• 항공우주시스템공학회지
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• 제7권3호
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• pp.1-6
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• 2013

Numerical optimization of two dimensional high lift configuration was performed with flow solver and optimization method based on RSM(Response Surface Model). Navier-Stokes solver with Spalart-Allmaras turbulence model was selected for the simulation of highly complex and separated flows on the flap. For the simultaneous optimization of both flap shape and setting (gap/overlap), 10 design variables (eight variables for flap shape variation and two variables for flap setting) were chosen. In order to generate the response surface model, 128 experimental points were selected for 10 design variables. The objective function considering maximum lift coefficient, lift to drag ratio and lift coefficient at specific angle of attack was selected to reduce flow separation on the flap surface. The present method was applied to two dimensional fowler flap in landing configuration. After applying the present method, it was shown that the optimized high lift configuration had less flow separation on the flap surface and lift to drag ratio was suppressed over entire angle of attack range.

• 한국기계가공학회지
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• 제10권5호
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• pp.117-124
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• 2011

Tire's performance plays important roles in improving vehicle's performances. Tire makers carry out a lot of research to improve tire's performance. They are making effort to meet multi purposes using various optimization methods. Recently, the tire makers perform the shape optimization using approximation models, which are surrogate models obtained by statistical method. Generally, the reason why we increase sampling points during optimization process, is to get more reliable approximation models, but the more we adopt sampling points, the more we need time to test. So it is important to select approximation model and proper number of sampling points to balance between reliability and time consuming. In this research, we studied to compare two kind cases for a approximation construction. First, we compare RSM and Kriging which are Curve Fitting Method and Interpolation Method, respectively. Second, we construct approximation models using three different number of sampling points. And then, we recommend proper approximation model and orthogonal array adopt tire's design optimization.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2004년도 춘계학술대회논문집
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• pp.404-409
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• 2004

A compressor loop pipe is the most important part in a refrigerator from the view of structural vibration and noise. Vibration energy generated from a compressor's inner body is transmitted to the shell and outside through the loop pipe. For this reason it is very important to design a compressor loop pipe. But, for geometrical complexity and dynamic nonlinearity of the loop pipe, analysis and design of the loop pipe is very difficult. So the statistical and experimental methods have to be used for design of this system. The response surface method (RSM) becomes a popular meta-modeling technique f3r the complex system as this loop pipe. As starting point of loop pile's optimization, FEA model and simple experimental model are used instead of the real loop pipe model. After RS model was constructed, using sensitivity-based optimizer performed optimization for the loop pipe. And the moving least square method (MLSM) was applied to reduce the approximation error.

• 한국자동차공학회논문집
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• 제14권1호
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• pp.1-7
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• 2006

This paper presents an ASF (Aluminum Space Frame) BIW optimal design, which minimizes the weight and satisfies multi-disciplinary constraints such as the static stiffness, vibration characteristics, low-speed crash, high-speed crash and occupant protection. As only one cycle CPU time for all the analyses is 12 hours, the ASF design having 11-design variable is a large scaled problem. In this study, ISCD-II and conservative least square fitting method is used for efficient RSM modeling. Then, ALM method is used to solve the approximate optimization problem. The approximate optimum is sequentially added to remodel the RSM. The proposed optimization method used only 20 analyses to solve the 11-design variable design problem. Also, the optimal design can reduce the] $15\%$ of total weight while satisfying all of the multi-disciplinary design constraints.

• 한국정밀공학회지
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• 제30권10호
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• pp.1079-1086
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• 2013

Optimal mount position of a front pillar trim considering heat resistant characteristics can be determined by two methods. One is conventional approximate optimization method which uses the statistical design of experiments (DOE) and response surface method (RSM). Generally, approximated optimum results are obtained through the iterative process by a trial and error. The quality of results depends seriously on the factors and levels assigned by a designer. The other is a methodology derived from previous work by the authors, which is called sequential design of experiments (SDOE), to reduce a trial and error procedure and to find an appropriate condition for using artificial neural network (ANN) systematically. An appropriate condition is determined from the iterative process based on the analysis of means. With this new technique and ANN, it is possible to find an optimum design accurately and efficiently.

