• Nuclear Engineering and Technology
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• v.26 no.1
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• pp.78-89
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• 1994

The radionuclide release rates from the engineered barrier composed of concrete structure and clay-based backfill in a low and intermediate level waste repository were assessed. Four types of release pathway were considered, and the contribution of each pathway to the total release were analyzed. To quantify the effect of uncertainties of input parameter values on the assessment of radionuclide release rates, the Latin Hypercube sampling method was used, and the resulting release rate distribution were determined through a goodness-of-fit test. Finally, the ranges of maxi-mum release rates ore estimated statistically with a confidence level of 95%.

• Nuclear Engineering and Technology
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• v.55 no.7
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• pp.2395-2406
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• 2023

To study the influence of parameter uncertainty in small pressurized water reactor (SPWR) once-through steam generator (OTSG), the nonlinear mathematical model of the SPWR is firstly established. Including the reactor core model, the OTSG model and the pressurizer model. Secondly, a control strategy that both the reactor core coolant average temperature and the secondary-side outlet pressure of the OTSG are constant is adopted. Then, the uncertainty quantification method is established based on Latin hypercube sampling and statistical method. On this basis, the quantitative platform for parameter uncertainty of the OTSG is developed. Finally, taking the uncertainty in primary-side flowrate of the OTSG as an example, the platform application work is carried out under the variable load in SPWR and step disturbance of secondary-side flowrate of the OTSG. The results show that the maximum uncertainty in the critical output parameters is acceptable for SPWR.

• Journal of Korea Water Resources Association
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• v.38 no.4 s.153
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• pp.281-292
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• 2005

The water balance analysis for the long-term water resources plan is a simple calculation that compares water demands with possible water supplies. For a watershed being considered the reports on the performance of the water balance analysis, however, have shown inconsistent results and thus have not earned credibility due to the uncertainty of the data acquired and models used. In this research, uncertainties in the water scarcity estimate were assessed through probability representation based on the Monte Carlo simulation using Latin Hypercube Sampling (LHS). The natural flow, municipal demand, industrial demand, agricultural demand, and return flow rate were selected as representative input variables for the water balance analysis, and their distributions were set based on the linear regression and the entropy theory. The statistical properties of the output variable samples were analyzed in comparison with a deterministic estimate of the water scarcity of an existing study. Application of LHS to three sub-basins of the Geum river basin showed the deterministic estimate could be overestimated or underestimated. The sensitivity analysis as well as the uncertainty analysis found that the return flow rate of the agricultural water is the most uncertain but is rarely sensitive to the output of the water balance analysis.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.36 no.9
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• pp.1065-1071
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• 2012

Wind energy is becoming one of the most preferable alternatives to conventional sources of electric power that rely on fossil fuels. For stable electric power generation, constant rotating speed control of a wind turbine is performed through pitch control and stall control of the turbine blades. Recently, variable pitch control has been implemented in modern wind turbines to harvest more energy at variable wind speeds that are even lower than the rated one. Although wind turbine pitch controllers are currently optimized using a step response via the Ziegler-Nichols auto-tuning process, this approach does not satisfy the requirements of variable pitch control. In this study, the variable pitch controller was optimized by a genetic algorithm using a neural network model that was constructed by the Latin Hypercube sampling method to improve the Ziegler-Nichols auto-tuning process. The optimized solution shows that the root mean square error, rise time, and settle time are respectively improved by more than 7.64%, 15.8%, and 15.3% compared with the corresponding initial solutions obtained by the Ziegler-Nichols auto-tuning process.

• Proceedings of the Korea Concrete Institute Conference
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• 2006.05a
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• pp.462-465
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• 2006

This paper is aimed at proposing the sampling method to reduce variance of statistical parameters in uncertainty analysis of concrete structures. The proposed method is a modification of Latin Hypercube sampling method. This uses specially modified tables of random permutations of rank number. Also, the Spearman coefficient is used to make modified tables. Numerical analysis is carried out to predict the uncertainty of axial shortening in prestressed concrete bridge. The numerical results show that the method is efficient for uncertainty analysis of complex structural system such as prestressed concrete bridges.

• Proceedings of the Korean Reliability Society Conference
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• 2000.04a
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• pp.239-246
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• 2000

This paper presents a new dynamic approach for assessing feasibility associated with the implementation of accident management strategies by the operators. This approach includes the combined use of both the concept of reliability physics and a dynamic event tree generation scheme. The reliability physics is based on the concept of a comparison between two competing variables, i.e., the requirement and the achievement parameter, while the dynamic event tree generation scheme on the continuous generation of the possible event sequences at every branch point up to the desired solution. This approach is applied to a cavity flooding strategy in a reference plant, which is to supply water into the reactor cavity using emergency fire systems in the station blackout sequence. The MAAP code and Latin Hypercube sampling technique are used to determine the uncertainty of the requirement parameter. It has been demonstrated that this combined methodology may contribute to assessing the success likelihood of the operator actions required during accidents and therefore to developing the accident management procedures.

