• Steel and Composite Structures
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• v.16 no.6
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• pp.681-701
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• 2014

The main aim of this study is to propose an efficient meta-heuristic algorithm for topology optimization of geometrically nonlinear single layer domes by serially integration of computational advantages of firefly algorithm (FA) and particle swarm optimization (PSO). During the optimization process, the optimum number of rings, the optimum height of crown and tubular section of the member groups are determined considering geometric nonlinear behaviour of the domes. In the proposed algorithm, termed as FA-PSO, in the first stage an optimization process is accomplished using FA to explore the design space then, in the second stage, a local search is performed using PSO around the best solution found by FA. The optimum designs obtained by the proposed algorithm are compared with those reported in the literature and it is demonstrated that the FA-PSO converges to better solutions spending less computational cost emphasizing on the efficiency of the proposed algorithm.

• Korean Journal of Computational Design and Engineering
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• v.15 no.5
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• pp.325-332
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• 2010

A lens system for mobile phone cameras is comprised of various lenses and designed so as to satisfy design requirements for responses such as a modular transfer function (MTF). However, it is difficult to manufacture and assemble camera modules to maintain the same performance compared with the designed camera modules, because of uncertainty. We should always design a lens system by considering uncertainty that can be caused by errors in the manufacturing and assembly process of mobile phone cameras. The robust optimization offers tools of making robust decisions with the consideration of design parameters, uncontrollable parameters, and the variance of the system. Using an efficient reliability analysis method and an optimization algorithm, we obtained robust optimization results that maximize the mean of MTF and minimize the standard deviation and proposed a new robust design process for a lens system.

• Korean Journal of Computational Design and Engineering
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• v.20 no.4
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• pp.412-419
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• 2015

This paper presents the results of the structural analysis and the optimization of a 6,500 m manned submersible. Nonlinear structural analysis for imperfect spheres with the maximum allowable out-of-roundness(OOR) was performed to calculate the thickness of the pressure hull. Dimensions of viewports were determined according to ASME PVHO standard. The design optimization of the spherical hull with openings was divided into two steps - the optimization of the detailed shape of the viewport reinforcements and the optimization of the viewport location in the spherical pressure hull.

• Journal of computational fluids engineering
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• v.10 no.2
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• pp.54-59
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• 2005

For the improvement of aerodynamic performance of the turbine blade in a turbopump for the liquid rocket engine, the optimization of turbine profile shape has been studied. The turbine in a turbopump in this study is a partial admission of impulse type, which has twelve nozzles and supersonic inflow. Due to the separated nozzles and supersonic expansion, the flow field becomes complicate and shows oblique shocks and flow separation. To increase the blade power, redesign ol the blade shape using CFD and optimization methods was attempted. The turbine cascade shape was represented by four design parameters. For optimization, a genetic algorithm based upon non-gradient search hue been selected as an optimizer. As a result, the final blade has about 4 percent more blade power than the initial shape.

• Journal of computational fluids engineering
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• v.14 no.1
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• pp.53-61
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• 2009

The objective of this research is to design blade planform to reduce high speed impulsive(HSI) noise from a non-lifting helicopter rotor using CFD method and optimization techniques. As for the aero-acoustic analysis, CFD technique for aerodynamic analysis and Kirchhoff's method for the acoustic analysis were used. As for the optimization method, Kriging-based genetic algorithm(GA) model as a high-fidelity optimization method was chosen. Design variables and constraints are determined for arbitrary blade planform. The result shows that the optimized blade planform with high swept-back and taper ratio can reduce HSI noise by suppressing generation of the strong shock wave on blade surface and propagation of the noise to the farfield flow region.

• Journal of the Korean Society for Precision Engineering
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• v.16 no.8
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• pp.53-58
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• 1999

Although the design of magnetic bearing needs a systematic optimization du e to several design variables, constraints, geometric limitations, nonlinearities, and so on, the present for magnetic bearings have been conducted in the linear region of the characteristics for magnetic by trial and error considering design constraints. This article, therefore, provides the possibility of a genetic algorithm(GA) based optimization with two dimensional-nonlinear finite element magnetic field analysis for design of a radial heteropolar magnetic bearing. The magnetic bearing design by GA based optimization makes good agreements with that by a commercial optimization software DOT using the sensitivity analysis.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2002.10a
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• pp.42-45
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• 2002

In this paper, multiphase optimization of machine Tool structure is presented. The final goal is to obtain 1) light weight, 2) statically and dynamically rigid. and 3) thermally stable structure. The entire optimization process is carried out in three phases. In the first phase, multiple static optimization problem with two objective functions is treated using Pareto genetic algorithm. where two objective functions are weight of the structure and static compliance. In the second phase, maximum receptance is minimized using simple genetic algorithm. And the last phase, thermal deflection to moving heat sources is analyzed using Predictor-Corrector Method. The method is applied to a high speed line center design which takes the shape of back-column structure.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2014.10a
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• pp.555-556
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• 2014

In this paper, topology optimization of two-dimensional acoustic lenses is presented by using the phase field method. The objective of the optimization is to maximize the acoustic pressure at a specified domain inside the acoustic domain for a given frequency, and the constraint is imposed on the amount of the material of the acoustic lens. Topology optimization of two-dimensional acoustic lenses are obtained as the steady state of the phase transition described by the Allen-Cahn equation. The Helmholtz equation modeling the wave propagation is solved by using a finite element method. The effectiveness of the proposed method is verified by applying it for several two-dimensional acoustic lens system design problems.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 1998.04a
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• pp.245-253
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• 1998

Recently, the importance of multi-objective optimization techniques and stochastic search methods is increasing. The stochastic search methods have the concepts of the survival of the fittest and natural selection such as genetic algorithms(GA), simulated annealing(SA) and evolution strategies (ES). As many accidents of oil tankers cause marine pollution, oil tankers of double hull or mid deck structure are being built to minimize the marine pollution. For the improvement of oil tanker design technique, an efficient optimization technique is proposed in this study. Multi-objective optimization problem of weight and cost of double hull and mid deck tanker is formulated. Discrete design variables are used considering real manufacturing, and the concept of relative production cost is also introduced. The ES method is used as an optimization technique, and the ES algorithm was developed to generate a more efficient Pareto optimal set.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2002.10a
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• pp.203-210
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• 2002

The great advantages on the Genetic Algorithms(GAs) are ease of implementation, and robustness in solving a wide variety of problems, several GAs based optimization models for solving complex structural problems were proposed. However, there are two major disadvantages in GAs. The first disadvantage, implementation of GAs-based optimization is computationally too expensive for practical use in the field of structural optimization, particularly for large-scale problems. The second problem is too difficult to find proper parameter for particular problem. Therefore, in this paper, a Distributed Hybrid Genetic Algorithms(DHGAs) is developed for structural optimization on a cluster of personal computers. The algorithm is applied to the minimum weight design of steel structures.