• Journal of Ocean Engineering and Technology
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• v.32 no.4
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• pp.215-221
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• 2018

In this study, the optimal design of a mooring system was carried out. Unlike almost all design methods, which are based on the deterministic method, this study focused on the probabilistic method. The probabilistic method, especially the design of experiment (DOE), could be a good way to cover some of the drawbacks of the deterministic approach. There various parameters for a mooring system, as widely known, including the weight, length, and stiffness of line. Scenarios for the mooring system parameters were produced using the Latin Hypercube Sampling method of the probabilistic approach. Next, a vessel-mooring system coupled analysis was performed in Orcaflex. A total of 50 scenarios were used in this study to optimize the initial design by means of a genetic algorithm. Finally, after determining the optimal process, a reliability analysis was performed to understand the system validity.

• Journal of The Geomorphological Association of Korea
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• v.23 no.3
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• pp.67-78
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• 2016

Soil carbon(C) is an essential property for characterizing soil quality. Understanding spatial patterns of soil C is particularly limited for mountain areas. This study aims to predict the spatial pattern of soil C using terrain analysis in a steep mountainous area. Specifically, model performances and prediction uncertainties were investigated based on the number of resampling repetitions. Further, important predictors for soil C were also identified. Finally, the spatial distribution of uncertainty was analyzed. A total of 91 soil samples were collected via conditioned latin hypercube sampling and a digital soil C map was developed using support vector regression which is one of the powerful machine learning methods. Results showed that there were no distinct differences of model performances depending on the number of repetitions except for 10-fold cross validation. For soil C, elevation and surface curvature were selected as important predictors by recursive feature elimination. Soil C showed higher values in higher elevation and concave slopes. The spatial pattern of soil C might possibly reflect lateral movement of water and materials along the surface configuration of the study area. The higher values of uncertainty in higher elevation and concave slopes might be related to geomorphological characteristics of the research area and the sampling design. This study is believed to provide a better understanding of the relationship between geomorphology and soil C in the mountainous ecosystem.

• Nuclear Engineering and Technology
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• v.56 no.5
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• pp.1654-1666
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• 2024

The goal of this research is to propose a way to maximize small modular reactor (SMR) utilization to gain better market feasibility in support of carbon neutrality. For that purpose, a comprehensive tool was developed, combining off-design thermohydraulic models, economic objective models (levelized cost of electricity, annual profit), non-economic models (saved CO2), a parameter input sampling method (Latin hypercube sampling, LHS), and a multi-objective evolutionary algorithm (Non-dominated Sorting Algorithm-2, NSGA2 method) for optimizing a SMR-combined heat and power cycle (CHP) system design. Considering multiple objectives, it was shown that NSGA2+LHS method can find better optimal solution sets with similar computational costs compared to a conventional weighted sum (WS) method. Out of multiple multi-objective optimal design configurations for a 105 MWe design generation rating, a chosen reference SMR-CHP system resulted in its levelized cost of electricity (LCOE) below $60/MWh for various heat prices, showing economic competitiveness for energy market conditions similar to South Korea. Examined economic feasibility may vary significantly based on CHP heat prices, and extensive consideration of the regional heat market may be required for SMR-CHP regional optimization. Nonetheless, with reasonable heat market prices (e.g. district heating prices comparable to those in Europe and Korea), SMR can still become highly competitive in the energy market if coupled with a CHP system.

• Nuclear Engineering and Technology
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• v.56 no.8
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• pp.2999-3029
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• 2024

To improve the predictive capability of a nuclear thermal hydraulic safety analysis code by developing a better constitutive equation for individual phenomenon has been the general research direction until now. This paper proposes a new method to directly use complex experimental data obtained from integral effect test (IET) to improve constitutive models holistically and simultaneously. The method relies on the sensitivity of a simulation result of IET data to the multiple constitutive equations utilized during the simulation, and the sensitivity of individual model determines the direction of modification for the constitutive model. To develop a robust and generalized method, a clustering algorithm using an artificial neural network, sample space size determination using non-parametric statistics, and sampling method of Latin hypercube sampling are used in a combined manner. The value of the proposed methodology is demonstrated by applying the method to the ATLAS DSP-05 IET experiment. A sensitivity of each observation parameter to the constitutive models is analyzed. The new methodology suggested in the study can be used to improve the code prediction results of complex IET data by identifying the direction for constitutive equations to be modified.

• 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 Korean Society of Coastal and Ocean Engineers
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• v.21 no.6
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• pp.495-507
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• 2009

The expected sliding distance for the lifetime of a caisson breakwater has a limitation to be used as the stability criterion of the breakwater. Since the expected sliding distance is calculated as the mean of simulated sliding distances for the lifetime, there is possibility for the actual sliding distance to exceed the expected sliding distance. To overcome this problem, the exceedance probability of the allowable sliding distance is used to assess the stability of sliding. Latin Hypercube sampling and Crude Monte Carlo simulation were used to calculate the exceedance probability. The doubly-truncated normal distribution was considered to complement the physical disadvantage of the normal distribution as the random variable distribution. In the case of using the normal distribution, the cross-sections of Okgye, Hwasun, and Donghae NI before reinforcement were found to be unstable in all the limit states. On the other hand, when applying the doubly-truncated normal distribution, the cross-sections of Hwasun and Donghae NI before reinforcement were evaluated to be unstable in the repairable limit state and all the limit states, respectively. Finally, the shortcoming of the expected sliding distance as the stability criterion was investigated, and we reasonably assessed the stability of sliding of caissons by using the exceedance probability of allowable sliding distance for the caisson breakwaters in Korea.

