• Journal of the Korean Society for Railway
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• v.12 no.2
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• pp.291-301
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• 2009

This paper presents the optimal design and analysis method of the train overhaul maintenance facility based on the simulation. Because the train is composed of a coach or more, we design the simulation model after analyzing the operation of train into train, coach, coach's body parts and wheel parts and soon. In simulation analysis, we consider the critical (dependent) factors and design (independent) parameters for the selection of alternatives and optimal design. Therefore, Multi Criteria Decision Making (MCDM) is proposed for the selection of alternatives and optimal method in order to find the optimal design factors. The case study for the above approach is used for the electronic locomotive overhaul maintenance facility. This paper provides a comprehensive framework for the train overhaul maintenance facility design using the simulation, MCDM and optimal methods. Therefore, the method developed for this research can be adopted for other enhancements in different but comparable situation.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.164-167
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• 1996

This paper presents an off-line P.I.D parameter estimation method during normal operation in power plant. The process parameters are estimated using the recursive least square method. The controller parameters are estimated on the basis of desired characteristics of the dynamic model of the closed-loop control.

• Proceedings of the KWS Conference
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• 2004.05a
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• pp.39-41
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• 2004

This study is to optimize the condition of pulse parameters using the response surface method in micro pulse TIG welding of pleated type metallic filter. The input parameters used were pulse current, base current, pulse duty, frequency and welding speed and the hydraulic pressure was used as the output parameter. The central composite design was designed using second order regression model, As the results, the optimal welding condition to manufacture the pleated type metallic filter was obtained.

• Journal of computational fluids engineering
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• v.21 no.3
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• pp.39-47
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• 2016

We conducted a multi-stage optimization to secure the desired performance of a centrifugal fan for home appliance in an early stage of product development. In optimization, the static pressure at the outlet of the fan is chosen as an objective function that is to be maximized, providing the required flow rate at the operating point of the fan. The optimization procedure begins with parameters for an initial baseline fan design. The baseline design is optimized by using a commercial optimization package. Accordingly, the corresponding blade models with a set of geometrical parameters are generated. Flow through a fan is simulated by solving the Reynolds-averaged Navier-Stokes equations. A multi-stage optimization scheme is employed to determine the family of optimum values for the parameters, leading to the pressure increase at the outlet of the fan. To validate the numerically obtained optimal design parameters, we fabricated the three types of fans using rapid prototyping and assessed the performance using a fan tester. Experimental results show that the design parameters at each stage satisfy the goal of optimization. The multi-stage optimization process turned out to be a useful tool in the development of a centrifugal fan.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.39 no.8
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• pp.813-821
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• 2015

In this paper, we analyzed the problems of a batch-type sterilizer and design a continuous sterilizer to control the temperature deviation. The temperature deviation is analyzed with respect to design parameters such as a nozzle diameter, hole diameter, and nozzle length. The significant temperature parameters are optimized using the response surface methodology. An experimental apparatus is developed using the optimized design parameters. Using a field test, we show that the target temperature is obtained in about 7.3 minutes and the temperature deviation is improved about $0.84^{\circ}C$. The optimized parameters from the test are equal to the analytical parameters.

• Advances in nano research
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• v.10 no.1
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• pp.59-69
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• 2021

Electrospinning is a cost-effective and versatile method for producing submicron fibers. Although this method is relatively simple, at the theoretical level the interactions between process parameters and their influence on the fiber morphology are not yet fully understood. In this paper, the aim was finding optimal electrospinning parameters in order to obtain the smallest fiber diameter by using Taguchi's methodology. The nanofibers produced by electrospinning a solution of Thermoplastic Polyurethane (TPU) in Dimethylformamide (DMF). Polymer concentration and process parameters were considered as the effective factors. Taguchi's L9 orthogonal design (4 parameters, 3 levels) was applied to the experiential design. Optimal electrospinning conditions were determined using the signal-to-noise (S/N) ratio with Minitab 17 software. The morphology of the nanofibers was studied by a Scanning Electron Microscope (SEM). Thereafter, a tensile tester machine was used to assess mechanical properties of nanofibrous scaffolds. The analysis of DoE experiments showed that TPU concentration was the most significant parameter. An optimum combination to reach smallest diameters was yielded at 12 wt% polymer concentration, 16 kV of the supply voltage, 0.1 ml/h feed rate and 15 cm tip-to-distance. An empirical model was extracted and verified using confirmation test. The average diameter of nanofibers at the optimum conditions was in the range of 242.10 to 257.92 nm at a confidence level 95% which was in close agreement with the predicted value by the Taguchi technique. Also, the mechanical properties increased with decreasing fibers diameter. This study demonstrated Taguchi method was successfully applied to the optimization of electrospinning conditions for TPU nanofibers and the presented scaffold can mimic the structure of Extracellular Matrix (ECM).

