• 한국방재학회 논문집
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• 제10권1호
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• pp.73-80
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• 2010

상수도관망의 최적설계는 단목적함수와 고정된 수리학적 변수로 구성된 비용최소화의 문제로 시작되었다. 하지만, 미래의 불확실한 수요량의 변동과 같이 상수도관망 내에 존재하는 여러 불확실성을 고려하여 설계하는 것이 실제 상수도관망의 거동을 보다 적절히 예측하는 것이다. 따라서 상수도관망 내 존재하는 불확실성을 양적으로 고려하는 다양한 방법이 연구되어 상수도관망의 최적설계에 반영되었고, 다목적함수를 사용한 최적화문제도 다루게 되었다. 본 연구에서는 관망의 절점에서의 수요량과 관의 조도계수를 불확실성을 가진 변수로 두고, 비용 최소화와 관망의 강건성 (Robustness)을 최대화 하는 두 가지 목적함수를 가진 다목적함수 최적화 문제를 다루었다. 최적화 과정은 비용최소화와 불확실성을 고려한 최종 최적화의 두 과정으로 나뉜다. 각 절점에서의 수요량과 관의 조도계수는 베타확률밀도함수 (Beta PDF)를 사용, Latin Hypercube 샘플링 방법으로 불확실성을 고려하였고, 다목적함수의 최적화는 유전자 알고리듬 (Multi-objective Genetic Algorithms, MOGA)을 사용하였다. 제안된 방법은 New York Tunnels이라는 실제 상수도관망에 적용하여 적용성을 검증 하였고 그 결과를 분석하였다. 다목적 최적화 문제에서 최적화가 진행될 수 록 초기 값에 모여 있던 점들이 그 점 주위를 시작으로 해 공간에 최적 해를 찾아 오른쪽 아래 부분으로 탐색해 나가는 것을 확인할 수 있었고 최적설계의 해는 해 공간에서 Pareto Front를 구성하며 파레토 최적해를 구하였다.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2006년도 추계학술대회 논문집
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• pp.247-248
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• 2006

• 한국전산유체공학회:학술대회논문집
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• 한국전산유체공학회 2011년 춘계학술대회논문집
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• pp.100-103
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• 2011

In the present work, a numerical study of fluid flow and heat transfer on the concave surface with impinging jet has been performed by solving three-dimensional Reynods-averaged Naver-Stokes(RANS) equations. The constant temperature condition was applied to the concave impingement surface. The inclination angle of jet nozzle and the distance between jet nozzles are chosen as design variables under equivalent mass flow rate of working fluid into cooling channel, and area averaged Nusselt number on concave impingement surface is set as the objective function. Thirteen training points are obtained by Latin Hypercube sampling method, and the PEA model is constructed by using the objective function values at the trainging points. And, the sequential quadratic programming is used to search for the optimal paint from the PBA model. Through the optimization, the optimal shape shows improved heat transfer rate as compared to the reference geometry.

• Journal of Electrical Engineering and Technology
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• 제7권1호
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• pp.69-74
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• 2012

This paper presents a distributed winding type axial flux permanent magnet synchronous generator (AFPMSG) with reduced the total harmonic distortion (THD), suitable for wind turbine generation systems. Although the THD of the proposed distributed winding type is more reduced than the concentrated winding type, the unbalance of the phase back EMF occurs. To improve the unbalance of the phase back EMF and the output power of the distributed winding type AFPMSG, the Kriging based on the latin hypercube sampling (LHS) is utilized. Finally, these optimization results are confirmed by experimental results. As a result, the unbalance of the phase back EMF and the output power of the distributed winding type AFPMSG were improved while maintaining the total harmonic distortion (THD) and the average phase back EMF.

• 대한기계학회:학술대회논문집
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• 대한기계학회 2008년도 추계학술대회B
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• pp.2729-2732
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• 2008

Cylindrical film-cooling hole is formulated numerically and optimized to enhance film-cooling effectiveness. The Kriging method is used an optimization technique with Reynolds-averaged Navier-Stokes analysis of fluid and heat transfer with shear stress transport model. The hole length-to-diameter ratio and injection angle are chosen as design variables and spatially averaged film-cooling effectiveness is considered as objective function which is to be maximized. Twelve training points obtained by Latin Hypercube Sampling for two design variables. Optimum shape shows the film-cooling effectiveness increased.

