Compressed Sparse Row (CSR) storage scheme. The approach has been validated by comparison with available results. The proposed method yielded smoother flow fronts and reduced the error in the pressure and temperature pattern that plagued the conventional fixed grid methods. The solution accuracy was considerably higher than that of the conventional method since we could proceed in the mesh refinement without a significant increase in the computation time. Various thermal engineering situations can be simulated by using the developed code.

The present paper aims to predict the hygrothermal behavior of massive wood panel considered as bio-based building material. In this context, we developed a macroscopic model coupled no linear heat, air, and moisture transfers that incorporates simultaneously the effect of thermal diffusion and infiltration phenomenon on the building material. The model inputs parameters were evaluated experimentally according to the recognized standards of material's characterization. Therefore, numerous series of hygrothermal calculation were carried out on the 1-D and 2-D configuration in order to assess the dimensionless effect on such wooden material. Two types of boundary conditions were considered and examined. The first are at the material scale of wood drying process. The second type of conditions is at the wall scale, where the conditions of the building ambiance are considered. Moreover, the model sensitivity to the driving potentials coupling and to the parameters variability was considered and examined. It has been found that the coupling in the model had a remarkable impact on both kinetics of temperature and moisture content.