• Journal of the Korean Crystal Growth and Crystal Technology
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• v.16 no.1
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• pp.38-42
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• 2006

Spark plasma sintering processes have been rapidly introduced recently to improve the quality and productivity of ceramic products and to solve the problem of environmental pollutions. Sintering temperatures and pressing pressures in the spark plasma sintering process are known to be the important factors highly affecting the quality of the ceramics. In this research, in order to see the effects of sintering temperatures and pressing pressures on the grain growth during the spark plasma sintering process of $Al_2O_3$ the grain growth processes associated with sintering temperatures and pressing pressures are simulated by the Monte Carlo method (MCM) and the finite element method (FEM). In this Part 1, the formulations for the simulation, which is the theoretical background of Part 2, are introduced.

• Journal of the Korean Society of Radiology
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• v.14 no.5
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• pp.503-510
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• 2020

Concrete is one of the most widely used materials as the shielding structures of a nuclear facilities. It is also the most generated radioactive waste in quantity while dismantling facilities. Since the concrete captures neutrons and generates various radionuclides, radiation measurement and analysis of the sample was fulfilled prior to dismantle facilities. An HPGe detector is used in general for the radiation measurement, and effective correction factors such as geometrical correction factor, self-absorption correction, and absolute detector efficiency have to be applied to the measured data to decide exact radioactivity of the sample. Correction factors are obtained by measuring data using a standard source with the same geometry and chemical states as the sample under the same measurement conditions. However, it is very difficult to prepare standard concrete sources because concrete is limited in pretreatment due to various constituent materials and high density. In addition, the concrete sample obtained by core drill is a volumetric source, which requires geometric correction for sample diameter and self absorption correction for sample density. Therefore in recent years, many researchers are working on the calculation of effective correction factors using Monte carlo simulation instead of measuring them using a standard source. In this study we calculated, using Geant4, one of the Monte carlo codes, the correction factors for the various diameter and density of the concrete core sample at the gamma ray energy emitted from the nuclides ¹⁵²Eu and ⁶⁰Co, which are the most generated in radioactive concrete.

• Proceeding of EDISON Challenge
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• 2016.03a
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• pp.67-74
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• 2016

초발수 표면은 표면이 젖지 않으면서 물방울이 자유롭게 움직일 수 있는 표면을 말한다. 이는 자연에서 많이 관찰되며 예를 들면 연잎, 나비와 곤충의 다리가 대표적이다. 일반적으로 초발수 표면은 물방울과 접촉할 때 이루는 각이 $150^{\circ}$보다 크며, 표면을 $5^{\circ}$정도 기울이면 물방울이 굴러가기 시작한다. 특히 연잎 표면을 자세히 보면 마이크로/나노단위의 미세한 돌기가 표면 위에 존재한다. 이러한 연잎 표면의 구조에 따른 특성을 모방하여 표면 위에 인위적으로 다양한 모양, 크기의 돌기를 만들어 표면의 초발수 특성을 향상시키는 연구가 활발히 이루어지고 있다. 연구가 다양하고 광범위하게 진행되면서, 학문적인 원인 분석 외에도 산업에서의 활용가능성이 주목받고 있다. 이에 따라 이 논문은 전산모사 방법을 사용하여 표면 돌기와 관련된 다양한 변수(돌기의 모양, 높이, 너비, 돌기 사이의 간격, 표면과 물 분자 간의 에너지)에 따라 표면의 초발수 특성이 어떻게 변하는지 연구해보고, 초발수 현상의 특징과 그 발생 원인을 이해하는데 목적을 두며 이러한 연구결과는 실제 산업현장에서 최적의 초발수 표면을 제작하는데 도움을 줄 것이다.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.18 no.3
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• pp.291-302
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• 2005

Due to the importance of the parameter in structural response, the uncertain elastic modulus was located at the center of stochastic analysis, where the response variability caused by the uncertain system parameters is pursued. However when we analyze the so-called stochastic systems, as many parameters as possible must be included in the analysis if we want to obtain the response variability that can reach a true one, even in an approximate sense. In this paper, a formulation to determine the statistical behavior of in-plane structures due to multiple uncertain material parameters, i.e., elastic modulus and Poisson's ratio, is suggested. To this end, the polynomial expansion on the coefficients of constitutive matrix is employed. In constructing the modified auto-and cross-correlation functions, use is made of the general equation for n-th moment. For the computational purpose, the infinite series of stochastic sub-stiffness matrices is truncated preserving required accuracy. To demons4rate the validity of the proposed formulation, an exemplary example is analyzed and the results are compared with those obtained by means of classical Monte Carlo simulation, which is based on the local averaging scheme.

