• The KSFM Journal of Fluid Machinery
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• v.19 no.2
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• pp.16-20
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• 2016

Design optimization of a transonic compressor rotor(NASA rotor 37) was carried out using response surface method(RSM) which is one of the optimization methods. A numerical simulation was conducted using ANSYS CFX by solving three-dimensional Reynolds-averaged Navier Stokes(RANS) equations. Response surfaces that were based on the results of the design of experiment(DOE) techniques were used to find an optimal shape of blade which has the maximum aerodynamic performance. Two objective functions, viz., the adiabatic efficiency and the loss coefficient were selected with three design configurations to optimize the blade shape. As a result, the efficiency of the optimized blade is found to be increased.

• Proceedings of the KIEE Conference
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• 2009.07a
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• pp.754_755
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• 2009

This paper deals with the characteristics analysis & optimum design of Synchronous Reluctance Motor (SynRM) with anisotropy rotor using a coupled Finite Element Method (FEM) & Response Surface Methodology (RSM). The focus of this paper is the characteristics analysis & optimum design relative to the output power on the basis of rotor materials of a SynRM. The coupled Finite Elements Analysis (FEA) & Preisach model have been used to evaluate nonlinear solutions. Comparisons are given with characteristics of normal synchronous reluctance motor and those of anisotropy rotor SynRM (ANISO-SynRM), respectively. The feasibility of using RSM with FEM in practical engineering problem is investigated with computational examples and comparison between the fitted response and the results obtained from an analytical solution according to the design variables of rotor in anisotropy rotor SynRM.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2004.11a
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• pp.972-975
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• 2004

In order to reduce the noise of BLDC motor, a systematic optimization procedure for rotor structure is presented. The noise index is defined as the sum of volume velocity of FE-model that are calculated at the dominant frequencies during dehydration process, which is based on the principle of radiation simple volume source. Then, the five design variables are selected to represent the shape and layout or rotor structure. This discrete design optimization problem for minimizing the noise index is solved by 3-level orthogonal array based effect analysis. Finally, the response surface method (RSM) combined optimization approach is employed for more refining the approximate optimum.

• Journal of the Korean Society of Safety
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• v.30 no.1
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• pp.8-13
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• 2015

The experimental design methodology was applied in the drop tube furnace (DTF) to predict the various combustion properties according to the operating conditions and to assess the coal plant safety. Response surface method (RSM) was introduced as a design of experiment, and the database for RSM was set with the numerical simulation of DTF. The dependent variables such as burnout ratios (BOR) of coal and $CO/CO_2$ ratios were mathematically described as a function of three independent variables (coal particle size, carrier gas flow rate, wall temperature) being modeled by the use of the central composite design (CCD), and evaluated using a second-order polynomial multiple regression model. The prediction of BOR showed a high coefficient of determination (R2) value, thus ensuring a satisfactory adjustment of the second-order polynomial multiple regression model with the simulation data. However, $CO/CO_2$ ratio had a big difference between calculated values and predicted values using conventional RSM, which might be mainly due to the dependent variable increses or decrease very steeply, and hence the second order polynomial cannot follow the rates. To relax the increasing rate of dependent variable, $CO/CO_2$ ratio was taken as common logarithms and worked again with RSM. The application of logarithms in the transformation of dependent variables showed that the accuracy was highly enhanced and predicted the simulation data well.

• Transactions of Materials Processing
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• v.30 no.3
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• pp.125-133
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• 2021

In this study, response surface method (RSM), back propagation neural network (BPNN), and genetic algorithm (GA) were used for modeling and multi-objective optimization of the parameters of AA5052-H32 in incremental sheet forming (ISF). The goal of optimization is to determine the maximum forming angle and minimum surface roughness, while varying the production process parameters, such as tool diameter, tool spindle speed, step depth, and tool feed rate. A Box-Behnken experimental design (BBD) was used to develop an RSM model and BPNN model to model the variations in the forming angle and surface roughness based on variations in process parameters. Subsequently, the RSM model was used as the fitness function for multi-objective optimization of the ISF process the GA. The results showed that RSM and BPNN can be effectively used to control the forming angle and surface roughness. The optimized Pareto front produced by the GA can be utilized as a rational design guide for practical applications of AA5052 in the ISF process

• Korean Chemical Engineering Research
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• v.53 no.4
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• pp.524-529
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• 2015

This study was conducted to optimize the medium composition for cold-adaptive protease production of Enterobacteriaceae sp. by response surface methodology (RSM). Yeast extract, and TritonX-100 were identified as the significant factors affecting protease from one-factor-at-a-time method. RSM studies for optimizing protease production of Enterobacteriaceae sp. have been carried out for three parameters including yeast extract concentration, TritonX-100 concentration, and culture pH. These significant factors were optimized as 6.690 g/L yeast extract, 0.018 g/L Triton$^{TM}$ X-10, and pH 6.677. The experimentally obtained protease activity was 8.03 U /L, and it became 1.5-fold increase before optimization.

• Korean Journal of Food Science and Technology
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• v.29 no.4
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• pp.752-757
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• 1997

Crude papain extracted at optimum condition was purified with an ethanol precipitation method. Four factors of protein recovery method were optimized by response surface methodology (RSM) and the function was expressed in terms of a quadratic polynomial equation. Adequacy of the model equation for optimum response values was tested and optimum conditions of protein recovery were 38.2 mg/mL of protein, ethanol concentration of 40% and precipitation temperature of $-8^{\circ}C$. The experimental value (68.97%) for recovery yield was closed to the predicted value (77.28%) under these conditions.

• Korean Chemical Engineering Research
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• v.53 no.2
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• pp.199-204
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• 2015

Response surface methodology (RSM) in combination with a 17-run central composite design (CCD) was applied to optimize the non-catalytic hydrolysis of lard using subcritical water to produce fatty acids (FA). The effects of three variables including temperature, molar ratio of water to oil and time, and their relationship on FA content were investigated. A quadratic regression model was employed to predict the FA contents. Optimum reaction conditions for maximizing the FA content were obtained as follows: reaction temperature of $288.5^{\circ}C$, molar ratio of water to oil of 39.5 and reaction time of 29.5 min. Under the optimum conditions, the predicted and experimentally obtained FA contents were 97.06% and 96.99%, respectively.

• 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.