• Journal of Mechanical Science and Technology
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• v.18 no.5
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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.

• Proceedings of the KSME Conference
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• 2000.11a
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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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• v.19 no.4
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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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• v.16 no.5
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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.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.24 no.10 s.181
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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.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.47 no.7
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• pp.535-540
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• 2019

The coupling between different models for MDO (Multi-disciplinary Optimization) greatly increases the complexity of the computational framework, while at the same time increasing CPU time and memory usage. To overcome these difficulties, POD (Proper Orthogonal Decomposition) and RBF (Radial Basis Function) are used to solve the optimization problem of determining the thickness of composites and sandwich cores when composite sandwich structures are used as aircraft wing skin materials. POD and RBF are used to construct surrogate models for the wing shape and the load data. Optimization is performed using the objective function and constraint function values which are obtained from the surrogate models.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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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.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.38 no.5
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• pp.414-420
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• 2010

This paper presents a validation of the accuracy of a reduced order model(ROM) and the efficiency of the design optimization using a Proper Orthogonal Decomposition(POD) to transonic wing/fuselage system. Three dimensional Euler equations are solved to extrude snapshot data of the full order aerodynamic analysis, and then a set of POD basis vectors reproducing the behavior of flow around the wing/fuselage system is calculated from these snapshots. In this study, reduced order model constructed through this procedure is applied to several validation cases, and then it is confirmed that the ROM has the capability of the prediction of flow field in the space of interest. Additionally, after the design optimization of the wing/fuselage system with the ROM is performed, results of the ROM are compared with results of the design optimization using response surface model(RSM). From these, it can be confirmed that the design optimization with the ROM is more efficient than RSM.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.25 no.5
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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.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.38 no.8
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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).