• Wind and Structures
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• v.21 no.6
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• pp.597-607
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• 2015

With an increasing demand of a renewable energy, new offshore wind turbine farms are being planned in some parts of the world. Foundation installation asks a significant cost of the total budget of offshore wind turbine (OWT) projects. Hence, a cost reduction from foundation parts is a key element when a cost-efficient designing of OWT budget. Mono-piles have been largely used, accounting about 78% of existing OWT foundations, because they are considered as a most economical alternative with a relatively shallow-water, less than 30 m of seawater depth. OWT design standards such as IEC, GL, DNV, API, and Eurocode are being developed in a form of reliability based limit state design method. In this paper, reliability analysis using the response surface method (RSM) and numerical simulation technique for an OWT mono-pile foundation were performed to investigate the sensitivities of mono-pile design parameters, and to find practical implications of RSM reliability analysis.

• Journal of Aerospace System Engineering
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• v.7 no.3
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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.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.10 no.5
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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.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2004.05a
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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.

• Transactions of the Korean Society of Automotive Engineers
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• v.14 no.1
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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.

• Journal of the Korean Society for Precision Engineering
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• v.30 no.10
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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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• v.31 no.8
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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.

• Journal of the Korean Applied Science and Technology
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• v.37 no.6
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• pp.1738-1751
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• 2020

The skin is divided into three parts: the epidermis, the dermis, and the subcutaneous fat, and the stratum corneum, which is located at the top of the epidermis, acts as a barrier that prevents drug delivery. Nanoemulsions are known to be effective in transdermal delivery of drugs through intercellular lipids because of their unique small particle size. In this study, phase inversion temperature (PIT) nanoemulsion containing α-bisabolol was optimized using response surface methodology (RSM) for effective skin absorption of α-bisabolol. As a preliminary experiment, the 25-2 fractional factorial design method and the 23 full factorial design method were performed. Box-Behnken design was performed based on the results of the factorial design method. The content of surfactant (6.3~12.6%), co-surfactant (5.2~7.8%) and α-bisabolol (0.5~5.0%) were used as factors, and the dependent variable was the particle size of the nanoemulsion. PIT nanoemulsion optimization was performed according to the RSM results, and as a result, the optimal nanoemulsion formulation conditions were predicted to be 10.4% surfactant content, 6.3% co-surfactant content, and 5.0% α-bisabolol content. As a result of the skin absorption test, the final skin absorption rate of the PIT nanoemulsion was 35.11±1.01%, and the final skin absorption rate of the general emulsion as a control was 28.25±1.69%, confirming that the skin absorption rate of the PIT nanoemulsion was better.

• Proceedings of the KSME Conference
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• 2001.06c
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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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• v.42 no.2
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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.