• Journal of Mechanical Science and Technology
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• 제18권5호
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• pp.814-819
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• 2004

The conceptual design of a rotorcraft system involves many different analysis disciplines. The decomposition of such a system into several subsystems can make analysis and design more efficient in terms of the total computation time. Adaptive parallel decomposition makes the structure of the overall design problem suitable to apply the multidisciplinary design optimization methodologies and it can exploit parallel computing. This study proposes a decomposition method which adaptively determines the number and sequence of analyses in each sub-problem corresponding to the available number of processors in parallel. A rotorcraft design problem is solved and as a result, the adaptive parallel decomposition method shows better performance than other previous methods for the selected design problem.

• 대한기계학회:학술대회논문집
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• 대한기계학회 2000년도 추계학술대회논문집A
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• pp.818-823
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• 2000

In practical design studies, most of designers solve multidisciplinary problems with complex design structure. These multidisciplinary problems have hundreds of analysis and thousands of variables. The sequence of process to solve these problems affects the speed of total design cycle. Thus it is very important for designer to reorder original design processes to minimize total cost and time. This is accomplished by decomposing large multidisciplinary problem into several multidisciplinary analysis subsystem (MDASS) and processing it in parallel. This paper proposes new strategy for parallel decomposition of multidisciplinary problem to raise design efficiency by using genetic algorithm and shows the relationship between decomposition and multidisciplinary design optimization (MDO) methodology.

• Journal of Magnetics
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• 제19권4호
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• pp.385-392
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• 2014

Uncertainties in loads, materials and manufacturing quality must be considered during electromagnetic devices design. This paper presents an effective methodology for robust optimization design based on the variance decomposition in order to keep higher accuracy of the robustness prediction. Sobol' theory is employed to estimate the response variance under some specific tolerance in design variables. Then, an optimal design is obtained by adding a criterion of response variance upon typical optimization problems as a constraint of the optimization. The main contribution of this paper is that the proposed method applies the variance decomposition to obtain a more accurate variance of the response, as well save the computational cost. The performance and robustness of the proposed algorithms are investigated through a numerical experiment with both an analytic function and the TEAM 22 problem.

• Journal of Mechanical Science and Technology
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• 제16권5호
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• pp.609-618
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• 2002

In practical designs, most of the multidisciplinary problems have a large-size and complicate design system. Since multidisciplinary problems have hundreds of analyses and thousands of variables, the grouping of analyses and the order of the analyses in the group affect the speed of the total design cycle. Therefore, it is very important to reorder and regroup the original design processes in order to minimize the total computational cost by decomposing large multidisciplinary problems into several multidisciplinary analysis subsystems (MDASS) and by processing them in parallel. In this study, a new decomposition method is proposed for parallel processing of multidisciplinary design optimization, such as collaborative optimization (CO) and individual discipline feasible (IDF) method. Numerical results for two example problems are presented to show the feasibility of the proposed method.

• 대한기계학회논문집A
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• 제24권10호
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• pp.2438-2450
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• 2000

The Design Axioms provide a general framework for design methodologies. The axiomatic design framework has been successfully applied to various design tasks. However, the axiomatic design has been rarely utilized in the detailed design process of structures where the optimization technology is generally carried out. The relationship between the axiomatic design and the optimization is investigated and Logical Decomposition method is developed for a systematic structural optimization. The entire optimization process is decomposed to satisfy the Independence Axiom. In the decomposition process, design variables are grouped according to sensitivities. The sensitivities are evaluated by the Analysis of Variance(ANOVA) to avoid considering only local values. The developed method is verified through examples such as the twenty -five members transmission tower and the two -bay-six-story frame.

