• Transactions of the Korean Society of Mechanical Engineers A
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• v.36 no.8
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• pp.921-927
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• 2012

Reliability-based design optimization (RBDO) can be used to determine the reliability of a system by means of probabilistic design criteria, i.e., the possibility of failure considering stochastic features of design variables and input parameters. To assure these criteria, various reliability analysis methods have been developed. Most of these methods assume that distribution functions are continuous. However, in real problems, because real data is often discrete in form, it is important to estimate the distributions for discrete information during reliability analysis. In this study, we employ the Akaike information criterion (AIC) method for reliability analysis to determine the best estimated distribution for discrete information and we suggest an RBDO method using AIC. Mathematical and engineering examples are illustrated to verify the proposed method.

• Journal of Advanced Marine Engineering and Technology
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• v.19 no.4
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• pp.17-26
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• 1995

In general existing air conditioning devices, which are carried out by convection heat transfer, are very popular compared with the radiation type air conditioning devices. But perconal convection tpe air conditioning units are unuseful air conditioning type because it handles amount of surrounded air to meet the temperature and humidity. In this view, this study is intended to develope personal dir conditioning units using a radiation type radiator. Liquid Droplet Radiator(L.P.R.) radiates the energy by means of thermal radiation. Radiative energy from L.P.R. is the infrared rays which heat the objects without lose of energy. It is a desirable heating method for the local area within the large room. In this study, the analysis uses the Monte Carlo methd to predict the temperature distribution in the droplet sheet and the net heat flux from the L.D.R.. And for this study and experiment was carried out to analyse the radiative and convective heat transfer characteristics in the L.D.R.. And the experiment was investigated the effects of inlet temperature, feed rate, optical thickness and droplet diameter on heat transfer characteristics of L.D.R.. The obtained results from the numerical and experimental studies of L.D.R. were as follows ; (1) The heat flux of L.D.R. was effected by extinction coefficient of droplet sheet, optical thickness and droplet temperature, surface area and emissivity of the droplet. And it was increased with the temperature, feed rate and optical thickness, on the other hand decreased with increasing of droplet diameter. (2) The experimental results for heat flux was ecalucted below 20% than that of the numerical solution by Monte Carlo method, but the tendency of the variation shows relatively good agreement.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.31 no.10
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• pp.10-20
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• 2003

In this study, the effective properties were numerically calculated for laminated plain weave textile composites with arbitrary s tacking orientation angles. A single-field macroelement with modified sub-domain integration was used in the analysis to reduce computer resource requirement while efficiently accounting for the internal microstructure. A sample calculation procedure based on the Monte Carlo method was employed to consider the random shift between the layers. Results showed that a significant deviation occurred when the orientation angles were near 0 deg for extensional modulus and Poisson's ratio and 45 deg for the shear modulus. It was also found that the average properties calculated by the 2-layer numerical specimen had large differences compared to the CLT results, which indicated that a caution must be needed when designig of thin plain weave composite structures.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.40 no.4
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• pp.336-345
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• 2012

In crack growth life, uncertainties are caused by variance of geometry, applied loads and material properties. Therefore, the reliability estimation for these uncertainties is required to keep the robustness of calculated life. The stress intensity factors are the most important variable in crack growth life calculation, but its equation is hard to know for complex geometry, therefore they are processed by the finite element analysis which takes long time. In this paper, the response surface is considered to increase efficiency of the reliability analysis for crack growth life of a turbine wheel. The approximation model of the stress intensity factors is obtained by the regression analysis for FEA data and the response surface of crack growth life is generated for selected factors. The reliability analysis is operated by the Monte Carlo Simulation for the response surface. The results indicate that the response surface could reduce computations that need for reliability analysis for the turbine wheel, which is hard to derive stress intensity factor equation, successfully.

• Journal of the Korea institute for structural maintenance and inspection
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• v.16 no.5
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• pp.29-39
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• 2012

The main objective is to predict the future degradation and maintenance budget for a suspension bridge system. Bayesian inference is applied to find the posterior probability density function of the source parameters (damage indices and serviceability), given ten years of maintenance data. The posterior distribution of the parameters is sampled using a Markov chain Monte Carlo method. The simulated risk prediction for decreased serviceability conditions are posterior distributions based on prior distribution and likelihood of data updated from annual maintenance tasks. Compared with conventional linear prediction model, the proposed quadratic model provides highly improved convergence and closeness to measured data in terms of serviceability, risky factors, and maintenance budget for bridge components, which allows forecasting a future performance and financial management of complex infrastructures based on the proposed quadratic stochastic regression model.

• 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.

• 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.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.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.33 no.1
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• pp.38-45
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• 2009

Oxy-fuel combustion of pulverized coal is one of the promising new technologies to reduce $CO_2$ and NOx from coal combustion. However, the stability of pulverized coal flame is reduced in the oxy-fuel combustion. This flame stability is concerned with the flame propagation that is affected by surrounding gas and coal characteristics, such as gas temperature, gas composition, coal volatile, coal activation energy and coal size. In this paper, a study on the influence of surrounding gas and coal characteristics on the flame propagation velocity in oxy-fuel combustion of pulverized coal was preformed. One dimensional model was used to calculate the flame propagation velocity of pulverized coal clouds. In this model, the radiation is considered to be the main source of heat exchange, and Monte Carlo method was adopted for accurate calculation of radiation heat flux. It was found that the flame propagation velocity become higher with the decrease of coal activation energy and the increase of coal volatile. Also, according to the increase of gas temperature and $O_2$ concentration, flame propagation velocity increased.

• Journal of the Korea Society of Computer and Information
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• v.15 no.5
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• pp.49-56
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• 2010

In this paper, we implement a Hadoop based cluster for cloud computing and evaluate the performance of this cluster based on application characteristics by executing RandomTextWriter, WordCount, and PI applications. A RandomTextWriter creates given amount of random words and stores them in the HDFS(Hadoop Distributed File System). A WordCount reads an input file and determines the frequency of a given word per block unit. PI application induces PI value using the Monte Carlo law. During simulation, we investigate the effect of data block size and the number of replications on the execution time of applications. Through simulation, we have confirmed that the execution time of RandomTextWriter was proportional to the number of replications. However, the execution time of WordCount and PI were not affected by the number of replications. Moreover, the execution time of WordCount was optimum when the block size was 64~256MB. Therefore, these results show that the performance of cloud computing system can be enhanced by using a scheduling scheme that considers application's characteristics.