• Proceedings of the Korea Association of Crystal Growth Conference
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• 1999.06a
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• pp.69-80
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• 1999

Computer simulations have played a central role in the development of out understanding of the atomic scale processes involved in crystal growth. The assumptions underlying computer modeling will be discussed and out recent work on modeling of the kinetic formation of thermodynamically unstable phases in alloys or mixtures will be reviewed. Our Monte Carlo computer simulations have reproduced the experimental results on the rapid recrystallization of laser-melted doped silicon. An analytical model for this phenomenon has been developed, and its applicability to other materials will be discussed.

• Journal of the Korean Crystal Growth and Crystal Technology
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• v.9 no.4
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• pp.365-370
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• 1999

Computer simulations have played a central role in the development of our understanding of the atomic scale processes involved in crystal growth. The assumptions underlying computer modeling will be discussed and our recent work on modeling of the kinetic formation of thermodynamically unstable phases in alloys or mixtures will be reviewed. Our Monte Carlo computer simulations have reproduced the experimental results on the rapid recrystallization of laser-melted doped silicon. An analytical model for this phenomenon has been developed, and its applicability to other materials will be discussed.

• Computers and Concrete
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• v.2 no.4
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• pp.325-343
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• 2005

Chloride ingress is a common cause of deterioration of reinforced concrete located in coastal zone. Modeling the chloride ingress is an important basis for designing reinforced concrete structures and for assessing the reliability of an existing structure. The modeling is also needed for predicting the deterioration of a reinforced structure. The existing deterministic solution for prediction model of corrosion initiation cannot reflect uncertainties which input variables have. This paper presents an approach to the fuzzy arithmetic based modeling of the chloride-induced corrosion of reinforcement in concrete structures that takes into account the uncertainties in the physical models of chloride penetration into concrete and corrosion of steel reinforcement, as well as the uncertainties in the governing parameters, including concrete diffusivity, concrete cover depth, surface chloride concentration and critical chloride level for corrosion initiation. There are a lot of prediction model for predicting the time of reinforcement corrosion of structures exposed to chloride-induced corrosion environment. In this work, RILEM model formula and Crank's solution of Fick's second law of diffusion is used. The parameters of the models are regarded as fuzzy numbers with proper membership function adapted to statistical data of the governing parameters instead of random variables of probabilistic modeling of Monte Carlo Simulation and the fuzziness of the time to corrosion initiation is determined by the fuzzy arithmetic of interval arithmetic and extension principle. An analysis is implemented by comparing deterministic calculation with fuzzy arithmetic for above two prediction models.

• Geophysics and Geophysical Exploration
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• v.17 no.3
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• pp.155-162
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• 2014

We performed MCNP modeling for density log, and examined its reliability and validity comparing the correction curves from physical borehole model. Based on the constructed numerical model, numerical modelings of density sonde in three-inch borehole were carried out under the various conditions such as the existence and type of casing or fluid, and also the stand-off between the sonde and borehole wall. These results of numerical modeling quantitatively reflect effects of casing and fluid in borehole, and moreover, demonstrate constant patterns with interval change from borehole wall. From this study, numerical modeling using MCNP shows a good applicability for well logging, and therefore, can be efficiently used for the calibration of well logging data under the various borehole conditions.

• 한국태양에너지학회:학술대회논문집
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• 2011.04a
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• pp.223-228
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• 2011

The volumetric solar receiver is a key element of solar power plants using air. The solar flux distribution inside the receiver should be a priori known for its heat transfer modeling. Previous works have not considered characteristics of the solar flux although they change with radiative properties of receiver materials and receiver geometries. A numerical method, which is based on the Monte Carlo ray-tracing method, was developed in the current work. The solar flux distributions inside multi-channeled volumetric solar receivers were calculated when light is concentrated at the KIER solar furnace. It turned out that 99 percentage of the concentrated solar energy is absorbed within 15 mm charmel length for the charmel radius smaller than 1.5 mm. If the concentrated light is assumed to be diffuse, the absorbed solar energy at the charmel entrance region is overpredicted while the light penetrates more deeply into the charmel. The developed method will help understand the solar flux when only a part of concentrated light is of interest. Furthermore, if the presented results are applied for heat transfer modeling of multi-channeled volumetric solar receivers, one could examine effects of receiver charmel properties and shape on air temperature profiles.