• Journal of the Korean Society for Railway
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• v.8 no.4
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• pp.337-341
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• 2005

Algorithm for passenger flow analysis based on DEM(Discrete Element Method) is newly developed. In the new algorithm, there are many similarity between multi phase flow and passenger flow. The velocity component of 1st phase corresponds to the direction vector of cell, each particle to each passenger, volume fraction to population density and the momentum equation of particle to the walking velocity equation of passenger, etc. And, the walking velocity of passenger is also represented by the function of population density. Key algorithms are developed to determine the position of passenger, population density and numbering to each passenger, To verify the effectiveness of new algorithm, passenger flow analysis for simple railway station model is conducted. The results for passenger flow in the model station are satisfying qualitatively and quantitatively.

• Journal of the Korean Society for Railway
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• v.9 no.3 s.34
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• pp.271-276
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• 2006

Insight into behaviour of pedestrians as welt as tools to assess passenger flow condition is important in such instances as planning and geometric design of railway station under regular and safety-critical circumstances. Algorithm for passenger flow analysis based on DEM (Discrete Element Method) is newly developed. There are lots of similarity between particle-laden two phase flow and passenger flow. The velocity component of 1st phase corresponds to the unit vector of calculation cell, each particle to passenger, volume fraction to population density and the particle velocity to the walking velocity, etc. And, the walking velocity of passenger is also represented by the function of population density. Key algorithms are developed to determine the position of passenger, population density and numbering to each passenger. To verify the effectiveness of new algorithm, passenger flow analysis for the basic models of railway station is conducted.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.28 no.4
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• pp.351-359
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• 2015

In order to exactly evaluate the seismic collapse capacity of a structure, probabilistic approach is required by considering uncertainties related to its structural properties and ground motion. Regardless of the types of uncertainties, they influence on the seismic response of a structures and their effects are required to be estimated. An incremental dynamic analysis(IDA) is useful to investigate uncertainty-propagation due to ground motion. In this study, a 3-story steel moment-resisting frame is selected for a prototype frame and analyzed using the IDA. The uncertainty-propagation is assessed with categorized parameters representing epistemic uncertainties, such as the seismic weight, the inherent damping, the yield strength, and the elastic modulus. To do this, the influence of the uncertainty-propagation to the seismic collapse capacity of the prototype frame is probabilistically evaluated using the incremental dynamic analyses based on the Monte-Carlo simulation sampling with the Latin hypercube method. Of various parameters related to epistemic uncertainty-propagation, the inherent damping is investigated to be the most influential parameter on the seismic collapse capacity of the prototype frame.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.36 no.3
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• pp.203-211
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• 2023

A Markov chain Monte Carlo (MCMC) simulation is proposed for probabilistic full waveform inversion (FWI) in a layered half-space. Dynamic responses on the half-space surface are estimated using the thin-layer method when a harmonic vertical force is applied. Subsequently, a posterior probability distribution function and the corresponding objective function are formulated to minimize the difference between estimations and observed data as well as that of model parameters from prior information. Based on the gradient of the objective function, a proposal distribution and an acceptance probability for MCMC samples are proposed. The proposed MCMC simulation is applied to several layered half-space examples. It is demonstrated that the proposed MCMC simulation for probabilistic FWI can estimate probabilistic material properties such as the shear-wave velocities of a layered half-space.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.30 no.2
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• pp.153-158
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• 2017

In this paper, we take into account topology optimization problems considering spatial randomness in the material property of elastic modulus. Based on 88 lines MATLAB Code, Monte Carlo analysis has been performed for MBB(messerschmidt-$b{\ddot{o}}lkow$-blohm) model using 5,000 random sample fields which are generated by using the spectral representation scheme. The random elastic modulus is assumed to be Gaussian in the spatial domain of the structure. The variability of the volume fraction of the material, which affects the optimum topology of the given problem, is given in terms of correlation distance of the random material. When the correlation distance is small, the randomness in the topology is high and vice versa. As the correlation distance increases, the variability of the volume fraction of the material decreases, which comply with the feature of the linear static analysis. As a consequence, it is suggested that the randomness in the material property is need to be considered in the topology optimization.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.24 no.4
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• pp.383-389
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• 2011

In this paper, a statistical calibration method is proposed in order to identify the variability of complex modulus for a rubber material due to operational temperature and experimental/model errors. To describe temperature- and frequency-dependent material properties, a fractional derivative model and a shift factor relationship are used. A likelihood function is defined as a product of the probability density functions where experimental values lie on the model. The variation of the fractional derivative model parameters is obtained by maximizing the likelihood function. Using the proposed method, the variability of a synthetic rubber material is estimated and applied to a rubber mount problem. The dynamic characteristics of the rubber mount are calculated using a finite element model of which material properties are sampled from Monte Carlo simulation. The calculated dynamic stiffnesses show very large variation.