• Asian-Australasian Journal of Animal Sciences
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• 제31권8호
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• pp.1315-1324
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• 2018

Objective: An experiment was conducted to investigate the effects of different diet formulations: F1 (Two complicated basal diets containing different crude protein levels plus tested feedstuff) vs F2 (A simple corn soybean meal [SBM] basal diet plus tested feedstuff) combined with total collection (TC) or chromic oxide ($Cr_2O_3$) marker or acid-insoluble ash (AIA) marker method, and freeze-dry or oven-dry (OD) technique on estimation of nutrient digestibility in diets fed to growing pigs. Methods: In F1, twelve barrows were allocated to two $6{\times}4$ Youden Squares. The treatment diets included a high protein basal (HPB) diet, a low protein basal (LPB) diet, a corn diet and a wheat bran (WB) diet formulated based on the HPB diet, and a SBM diet and a rapeseed meal (RSM) diet formulated based on the LPB diet. In F2, eight barrows were allocated to two $4{\times}4$ Latin Squares. The treatment diets included a corn basal diet, a SBM basal diet formulated based on the corn diet, and a WB diet and a RSM diet formulated based on the SBM diet. Results: Concentration of digestible (DE) and metabolizable energy (ME), and the apparent total tract digestibility of gross energy, ash, neutral detergent fibre, and acid detergent fibre determined by $Cr_2O_3$ marker method were greater than those determined by TC and AIA marker methods in HPB, LPB, and RSM diets formulated by F1 and in corn diet formulated by F2 (p<0.05). The DE values in WB and both DE and ME values in SBM and RSM estimated using F1 were greater than those estimated using F2 (p<0.05). Conclusion: From the accuracy aspect, the AIA marker or TC method combined with OD technique is recommended for determining the energy concentration and nutrient digestibility of components in diets fed to growing pigs.

• 한국응용과학기술학회지
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• 제37권6호
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• pp.1738-1751
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• 2020

피부는 표피, 진피, 피하지방의 세 부분으로 나누어져 있으며, 표피의 가장 윗부분에 존재하는 각질층은 약물의 피부 전달을 방해하는 장벽 역할을 한다. 나노에멀젼은 특유의 작은 입자크기 때문에 세포간 지질을 통과하여 약물의 피부전달에 효과적이라고 알려져 있다. 본 연구에서는 α-bisabolol의 효과적인 피부흡수를 위해 반응표면분석법(response surface methodology, RSM)을 이용하여 상반전온도(phase inversion temperature, PIT) 유화법으로 제조한 α-bisabolol을 함유한 나노에멀젼을 최적화하였다. 예비실험으로 25-2 일부요인배치법과 23 요인배치법이 수행되었다. 요인배치법의 결과를 바탕으로 계면활성제(6.3~12.6%), 보조계면활성제(5.2~7.8%) 및 α-bisabolol (0.5~5.0%) 함량을 인자로 하고 반응 변수를 나노에멀젼의 입자크기로 하는 Box-Behnken design을 수행하였다. RSM 결과에 따라 PIT 나노에멀젼 최적화를 수행하였고, 그 결과 최적의 나노에멀젼 처방 조건은 계면활성제 함량 10.4%, 보조계면활성제 함량 6.3%, α-bisabolol 함량 5.0%로 예측되었다. 피부흡수시험 결과 PIT 나노에멀젼의 최종 피부흡수율은 35.11±1.01%, 대조군인 일반에멀젼의 최종 피부흡수율은 28.25±1.69%로 PIT 나노에멀젼의 피부흡수율이 더 우수함을 확인하였다.