• ETRI Journal
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• v.28 no.2
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• pp.182-190
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• 2006

The circuit modeling of interdigitated capacitors fabricated by high-k low-temperature co-fired ceramic (LTCC) sheets was investigated. The s-parameters of each test structure were measured from 50 MHz to 10 GHz, and the modeling was performed using these measured sparameters up to the first resonant frequency. Each test structure was divided into appropriate building blocks. The equivalent circuit of each building block was composed based on the partial element equivalent circuit (PEEC) method. Modeling was executed to optimize the parameters in the equivalent circuit of each building block. The validity of the extracted parameters was verified by the predictive modeling for the test structures with different geometry. After that, Monte Carlo analysis and sensitivity analysis were performed based on the extracted parameters. The modeling methodology can allow a device designer to improve the yield and to save time and cost for the design and manufacturing of devices.

• Computers and Concrete
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• v.26 no.2
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• pp.127-136
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• 2020

There is an inherent randomness for concrete microstructure even with the same manufacturing process. Meanwhile, the concrete material under the aqueous environment is usually not fully saturated by water. This study aimed to develop a stochastic micromechanical framework to investigate the probabilistic behavior of the unsaturated concrete from microscale level. The material is represented as a multiphase composite composed of the water, the pores and the intrinsic concrete (made up by the mortar, the coarse aggregates and their interfaces). The differential scheme based two-level micromechanical homogenization scheme is presented to quantitatively predict the concrete's effective properties. By modeling the volume fractions and properties of the constituents as stochastic, we extend the deterministic framework to stochastic to incorporate the material's inherent randomness. Monte Carlo simulations are adopted to reach the different order moments of the effective properties. A distribution-free method is employed to get the unbiased probability density function based on the maximum entropy principle. Numerical examples including limited experimental validations, comparisons with existing micromechanical models, commonly used probability density functions and the direct Monte Carlo simulations indicate that the proposed models provide an accurate and computationally efficient framework in characterizing the material's effective properties. Finally, the effects of the saturation degrees and the pore shapes on the concrete macroscopic probabilistic behaviors are investigated based on our proposed stochastic micromechanical framework.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.37 no.9
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• pp.1133-1140
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• 2013

A computer model is a useful tool that provides solution via physical modeling instead of expensive testing. In reality, however, it often does not agree with the experimental data owing to simplifying assumption and unknown or uncertain input parameters. In this study, a Bayesian approach is proposed to calibrate the computer model in a probabilistic manner using the measured data. The elasto-plastic analysis of a pyrotechnically actuated device (PAD) is employed to demonstrate this approach, which is a component that delivers high power in remote environments by the combustion of a self-contained energy source. A simple mathematical model that quickly evaluates the performance is developed. Unknown input parameters are calibrated conditional on the experimental data using the Markov Chain Monte Carlo algorithm, which is a modern computational statistics method. Finally, the results are applied to determine the reliability of the PAD.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.35 no.10
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• pp.989-996
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• 2011

An understanding of the concentrated solar flux is critical for the analysis and design of solar-energy-utilization systems. The current work focuses on the development of an algorithm that uses the Monte Carlo ray-tracing method with excellent flexibility and expandability; this method considers both solar limb darkening and the surface slope error of reflectors, thereby analyzing the solar flux. A comparison of the modeling results with measurements at the solar furnace in Korea Institute of Energy Research (KIER) show good agreement within a measurement uncertainty of 10%. The model evaluates the concentration performance of the KIER solar furnace with a tracking accuracy of 2 mrad and a maximum attainable concentration ratio of 4400 sun. Flux variations according to measurement position and flux distributions depending on acceptance angles provide detailed information for the design of chemical reactors or secondary concentrators.

• Proceedings of the Korean Geotechical Society Conference
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• 2002.03a
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• pp.651-656
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• 2002

In general, the most important problem in slope stability analysis is that there is no definite way to describe the natural three-dimensional Joint network. Therefore, the many approaches were tried to anlayze the slope stability. Numerical modeling approach is one of the branch to resolve the complexity of natural system. UDEC, FLAC, and SWEDGE are widely used commercial code for the purpose on stability analysis. For the purpose on the more appropriate application of these kind of code, however, three-dimensional distribution of joint network must be identified in more explicit way. Remaining problem is to definitely describe the three dimensional network of joint and bedding, but it is almost impossible in practical sense. Three dimensional joint generation method with random number generation and the results of generation to UDEC have been applied to settle the refered problems in field site. However, this approach also has a important problem, and it is that joint network is generated only once. This problem lead to the limitation on the application to field case, in practical sense. To get rid of this limitation, Monte Carlo Simulation is proposed in this study 1) statistical analysis of input values and definition of the applied system with statistical parameter, 2) instead of the consideration of generated network as a real system, generated system is just taken as one reliable system, 3) present the design parameters, through the statistical analysis of ouput values Results of this study are not only the probability of failure, but also area of failure block, shear strength, normal strength and failure pattern, and all of these results are described in statistical parameters. The results of this study, shear strength, failure area, pattern etc, can provide the direct basement on the design, cutoff angle, support pattern, support strength and etc.