• 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.

• Journal of the Korea institute for structural maintenance and inspection
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• v.14 no.5
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• pp.119-127
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• 2010

This paper provides a new approach for durability prediction of reinforced concrete structures exposed to carbonation. In this method, the prediction can be updated successively by a Bayes' theorem when additional data are available. The stochastic properties of model parameters are explicitly taken into account in the model. To simplify the procedure of the model, the probability of the durability limit is determined based on the samples obtained from the Latin Hypercube Sampling(LHS) technique. The new method may be very useful in design of important concrete structures and help to predict the remaining service life of existing concrete structures which have been monitored. For using the new method, in which the prior distribution is developed to represent the uncertainties of the carbonation velocity using data of concrete structures(3700 specimens) in Korea and the likelihood function is used to monitor in-situ data. The posterior distribution is obtained by combining a prior distribution and a likelihood function. Efficiency of the LHS technique for simulation was confirmed through a comparison between the LHS and the Monte Calro Simulation(MCS) technique.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.18 no.6
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• pp.262-274
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• 2019

The purpose of this study is to estimate the minimum proportion of probe vehicles for obtaining expressway traffic information using VISSIM, a micro traffic simulation model, between Yongin IC and Yangji IC on Yeongdong Expressway. 7,200 scenarios were created for the experiment, and 40 scenarios were adopted using the Latin hypercube sampling method because it was difficult to perform all the scenarios through experiments. The reliability of the experiment was improved by adding a situation when the general situation and the accident situation exist. In the experiments, the average travel time of probe vehicles at different market penetration rates were compared with the average travel time of the entire vehicles. As a result, the minimum market penetration rate of probe vehicles for obtaining expressway traffic information was found to be 45%. In addition, it is estimated that 25% market penetration rate of probe vehicle can meet 70% of traffic situations in accident scenario.

• 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.

• 한국전산유체공학회:학술대회논문집
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• 2008.03a
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• pp.229-232
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• 2008

This study presents a numerical procedure to optimize the shape of stepped circular pin-fins to enhance turbulent heat transfer. The KRG method is used as an optimization technique with Reynolds-averaged Navier-Stokes analysis of fluid flow and heat transfer with shear stress transport turbulent model. The objective function is defined as a linear combination of heat transfer and friction loss related terms with a weighting factor. Ten training points are obtained by Latin Hypercube Sampling for two design variables. Optimum shape has been successfully obtained with the increased objective function.

• 한국전산유체공학회:학술대회논문집
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• 2008.10a
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• pp.229-232
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• 2008

This study presents a numerical procedure to optimize the shape of stepped circular pin-fins to enhance turbulent heat transfer. The KRG method is used as an optimization technique with Reynolds-averaged Navier-Stokes analysis of fluid flow and heat transfer with shear stress transport turbulent model. The objective function is defined as a linear combination of heat transfer and friction loss related terms with a weighting factor. Ten training points are obtained by Latin Hypercube Sampling for two design variables. Optimum shape has been successfully obtained with the increased objective function.

• The KSFM Journal of Fluid Machinery
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• v.11 no.1
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• pp.52-60
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• 2008

Staggered dimples printed on opposite walls of an internal cooling channel are formulated numerically and optimized to enhance heat transfer performance. Nusselt number and friction factor based objectives are considered and a weighted average surrogate model is used to approximate the data generated by numerical simulation. The dimpled channel shape is defined by three geometric design variables, and the design point within design space are selected using Latin hypercube sampling. A weighted-sum method for multi-objective optimization is applied to integrate multiple objectives into a single objective. By the optimization, the objective function value is improved largely and heat transfer rate is increase much higher than pressure loss increase due to shape deformation. Channel with vertically non-symmetric optimum dimples is tested and found that the best appears if dimples on opposite wall are displaced by one quarter of dimple spacing.

• The KSFM Journal of Fluid Machinery
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• v.10 no.3 s.42
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• pp.39-46
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• 2007

This study presents a numerical procedure to optimize the shape of staggered dimple surface to enhance turbulent heat transfer in a rectangular channel. The RBNN method is used as an optimization technique with Reynolds-averaged Navier-Stokes analysis of fluid flow and heat transfer with shear stress transport (SST) turbulence model. The dimple depth-to-dimple print diameter (d/D), channel height-to-dimple print diameter ratio (H/D), and dimple print diameter-to-pitch ratio (D/S) are chosen as design variables. The objective function is defined as a linear combination of heat transfer related term and friction loss related term with a weighting factor. Latin Hypercube Sampling (LHS) is used to determine the training points as a mean of the design of experiment. The optimum shape shows remarkable performance in comparison with a reference shape.

• The KSFM Journal of Fluid Machinery
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• v.11 no.2
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• pp.46-54
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• 2008

This study presents a numerical optimization to optimize an axial flow fan blade to increase the efficiency. The radial basis neural network is used as an optimization method with the numerical analysis by Reynolds-averaged Navier-Stokes equations using SST model as turbulence closure. Four design variables related to airfoil maximum camber, maximum camber location, leading edge radius and trailing edge radius, respectively, are selected, and efficiency is considered as objective function which is to be maximized. Thirty designs are evaluated to get the objective function values of each design used to train the neural network. Optimum shape shows the efficiency increased by 1.0%.

• Journal of the Korea Society for Simulation
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• v.32 no.3
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• pp.67-74
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• 2023

A semiconductor fabrication facility(FAB) is one of the most capital-intensive and large-scale manufacturing systems which operate under complex and uncertain constraints through hundreds of fabrication steps. To improve fab performance with intuitive scheduling, practitioners have used weighted-sum scheduling. Since the determination of weights in the scheduling significantly affects fab performance, they often rely on simulation-based decision making for obtaining optimal weights. However, a large-scale and high-fidelity simulation generally is time-intensive to evaluate with an exhaustive search. In this study, we investigated three sampling methods (i.e., Optimal latin hypercube sampling(OLHS), Genetic algorithm(GA), and Decision tree based sequential search(DSS)) for the optimization. Our simulation experiments demonstrate that: (1) three methods outperform greedy heuristics in performance metrics; (2) GA and DSS can be promising tools to accelerate the decision-making process.