• Title/Summary/Keyword: and optimization

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Simulation Optimization Methods with Application to Machining Process (시뮬레이션 최적화 기법과 절삭공정에의 응용)

  • 양병희
    • Journal of the Korea Society for Simulation
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    • v.3 no.2
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    • pp.57-67
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    • 1994
  • For many practical and industrial optimization problems where some or all of the system components are stochastic, the objective functions cannot be represented analytically. Therefore, modeling by computer simulation is one of the most effective means of studying such complex systems. In this paper, with discussion of simulation optimization techniques, a case study in machining process for application of simulation optimization is presented. Most of optimization techniques can be classified as single-or multiple-response techniques. The optimization of single-response category, these strategies are gradient based search methods, stochastic approximate method, response surface method, and heuristic search methods. In the multiple-response category, there are basically five distinct strategies for treating the responses and finding the optimum solution. These strategies are graphical method, direct search method, constrained optimization, unconstrained optimization, and goal programming methods. The choice of the procedure to employ in simulation optimization depends on the analyst and the problem to be solved.

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A Study on the Multidisciplinary Design Optimization Using Collaborative Optimization Approach (협동 최적화 접근 방법에 의한 타분야 최적 설계에 관한 연구)

  • 노명일;이규열
    • Korean Journal of Computational Design and Engineering
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    • v.5 no.3
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    • pp.263-275
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    • 2000
  • Multidisciplinary design optimization(MDO) can yield optimal design considering all the disciplinary requirements concurrently. A method to implement the collaborative optimization(CO) approach, one of the MDO methodologies, is developed using a pre-compiler “EzpreCompiler”, a design optimization library “EzOptimizer”, and a common object request broker architecture(CORBA) in distributed computing environment. The CO approach is applied to a mathematical example to show its applicability and equivalence to standard optimization(SO) formulation. In a realistic engineering problem such as optimal design of a two-member hub frame, optimal design of a speed reducer and initial design of a bulk carrier, the CO yields better results than the SO. Furthermore, the CO allows the distributed processing using the CORBA, which leads to reduction of overall computation time.

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A STUDY ON THE EFFICIENCY OF AERODYNAMIC DESIGN OPTIMIZATION USING DISTRIBUTED COMPUTATION (분산컴퓨팅 환경에서 공력 설계최적화의 효율성 연구)

  • Kim Y.-J.;Jung H.-J.;Kim T.-S.;Joh C.-Y.
    • 한국전산유체공학회:학술대회논문집
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    • 2005.10a
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    • pp.163-167
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    • 2005
  • A research to evaluate efficiency of design optimization was performed for aerodynamic design optimization problem in distributed computing environment. The aerodynamic analyses which take most of computational work during design optimization were divided into several jobs and allocated to associated PC clients through network. This is not a parallel process based on domain decomposition rather than a simultaneous distributed-analyses process using network-distributed computers. GBOM(gradient-based optimization method), SAO(Sequential Approximate Optimization) and RSM(Response Surface Method) were implemented to perform design optimization of transonic airfoil and to evaluate their efficiencies. One dimensional minimization followed by direction search involved in the GBOM was found an obstacle against improving efficiency of the design process in distributed computing environment. The SAO was found quite suitable for the distributed computing environment even it has a handicap of local search. The RSM is apparently the fittest for distributed computing environment, but additional trial and error works needed to enhance the reliability of the approximation model are annoying and time-consuming so that they often impair the automatic capability of design optimization and also deteriorate efficiency from the practical point of view.