• Nuclear Engineering and Technology
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• 제54권5호
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• pp.1712-1725
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• 2022

For improving the seismic performance of the nuclear power plant (NPP) piping system, attempts have been made to apply a dynamic absorber (DA). However, the current piping DA design method is limited because it cannot provide the globally optimum values for the target design seismic loading. Therefore, this study proposes a seismic time history analysis-based DA optimal design method for piping. To this end, the Kriging approach is introduced to reduce the numerical cost required for seismic time history analyses. The appropriate design of the experiment method is used to increase the efficiency in securing response data. A gradient-based method is used to efficiently deal with the multi-dimensional unconstrained optimization problem of the DA optimal design. As a result, the proposed method showed an excellent response reduction effect in several responses compared to other optimal design methods. The proposed method showed that the average response reduction rate was about 9% less at the maximum acceleration, about 5% less at the maximum value of the response spectrum, about 9% less at the maximum relative displacement, and about 4% less at the maximum combined stress compared to existing optimal design methods. Therefore, the proposed method enables an effective optimal DA design method for mitigating seismic response in NPP piping in the future.

• International Journal of Reliability and Applications
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• 제2권3호
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• pp.209-240
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• 2001

Computer-based simulation and analysis is used extensively in engineering for a variety of tasks. Despite the steady and continuing growth of computing power and speed, the computational cost of complex high-fidelity engineering analyses and simulations limit their use in important areas like design optimization and reliability analysis. Statistical approximation techniques such as design of experiments and response surface methodology are becoming widely used in engineering to minimize the computational expense of running such computer analyses and circumvent many of these limitations. In this paper, we compare and contrast five experimental design types and four approximation model types in terms of their capability to generate accurate approximations for two engineering applications with typical engineering behaviors and a wide range of nonlinearity. The first example involves the analysis of a two-member frame that has three input variables and three responses of interest. The second example simulates the roll-over potential of a semi-tractor-trailer for different combinations of input variables and braking and steering levels. Detailed error analysis reveals that uniform designs provide good sampling for generating accurate approximations using different sample sizes while kriging models provide accurate approximations that are robust for use with a variety of experimental designs and sample sizes.

• International Journal of Fluid Machinery and Systems
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• 제9권3호
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• pp.265-276
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• 2016

Energy systems working coherently in different conditions may not have a specific design which can provide optimal performance. A system working for a longer period at lower efficiency implies higher energy consumption. In this effort, a methodology demonstrated by a jet pump design and optimization via numerical modeling for fluid dynamics and implementation of an evolutionary algorithm for the optimization shows a reduction in computational costs. The jet pump inherently has a low efficiency because of improper mixing of primary and secondary fluids, and multiple momentum and energy transfer phenomena associated with it. The high fidelity solutions were obtained through a validated numerical model to construct an approximate function through surrogate analysis. Pareto-optimal solutions for two objective functions, i.e., secondary fluid pressure head and primary fluid pressure-drop, were generated through a multi-objective genetic algorithm. For the jet pump geometry, a design space of several design variables was discretized using the Latin hypercube sampling method for the optimization. The performance analysis of the surrogate models shows that the combined surrogates perform better than a single surrogate and the optimized jet pump shows a higher performance. The approach can be implemented in other energy systems to find a better design.

• 한국전산구조공학회:학술대회논문집
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• 한국전산구조공학회 2006년도 정기 학술대회 논문집
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• pp.275-282
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• 2006

System algorithms estimated by deterministic input may occur the error between predicted and actual output. Especially, actual system can't predict the exact outputs due to uncertainty and tolernce of input parameters. A single output to a set of inputs has a limited value without the variation. Hence, we should consider various scatters caused by the load assessment, material characteristics, stress analysis and manufacturing methods in order to perform the robust design or etimate the reliability of structure. The system design with uncertainty should perform the probabilistic structural optimization with the statistical response and the reliability. This method calculated the probability distributions of the characteristics such as stress by combining stress analysis, response surface methodology and Monte Carlo simulation and got the probabilistic sensitivity. The sensitivity of structural response with respect to in constant design variables was estimated by fracture probability. Therefore, this paper proposed the probabilistic reliability design method for fracture of uncorved freight end beam and the design criteria by fracture probability.

• 한국해양공학회지
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• 제32권4호
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• pp.215-221
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• 2018

In this study, the optimal design of a mooring system was carried out. Unlike almost all design methods, which are based on the deterministic method, this study focused on the probabilistic method. The probabilistic method, especially the design of experiment (DOE), could be a good way to cover some of the drawbacks of the deterministic approach. There various parameters for a mooring system, as widely known, including the weight, length, and stiffness of line. Scenarios for the mooring system parameters were produced using the Latin Hypercube Sampling method of the probabilistic approach. Next, a vessel-mooring system coupled analysis was performed in Orcaflex. A total of 50 scenarios were used in this study to optimize the initial design by means of a genetic algorithm. Finally, after determining the optimal process, a reliability analysis was performed to understand the system validity.