• Composites Research
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• v.31 no.5
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• pp.251-259
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• 2018

In this paper, statistical volume element modeling method was developed for multi-scale progressive failure analysis of fiber reinforced composite materials. Big-size statistical volume elements (BSVEs) was considered to minimize the size effect in the micro-scale, by including as many fibers as possible. For that purpose, a mesh cutting method is suggested and adapted into the fiber model generator that creates finite element domain rapidly. The fiber defect model was also developed based on the experimental distribution of the fiber strength. The size effects from the local load sharing (LLS) are evaluated by increasing the fiber inclusion in the micro-scale model. Finally, continuum damage mechanics (CDM) model to the fiber direction was extracted from numerical analysis on BSVEs. And it was compared with strength prediction from typical representative volume element (RVE) model.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.16 no.4
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• pp.353-367
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• 2003

This paper describes the extension of the numerical model, which was developed to simulate the nonlinear behavior of reinforced concrete (RC) structures subjected to monotonic in plane shear and introduced in the companion paper, to simulate effectively the behavior of RE structures under cyclic loadings. While maintaining all the basic assumptions adopted in defining the constitutive relations of concrete under monotonic loadings, a hysteretic stress strain relation of concrete, which across the tension compression region, is defined. In addition, unlike previous simplified stress strain relations, curved unloading and reloading branches inferred from the stress strain relation of steel considering the Bauschinger effect we used. The modifications of the stress strain relation of steel are also introduced to reflect pinching effect depending on the shear span ratio and an average stress distribution in a cracked RC element. Finally, correlation studies between analytical results and experimental studies are conducted to establish the validity of the proposed model.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2004.07b
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• pp.1117-1120
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• 2004

경제적인 공정분석과 최적화를 위해서는 컴퓨터를 이용한 플라즈마 예측모델이 요구되고 있다. 본 연구에서는 일반화된 회귀 신경망 (GRNN)을 이용하여 플라즈마 증착공정 모델을 개발한다. GRNN의 예측성능은 패턴층 뉴런의 가우시안 함수를 구성하는 학습인자, 즉 spread에 의존한다. 종래의 모델에서는 모든 가우시안 함수의 spread가 동일한 값에서 최적화되었으며, 이로 인해 모델의 예측성능을 향상시키는 데에는 한계가 있었다. 본 연구에서는 유전자 알고리즘 (GA)를 이용하여 다변수 spread를 최적화하는 기법을 개발하였으며, 그 성능을 PECVD 공정에 의해 증착된 SiN 박막의 증착률에 적용하여 평가하였다. $2^{6-1}$ 부분인자 실험계획법에 의해 수집된 데이터를 이용하여 신경망을 학습하였고, 모델적합성 점검을 위해 별도의 12번의 실험을 수행하였다. 가우시안 함수의 spread는 0.2에서 2.0까지 0.2간격으로 증가시켰으며, 최적화한 GA-GRNN모델의 예측성능은 6.6 ${\AA}/min$이었다. 이는 종래의 방식으로 최적화한 모델의 예측성능 (13.5 ${\AA}/min$)과 비교하여 50.7% 향상된 예측성능이며, 이러한 향상은 제안한 GA-GRNN 모델이 플라즈마 공정 모델의 예측성능을 증진하는데 매우 효과적임을 보여준다.

• Journal of the Korea institute for structural maintenance and inspection
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• v.22 no.6
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• pp.130-137
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• 2018

Many constitutive models for concrete have been developed to predict the nonlinear behavior of concrete members considerably. The constitutive model for reinforcing bar that include the tension stiffening effect due to the bond characteristics between steel bars and concrete is being studied but the bilinear model is generally used. It was found that the buckling of the longitudinal reinforcing bars is controlled the nonlinear behavior of hybrid precast concrete panel, which is being developed for core wall. In this study, the constitutive models that can consider the embedding and buckling effects of reinforcing bar are investigated and a new model combing these constitutive models is proposed. In order to verify the proposed model, the analysis results are compared with experimental results of the concrete wall and hybrid precast concrete panel. The analysis of embedding-effect-only modeling predicted that the deformation increases continually without the decrease in the load carrying capacity. However, the analysis results of proposed model showed good agreement with some experimental results, thus verifying the proposed computational model.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2011.04a
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• pp.290-291
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• 2011

본 논문에서는 열-전기-기계 하중 하의 지능형 복합재료 보 모델을 전산점근해석기법에 기초하여 개발하였다. 열-전기-기계 하중 하의 구조물은 지난 십년간 많은 연구가 있어왔으나, 주로 고전적 보 모델에 기반을 두어 진행되어져 왔다. 멀티피직스 환경하의 구조물은 여러 가지 하중의 조합과 이에 따른 연성효과의 고려가 필수적이다. 따라서 공학적인 가정이 없는 점근해석기법은 보다 정확한 등가 보 모델을 개발하는데 있어 기반요소가 될 수 있다. 본 연구에서는 3차원 멀티피직스 구성방정식으로부터 출발하여 점근기법을 적용 체계적으로 등가 보 모델을 유도하고 그 해석 결과를 고찰하고자 한다.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.37 no.11
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• pp.1096-1103
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• 2009

