• Transactions of the Korean Society of Mechanical Engineers A
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• v.29 no.8 s.239
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• pp.1051-1060
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• 2005

Nonlinear Response Optimization using Equivalent Static Loads (NROESL) method/algorithm is proposed to perform optimization of non-linear response structures. The conventional method spends most of the total design time on nonlinear analysis. The NROESL algorithm makes the equivalent static load cases for each response and repeatedly performs linear response optimization and uses them as multiple loading conditions. The equivalent static loads are defined as the loads in the linear analysis, which generates the same response field as those in non-linear analysis. The algorithm is validated for the convergence and the optimality. The proposed algorithm is applied to a simple mathematical problem to verify the convergence and the optimality.

• 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.

• 한국전산유체공학회:학술대회논문집
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• 2007.10a
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• pp.92-99
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• 2007

In this paper, optimization of the vaned centrifugal compressor was carried out at a given mass flow rate condition. Firstly, impeller optimization was conducted using response surface method (RSM) which is one of optimization methods. After the optimization of the impeller was completed, diffuser optimization was performed with the optimized impeller. In these processes, Navier-Stokes solver was used to calculate the flow inside the centrifugal compressor. And the optimization is performed with Box-Behnken design method which is efficient for fitting second-order response surfaces to reduce the number of calculations required. As a result, compared with the reference model, the efficiency and the pressure ratio of the optimized impeller and diffuser are found to be increased. The performance at off-design conditions is presented.

• Journal of Korean Institute of Industrial Engineers
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• v.39 no.5
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• pp.342-350
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• 2013

Dual response surface optimization (DRSO), inspired by Taguchi's philosophy, attempts to optimize the process mean and variability by using response surface methodology. Researches on DRSO were extensively done in 1990's and have been matured recently. This paper reviews the existing DRSO methods from the decision making perspective. More specifically, this paper classifies the existing DRSO methods based on the optimization criterion and the timing of preference articulation. Also, some of case studies are reviewed. Extension to multiresponse optimization, triple response surface optimization, and application of data mining method are suggested as future research issues.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2003.10a
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• pp.145-150
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• 2003

The present study aims to optimize the performance of the Two-Stage Gas Gun by using the experimentally obtained data. RSM(Response Surface Method) was adopted in the optimization process to find the operating parameter than can maximize the projectile speed with the minimum number of tests. To decide the test points which results can consist of the response surface, 3$^{k}$ full factorial method was used, and the design variables were chosen with piston mass and 2$^{nd}$ driver fill pressure. The response surface was composed by nine test results and consequently the optimization was done with GENOCOP III, inherently GA code, in order to seek the optimal test point. The optimal test condition from the response surface was verified by the experiment. Results showed that the optimization process with response surface can successfully predict the test results fairly well. This study shows the possibility of performance optimization for the experimental facilities using numerical optimization algorithm.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.29 no.1 s.232
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• pp.45-52
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• 2005

A general approximate optimization technique by sequential design domain(SDD) did not save response values for getting an approximate function in each step. It has a disadvantage at aspect of an expense. In this paper, previous response values are recycled for constructing an approximate function. For this reason, approximation function is more accurate. Accordingly, even if we did not determine move limit, a system is converged to the optimal design. Size and shape optimization using approximate optimization technique is carried out with SDD. Algorithm executing Pro/Engineer and ANSYS are automatically adopted in the approximate optimization program by SDD. Convergence criterion is defined such that optimal point must be located within SDD during the three steps. The PLBA(Pshenichny-Lim-Belegundu-Arora) algorithm is used to solve approximate optimization problems. This algorithm uses the second-order information in the direction finding problem and uses the active set strategy.

• Proceedings of the KSME Conference
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• 2002.08a
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• pp.635-638
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• 2002

In this study, three-dimensional imcompressible viscous flow analysis and optimization using response surface method are presented for the design of a jet fan. Steady, imcompressible, three-dimensional Reynolds averaged Wavier-Stokes equations are used as governing equations, and standard $k-{\varepsilon}$ turbulence model is chosen as a turbulence model. Governimg equations are discretized using finite volume method. Sweep angles are used as design variables for the shape optimization of the impeller in response surface method. The experimental points which are needed to construct response surface are obtained from the D-optimal design and finally the shape of impeller Is achieved from using a numerical optimization for the response surface which is obtained from CFD.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.34 no.12
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• pp.1811-1820
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• 2010

Most components in the real world show nonlinear response. The nonlinearity may arise because of contact between the parts, nonlinear material, or large deformation of the components. Structural optimization considering nonlinearities is fairly expensive because sensitivity information is difficult to calculate. To overcome this difficulty, the equivalent load method was proposed for nonlinear response optimization. This method was originally developed for size and shape optimization. In this study, the equivalent load method is modified to perform topology optimization considering all kinds of nonlinearities. Equivalent load is defined as the load for linear analysis that generates the same response field as that for nonlinear analysis. A simple example demonstrates that results of the topology optimization using equivalent load are very similar to the numerical results. Nonlinear response topology optimization is performed with a practical example and the results are compared with those of conventional linear response topology optimization.

• Industrial Engineering and Management Systems
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• v.9 no.1
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• pp.10-19
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• 2010

This paper presents an extended computing procedure for the global optimization of the triple response system (TRS) where the response functions are nonconvex (nonconcave) quadratics and the input factors satisfy a radial region of interest. The TRS arising from response surface modeling can be approximated using a nonlinear mathematical program involving one primary (objective) function and two secondary (constraints) functions. An optimization algorithm named triple response surface algorithm (TRSALG) is proposed to determine the global optimum for the nondegenerate TRS. In TRSALG, the Lagrange multipliers of target (secondary) functions are computed by using the Hooke-Jeeves search method, and the Lagrange multiplier of the radial constraint is located by using the trust region (TR) method at the same time. To ensure global optimality that can be attained by TRSALG, included is the means for detecting the degenerate case. In the field of numerical optimization, as the family of TR approach always exhibits excellent mathematical properties during optimization steps, thus the proposed algorithm can guarantee the global optimal solution where the optimality conditions are satisfied for the nondegenerate TRS. The computing procedure is illustrated in terms of examples found in the quality literature where the comparison results with a gradient-based method are used to calibrate TRSALG.

• Journal of the Korean Operations Research and Management Science Society
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• v.40 no.3
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• pp.49-61
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• 2015

We study the problem of multiple response optimization (MRO) and focus on the selection of input levels which will produce desirable output quality. We propose an interactive multiple objective optimization approach to the input design. The earlier interactive methods utilized for MRO communicate with the decision maker only using the response variable values, in order to improve the current response values, thereby resulting in the corresponding design solution automatically. In their interaction steps of preference articulation, no account is taken of any active changes in design variable values. On the contrary, our approach permits the decision maker to change the design variable values in its interaction stage, which makes possible the consideration of the preference or economics of the design variable side. Using some typical value functions, we also demonstrate that our method converges reasonably well to the known optimal solutions.