• 제목/요약/키워드: Sequential approximate optimization

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Minimization of differential column shortening and sequential analysis of RC 3D-frames using ANN

  • Njomo, Wilfried W.;Ozay, Giray
    • Structural Engineering and Mechanics
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    • 제51권6호
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    • pp.989-1003
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    • 2014
  • In the preliminary design stage of an RC 3D-frame, repeated sequential analyses to determine optimal members' sizes and the investigation of the parameters required to minimize the differential column shortening are computational effort consuming, especially when considering various types of loads such as dead load, temperature action, time dependent effects, construction and live loads. Because the desired accuracy at this stage does not justify such luxury, two backpropagation feedforward artificial neural networks have been proposed in order to approximate this information. Instead of using a commercial software package, many references providing advanced principles have been considered to code a program and generate these neural networks. The first one predicts the typical amount of time between two phases, needed to achieve the minimum maximorum differential column shortening. The other network aims to prognosticate sequential analysis results from those of the simultaneous analysis. After the training stages, testing procedures have been carried out in order to ensure the generalization ability of these respective systems. Numerical cases are studied in order to find out how good these ANN match with the sequential finite element analysis. Comparison reveals an acceptable fit, enabling these systems to be safely used in the preliminary design stage.

듀얼 레일 형상에 적합한 철도차량의 차륜 형상 설계 (Design of Railway Vehicle Wheel Profile Suitable for Dual-rail Profile)

  • 변성광;이동형;최하영
    • 한국기계가공학회지
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    • 제16권3호
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    • pp.30-37
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    • 2017
  • When a wheel profile of a train-tram is designed, both train and tram tracks should be considered. This study designed a wheel profile that enables high-speed driving(200km/h) on the train track and low speed driving on the tram track with multiple sharp curves. The study used the approximation optimization method to reduce cost and time, used the sequential quadratic programming method as the optimized algorithm, and the central composite design and response surface method as an approximate model. The optimized wheel shape based on this approximation optimization method reduced wear of the initial wheel showed a better performance in terms of derailment and lateral force.

반응표면법의 향상된 최적화 알고리즘 구성에 관한 연구 (The Study for Construction of the Improved Optimization Algorithm by the Response Surface Method)

  • 박정선;이동주;임종빈
    • 한국항공운항학회지
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    • 제13권3호
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    • pp.22-33
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    • 2005
  • Response Surface Method (RSM) constructs approximate response surfaces using sample data from experiments or simulations and finds optimum levels of process variables within the fitted response surfaces of the interest region. It will be necessary to get the most suitable response surface for the accuracy of the optimization. The application of RSM plan experimental designs. The RSM is used in the sequential optimization process. The first goal of this study is to improve the plan of central composite designs of experiments with various locations of axial points. The second is to increase the optimal efficiency applying a modified method to update interest regions.

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Conceptual Design Optimization of Tensairity Girder Using Variable Complexity Modeling Method

  • Yin, Shi;Zhu, Ming;Liang, Haoquan;Zhao, Da
    • International Journal of Aeronautical and Space Sciences
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    • 제17권1호
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    • pp.29-36
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    • 2016
  • Tensairity girder is a light weight inflatable fabric structural concept which can be used in road emergency transportation. It uses low pressure air to stabilize compression elements against buckling. With the purpose of obtaining the comprehensive target of minimum deflection and weight under ultimate load, the cross-section and the inner pressure of tensairity girder was optimized in this paper. The Variable Complexity Modeling (VCM) method was used in this paper combining the Kriging approximate method with the Finite Element Analysis (FEA) method, which was implemented by ABAQUS. In the Kriging method, the sample points of the surrogate model were outlined by Design of Experiment (DOE) technique based on Optimal Latin Hypercube. The optimization framework was constructed in iSIGHT with a global optimization method, Multi-Island Genetic Algorithm (MIGA), followed by a local optimization method, Sequential Quadratic Program (SQP). The result of the optimization gives a prominent conceptual design of the tensairity girder, which approves the solution architecture of VCM is feasible and efficient. Furthermore, a useful trend of sensitivity between optimization variables and responses was performed to guide future design. It was proved that the inner pressure is the key parameter to balance the maximum Von Mises stress and deflection on tensairity girder, and the parameters of cross section impact the mass of tensairity girder obviously.

순차적 실험계획법과 마이크로 유전알고리즘을 이용한 최적화 알고리즘 개발 (Development of Optimization Algorithm Using Sequential Design of Experiments and Micro-Genetic Algorithm)

  • 이정환;서명원
    • 대한기계학회논문집A
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    • 제38권5호
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    • pp.489-495
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    • 2014
  • 마이크로 유전알고리즘은 적은 수의 개체 사용 및 무작위 개체 구성을 통한 돌연변이 기능 대체의 특징을 갖는 진화연산을 수행하여 일반적인 유전알고리즘이 갖는 각 세대당 많은 계산 량이 요구되는 단점을 극복하고자 하였다. 이러한 마이크로 알고리즘은 특히 설계변수가 3~5 개를 갖는 문제에 효율적이라는 것이 많은 연구자들에 의하여 알려졌다. 따라서 본 연구의 목적은 순차적 실험계획법과 마이크로 유전알고리즘을 이용한 최적화 알고리즘을 개발하는 것이며, 이를 수학예제와 구조물 문제에 적용하여 실용성을 확인하고자 한다. 순차적 실험계획법은 저자들의 선행연구에서 제안되었으며, 실험계획법과 반응표면법을 이용하는 근사최적화 기법에 의한 시행착오적인 반복과정을 최소화하고자 하는 방법으로써, 행렬실험과 평균분석을 반복 적용하는 개념이다.

