• 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.

• 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.

• 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 Fluid Machinery and Systems
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• v.3 no.1
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• pp.29-38
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• 2010

This paper presents a procedure for the design optimization of a centrifugal compressor. The centrifugal compressor consists of a centrifugal impeller, vaneless diffuser and volute. And, optimization techniques based on the radial basis neural network method are used to optimize the impeller of a centrifugal compressor. The Latin-hypercube sampling of design-of-experiments is used to generate the thirty design points within design spaces. Three-dimensional Reynolds-averaged Navier-Stokes equations with the shear stress transport turbulence model are discretized by using finite volume approximations and solved on hexahedral grids to evaluate the objective function of the total-to-total pressure ratio. Four variables defining the impeller hub and shroud contours are selected as design variables in this optimization. The results of optimization show that the total-to-total pressure ratio of the optimized shape at the design flow coefficient is enhanced by 2.46% and the total-to-total pressure ratios at the off-design points are also improved significantly by the design optimization.

• Proceedings of the Korean Society of Soil and Groundwater Environment Conference
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• 2004.09a
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• pp.302-306
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• 2004

Probabilistic approaches are applied to the problem of groundwater remediation design to consider the risk of design and heterogeneity of real condition. Hydraulic conductivity fields are generated by two methods. First, the homogeneous domains which have the hydraulic conductivity with log-normal distribution are constructed by using Latin Hypercube method. Second, random fields with a certain spatial correlation are also generated. The optimal solutions represented by cumulative distribution function (CDF) of relative cost are calculated by three different manners. The one uses the homogeneous domains with the optimal design of base condition. It shows that ver)'wide range of cost and the influences of different penalty values. The other one uses the random field with same design and shows narrow range of cost. These CDF can reflect on the risk of optimal solution in a simple exampie condition and be effective in estimating the cost of groundwater remediation.

• Journal of Electrical Engineering and Technology
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• v.9 no.1
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• pp.184-189
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• 2014

This paper presents a multi-objective optimization approach to design rotor slot geometry of three-phase squirrel cage induction machine to achieve NEMA design D torque-speed (T-S) characteristics with high efficiency. The multi-objective Particle Swarm Optimization (MOPSO) algorithm combined with the adaptive response surface method and Latin hypercube sampling strategy is applied to obtain the Pareto optimal designs. In order to demonstrate the validity of the suggested optimal algorithm, an application to rotor slot design of three-phase induction motor is presented.

• The KSFM Journal of Fluid Machinery
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• v.12 no.4
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• pp.44-53
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• 2009

Numerical design optimization of a fan-shaped hole for film-cooling has been carried out to improve film-cooling effectiveness by combining a three-dimensional Reynolds-averaged Navier-Stokes analysis with the radial basis neural network method, a well known surrogate modeling technique for optimization. The injection angle of hole, lateral expansion angle of hole and ratio of length-to-diameter of the hole are chosen as design variables and spatially averaged film-cooling effectiveness is considered as an objective function which is to be maximized. Twenty training points are obtained by Latin Hypercube sampling for three design variables. Sequential quadratic programming is used to search for the optimal point from the constructed surrogate. The film-cooling effectiveness has been successfully improved by the optimization with increased value of all design variables as compared to the reference geometry.

• The KSFM Journal of Fluid Machinery
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• v.16 no.2
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• pp.35-41
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• 2013

Inlet part of a printed circuit heat exchanger has been optimized by using three-dimensional Reynolds-Averaged Navier-Stokes analysis and surrogate modeling techniques. Kriging model has been used as the surrogate model. The objective function for the optimization has been defined as a linear combination of uniformity of mass flow rate and the pressure loss with a weighting factor. For the optimization, the angle of the inlet plenum wall, radius of curvature of the inlet plenum wall, and width of the inlet pipes have been selected as design variables. Twenty six design points are obtained by Latin Hypercube Sampling in design space. Through the optimization, considerable improvement in the objective function has been obtained in comparison with the reference design of PCHE.

• Journal of the Korean Society of Marine Environment & Safety
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• v.27 no.1
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• pp.161-171
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• 2021

This paper presents the findings of a comparative study on minimum weight design and sensitivity evaluation using different experimental design methods for the structural design of an active-type deck support frame (DSF) developed for the float-over installation of an of shore plant topside. The thickness sizing variables of the structural members of a passive-type DSF were considered the design factors, and the output responses were defined using the weight and strength performances. The design of the experimental methods applied in the comparative study of the minimum weight design and the sensitivity evaluation were the orthogonal array design, Box- Behnken design, and Latin hypercube design. A response surface method was generated for each design of the experiment to evaluate the approximation performance of the design space exploration according to the experimental design, and the accuracy characteristics of the approximation were reviewed. Regarding the minimum weight design, the design results, such as numerical costs and weight minimization, of the experimental design for the best design case, were evaluated. The Box- Behnken design method showed the optimum design results for the structural design of the passive-type DSF.

• International Journal of Fluid Machinery and Systems
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• v.2 no.1
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• pp.1-12
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• 2009

Surrogate modeling is applied to a compressor blade shape optimization to modify its stacking line and thickness to enhance adiabatic efficiency and total pressure ratio. Six design variables are defined by parametric curves and three objectives; efficiency, total pressure and a combined objective of efficiency and total pressure are considered to enhance the performance of compressor blade. Latin hypercube sampling of design of experiments is used to generate 55 designs within design space constituted by the lower and upper limits of variables. Optimum designs are found by formulating a PRESS (predicted error sum of squares) based averaging (PBA) surrogate model with the help of a gradient based optimization algorithm. The optimum designs using the current variables show that, to optimize the performance of turbomachinery blade, the adiabatic efficiency objective is improved substantially while total pressure ratio objective is increased a very small amount. The multi-objective optimization shows that the efficiency can be increased with the less compensation of total pressure reduction or both objectives can be increased simultaneously.