• Computers and Concrete
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• 제9권1호
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• pp.35-50
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• 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
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• 제20권10호
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• pp.1722-1729
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• 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
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• 제37권3호
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• pp.179-185
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• 2024

본 연구는 Representative Volume Element(RVE) 모델을 사용하여 초기 공극 결함이 있는 단방향 섬유강화 복합재의 횡방향 인장 강도 변화에 대해 정량적 평가 및 조사되었다. 초기 공극 결함을 표본오차와 신뢰 수준을 기준으로 적정 표본의 수가 계산된 후, 총 5000개의 초기 공극 결함이 있는 RVE 모델이 표본 집단으로 생성되었다. 표본 집단은 차원 축소법과 밀도 기반 군집 분석을 통해 유사도 분석이 진행되었으며 편향되지 않은 표본 집단임이 확인 및 검증되었다. 검증된 표본 분석 결과는 복합재 구조의 신뢰성 해석에 적용될 수 있게 Weibull 분포로 표현되었다.

• 한국분말재료학회지
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• 제24권1호
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• pp.17-23
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• 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
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• 제21권5호
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• pp.1085-1103
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• 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
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• 제35권3호
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• pp.188-195
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• 2022

본 논문은 멀티스케일 공정-구조 해석의 방법론을 제안하고 단섬유층과 직물층으로 이루어진 배터리 하우징 파트에 적용한다. 특별히 마이크로스케일 대표체적요소(RVE: Representative Volume Element)안 기지의 불확정성을 고려하였다. 마이크로스케일의 RVE내 기지 물성의 랜덤한 공간내 분포는 KLE(Karhunen-Loeve Expansion)을 통해 구현하였다. 공간상 랜덤분포된 기지 물성을 갖는 RVE의 유효 물성을 전산균질화를 통해 얻어 매크로스케일 유한요소 모델에 매핑하였다. 또한 하이브리드 공정해석을 통해 압축 성형 해석으로부터 얻은 잔류응력과 섬유배향을 매핑한 유한요소 모델과 드레이핑 공정 해석결과로부터 얻어진 섬유배향을 매핑한 모델을 결합하였다. 본 연구에 제안된 방법은 배터리 하우징 뿐만 아니라 다양한 재료 구성을 갖는 복합재료의 공정-구조해석을 통해 설계요구도를 엄밀하게 평가할 수 있을 것이라 기대된다.

• 소성∙가공
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• 제32권1호
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• pp.28-34
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• 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
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• 제34권1호
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• pp.70-75
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• 2021

본 논문은 단섬유 칩으로 구성된 Sheet Molding Compound(SMC) 복합재료를 실험적으로 관찰된 특징들을 바탕으로 메소스케일(meso-scale) 대표체적요소(RVE: Representative Volume Element)를 재구성하는 새로운 알고리즘을 제시한다. 전산해석을 이용하여 SMC 복합재료의 비등방성 거동의 정확한 예측은 어려운 문제이다. 이를 극복하기 위해, SMC 복합재료를 위한 일련의 이미지 프로세싱 기술과 재구성 알고리즘 및 유한요소(FE: Finite Element) 생성기로 구성된 SMC RVE 모델을 개발하였다. 첫째, micro-CT 이미지 프로세싱은 SMC 물성에 직접적인 상관관계를 가지는 섬유칩의 배향 및 분산의 확률적 분포를 평가한다. 둘째, 해당 통계적 분포를 바탕으로 섬유칩 간의 겹침효과를 고려한 섬유칩 팩킹 재구성 알고리즘을 개발한다. 마지막으로, SMC 복합재료 멀티스케일 해석을 이용하여 매크로스케일(macro-scale)에서의 거동을 파악하고 실험데이터를 통해 검증을 수행한다.

• Steel and Composite Structures
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• 제44권5호
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• pp.685-690
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• 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
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• 제90권4호
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• pp.403-415
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• 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.