크리깅을 이용한 개선된 확률론적 최적화 알고리즘 (An Improved Stochastic Algorithm Using Kriging for Practical Optimal Designs)

  • 임종빈;박정선;노영희
    • 한국항공우주학회지
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    • 제34권9호
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    • pp.33-44
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    • 2006
  • 최근 공학적 설계문제들이 복잡해짐에 따라 크리깅을 이용한 근사최적화에 관한 연구가 활발하다. 따라서 본 논문에서는 개선된 확률론적 최적화 알고리즘을 제안함으로써 크리깅을 이용한 근사최적설계의 정확성과 효율성을 높이고자한다. 순차적 근사최적화 시 확률적인 설계영역으로의 이동을 위해 새로운 방법인 확률론적 국부화기법(SLM)을 제안하며, 고전적 계획법, 공간충진 계획법의 두 실험계획법을 사용함으로써 실험점 선정의 효율성을 높이고, 실험계획법의 종류에 따른 결과를 비교, 분석하였다. 또한 3부재 트러스, Sandgren의 압력용기 그리고 하니콤 인공위성 플랫폼 최적설계의 실제 공학적 문제에 적용함으로써 효율성을 검증하고자 한다.

승용차용 알루미늄 시트의 경량화 및 내구성능 향상을 위한 최적설계 (Optimal Design of the Passenger Vehicle Aluminum Seat for Weight Reduction and Durability Performance Improvement)

  • 김병길;김민수;김범진;허승진
    • 한국자동차공학회논문집
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    • 제13권3호
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    • pp.58-63
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    • 2005
  • In order to minimize weight of vehicle seat, an optimum design of aluminum seat is presented while satisfying stress and fatigue life constraints. In this study, the analysis model is validated by comparing it's stress with that of test. Then, two-level orthogonal array is used to estimate the design sensitivity for 7 design variables. Finally, the sequential approximate optimization (SAO) is performed using the constructed RSM models. The approximate RSM models are sequentially updated using the analysis results corresponding to the approximate optimum obtained during the SAO. After 14 analyses, the SAO gives an optimal design that can reduce 16.7$\%$ of weight while increasing 369$\%$ of fatigue life and satisfying stress constraint.

사출금형의 균일 냉각을 위한 냉각회로의 다중목적함수 최적설계 (Multi-objective Optimization of an Injection Mold Cooling Circuit for Uniform Cooling)

  • 박창현;박정민;최재혁;이병옥;최동훈
    • 한국자동차공학회논문집
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    • 제20권1호
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    • pp.124-130
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    • 2012
  • An injection mold cooling circuit for an automotive front bumper was optimally designed in order to simultaneously minimize the average of the standard deviations of the temperature and the difference in mean temperatures of the upper and lower molds for uniform cooling. The temperature distribution for a specified design was evaluated by Moldflow Insight 2010, a commercial injection molding analysis tool. For efficient design, PIAnO (Process Integration, Automation and Optimization), a commercial PIDO tool, was used to integrate and automate injection molding analysis procedure. The weighted-sum method was used to handle the multi-objective optimization problem and PQRSM, a function-based sequential approximate optimizer equipped in PIAnO, to handle numerically noisy responses with respect to the variation of design variables. The optimal average of the standard deviations and difference in mean temperatures were found to be reduced by 9.2% and 56.52%, respectively, compared to the initial ones.

순차적 반응표면법을 이용한 마이크로 정적 믹서의 최적설계 (Parameter Optimization of a Micro-Static Mixer Using Successive Response Surface Method)

  • 한석영;맹주성;김성훈
    • 대한기계학회논문집A
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    • 제28권9호
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    • pp.1314-1319
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    • 2004
  • In this study, parameter optimization of micro-static mixer with a cantilever beam was accomplished for maximizing the mixing efficiency by using successive response surface approximations. Variables were chosen as the length of cantilever beam and the angle between horizontal and the cantilever beam. Sequential approximate optimization method was used to deal with both highly nonlinear and non-smooth characteristics of flow field in a micro-static mixer. Shape optimization problem of a micro-static mixer can be divided into a series of simple subproblems. Approximation to solve the subproblems was performed by response surface approximation, which does not require the sensitivity analysis. To verify the reliability of approximated objective function and the accuracy of it, ANOVA analysis and variables selection method were implemented, respectively. It was verified that successive response surface approximation worked very well and the mixing efficiency was improved very much comparing with the initial shape of a micro-static mixer.

프런트 필라 트림의 내열특성 향상을 위한 순차적 실험계획법과 인공신경망 기반의 최적설계 (Optimum Design based on Sequential Design of Experiments and Artificial Neural Network for Heat Resistant Characteristics Enhancement in Front Pillar Trim)

  • 이정환;서명원
    • 한국정밀공학회지
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    • 제30권10호
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    • pp.1079-1086
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    • 2013
  • Optimal mount position of a front pillar trim considering heat resistant characteristics can be determined by two methods. One is conventional approximate optimization method which uses the statistical design of experiments (DOE) and response surface method (RSM). Generally, approximated optimum results are obtained through the iterative process by a trial and error. The quality of results depends seriously on the factors and levels assigned by a designer. The other is a methodology derived from previous work by the authors, which is called sequential design of experiments (SDOE), to reduce a trial and error procedure and to find an appropriate condition for using artificial neural network (ANN) systematically. An appropriate condition is determined from the iterative process based on the analysis of means. With this new technique and ANN, it is possible to find an optimum design accurately and efficiently.