• Journal of Radiation Protection and Research
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• v.25 no.4
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• pp.233-239
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• 2000

The sensitivity analysis of input parameters was Performed fer an ingestion dose assessment model (U. S. NRC's Regulatory Guide 1.109 model) from routine releases of radionuclides. In this study, three kinds of typical Korean foodstuffs (rice, leaff vegetables, milk) and two kinds of radionuclides $(^{l37}Cs,\;^{131}I)$ were considered. The values of input parameters were sampled using a Latin hypercube sampling technique based on Monte Carlo approach. Sensitivity indices, which represent the influence or the importance of input parameters for predictive results, were quantitatively expressed by the partial rank correlation coefficients. As the results, the ratio of the interception fraction to the yield of agricultural plants and the human consumption rate were sensitive input parameters for the considered foodstuffs and radionuclides. Additionally, in case of milk, the transfer factor of radionuclides from animal intake to milk and the daily intake rate of feedstuffs were sensitive input parameters. The weathering removal half-life and the delay time from food production to human consumption were relatively sensitive for $^{137}Cs$ and $^{131}I$ depositions, respectively.

• Journal of Korean Tunnelling and Underground Space Association
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• v.19 no.2
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• pp.143-159
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• 2017

A probabilistic fragility assessment procedure is developed in this paper to predict risks of damage arising from seismic loading to the two-cell RC box tunnel. Especially, the paper focuses on establishing a simplified methodology to derive fragility curves which are an indispensable ingredient of seismic fragility assessment. In consideration of soil-structure interaction (SSI) effect, the ground response acceleration method for buried structure (GRAMBS) is used in the proposed approach to estimate the dynamic response behavior of the structures. In addition, the damage states of tunnels are identified by conducting the pushover analyses and Latin Hypercube sampling (LHS) technique is employed to consider the uncertainties associated with design variables. To illustrate the concepts described, a numerical analysis is conducted and fragility curves are developed for a large set of artificially generated ground motions satisfying a design spectrum. The seismic fragility curves are represented by two-parameter lognormal distribution function and its two parameters, namely the median and log-standard deviation, are estimated using the maximum likelihood estimates (MLE) method.

• Nuclear Engineering and Technology
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• v.21 no.1
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• pp.18-31
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• 1989

To analyze the impact of air pollution control on electricity generation cost, a computer program was developed. POGEN calculates levelized discounted power generation cost including additional air pollution control cost for coal power plant. Pollution subprogram calculates total capital and variable costs using governing equations for flue gas control. The costs are used as additional input for levelized discounted power generation cost subprogram. Pollution output for Rue Gas Desulphurization direct cost was verified using published cost data of well experienced industrialized countries. The power generation costs for the year 2001 were estimated by POGEN for three different regulatory scenarios imposed on coal power plant, and by levelized discounted power generation cost subprogram for nuclear power. Because of uncertainty expected in input variables for future plants, sensitivity and uncertainty analysis were made to check the importance and uncertainty propagation of the input variables using Latin Hypercube Sampling and Multiple Least Square method. Most sensitive parameter for levelized discounted power generation cost is discount rate for both nuclear and coal. The control cost for flue gas alone reaches additional 9-11 mills/kWh with standard deviation less than 1.3 mills/kWh. This cost will be nearly 20% of power generation cost and 40% of one GW capacity coal power plant investment cost. With 90% confidence, the generation cost of nuclear power plant will be 32.6-51.9 mills/kWh, and for the coal power plant it will be 45.5-50.5 mills/kWh. Nuclear is favorable with 95% confidence under stringent future regulatory requirement in Korea.

• Nuclear Engineering and Technology
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• v.19 no.2
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• pp.85-98
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• 1987

To analyze the aerosol dynamics in severe accidents of LMFBR, a new computer code entitled MCAD (Multicomponent Aerosol Dynamics) has been developed. The code can treat two component aerosol system using relative collision probability of each particles as sequences of accident scenarios. Coagulation and removal mechanisms incorporating Brownian diffusion and gravitational sedimentation are included in this model. In order to see the effect of particle geometry, the code makes use of the concept of density correction factor and shape factors. The code is verified using the experimental result of NSPP-300 series and compared to other code. At present, it fits the result of experiment well and agrees to the existing code. The input variables included are very uncertain. Hence, it requires uncertainty and sensitivity analysis as a supplement to code development. In this analysis, 14 variables are selected to analyze. The input variables are compounded by experimental design method and Latin hypercube sampling. Their results are applied to Response surface method to see the degree of regression. The stepwise regression method gives an insight to which variables are significant as time elapse and their reasonable ranges. Using Monte Carlo Method to the regression model of LHS, the confidence level of the results of MCAD and their variables is improved.