The predictions of the material behavior for the structural stability of thrust chamber mixing head at very-low temperatures are very important since the head is highly pressurized by the liquid oxygen with very-low temperatures and experiences impact load by the thrust of combustion chamber. The constitutive equation to express tensile deformation behavior of the material at very-low temperature to predict deformation behavior of the mixing head is formulated by composition of thermal component and athermal component based on dislocation energy barrier model suggested by Kocks. Also, increase of thermal stress components by the increase of obstacles at low temperatures is formulated to the equation similar with Ramberg-Osgood equation. The suggested model predicted well the material's behavior at the wide temperature ranges from very-low temperature to ambient temperature.

• Proceedings of the Materials Research Society of Korea Conference
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• 2010.05a
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• pp.34.1-34.1
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• 2010

국내외적으로 다양한 형태의 저차원 구조 나노재료를 화학센서에 응용하고자 하는 연구가 매우 활발히 진행되고 있다. 현재까지 발표된 많은 연구는 단일 산화물 반도성 재료를 이용한 센서 특성 측정 및 센싱특성 향상 연구가 주를 이루고 있다. 이에 비해 2 가지 이상의 물질로 구성되는 이종접합(heterojuction) 나노재료는 단일 재료에 비해 화학센서로서의 특성을 향상시킬 여러 가능성이 제시되고 있으나, 이러한 이종접합 구조의 가스감응특성에 대한 기본 모델 제시는 부족한 상태이다. 본 연구에서는 $SnO_2$-ZnO 등의 코아-쉘 구조 나노섬유 및 ZnO-$SnO_2$ 이종접합 구조 나노섬유 나노재료를 제조하였다. 이러한 나노재료의 가스 감응특성을 조사하고 그 결과들을 바탕으로 이종접합 나노재료의 가스 감응특성에 대한 메커니즘을 제시하고자 한다.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.1
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• pp.96-101
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• 2009

This paper presents a method for identifying the parameter set of inelastic constitutive equations, which is based on an Evolutionary Algorithm. The advantage of the method is that appropriate parameters can be identified even when the measured data are subject to considerable errors and the model equations are inaccurate. The design of experiments suited for the parameter identification of a material model by Chaboche under the uniaxial loading and stationary temperature conditions was first considered. Then the parameter set of the model was identified by the proposed method from a set of experimental data. In comparison to those by other methods, the resultant stress-strain curves by the proposed method correlated better to the actual material behaviors.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.33 no.2
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• pp.81-93
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• 2020

The fatigue characteristics of glass fiber reinforced plastic (GFRP) composites were studied under repeated loads using the finite element method (FEM). To realize the material characteristics of GFRP composites, Digimat, a mean-field homogenization tool, was employed. Additionally, the micro-structures and material models of GFRP composites were defined with it to predict the fatigue behavior of composites more realistically. Specifically, the fatigue characteristics of polybutylene terephthalate with short fiber fractions of 30wt% were investigated with respect to fiber orientation, stress ratio, and thickness. The injection analysis was conducted using Moldflow software to obtain the information on fiber orientations. It was mapped over FEM concerned with fatigue specimens. LS-DYNA, a typical finite element commercial software, was used in the coupled analysis of Digimat to calculate the stress amplitude of composites. FEMFAT software consisting of various numerical material models was used to predict the fatigue life. The results of coupled analysis of linear and nonlinear material models of Digimat were analyzed to identify the fatigue characteristics of GFRP composites using FEMFAT. Neuber's rule was applied to the linear material model to analyze the fatigue behavior in LCF regimen. Additionally, to evaluate the morphological and mechanical structure of GFRP composites, the coupled and fatigue analysis were conducted in terms of thickness.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.34 no.10
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• pp.1377-1384
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• 2010

The objective of this study is to determine the best material model that represents the deformation behavior of the Sn37Pb solder alloy accurately. First, a specimen is fabricated and subjected to a thermal cycle with temperatures ranging from the room temperature to $125^{\circ}C$. An experiment is conducted to examine deformation by Moire interferometry. Three different constitutive equation models are used in the finite element analysis (FEA) of the thermal cycle. In order to minimize the difference between the FEA results and the experimental results, the material parameters of the solder alloy are considered to be unknown and are determined by conducting optimization. As a result of the study, the Anand model is found to represent the deformation behavior of the solder most accurately.