• 한국신재생에너지학회:학술대회논문집
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• 2009.11a
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• pp.427-430
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• 2009

풍력 터빈 블레이드용 익형의 경우 운용 조건에서 높은 양항비를 가지도록 설계되나 풍속, 풍향의 변동에 의해 운용조건에 변화가 발생할 경우 성능의 저하가 발생할 수 있다. 따라서 운용조건의 변동이 발생하더라도 공력 성능이 크게 변하지 않는 익형이 요구된다. 본 연구에서는 이러한 운용조건의 불확실성을 고려하여 풍력 터빈 블레이드용 익형의 신뢰성 기반 강건 최적 설계를 수행하였다. 익형 설계를 위해서 여러 익형 형상 변수들을 고려할 수 있는 익형 모델링 함수를 정의하였고 기저형상으로는 NREL에서 개발한 S809 익형을 사용하였다.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.34 no.8
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• pp.24-32
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• 2006

Using a deterministic design optimization, the effect of uncertainty can result in violation of constraints and deterioration of performances. For this reason, design optimization is required to guarantee reliability for constraints and ensure robustness for an objective function under uncertainty. Therefore, this study drew Monte Carlo Simulation(MCS) for the evaluation of reliability and robustness, and selected an artificial neural network as an approximate model that is suitable for MCS. Applying to the aero-structural optimization problem of aircraft wing, we can explore robuster optima satisfying the sigma level of reliability than the baseline.

• Journal of Ocean Engineering and Technology
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• v.26 no.6
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• pp.80-85
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• 2012

The methods of robust design optimization (RDO) and reliability-based robust design optimization (RBRDO) were implemented in the present study. RBRDO is an integrated method that accounts for the design robustness of an objective function and for the reliability of constraints. The objective function in RBRDO is expressed in terms of the mean and standard deviation of an original objective function. Thus, a multi-objective formulation is employed. The regressive approximate models are generated via the moving least squares method (MLSM) and constraint-feasible moving least squares method (CF-MLSM), which make it possible to realize the feasibility regardless of the multimodality/nonlinearity of the constraint function during the approximate optimization processes. The regression model based RBRDO is newly devised and its numerical characteristics are explored using the design of an actively controlled ten bar truss structure.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.35 no.2
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• pp.163-168
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• 2011

Robust optimization or reliability-based design optimization are some of the methodologies that are employed to take into account the uncertainties of a system at the design stage. For applying such methodologies to solve industrial problems, accurate and efficient methods for estimating statistical moments and failure probability are required, and further, the results of sensitivity analysis, which is needed for searching direction during the optimization process, should also be accurate. The aim of this study is to employ the function approximation moment method into the sensitivity analysis formulation, which is expressed as an integral form, to verify the accuracy of the sensitivity results, and to solve a typical problem of reliability-based design optimization. These results are compared with those of other moment methods, and the feasibility of the function approximation moment method is verified. The sensitivity analysis formula with integral form is the efficient formulation for evaluating sensitivity because any additional function calculation is not needed provided the failure probability or statistical moments are calculated.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.38 no.5
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• pp.575-584
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• 2014

In this study, the NREL 5 MW wind turbine tower model was optimized according to the multi-body dynamics and reliability-based design. The mathematical model was defined as a link-joint system including dynamic characteristics derived from Timoshenko's beam theory. For the optimization problem, the sensitivities to variations in the tower thicknesses and inner and outer diameters were acquired and arranged in terms of safety and efficiency according to bending stress and buckling standards. An optimal design was calculated with the advanced first-order second moment method and used to define a finite element model for validation. The finite element model was simulated by static analysis. The relationship between the multi-body dynamic and finite element method throughout the process was investigated, and the optimal model, which had high endurance despite its low mass, was determined.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.36 no.2
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• pp.99-104
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• 2008

This study performed robust design optimization of fighter wing planform, considering uncertainty based on neural network model. To construct uncertainty model, aerodynamic performance and their sensitivity were evaluated by 3-dimensional Euler equations and adjoint variable method at experimental points selected from central composite design. In addition, because a neural network model has the advantage of capturing non-linear characteristic, it was possible to predict sensitivity of the aerodynamic performance efficiently and accurately . From the results of robust design optimization, it could be confirmed that the robustness of the objective function and constraints were improved if the variation of uncertainty and sigma level were increased.

• Proceedings of the Korean Society Of Semiconductor Equipment Technology
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• 2006.10a
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• pp.35-38
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• 2006

디스플레이 산업에 이용되는 적층판(layered plates)의 결합공정 중 냉각공정에서 열팽창계수의 차이로 인해 잔류응력이 발생하고 심하면 적층판에 크랙(crack)이 발생한다. 본 연구에서는 적층판의 결합공정을 대상으로 현상을 분석하고 이 과정을 시뮬레이션하는 해석 프로그램을 개발하였다. 또한 이를 토대로 향후의 새로운 제품에 대해서도 크랙과 같은 문제점을 최소화 할 수 있는 신뢰성 있는 공정 셋업을 제시하기 위해 차원감소법(dimension reduction method)과 근사화 방법인 반응표면법(response surface method), 순차적 근사최적화 기법(Sequential Approximate Optimization, SAO)을 이용하여 신뢰성기반의 강건최적설계를 하였다.

• Proceedings of the KSME Conference
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• 2008.11a
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• pp.678-683
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• 2008

Moment-based reliability analysis is the method to calculate reliability using Pearson System with first-four raw moments obtained from simulation model. But it is too expensive to calculate first four moments from complicate simulation model. To overcome this drawback the MD(multiplicative decomposition) method which approximates simulation model to kriging metamodel and calculates first four raw moments explicitly with multiplicative decomposition techniques. In general, kriging metamodel is an interpolation model that is decomposed of global model and local model. The global model, in general, can be used as the constant global model, the 1st order global model, or the 2nd order global model. In this paper, the influences of global models on the accuracy and robustness of raw moments are examined and compared. Finally, we suggest the best global model which can provide exact and robust raw moments using MD method.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.38 no.4
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• pp.379-384
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• 2014

The most critical rotating parts of a gas turbine engine are turbine blades and disc, given that they must operate under severe conditions such as high turbine inlet temperature, high speeds, and high compression ratios. Owing to theses operating conditions and high rotational speed energy, some failures caused by turbine disks and blades are categorized into catastrophic and critical, respectively. To maximize the margin of structural integrity, we aim to optimize the vulnerable area of disc-blade interface region. Then, to check the robustness of the obtained optimized solution, we evaluated structural reliability under uncertainties such as dimensional tolerance and fatigue life variant. The results highlighted the necessity for and limitations of optimization which is one of deterministic methods, and pointed out the requirement for introducing reliability-based design optimization which is one of stochastic methods. Thermal-structural coupled-filed analysis and contact analysis are performed for them.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.34 no.11
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• pp.1691-1696
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• 2010

The efforts of reflecting the system's uncertainties in design step have been made and robust optimization or reliabilitybased design optimization are examples of the most famous methodologies. The statistical moments of a performance function and the constraints corresponding to probability conditions are involved in the formulation of these methodologies. Therefore, it is essential to effectively and accurately calculate them. The sensitivities of these methodologies have to be determined when nonlinear programming is utilized during the optimization process. The sensitivity of statistical moments and probability constraints is expressed in the integral form and limited to the normal random variable; we aim to expand the sensitivity formulation to nonnormal variables. Additional functional calculation will not be required when statistical moments and failure or satisfaction probabilities are already obtained at a design point. On the other hand, the accuracy of the sensitivity results could be worse than that of the moments because the target function is expressed as a product of the performance function and the explicit functions derived from probability density functions.