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Design and Optimization of Suspension with Optical Flying Head Using Integrated Optimization Frame (통합최적프레임을 사용한 광부상헤드를 탑재한 서스팬션의 최적화)

  • Kim, Ji-Won;Park, Kyoung-Su;Yoon, Sang-Joon;Choi, Dong-Hoon;Park, Young-Pil;Lee, Jong-Soo;Park, No-Cheol
    • Transactions of the Society of Information Storage Systems
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    • v.1 no.2
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    • pp.161-168
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    • 2005
  • This paper optimizes the optical flying head(OFH) suspension using the integrated optimization frame, which automatically integrates the analysis with the optimization and effectively implements the repetitive works between them. The problem formulation for the optimization is suggested to improve the dynamic compliance of OFH and to shift the resonant frequencies caused tracking errors to high frequency domain. Furthermore, the minimization of the effective suspension mass that leads to decrease the so-called 'lift-off' as the disk-head separation acceleration divided by the suspension load is taken into consideration. In particular, this study is carried out the optimal design considering the process of modes tracking through the entire optimization processes. The advanced suspension that reduces the effective mass of the suspension and increases the resonant frequencies of sway and $2^{nd}$ torsion over 10kHz is achieved by using the integrated optimization frame.

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The Integrated Design Optimization Technique for Spatial Structures

  • Lee, Sang-Jin
    • Architectural research
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    • v.14 no.1
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    • pp.19-26
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    • 2012
  • The technique of integrated design optimization is proposed to design spatial structures. Various element technologies such as topology optimization, layout editing and size optimization processes are used in an integrated manner to improve the performance of spatial structures. In order to demonstrate the present technique, a unit spatial structure is optimized and numerical results are described here.

Simple Bacteria Cooperative Optimization with Rank Replacement

  • Jung, Sung-Hoon
    • Journal of the Korean Institute of Intelligent Systems
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    • v.19 no.3
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    • pp.432-436
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    • 2009
  • We have developed a new optimization algorithm termed simple bacteria cooperative optimization (sBCO) based on bacteria behavior patterns [1]. In [1], we have introduced the algorithm with basic operations and showed its feasibility with some function optimization problems. Since the sBCO was the first version with only basic operations, its performance was not so good. In this paper, we adopt a new operation, rank replacement, to the sBCO for improving its performance and compare its results to those of the simple genetic algorithm (sGA) which has been well known and widely used as an optimization algorithm. It was found from the experiments with four function optimization problems that the sBCO with rank replacement was superior to the sGA. This shows that our algorithm can be a good optimization algorithm.

Shape Optimization to Minimize The Response Time of Direct-acting Solenoid Valve

  • Shin, Yujeong;Lee, Seunghwan;Choi, Changhwan;Kim, Jinho
    • Journal of Magnetics
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    • v.20 no.2
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    • pp.193-200
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    • 2015
  • Direct-acting solenoid valves are used in the automotive industry due to their simple structure and quick response in controlling the flow of fluid. We performed an optimization study of response time in order to improve the dynamic performance of a direct-acting solenoid valve. For the optimal design process, we used the commercial optimization software PIAnO, which provides various tools for efficient optimization including design of experiments (DOE), approximation techniques, and a design optimization algorithm. 35 sampling points of computational experiments are performed to find the optimum values of the design variables. In all cases, ANSYS Maxwell electromagnetic analysis software was used to model the electromagnetic dynamics. An approximate model generated from the electromagnetic analysis was estimated and used for the optimization. The best optimization model was selected using the verified approximation model called the Kriging model, and an optimization algorithm called the progressive quadratic response surface method (PQRSM).

Aircraft derivative design optimization considering global sensitivity and uncertainty of analysis models

