• Computers and Concrete
• /
• v.9 no.1
• /
• pp.35-50
• /
• 2012

The 2D representative volume element (RVE) for softening quasi-brittle materials like concrete is determined. Two alternative methods are presented to determine a size of RVE in concrete subjected to uniaxial tension by taking into account strain localization. Concrete is described as a heterogeneous three-phase material composed of aggregate, cement matrix and bond. The plane strain FE calculations of strain localization at meso-scale are carried out with an isotropic damage model with non-local softening.

• Journal of Mechanical Science and Technology
• /
• v.20 no.10
• /
• pp.1722-1729
• /
• 2006

Predicting the mechanical properties of the 3-D scaffold using finite element method (FEM) simulation is important to the practical application of tissue engineering. However, the porous structure of the scaffold complicates computer simulations, and calculating scaffold models at the pore level is time-consuming. In some cases, the demands of the procedure are too high for a computer to run the standard code. To address this problem, the representative volume element (RVE) theory was introduced, but studies on RVE modeling applied to the 3-D scaffold model have not been focused. In this paper, we propose an improved FEM-based RVE modeling strategy to better predict the mechanical properties of the scaffold prior to fabrication. To improve the precision of RVE modeling, we evaluated various RVE models of newly designed 3-D scaffolds using FEM simulation. The scaffolds were then constructed using microstereolithography technology, and their mechanical properties were measured for comparison.

• Composites Research
• /
• v.37 no.3
• /
• pp.179-185
• /
• 2024

This study quantitatively evaluated and investigated the changes in transverse tensile strength of unidirectional fiber-reinforced composites with initial void defects using a Representative Volume Element (RVE) model. After calculating the appropriate sample size based on margin of error and confidence level for initial void defects, a sample group of 5000 RVE models with initial void defects was generated. Dimensional reduction and density-based clustering analysis were conducted on the sample group to assess similarity, confirming and verifying that the sample group was unbiased. The validated sample analysis results were represented using a Weibull distribution, allowing them to be applied to the reliability analysis of composite structures.

• Journal of Powder Materials
• /
• v.24 no.1
• /
• pp.17-23
• /
• 2017

In this study, a finite element analysis approach is proposed to predict the fluid-structure interaction behavior of active materials for lithium-ion batteries (LIBs), which are mainly composed of graphite powder. The porous matrix of graphite powder saturated with fluid electrolyte is considered a representative volume element (RVE) model. Three different RVE models are proposed to consider the uncertainty of the powder shape and the porosity. P-wave modulus from RVE solutions are analyzed based on the microstructure and the interaction between the fluid and the graphite powder matrix. From the results, it is found that the large surface area of the active material results in low mechanical properties of LIB, which leads to poor structural durability when subjected to dynamic loads. The results obtained in this study provide useful information for predicting the mechanical safety of a battery pack.

• Steel and Composite Structures
• /
• v.21 no.5
• /
• pp.1085-1103
• /
• 2016

The present paper is aimed to evaluate and compare the effective elastic properties of CNT- and graphene-based nanocomposites using 3-D nanoscale representative volume element (RVE) based on continuum mechanics using finite element method (FEM). Different periodic displacement boundary conditions are applied to the FEM model of the RVE to evaluate various elastic constants. The effects of the matrix material, the volume fraction and the length of reinforcements on the elastic properties are also studied. Results predicted are validated with the analytical and/or semiempirical results and the available results in the literature. Although all elastic stiffness properties of CNT- and graphene-based nanocomposites are found to be improved compared to the matrix material, but out-of-plane and in-plane stiffness properties are better improved in CNT- and graphene-based nanocomposites, respectively. It is also concluded that long nanofillers (graphene as well as CNT) are more effective in increasing the normal elastic moduli of the resulting nanocomposites as compared to the short length, but the values of shear moduli, except $G_{23}$ of CNT nanocomposite, of nanocomposites are slightly improved in the case of short length nanofillers (i.e., CNT and graphene).

