• Title/Summary/Keyword: structural optimization

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Topology Optimization

  • 박연규
    • CDE review
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    • v.3 no.2
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    • pp.89-92
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    • 1997
  • 이 글에서 소개하는 topology optimization은 structural optimization의 한 분야로서 최근 10여년 동안 급격하게 발전되어 온 분야이다. Structural optimization은 오랜 역사(일반적으로 최초의 structural optimization은 17세기 Galileo에 의하여 되어졌다고 받아들임)를 가지고 발달되어 왔음에도 불구하고 아직도 최적화 방법론과 응용 관점에서 빠르게 발전되고 있다. 이 분야는 사회적인 요구(한정된 자원과 에너지, 안전도, 환경문제)와 컴퓨터 관련 학문(고성능 컴퓨터, computational geometry, finite element method)의 발달에 힘입어 최근 30년간 많은 진전이 있었다.

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A Study on the Ranked Bidirectional Evolutionary Structural Optimization (등급 양방향 진화적 구조 최적화에 관한 연구)

  • Lee, Yeong-Sin;Ryu, Chung-Hyeon;Myeong, Chang-Mun
    • Transactions of the Korean Society of Mechanical Engineers A
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    • v.25 no.9
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    • pp.1444-1451
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    • 2001
  • The evolutionary structural optimization(ESO) method has been under continuous development since 1992. The bidirectional evolutionary structural optimization(BESO) method is made of additive and removal procedure. The BESO method is very useful to search the global optimum and to reduce the computational time. This paper presents the ranked bidirectional evolutionary structural optimization(R-BESO) method which adds elements based on a rank, and the performance indicator which can estimate a fully stressed model. The R-BESO method can obtain the optimum design using less iteration number than iteration number of the BESO.

Solving structural optimization problems with discrete variables using interactive fuzzy search algorithm

  • Mortazavi, Ali
    • Structural Engineering and Mechanics
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    • v.79 no.2
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    • pp.247-265
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    • 2021
  • The current investigation deals with assessing the search performance of a recently developed, parameter-free, and self-adaptive search algorithm so-called Interactive Fuzzy Search Algorithm (IFSA) in solving weight minimization of the constrained structural optimization problems with discrete variables. The proposed IFSA combines the navigation pattern of the Interactive Search Algorithm (ISA) with the decision-making competence of fuzzy reasoning. The fuzzy module of the proposed IFSA permanently monitors the search process and adjusts each agent's search behavior by considering the governing condition of the current problem. In structural optimization, due to construction limitations, it is more realistic to select the sizing variables from a discrete domain. Thus, in this study, to empirically evaluate the search capability of the IFSA, it is applied to solve a suite of structural optimization problems with the discrete design variables. The attained outcomes are compared with the ISA and some other related methods addressed in the relevant literature. The acquired accuracy level and demanded number of objective function evaluations indicates that the IFSA, comparatively, using lower computational cost could found lighter structural systems. Also, the comparison of the attained standard deviation values shows that the IFSA demonstrates higher stability during the optimization process. These superior outcomes designate that the fuzzy decision-making mechanism of the IFSA could work properly in dynamically adapting the search behavior of the algorithm with the governing condition of the current problem. Consequently, the promising gained results reveal that IFSA can effectively be applied in solving the structural optimization problems with discrete search domains.

Structural Optimization of the Lower Parts in a Humanoid Considering Dynamic Characteristics (동적 특성을 고려한 휴머노이드 하체 부품의 구조최적설계)

  • Hong, Eul-Pyo;Lee, Il-Kwon;You, Bum-Jae;Kim, Chang-Hwan;Park, Gyung-Jin
    • Transactions of the Korean Society of Mechanical Engineers A
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    • v.32 no.10
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    • pp.882-889
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    • 2008
  • A humanoid is a robot with its overall appearance based on that of the human body. When the humanoid moves or walks, dynamic forces act on the body structure. Although the humanoid keeps the balance by using a precise control, the dynamic forces generate unexpected deformation or vibration and cause difficulties on the control. Generally, the structure of the humanoid is designed by the designer's experience and intuition. Then the structure can be excessively heavy or fragile. A humanoid design scenario for a systematic design is proposed to reduce the weight of the structure while sufficient strength is kept. Lower parts of the humanoid are selected to apply the proposed design scenario. Multi-body dynamics is employed to calculate the external dynamic forces on the parts and structural optimization is carried out to design the lower parts. Because structural optimization using dynamic forces directly is fairly difficult, linear dynamic response structural optimization using equivalent static loads is utilized. Topology and shape optimizations are adopted for two steps of initial and detailed designs, respectively. Various commercial software systems are used for analysis and optimization. Improved designs are obtained and the design results are discussed.

A Study on the Comparison of Performances Between Direct Method and Approximation Method in Structural Optimization (구조최적설계시 직접법 및 근사법 알고리즘의 성능 비교에 관한 연구)

  • 박영선;이상헌;박경진
    • Transactions of the Korean Society of Mechanical Engineers
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    • v.18 no.2
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    • pp.313-322
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    • 1994
  • Structural optimization has been developed by two methods. One is the direct method which applies the Nonlinear Programming (NLP) algorithm directly to the structural optimization problem. This method is known to be very excellent mathematically. However, it is very expensive for large-scale problems due to the one-dimensional line search. The other method is the approximation method which utilizes the engineering senses very well. The original problem is approximated to a simple problem and an NLP algorithm is adopted for solving the approximated problems. Practical solutions are obtained with low cost by this method. The two methods are compared through standard structural optimization problems. The Finite element method with truss and beam elements is used for the structural and sensitivity analyses. The results are analyzed based on the convergence performances, the number is function calculations, the quality of the cost functions, and etc. The applications of both methods are also discussed.