  • Park, Hyeong-Uk;Chung, Joon;Lee, Jae-Woo
    • International Journal of Aeronautical and Space Sciences
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    • v.17 no.2
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    • pp.268-283
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    • 2016
  • Aircraft manufacturing companies have to consider multiple derivatives to satisfy various market requirements. They modify or extend an existing aircraft to meet new market demands while keeping the development time and cost to a minimum. Many researchers have studied the derivative design process, but these research efforts consider baseline and derivative designs together, while using the whole set of design variables. Therefore, an efficient process that can reduce cost and time for aircraft derivative design is needed. In this research, a more efficient design process is proposed which obtains global changes from local changes in aircraft design in order to develop aircraft derivatives efficiently. Sensitivity analysis was introduced to remove unnecessary design variables that have a low impact on the objective function. This prevented wasting computational effort and time on low priority variables for design requirements and objectives. Additionally, uncertainty from the fidelity of analysis tools was considered in design optimization to increase the probability of optimization results. The Reliability Based Design Optimization (RBDO) and Possibility Based Design Optimization (PBDO) methods were proposed to handle the uncertainty in aircraft conceptual design optimization. In this paper, Collaborative Optimization (CO) based framework with RBDO and PBDO was implemented to consider uncertainty. The proposed method was applied for civil jet aircraft derivative design that increases cruise range and the number of passengers. The proposed process provided deterministic design optimization, RBDO, and PBDO results for given requirements.

Study on Aerodynamic Optimization Design Process of Multistage Axial Turbine

  • Zhao, Honglei;Tan, Chunqing;Wang, Songtao;Han, Wanjin;Feng, Guotai
    • Proceedings of the Korean Society of Propulsion Engineers Conference
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    • 2008.03a
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    • pp.130-135
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    • 2008
  • An aerodynamic optimization design process of multistage axial turbine is presented in this article: first, applying quasi-three dimensional(Q3D) design methods to conduct preliminary design and then adopting modern optimization design methods to implement multistage local optimization. Quasi-three dimensional(Q3D) design methods, which mainly refer to S2 flow surface direct problem calculation, adopt the S2 flow surface direct problem calculation program of Harbin Institute of Technology. Multistage local optimization adopts the software of Numeca/Design3D, which jointly adopts genetic algorithm and artificial neural network. The major principle of the methodology is that the successive design evaluation is performed by using an artificial neural network instead of a flow solver and the genetic algorithms may be used in an efficient way. Flow computation applies three-dimensional viscosity Navier Stokes(N-S) equation solver. Such optimization process has three features: (i) local optimization based on aerodynamic performance of every cascade; (ii) several times of optimizations being performed to every cascade; and (iii) alternate use of coarse grid and fine grid. Such process was applied to optimize a three-stage axial turbine. During the optimization, blade shape and meridional channel were respectively optimized. Through optimization, the total efficiency increased 1.3% and total power increased 2.4% while total flow rate only slightly changed. Therefore, the total performance was improved and the design objective was achieved. The preliminary design makes use of quasi-three dimensional(Q3D) design methods to achieve most reasonable parameter distribution so as to preliminarily enhance total performance. Then total performance will be further improved by adopting multistage local optimization design. Thus the design objective will be successfully achieved without huge expenditure of manpower and calculation time. Therefore, such optimization design process may be efficiently applied to the aerodynamic design optimization of multistage axial turbine.

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A modified particle swarm approach for multi-objective optimization of laminated composite structures

  • Sepehri, A.;Daneshmand, F.;Jafarpur, K.
    • Structural Engineering and Mechanics
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    • v.42 no.3
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    • pp.335-352
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    • 2012
  • Particle Swarm Optimization (PSO) is a stochastic population based optimization algorithm which has attracted attentions of many researchers. This method has great potentials to be applied to many optimization problems. Despite its robustness the standard version of PSO has some drawbacks that may reduce its performance in optimization of complex structures such as laminated composites. In this paper by suggesting a new variation scheme for acceleration parameters and inertial weight factors of PSO a novel optimization algorithm is developed to enhance the basic version's performance in optimization of laminated composite structures. To verify the performance of the new proposed method, it is applied in two multi-objective design optimization problems of laminated cylindrical. The numerical results from the proposed method are compared with those from two other conventional versions of PSO-based algorithms. The convergancy of the new algorithms is also compared with the other two versions. The results reveal that the new modifications inthe basic forms of particle swarm optimization method can increase its convergence speed and evade it from local optima traps. It is shown that the parameter variation scheme as presented in this paper is successful and can evenfind more preferable optimum results in design of laminated composite structures.