Recently, due to their extraordinary strength-to-weight ratio and multi-functional applications, three-dimensional woven fiberglass sandwich structures have become a well-received topic by researchers and manufacturers. Nevertheless, using light and foam absorber materials as injected fillers within sandwich cores can improve their overall mechanical performance and, in particular, their fracture toughness behavior. This study evaluates the fracture toughness of three-dimensional woven fiberglass sandwich panels filled with natural nano-structured zeolite/polyurethane foams injected between their parallel panels. The Single-Edge Notched Bend test was carried out to understand the effect of the injected foam on the mode-I fracture toughness response. It is demonstrated that the polyurethane foam reinforced with natural nano-structured zeolite particles highly improved the fracture toughness of sandwich core panels. It was found that the presence of vertical glass yarns within the sandwich panel gallery resulted in a significantly higher toughness compared with typical sandwich panels of no reinforcing vertical columns confirmed by the crack propagation and observed failure mode. The SEM and EDX analyses were used to better understand the correlations amongst the specimen morphology, the cracks behavior, and the toughness exhibited by the fabricated specimens.

This paper presents a numerical investigation on the flexural performance of a novel cementitious reinforced GEM-TECH material using finite element method. A discrete nonlinear FE model using the commercial software ANSYS was employed to model a steel-reinforced GEM-TECH beam. Element SOLID65 was used to model the cementitious material while LINK180 element was used to model the reinforcing bars and stirrups. For model validation, FEA results and crack plots were compared to those obtained from the experimental results of five reinforced GEM-TECH beams: three beams designed with target density of 1810 kg/m3 and two beams with target density of 1600 kg/m3. Both load-deflection plots and the failure mode crack plots predicted by the FE model were in good agreement with the experimental results.

This paper presents an approach of linking finite element method with artificial neural network to predict J-Integral parameter in desirable airfoil condition. Finite Element (FE) and Artificial Neural Network (ANN) have been employed for the purpose. In other words, a prediction of finite element results has been done using ANN. Ultimately results of two methods have been compared for different cases. Wing fracture is a well-known problem of the planes which depends on various parameters. The J-integral is a vital parameter in evaluations of structure fracture phenomena. On the other hand residual stresses play an influential role in fracture formation. In the current work, effect of residual stresses and crack depth on J-Integral has been investigated in a standard NACA0012-34 airfoil. As will be seen, residual stresses and crack depth influence J-Integral values. It also will be shown that predictions of ANN method are in a good agreement with those obtained by finite element method.

Buckling is one of the most complicated concepts in mechanical engineering. Buckling often happens by compressive loads on thin structures. Thermal gradient between two ends of a column may cause a deflection in it. This will add an extra deformation to the one provided by compressive loads on the column. This phenomenon occurs when two ends of the column are at different temperatures, which can be seen at various structures. Because of the considered temperature gradients, the critical load of the column will decrease. In the current paper, various columns are modeled and the effect of thermal gradient and compressive load and other parameters on the Bi-material columns are studied. In other words, influences of compressive load and temperature gradient on critical load of Bi-material columns with interface crack are investigated. Effect of change in each parameter on critical load of column and crack opening was investigated. First, the thermal gradient was only applied to the model and in the next step; only the effect of mechanical loading was studied. Furthermore, artificial neural network (ANN) was used to extend the results to a bigger range of temperature conditions through the columns. Based on the results, ANN and finite element results are in a good agreement and the thermal effects may have a significant role in buckling of the column.