• Composites Research
• /
• v.35 no.3
• /
• pp.188-195
• /
• 2022

This paper proposes a multiscale process-structural analysis methodology and applies to a battery housing part made of the short fiber-reinforced and fabric-reinforced composite layers. In particular, uncertainties of the material properties within the microscale representative volume element (RVE) were considered. The random spatial distribution of matrix properties in the microscale RVE was realized by the Karhunen-Loeve Expansion (KLE) method. Then, effective properties of the RVE reflecting on spatially varying matrix properties were obtained by the computational homogenization and mapped to a macroscale FE (finite element) model. Morever, through the hybrid process simulation, a FE (finite element) model mapping residual stress and fiber orientation from compression molding simulation is combined with one mapping fiber orientation from the draping process simulation. The proposed method is expected to rigorously evaluate the design requirements of the battery housing part and composite materials having various material configurations.

• Transactions of Materials Processing
• /
• v.32 no.1
• /
• pp.28-34
• /
• 2023

In this research, a representative volume element (RVE)-based FE Model is presented to estimate the mechanical properties of additively manufactured continuous fiber-reinforced composites with different fiber orientations. To construct the model, an ABAQUS Python script has been implemented to produce matrix and fiber in the desired orientations at the RVE. A script has also been developed to apply the periodic boundary conditions to the RVE. Experimental tests were conducted to validate the numerical models. Tensile specimens with the fiber directions aligned in the 0, 45, and 90 degrees to the loading direction were manufactured using a continuous fiber 3D printer and tensile tests were performed in the three directions. Tensile tests were also simulated using the RVE models. The predicted Young's moduli compared well with the measurements: the Young's modulus prediction accuracy values were 83.73, 97.70, and 92.92 percent for the specimens in the 0, 45, and 90 degrees, respectively. The proposed method with periodic boundary conditions precisely evaluated the elastic properties of additively manufactured continuous fiber-reinforced composites with complex microstructures.

• Composites Research
• /
• v.34 no.1
• /
• pp.70-75
• /
• 2021

This paper presents a novel algorithm to reconstruct meso-scale representative volume elements (RVE), referring to experimentally observed features of Sheet Molding Compound (SMC) composites. Predicting anisotropic mechanical properties of SMC composites is challenging in the multiscale virtual test using finite element (FE) models. To this end, an SMC RVE modeler consisting of a series of image processing techniques, the novel reconstruction algorithm, and a FE mesh generator for the SMC composites are developed. First, micro-CT image processing is conducted to estimate probabilistic distributions of two critical features, such as fiber chip orientation and distribution that are highly related to mechanical performance. Second, a reconstruction algorithm for 3D fiber chip packing is developed in consideration of the overlapping effect between fiber chips. Third, the macro-scale behavior of the SMC is predicted by the multiscale analysis.

• Steel and Composite Structures
• /
• v.44 no.5
• /
• pp.685-690
• /
• 2022

In this paper, the equivalent material properties of cellular metamaterials with different types of perforations have been presented using finite element (FE) simulation of tensile test in Abaqus commercial software. To this end, a Representative Volume Element (RVE) has been considered for each type of cellular metamaterial with regular array of circular, square, oval and rectangular perforations. Furthermore, both straight and perpendicular patterns of oval and rectangular perforations have been studied. By applying Periodic Boundary conditions (PBC) on the RVE, the actual behavior of cellular material under uniaxial tension has been simulated. Finally, the effective Young's modulus, Poisson's ratio and mass density of various metamaterials have been presented as functions of relative density of the RVE

• Structural Engineering and Mechanics
• /
• v.90 no.4
• /
• pp.403-415
• /
• 2024

We looked at how the damping qualities of epoxy composites changed when different amounts of graphite nanoplatelets (GNP) were added, from 0% to 6% by weight. A mix of free and forced vibration tests helped us find the key GNP content that makes the damper ability better the most. We also created a Representative Volume Element (RVE) model to guess how the alloys would behave mechanically and checked these models against testing data. An Artificial Neural Network (ANN) was also used to guess how these compounds would react to motion. With proper hyperparameter tweaking, the ANN model showed good correlation (R²=0.98) with actual data, indicating its ability to predict complex material behavior. Combining these methods shows how GNPs impact epoxy composite mechanical properties and how machine learning might improve material design. We show how adding GNPs to epoxy composites may considerably reduce vibration. These materials may be used in industries that value vibration damping.