• 대한기계학회:학술대회논문집
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• 대한기계학회 2001년도 춘계학술대회논문집C
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• pp.369-375
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• 2001

For Optimization technology Was Developed in 1960, the Optimization Technology have grown into a full-featured, robust, highly rated and highly used. And Optimization techniques, having reached a degree of maturity over the past several years, are being used in a wide spectrum of industries, including aerospace, automotive, chemical, electrical, and manufacturing industries. With rapidly advancing computer technology, computers are becoming more powerful, and correspondingly, the size and the complexity of the problems being solved using Optimization techniques are also increasing. But Optimization techniques with analysis solver have many problems. For instance, the difficulties that a particular interface must be coded for each design problem and that the designer should be familiar with the optimization program as well as the analysis program. The purpose of this paper is Optimal Design Framework for Mechanical systems design. This Design Framework has two Optimizers, ADS (local optimizer) and RSM(Response Surface Method), and graphic user interfaces for formulation and optimum design problem and controlling the design process. Current Design Framework tested by two analysis solver, ADAMS and ANSYS. First this paper focused on the core Framework and their conception. In the second of the paper, I cover subjects such as Design Framework Operation. Next, The validity and effectiveness of Design Framework are shown by applying it to many practical design problems and obtaining satisfactory results. Finally, if you are an advanced Operator, you might want to use Response Surface Method, so that cover the result applied by RSM. here.

• Nuclear Engineering and Technology
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• 제42권2호
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• pp.219-230
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• 2010

In nuclear power plants (NPPs), periodic sensor calibrations are required to assure that sensors are operating correctly. By checking the sensor's operating status at every fuel outage, faulty sensors may remain undetected for periods of up to 24 months. Moreover, typically, only a few faulty sensors are found to be calibrated. For the safe operation of NPP and the reduction of unnecessary calibration, on-line instrument calibration monitoring is needed. In this study, principal component-based auto-associative support vector regression (PCSVR) using response surface methodology (RSM) is proposed for the sensor signal validation of NPPs. This paper describes the design of a PCSVR-based sensor validation system for a power generation system. RSM is employed to determine the optimal values of SVR hyperparameters and is compared to the genetic algorithm (GA). The proposed PCSVR model is confirmed with the actual plant data of Kori Nuclear Power Plant Unit 3 and is compared with the Auto-Associative support vector regression (AASVR) and the auto-associative neural network (AANN) model. The auto-sensitivity of AASVR is improved by around six times by using a PCA, resulting in good detection of sensor drift. Compared to AANN, accuracy and cross-sensitivity are better while the auto-sensitivity is almost the same. Meanwhile, the proposed RSM for the optimization of the PCSVR algorithm performs even better in terms of accuracy, auto-sensitivity, and averaged maximum error, except in averaged RMS error, and this method is much more time efficient compared to the conventional GA method.

• 대한기계학회논문집A
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• 제29권3호
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• pp.455-461
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• 2005

This research studied robust design of composite hand for LTR (LCD glass Transfer Robot). $1^{st}$ DOE (Design of Experiment) was conducted to find out vital few Xs. 108 experiments were performed and their results were statistically analyzed. Pareto chart analysis shows that the geometric parameters (height and width of composite beam) are more important than material parameters $(E_{1},\;E_{2})$ or stacking sequence angle. Also, the stacking sequence of mid-layer is more important than that of outer-layer. The main effect plots shows that the maximum deflection of LTR hand is minimized with increasing height, width of beam and layer thickness. $2^{nd}$ DOE was conducted to obtain RSM (Response Surface Method) equation. 25 experiments were conducted. The CCD (Central Composite Design) technique with four factors was used. The coefficient of determination $(R^{2})$ for the calculated RSM equation was 0.989. Optimum design was conducted using the RSM equation. Multi-island genetic algorithm was used to optimum design. Optimum values for beam height, beam width, layer thickness and beam length were 24.9mm, 186.6mnL 0.15mm and 2402.4mm respectively. An approximate value of 0.77mm in deflection was expected to be a maximum under the optimum conditions. Six sigma robust design was conducted to find out guideline for control range of design parameter. To acquire six sigma level reliability, the standard deviation of design parameter should be con trolled within $2{\%}$ of average design value