• Nuclear Engineering and Technology
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• v.27 no.3
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• pp.292-300
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• 1995

This paper present a new dynamic HRA (Human Reliability Analysis) method and its application for Quantifying the human error probabilities in implementing an accident management action. For comparisons of current HRA methods with the new method, the characteristics of THERP, HCR, and SLIM-MAUD, which are most frequently used methods in PSAs, are discussed. The action associated with the implementation of the cavity flooding during a station blackout sequence is considered for its application. This method is based on the concepts of the quantified correlation between the performance requirement and performance achievement. The MAAP 3.0B code and Latin Hypercube sampling technique are used to determine the uncertainty of the performance achievement parameter. Meanwhile, the value of the performance requirement parameter is obtained from interviews. Based on these stochastic distributions obtained, human error probabilities are calculated with respect to the various means and variances of the timings. It is shown that this method is very flexible in that it can be applied to any kind of the operator actions, including the actions associated with the implementation of accident management strategies.

• Journal of the Korea institute for structural maintenance and inspection
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• v.19 no.6
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• pp.72-79
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• 2015

An efficient and practical reliability evaluation method is proposed for the coastal structures in this paper. It is capable of evaluating reliability of real complicated coastal structures considering uncertainties in various sources of design parameters, such as wave and current loads, resistance-related design variables including Young's modulus and compressive strength of the reinforced concrete, soil parameters, and boundary conditions. It is developed by intelligently integrating the Latin Hypercube sampling (LHS), Monte Carlo simulation (MCS) and the finite element method (FEM). The LHS-based MCS is used to significantly reduce the computational effort by limiting the number of simulation cycles required for the reliability evaluation. The applicability and efficiency of the proposed method were verified using a caisson-type breakwater structure in the numerical example.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.17 no.5
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• pp.95-100
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• 2008

An active micro-mixer, which is composed of an oscillating micro-stirrer in the micro-channel to provide effective mixing was optimized. The effects of molecular diffusion and disturbance by the stirrer were considered with regard to two types of mixer models: the simple straight micro-channel and micro-channel with an oscillating stirrer. Two types of mixer models were studied by analyzing mixing behaviors such as their interaction after the stirrer. The mixing was calculated by Lattice Boltzmann methods using the D2Q9 model. In this study, the time-averaged mixing index formula was used to estimate the mixing performance of time-dependent flow. The mixing indices of the two models were compared. From the results, it was found that the mixer with an oscillating stirrer was much more enhanced and stabilized. Therefore, an approximate optimization of an active micro-mixer with an oscillating stirrer was performed using Kriging method with OLHD(Optimal Latin Hypercube Design) in order to determine the optimal design variables. The design parameters were established as the frequency, the length and the angle of the stirrer. The optimal values were obtained as 1.0346, 0.66D and $\pm45^{\circ}$, respectively. It was found that the mixing index of the optimal design increased by 88.72% compared with that of the original design.

• Earthquakes and Structures
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• v.10 no.1
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• pp.141-161
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• 2016

In seismic fragility and risk analysis, the definition of structural limit state (LS) capacities is of crucial importance. Traditionally, LS capacities are defined according to design code provisions or using deterministic pushover analysis without considering the inherent randomness of structural parameters. To assess the effects of structural randomness on LS capacities, ten structural parameters that include material strengths and gravity loads are considered as random variables, and a probabilistic pushover method based on a correlation-controlled Latin hypercube sampling technique is used to estimate the uncertainties in LS capacities for four typical reinforced concrete frame buildings. A series of ten LSs are identified from the pushover curves based on the design-code-given thresholds and the available damage-controlled criteria. The obtained LS capacities are further represented by a lognormal model with the median $m_C$ and the dispersion ${\beta}_C$. The results show that structural uncertainties have limited influence on $m_C$ for the LSs other than that near collapse. The commonly used assumption of ${\beta}_C$ between 0.25 and 0.30 overestimates the uncertainties in LS capacities for each individual building, but they are suitable for a building group with moderate damages. A low uncertainty as ${\beta}_C=0.1{\sim}0.15$ is adequate for the LSs associated with slight damages of structures, while a large uncertainty as ${\beta}_C=0.40{\sim}0.45$ is suggested for the LSs near collapse.