• Computational Structural Engineering
• /
• v.10 no.4
• /
• pp.143-154
• /
• 1997

Astochastic finite element model which reflects both the effect of discontinuities and the uncertainty of material properties in underground rock mass has been developed. Latin Hypercube Sampling technique has been mobilized and compared with the Monte Carlo simulation method. To consider the effect of discontinuities, the joint finite element model, which is known to be suitable to explain faults, cleavage, things of that nature, has been used in this study. To reflect the uncertainty of material properties, multi-random variables are assumed as the joint normal stiffness and the joint shear stiffness, which could be simulated in terms of normal distribution. The developed computer program in this study has been verified by practical example and has been applied to analyze the circular cavern with discontinuous rock mass.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
• /
• 2005.07a
• /
• pp.240-241
• /
• 2005

In this paper, Latin Hypercube Sampling based the neural network model for the electrical characteristics of $HfO_2$ thin films grown by metal organic molecular beam epitaxy was investigated. The accumulation capacitance and the hysteresis index are extracted to be the main responses to examine the characteristics of $HfO_2$ thin films. X-ray diffraction was used to analyze the characteristic variation for the different process conditions. The initial weights and biases are selected by Latin Hypercube Sampling method. This modeling methodology can allow us to optimize the process recipes and improve the manufacturability.

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

• Korean Journal of Computational Design and Engineering
• /
• v.16 no.6
• /
• pp.397-406
• /
• 2011

Since tolerance allocation in a mobile phone camera manufacturing process greatly affects production cost and reliability of optical performance, a systematic design methodology for allocating optimal tolerances is required. In this study, we proposed the tolerance optimization procedure for determining tolerances that minimize production cost while satisfying the reliability constraints on important optical performance indices. We employed Latin hypercube sampling for evaluating the reliabilities of optical performance and a function-based sequential approximate optimization technique that can reduce computational burden and well handle numerical noise in the tolerance optimization process. Using the suggested tolerance optimization approach, the optimal production cost was decreased by 30.3 % compared to the initial cost while satisfying the two constraints on the reliabilities of optical performance.

• The KSFM Journal of Fluid Machinery
• /
• v.12 no.3
• /
• pp.7-12
• /
• 2009

In this paper, shape optimization based on three-dimensional flow analysis has been performed for impeller design of centrifugal compressor. To evaluate the objective function of an isentropic efficiency, Reynolds-averaged Navier-Stokes equations are solved with SST (Shear Stress Transport) turbulence model. The governing equations are discretized by finite volume approximations. The optimization techniques based on the radial basis neural network method are used for the optimization. Latin hypercube sampling as design of experiments is used to generate thirty design points within design space. Sequential quadratic programming is used to search the optimal point based on the radial basis neural network model. Four geometrical variables concerning impeller shape are selected as design variables. The results show that the isentropic efficiency is enhanced effectively from the shape optimization by the radial basis neural network method.

• Smart Structures and Systems
• /
• v.12 no.6
• /
• pp.641-659
• /
• 2013

In this paper is studied the influence of the uncertain mass distribution over the floors on the choice of the optimal parameters of a hybrid control system for tall buildings subjected to wind load. In particular, an optimization procedure is developed for the robust design of a hybrid control system that is based on an enhanced Monte Carlo simulation technique and the genetic algorithm. The large computational effort inherent in the use of a MC-based procedure is reduced by the employment of the Latin Hypercube Sampling. With reference to a tall building modeled as a multi degrees of freedom system, several numerical analyses are carried out varying the parameters influencing the floors' masses, like the coefficient of variation of the distribution and the correlation between the floors' masses. The procedure allows to obtain optimal designs of the control system that are robust with respect to the uncertainties on the distribution of the dead and live loads.

• Water Engineering Research
• /
• v.3 no.4
• /
• pp.215-234
• /
• 2002

Uncertainty in dam breach flood routing results was analyzed in order to provide the basis fer the investigation of their effects on the flood damage assessments and dam safety risk assessments. The Monte Carlo simulation based on Latin Hypercube Sampling technique was used to generate random values for two uncertain input parameters (i.e., dam breach parameters and Manning's n roughness coefficients) of a dam breach flood routing analysis model. The flood routing results without considering the uncertainty in two input parameters were compared with those with considering the uncertainty. This paper showed that dam breach flood routing results heavily depend on the two uncertain input parameters. This study indicated that the flood damage assessments in the downstream areas can be critical if uncertainty in dam breach flood routing results are considered in a reasonable manner.

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

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

• Journal of the Korean Society of Manufacturing Process Engineers
• /
• v.14 no.3
• /
• pp.85-91
• /
• 2015

The press-fit shaft is an important part used in automobiles, vessels, and trains. This study proposes an optimized design method to reduce damage that may occur in the press-fitted shaft by modifying the shape of the boss step of the press-fitted shaft. To reduce the time and cost of running the optimized design method, an approximate design optimization is applied and an optimized algorithm is generated using a genetic algorithm that is widely used in engineering fields and an approximate model using a response surface method. The planned experiments for the data that are needed to generate the approximate model use a central composite design (CCD) and Latin hypercube sampling (LHS), and the results of the approximate optimization using the above two design of experiments are to be compared.