• 한국항공우주학회지
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• 제47권7호
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• pp.535-540
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• 2019

MDO(Multi-disciplinary Optimization)를 위한 서로 다른 모델 간의 결합은 계산 프레임 워크의 복잡성을 크게 증가시키는 동시에 CPU 시간과 메모리 사용을 증가시킨다. 이러한 어려움을 극복하기 위해 POD(Proper Orthogonal Decomposition)와 RBF(Radial Basis Function)를 사용하여 복합 샌드위치 구조가 항공기 날개 스킨 재료로 사용될 때 복합재와 샌드위치 코어의 두께를 결정하는 최적화 문제의 해를 구했다. POD와 RBF를 사용하여 날개 형상과 하중 데이터에 대한 대리 모델을 만들었으며 대리 모델에 의해 얻어진 목적 함수 및 제약 함수 값을 사용하여 최적해를 구하였다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1996년도 한국자동제어학술회의논문집(국내학술편); 포항공과대학교, 포항; 24-26 Oct. 1996
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• pp.36-39
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• 1996

In chemical processes, measurement errors reduce the credibility of information and cause inconsistency in material and energy balances. Because multicomposition flows and temperature measurements make material and energy balances nonlinear equations, data reconciliation becomes a nonlinear constrained optimization problem. In multicomposition processes, if we follow general optimization procedure, the number of measurement variables is so large that data reconciliation requires much computation time. We propose the decomposition procedure to reduce the computation time without the decrease of accuracy of data reconciliation. Decomposition procedure finds global variables, that can reduce the nonlinearity of constraints, and divides two sub-optimization problems. Once we optimize the global variables at upper level, we can easily optimize the remain variables at tower level, We can obtain the short computational time and the same accuracy as SQP optimization method.

• 한국항공우주학회지
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• 제38권5호
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• pp.414-420
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• 2010

본 연구에서는 천음속 날개/동체 모텔에 대한 축소모델 (Reduced Order Model; ROM)의 정확성을 검증하고, Proper Orthogonal Decomposition(POD)을 이용한 최적설계를 통해 그 효율성을 검토하였다. full order 공력해석을 통한 Snapshot을 추출하기 위해 삼차원 오일러 방정식을 이용하였으며, 이들 Snapshot들을 통해 날개/동체 모델 주위 유동장의 거동을 모사하는 POD의 기저벡터를 계산 하였다. 이러한 과정을 거쳐 구축된 축소모텔은 6개의 Case들로 검증하였으며, 그 결과 ROM을 이용해 관심영역에 대한 유동장의 예측을 할 수 있다는 것을 확인하였다. 그리고 ROM을 통한 날개/동체 모델의 최적설계를 수행 하였으며, 그 결과는 반응면모델 (Response Surface Model; RSM)을 이용한 최적설계 결과와 비교 하였다. 이를 통해 ROM을 바탕으로한 최적설계가 RSM을 이용한 것보다 효율적임을 확인하였다.

• 대한기계학회논문집A
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• 제25권5호
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• pp.883-890
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• 2001

In practical design studies, most of designers solve multidisciplinary problems with large size and complex design system. These multidisciplinary problems have hundreds of analysis and thousands of variables. The sequence of process to solve these problems affects the speed of total design cycle. Thus it is very important for designer to reorder the original design processes to minimize total computational cost. This is accomplished by decomposing large multidisciplinary problem into several multidisciplinary analysis subsystem (MDASS) and processing it in parallel. This paper proposes new strategy for parallel decomposition of multidisciplinary problem to raise design efficiency by using genetic algorithm and shows the relationship between decomposition and multidisciplinary design optimization (MDO) methodology.

• 대한전기학회논문지
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• 제38권8호
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• pp.585-592
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• 1989

This paper presents an efficient algorithm for the reactive planning of transmission network under normal operating conditions. The optimal operation of a power system is a prerequisite to obtain the optimal investment planning. The operation problem is decomposed into a P-optimization module and a Q-optimization module, but both modules use the same objective function of generation cost. In the investment problem, a new variable decomposition technique is adopted which can operate the operation and the investment variables. The optimization problem is solved by using the gradient projection method (GPM).