• International Journal of Naval Architecture and Ocean Engineering
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• v.7 no.4
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• pp.750-769
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• 2015

The geometry of engineering systems affects their performances. For this reason, the shape of engineering systems needs to be optimized in the initial design stage. However, engineering system design problems consist of multi-objective optimization and the performance analysis using commercial code or numerical analysis is generally time-consuming. To solve these problems, many engineers perform the optimization using the approximation model (response surface). The Response Surface Method (RSM) is generally used to predict the system performance in engineering research field, but RSM presents some prediction errors for highly nonlinear systems. The major objective of this research is to establish an optimal design method for multi-objective problems and confirm its applicability. The proposed process is composed of three parts: definition of geometry, generation of response surface, and optimization process. To reduce the time for performance analysis and minimize the prediction errors, the approximation model is generated using the Backpropagation Artificial Neural Network (BPANN) which is considered as Neuro-Response Surface Method (NRSM). The optimization is done for the generated response surface by non-dominated sorting genetic algorithm-II (NSGA-II). Through case studies of marine system and ship structure (substructure of floating offshore wind turbine considering hydrodynamics performances and bulk carrier bottom stiffened panels considering structure performance), we have confirmed the applicability of the proposed method for multi-objective side constraint optimization problems.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.37 no.6
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• pp.519-526
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• 2009

This paper describes a research result of a external shape optimization study to maximize the range of the guided missile with canards and tailfins in atmospheric flight. For this purpose, the external shape optimization program which can enhance the range of a missile was developed, incorporated with the trajectory analysis and the optimization technique. In the trajectory analysis part, Missile DATCOM which utilizes the semi-empirical method was directly connected to the trajectory code to supply the aerodynamic coefficients efficiently at every time step. In the gliding flight trajectory after apogee, a maximum $C_L/C_D$ trim condition calculation module was attached under the assumption of the missile continuously flying at maximum $C_L/C_D$ condition. In the optimization part, a Response Surface Method(RSM) was adopted to reduce the computing time.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.11
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• pp.7754-7760
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• 2015

The outer tie rod is one of the part of steering system, the optimization process was executed to find the lightweight design. The inner tie rod was considered in the optimum design of an outer tie rod. it could be closer to the test condition than in the case of considering outer tie rod only. The aluminum forging material was considered as a weight reduction proposal. The target of optimization was the shape of the minimum weight to resist at the load of buckling. RSM and Kriging interpolation method were applied as a optimization method to consider the nonlinear shape optimization problem. Then, 16.3%, 16.6% of weight reduction was obtained from the result comparing with that of the initial model. The results of meta model optimization was compared with that of finite element method. The error values of buckling load estimation were 2.6%, 2.04%. and those of weight estimation were 0.17%, 0.13%. Therefore, it seemed that the result of Kriging model could be obtained closer to optimum value than that of RSM model.

• JSTS:Journal of Semiconductor Technology and Science
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• v.11 no.3
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• pp.135-145
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• 2011

Accurate process characterization and optimization are the first step for a successful advanced process control (APC), and they should be followed by continuous monitoring and control in order to run manufacturing processes most efficiently. In this paper, process characterization and recipe optimization methods with multiple outputs are presented in high density plasma-chemical vapor deposition (HDP-CVD) silicon dioxide deposition process. Five controllable process variables of Top $SiH_4$, Bottom $SiH_4$, $O_2$, Top RF Power, and Bottom RF Power, and two responses of interest, such as deposition rate and uniformity, are simultaneously considered employing both statistical response surface methodology (RSM) and neural networks (NNs) based genetic algorithm (GA). Statistically, two phases of experimental design was performed, and the established statistical models were optimized using performance index (PI). Artificial intelligently, NN process model with two outputs were established, and recipe synthesis was performed employing GA. Statistical RSM offers minimum numbers of experiment to build regression models and response surface models, but the analysis of the data need to satisfy underlying assumption and statistical data analysis capability. NN based-GA does not require any underlying assumption for data modeling; however, the selection of the input data for the model establishment is important for accurate model construction. Both statistical and artificial intelligent methods suggest competitive characterization and optimization results in HDP-CVD $SiO_2$ deposition process, and the NN based-GA method showed 26% uniformity improvement with 36% less $SiH_4$ gas usage yielding 20.8 ${\AA}/sec$ deposition rate.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.38 no.7
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• pp.716-726
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• 2010