Micro Genetic Algorithms in Structural Optimization and Their Applications (마이크로 유전알고리즘을 이용한 구조최적설계 및 응용에 관한 연구)

  • 김종헌;이종수;이형주;구본홍
    • Proceedings of the Computational Structural Engineering Institute Conference
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    • 2002.04a
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    • pp.225-232
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    • 2002
  • Simple genetic algorithm(SGA) has been used to optimize a lot of structural optimization problems because it can optimize non-linear problems and obtain the global solution. But, because of large evolving populations during many generations, it takes a long time to calculate fitness. Therefore this paper applied micro-genetic algorithm(μ -GA) to structural optimization and compared results of μ -GA with results of SGA. Additionally, the Paper applied μ -GA to gate optimization problem for injection molds by using simulation program CAPA.

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Structural Optimization of a Manifold Valve for Pressure Vessel (압력용기 매니폴드 밸브의 구조최적설계)

  • Bae, Tae-Sung;Kim, Si-Pom;Lee, Kwon-Hee
    • Journal of the Korean Society for Precision Engineering
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    • v.26 no.12
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    • pp.102-109
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    • 2009
  • This study proposes the structural optimization of a manifold valve. FE analysis is performed to evaluate the strength of a manifold valve. In addition, the structural optimization technique is applied to reduce its weight. In this study, the optimization method using the kriging interpolation method is adopted to obtain the minimum weight satisfying the strength constraint. The maximum stress and the weight are replaced by the metamodels. In this process, tile sample points are generated by latin-hypercube design. Optimum designs are obtained by ANSYS Workbench and the in-house program.

Parallel Genetic Algorithm for Structural Optimization on a Cluster of Personal Computers (구조최적화를 위한 병렬유전자 알고리즘)

  • 이준호;박효선
    • Proceedings of the Computational Structural Engineering Institute Conference
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    • 2000.10a
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    • pp.40-47
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    • 2000
  • One of the drawbacks of GA-based structural optimization is that the fitness evaluation of a population of hundreds of individuals requiring hundreds of structural analyses at each CA generation is computational too expensive. Therefore, a parallel genetic algorithm is developed for structural optimization on a cluster of personal computers in this paper. Based on the parallel genetic algorithm, a population at every generation is partitioned into a number of sub-populations equal to the number of slave computers. Parallelism is exploited at sub-population level by allocationg each sub-population to a slave computer. Thus, fitness of a population at each generation can be concurrently evaluated on a cluster of personal computers. For implementation of the algorithm a virtual distributed computing system in a collection of personal computers connected via a 100 Mb/s Ethernet LAN. The algorithm is applied to the minimum weight design of a steel structure. The results show that the computational time requied for serial GA-based structural optimization process is drastically reduced.

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Structural Optimization Using Micro-Genetic Algorithm (마이크로 유전자 알고리즘을 이용한 구조 최적설계)

  • 한석영;최성만
    • Proceedings of the Korean Society of Machine Tool Engineers Conference
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    • 2003.04a
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    • pp.9-14
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    • 2003
  • SGA (Single Genetic Algorithm) is a heuristic global optimization method based on the natural characteristics and uses many populations and stochastic rules. Therefore SGA needs many function evaluations and takes much time for convergence. In order to solve the demerits of SGA, $\mu$GA(Micro-Genetic Algorithm) has recently been developed. In this study, $\mu$GA which have small populations and fast convergence rate, was applied to structural optimization with discrete or integer variables such as 3, 10 and 25 bar trusses. The optimized results of $\mu$GA were compared with those of SGA. Solutions of $\mu$GA for structural optimization were very similar or superior to those of SGA, and faster convergence rate was obtained. From the results of examples, it is found that $\mu$GA is a suitable and very efficient optimization algorithm for structural design.

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Nonlinear Dynamic Response Structural Optimization of an Automobile Frontal Structure Using Equivalent Static Loads (등가정하중법을 이용한 차량 전면 구조물의 비선형 동적 반응 구조최적설계)

  • Yoon, Shic;Jeong, Seong-Beom;Park, Gyung-Jin
    • Proceedings of the KSME Conference
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    • 2008.11a
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    • pp.1156-1161
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    • 2008
  • Nonlinear dynamic analysis is generally used in automobile crash analysis and structural optimization considering crashworthiness uses the results of nonlinear dynamic analysis. Automobile crash optimization has high nonlinearity and difficulty in calculating sensitivity. Recently the equivalent static load (ESL) method has been proposed in order to overcome these difficulties. The ESL is the static load set generating the same displacement field as the nonlinear dynamic displacement field at each time step in dynamic analysis. From various researches regarding the ESL method, it has been proved that the ESL method is fairly useful. The ESL method can mathematically optimize a crash optimization problem through nonlinear analysis and well developed static optimization. The ESL is applied to nonlinear dynamic structural optimization of the automobile frontal impact problem. An automobile bumper is optimized. The mass of the structure is minimized while some constraints are satisfied.

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