• Journal of Biomedical Engineering Research
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• v.30 no.6
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• pp.482-488
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• 2009

Conventional devices for transanal endoscopic microsurgery that are currently used clinically for intestine or rectal cancer patients have 40 mm external diameter of rectoscope tube - induces anal damage and long-term postoperative pain for small-sized patients. In this paper, we designed rectum and rectoscope models and calculated the changing trends of operation area of the surgical tools in accordance with the step-by-step variation of design parameters of the rectoscope tube - external diameter, axial length, and distal angle. Using the results of computer simulation, we suggested an optimal set of design parameters that minimizes external diameter of the rectoscope tube and at the same time, maintains similar operation area of the surgical tools compared with commercialized devices (${\geq}\;4274.7mm^2$). The results of the simulation showed that the optimal design parameters were 35 mm external diameter, 100 mm axial length, and $45^{\circ}$ distal angle of the rectoscope tube. This result can be applied to the development of endoscopic microsurgery device that can minimize side effects to the intestine or rectal cancer patients.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.53 no.8
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• pp.561-570
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• 2004

In this paper, new architectures and comprehensive design methodologies of Genetic Algorithms(GAs) based Genetically optimized Neurofuzzy Networks(GoNFN) are introduced, and a series of numeric experiments are carried out. The proposed GoNFN is based on the rule-based Neurofuzzy Networks(NFN) with the extended structure of the premise and the consequence parts of fuzzy rules being formed within the networks. The premise part of the fuzzy rules are designed by using space partitioning in terms of fuzzy sets defined in individual variables. In the consequence part of the fuzzy rules, three different forms of the regression polynomials such as constant, linear and quadratic are taken into consideration. The structure and parameters of the proposed GoNFN are optimized by GAs. GAs being a global optimization technique determines optimal parameters in a vast search space. But it cannot effectively avoid a large amount of time-consuming iteration because GAs finds optimal parameters by using a given space. To alleviate the problems, the dynamic search-based GAs is introduced to lead to rapidly optimal convergence over a limited region or a boundary condition. In a nutshell, the objective of this study is to develop a general design methodology o GAs-based GoNFN modeling, come up a logic-based structure of such model and propose a comprehensive evolutionary development environment in which the optimization of the model can be efficiently carried out both at the structural as well as parametric level for overall optimization by utilizing the separate or consecutive tuning technology. To evaluate the performance of the proposed GoNFN, the models are experimented with the use of several representative numerical examples.

• The Journal of the Acoustical Society of Korea
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• v.40 no.4
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• pp.347-358
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• 2021

The objective of this study is to optimize the shape of doubly curved surface where a conformal array is equipped. That surface is modeled with a double-ellipsoid solid controlled by four parameters. By analyzing the performance of the conformal array beams with the beam pattern synthesis, two design parameters are determined. Then, we define the weighted object function which is formulated as the sum of sharp indexes for directivity index, the elevation resolution, and the bearing resolution. The direct calculation on all grids is used to evaluate the weighted object function and seek the optimal value of two design parameters when the weightings are given. In the simulation, four kinds of weighting cases are respectively applied to evaluate the weighted object function. The optimal shapes of double-ellipsoid solid are shown in each case. Especially, when the uniform weightings are used, the double-ellipsoid solid with more smooth surface is obtained.

• Structural Engineering and Mechanics
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• v.86 no.3
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• pp.337-348
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• 2023

The optimization of reinforced concrete (RC) cantilever retaining walls is a complex problem and requires the use of advanced techniques like metaheuristic algorithms. For this purpose, an optimization model must first be developed, which involves mathematical complications, multidisciplinary knowledge, and programming skills. This task has proven to be too arduous and has halted the mainstream acceptance of optimization. Therefore, it is necessary to unravel the complications of optimization into an easily applicable form. Currently, the most commonly used method for designing retaining walls is by following the proportioning limits provided by the ACI handbook. However, these limits, derived manually, are not verified by any optimization technique. There is a need to validate or modify these limits, using optimization algorithms to consider them as optimal limits. Therefore, this study aims to propose updated proportioning limits for the economical design of a RC cantilever retaining wall through a comprehensive parametric investigation using the genetic algorithm (GA). Multiple simulations are run to examine various design parameters, and trends are drawn to determine effective ranges. The optimal limits are derived for 5 geometric and 3 reinforcement variables and validated by comparison with their predecessor, ACI's preliminary proportioning limits. The results indicate close proximity between the optimized and code-provided ranges; however, the use of optimal limits can lead to additional cost optimization. Modifications to achieve further optimization are also discussed. Besides the geometric variables, other design parameters not covered by the ACI building code, like reinforcement ratios, bar diameters, and material strengths, and their effects on cost optimization, are also discussed. The findings of this investigation can be used by experienced engineers to refine their designs, without delving into the complexities of optimization.