In this research, design optimization of an aircraft wing has been performed using the fully automated Multidisciplinary Design Optimization (MDO) framework, which integrates aerodynamic and structural analysis considering nonlinear structural behavior. A computational fluid dynamics (CFD) mesh is generated automatically from parametric modeling using CATIA and Gambit, followed by an automatic flow analysis using FLUENT. A computational structure mechanics (CSM) mesh is generated automatically by the parametric method of the CATIA and visual basic script of NASTRAN-FX. The structure is analyzed by ABAQUS. Interaction between CFD and CSM is performed by a fully automated system. The Response Surface Method (RSM) is applied for optimization, helping to achieve the global optimum. The optimization design result demonstrates successful application of the fully automated MDO framework.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.36 no.3
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• pp.229-237
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• 2008

In this research, a MDO(multi-disciplinary design optimization) framework, which integrates aerodynamic and structural analysis to design an aircraft wing, is constructed. Whole optimization process is automated by a parametric-modeling approach. A CFD mesh is generated automatically from parametric modeling of CATIA and Gridgen followed by automatic flow analysis using Fluent. Finite element mesh is generated automatically by parametric method of MSC.Patran PCL. Aerodynamic load is transferred to Finite element model by the volume spline method. RSM(Response Surface Method) is applied for optimization, which helps to achieve global optimum. As the design problem to test the current MDO framework, a wing weight minimization with constraints of lift-drag ratio and deflection of the wing is selected. Aspect ratio, taper ratio and sweepback angle are defined as design variables. The optimization result demonstrates the successful construction of the MDO framework.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.28 no.10
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• pp.1458-1466
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• 2004

Deterministic optimum designs that are obtained without consideration of uncertainties could lead to unrealiable designs. Such deterministic engineering optimization tends to promote the structural system with less reliability redundancy than obtained with conventional design procedures using the factor of safety. Consequently, deterministic optimized structures will usually have higher failure probabilities than unoptimized structures. This paper proposes the reliability based design optimization technique fur apressure tank considering temperature effect. This paper presents an efficient and stable reliability based design optimization method by using the advanced first order second moment method, which evaluates a probabilistic constraint for more accuracy. In addition, the response surface method is utilized to approximate the performance functions describing the system characteristics in the reliability based design optimization procedure.

• 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.

• 유체기계공업학회:학술대회논문집
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• 2006.08a
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• pp.543-544
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• 2006

Shape optimization of a transonic axial compressor rotor operating at the design flow condition has been performed using three-dimensional Navier-Stokes analysis and three different surrogate models: i.e.., Response Surface Method(RSM), Kriging Method, and Radial Basis Function(RBF). Three design variables of blade sweep, lean and skew are introduced to optimize the three-dimensional stacking line of the rotor blade. The object function of the shape optimization is selected as an adiabatic efficiency. Throughout the shape optimization of the rotor blade, the adiabatic efficiency is increased for the three different surrogate models. Detailed flow characteristics at the optimal blade shape obtained by different optimization method are drawn and discussed.

• Journal of Mechanical Science and Technology
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• v.18 no.10
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• pp.1829-1836
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• 2004

This study presents the performance optimization of hypervelocity launcher system by using the experimentall data. During the optimization, the RSM (Response Surface Method) is adopted to find the operating parameters that could maximize the projectile speed. To construct a reliable response surface model, 3 full factorial method is used with the selected design variables, such as piston mass and 2 driver fill pressure. Nine test data could successfully construct the reasonable response surface, which used to yield the optimal operational conditions of the system using the genetic algorithm. The optimization results are confirmed by the experimental test with a good accuracy. Thus, the optimization can improve the performance of the facility.