• 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.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.

• 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.

• 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.

• International Journal of Reliability and Applications
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• v.2 no.1
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• pp.37-48
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• 2001

This paper presents a new dynamic human reliability analysis method and its application for quantifying the human error probabilities in implementing management action. For comparisons of current HRA methods with the new method, the characteristics of THERP, HCR, and SLIM-MAUD, which are most frequency used method in PSAs, are discussed. The action associated with 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 obtained, human error probabilities are calculated with respect to the various means and variances of the things. 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.

• Proceedings of the KIEE Conference
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• 2008.04c
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• pp.65-67
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• 2008

An adaptive response surface method with Latin Hypercube sampling strategy is employed to optimize a magnet pole shape of large scale BLDC motor to minimize the cogging torque. The proposed algorithm consists of the multi-objective Pareto optimization and ($1+{\lambda}$) evolution strategy to find the global optimal points with relatively fewer sampling data. In the adaptive RSM, an adaptive sampling point insertion method is developed utilizing the design sensitivities computed by using finite element method to set a reasonable response surface with a relatively small number of sampling points. The developed algorithm is applied to the shape optimization of PM poles for 6MW BLDC motor.

• Proceedings of the KIEE Conference
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• 2008.07a
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• pp.701-702
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• 2008

An adaptive response surface method with Latin Hypercube sampling strategy is employed to optimize a magnet pole shape of large scale BLDC motor to minimize the cogging torque. The proposed algorithm consists of the multi-objective Pareto optimization and (1+${\lambda}$) evolution strategy to find the global optimal points with relatively fewer sampling data. In the adaptive RSM, an adaptive sampling point insertion method is developed utilizing the design sensitivities computed by using finite element method to get a reasonable response surface with a relatively small number of sampling points. The developed algorithm is applied to the shape optimization of PM poles for 6 MW BLDC motor, and the cogging torque is reduced to 19% of the initial one.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.19 no.6
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• pp.55-60
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• 2020

The injection molding technique is a processing method widely used for the production of plastic parts. In this study, the gate position, gate size, packing time, and melt temperature were optimized to minimize both the stress and deformation that occur during the injection molding process of medical suction device components. We used a central composite design and Latin hypercube sampling to acquire the data and adopted the response surface method as an approximation method. The efficiency of the optimization of the injection molding problem was determined by comparing the results of a genetic algorithm, sequential quadratic programming, and a non-dominant classification genetic algorithm.