Computational Materials Science for Polymers

The kinetic model, encompassing the curing and reversion phenomena of the NR/SBR rubber vulcanization process, was developed by means of the finite element method simultaneously with heat transfer equations, including heat generation due to curing reactions. The vulcanization simulation was conducted for three spheres of different diameters (1, 5 and 10 cm) and two rubber wheels, one of which was a commercial product of the rubber industry. The proposed advanced simulation model, based on products’ two-dimensional axisymmetry, includes cooling after vulcanization, during which the crosslinking reactions continue to take place as a result of the products’ heated interiors. As a criterion for removing the product from the mold, an average vulcanization degree of 0.9 was set, whereby, during cooling, the vulcanization degree increases, due to crosslinking reactions. Based on the minimal difference between the maximal and minimal vulcanization degrees, which did not exceed a value of 0.0142, the optimal process parameters for each product were determined, achieving homogeneity and obtaining high-quality rubber products, while simultaneously ensuring a more efficient vulcanization process and enhanced cost effectiveness for the rubber industry. Full article

The aim was to investigate the relationship of charred layer parameters (also wood fire resistance) and moisture content of European larch (Larix decidua L.) wood. For this purpose, finite element model (FEM) was developed. To develop FEM, ANSYS software and transient thermal analysis were applied. To validate developed FEM, the medium-scale fire tests were provided in the laboratory chamber. In the fire tests the beams made of larch wood have undergone the thermal loading with radiant panel. The FEM validation results showed very strong correspondence of numerical and experimental results, when achieving the overall accuracy of 93.4%. Validated FEM was further used to determine the relationship between the larch beams moisture content and formation of charred layer, i.e., its parameters. The results from the simulation pointed out the fact, the wetter the wood, the higher its fire resistance. This is very important information for studying the formation of a charred layer and a layer of degraded wood. After increasing the moisture content from 10% to 30%, the area of the charred layer decreased by approximately 20%. The area of degraded wood decreased by almost 30%, so it can be stated that the area of the charred layer of wood and degraded wood decreases exponentially with increasing wood moisture content. Full article

We investigated, via a phase-field model simulation, the effects of a matrix’s properties and a filler’s characters on the polytetrafluoroethylene (PTFE) crystal growth process in composites under various supercooling degrees. The results show that the supercooling degree has a deciding influence on the crystal growth process. The intrinsic properties of PTFE polymer, such as anisotropic strength and phase transition latent heat, affect the growth rate, orientation, and interfacial integrity of the crystal trunk and the branching of the PTFE crystal growth process. The factors of the PTFE crystallization process, such as anisotropic strength and phase translation interface thickness, affect the uniformity and crystallization degree of the PTFE crystal. In the composites, the biphasic interface induces the crystal growth direction via the polymer chain segment migration rate, of which the degree depends on the shapes of the filler and the PTFE crystal nucleus. According to the results, choosing the low molecular weight PTFE and mixture filler with various particle sizes and surface curvatures as the raw materials of PTFE-based composites improves the crystallization of the PTFE matrix. Full article

Prediction of mechanical properties is an essential part of material design. State-of-the-art simulation-based prediction requires data on microstructure and inter-component interactions of material. However, due to high costs and time limitations, such parameters, which are especially required for the simulation of advanced properties, are not always available. This paper proposes a data-driven approach to predicting the labor-consuming fracture toughness based on a series of standard, easy-to-measure mechanical characteristics. Three supervised machine-learning (ML) models (artificial neural networks, a random forest algorithm, and gradient boosting) were designed and tested for the prediction of mechanical properties of pultruded composites. A considerable dataset of mechanical properties was acquired as results of standard tensile, compression, flexure, in-plane shear, and Charpy tests and utilized as the input to predict the fracture toughness. Furthermore, this study investigated the correlations between the obtained mechanical characteristics. Analysis of ML performance showed that fracture toughness had the highest correlations with longitudinal bending and transverse tension and a strong correlation with the longitudinal compression modulus and tensile strength. The gradient boosting decision tree-based algorithms demonstrated the best prediction performance for fracture toughness, with an MSE less than 10% of the average value, providing a prediction within the range of experimental error. The ML algorithms showed potential in terms of determining which macro-level parameters can be used to predict micro-level material characteristics and how. The results provide inspiration for future pultruded composite material design and can enhance the numerical simulations of material. Full article

The Poisson effect, measured by the Poisson’s ratio, plays an important role in the regulation of the elastic properties of composite materials, but it is not considered in the conventional Voigt (iso-strain) and Reuss (iso-stress) formulas, which explains why the formulas are inaccurate even if the iso-strain or the iso-stress conditions are satisfied. To consider the Poisson effect, we derived a set of new formulas based on the governing equations of elasticity. The obtained formulas show greater mathematical complexity. To further understand how the Poisson effect influences composite elastic properties, two types of finite element models (FEM) were constructed to simulate the situations where the Poisson effect does or does not have an influence. The results show that the conventional Voigt and Reuss formulas are special cases of the newly derived ones. The Poisson effect induces secondary strains and stresses into the phase materials, which demands more strain energy to achieve the same deformation in the primary (loading) direction, and thus increases composite stiffness; the magnitude of the increase is dependent on the contrast of phase properties. The findings may have significant impact on the study of the emerging nanocomposites and functionally graded materials, where the conventional Voigt and Reuss formulas have wide applications. Full article

The alignment of cellulose by an electric field is an interesting subject for cellulose material processing and its applications. This paper reports an atomistic molecular dynamics simulation of the crystalline cellulose nanofiber (CNF) model in varying electric field directions and strengths. GROMACS software was used to study crystalline cellulose 1β consisting of 18 chains in an aqueous environment at room temperature, and an electric field was applied along the cellulose chain direction and the perpendicular direction with varying field strength. The root-mean-square displacement, radius of gyration, end-to-end length, and hydrogen bond population of the crystalline CNF model were analyzed to determine the effects of the applied electric field on the structure of the CNF model. The results suggest that the nanosecond electric field can induce the orientation of the CNF along the applied electric field direction. The alignment rate and ability to maintain the alignment depend on the electric field strength. Analysis of the radius of gyration, end-to-end length, and bond lengths for intrachain and interchain hydrogen bonds revealed no significant effect on the cellulose structure. Cellulose alignment in an electric field has the potential to broaden the design of electric field-induced processing techniques for cellulose filaments, thin films, and electro-active cellulose composites. Full article

Low-density polyethylene (LDPE) has excellent dielectric properties and is extensively used in electrical equipment. Hexagonal boron nitride (h-BN) is similar to a graphite-layered structure, and alumina fiber (Al₂O₃) has high-temperature resistance and a strong performance. Herein, we prepared Al₂O₃-h-BN/LDPE nanocomposites by using LDPE as the matrix material and h-BN and Al₂O₃ as the fillers. The influence of different doping contents and the mass ratio of Al₂O₃ and h-BN (1:1) to LDPE on the electrical properties and thermal conductivity of the nanocomposites was examined. The results showed that the suppression effect on space charge was the most obvious and average. The charge density was the lowest and had the minimum decay rate when the doping content was 2%. The breakdown strength of the film reached the maximum value of 340.1 kV/mm, which was 12.3% higher than that of the pure LDPE (302.8 kV/mm). The thermal diffusivity of the composite sample was also higher than that of the single h-BN-doped sample when the content of h-BN and Al₂O₃ was 7%. The thermal conductivity was 59.3% higher than that of the pure LDPE sample and 20% higher than that of h-BN/LDPE. Full article