• 대한기계학회논문집B
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• 제28권11호
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• pp.1332-1338
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• 2004

This paper presents the response surface optimization method using three-dimensional Navier-Stokes analysis to optimize the shape of a forward-curved blades centrifugal fan. For numerical analysis, Reynolds-averaged Navier-Stokes equations with k-$\varepsilon$ turbulence model are discretized with finite volume approximations. In order to reduce huge computing time due to a large number of blades in forward-curved blades centrifugal fan, the flow inside of the fan is regarded as steady flow by introducing the impeller force models. Three geometric variables, i.e., location of cut off, radius of cut off, and width of impeller, and one operating variable, i.e., flow rate, were selected as design variables. As a main result of the optimization, the efficiency was successfully improved. And, optimum design flow rate was found by using flow rate as one of design variables. It was found that the optimization process provides reliable design of this kind of fans with reasonable computing time.

• 한국전산구조공학회:학술대회논문집
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• 한국전산구조공학회 2000년도 봄 학술발표회논문집
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• pp.168-173
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• 2000

Design of experiments is utilized for exploring the design space and for building response surface models in order to facilitate the effective solution of multi-objective optimization problems. Response surface models provide an efficient means to rapidly model the trade-off among many conflicting goals. In robust design, it is important not only to achieve robust design objectives but also to maintain the robustness of design feasibility under the effects of variations, called uncertainties. However, the evaluation of feasibility robustness often needs a computationally intensive process. To reduce the computational burden associated with the probabilistic feasibility evaluation, the first-order Taylor series expansions are used to derive individual mean and variance of constraints. For robust design applications, these constraint response surface models are used efficiently and effectively to calculate variances of constraints due to uncertainties. Robust optimization of automotive seat is used to illustrate the approach.

• 지식경영연구
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• 제20권3호
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• pp.39-47
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• 2019

Response surface methodology (RSM) is a group of statistical modeling and optimization methods to improve the quality of design systematically in the quality engineering field. Its final goal is to identify the optimal setting of input variables optimizing a response. RSM is a kind of knowledge management tool since it studies a manufacturing or service process and extracts an important knowledge about it. In a real problem of RSM, it is a quite frequent situation that considers multiple responses simultaneously. To date, many approaches are proposed for solving (i.e., optimizing) a multi-response problem: process capability function approach, desirability function approach, loss function approach, and so on. The process capability function approach first estimates the mean and standard deviation models of each response. Then, it derives an individual process capability function for each response. The overall process capability function is obtained by aggregating the individual process capability function. The optimal setting is given by maximizing the overall process capability function. The existing process capability function methods usually use the arithmetic mean or geometric mean as an aggregation operator. However, these operators do not guarantee the Pareto optimality of their solution. Moreover, they may bring out an unacceptable result in terms of individual process capability function values. In this paper, we propose a maximin-based process capability function method which uses a maximin criterion as an aggregation operator. The proposed method is illustrated through a well-known multiresponse problem.

• 한국시뮬레이션학회논문지
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• 제32권1호
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• pp.23-34
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• 2023

본 논문의 목적은 이항출력 실험을 이용할 경우에 확률적 전역 최적화 방법론들을 검토하고 알고리즘들간의 성능을 비교하기 위한 것이다. 모 성공확률은 알수 없고 확률적 특성을 갖기 때문에 확률적 전역 최적화 방법론에서는 모 성공확률 대신 성공확률의 추정치를 이용한다. 언덕오르기 알고리즘 , 단순랜덤탐색, 랜덤재출발 랜덤탐색, 랜덤 최적화, 담금질 기법 및 군집기반의 알고리즘인 입자 군집 최적화 알고리즘을 확률적 전역 최적화 알고리즘으로 사용하였다. 알고리즘의 비교를 위하여 두가지 테스트 함수(하나는 단봉이고 나머지는 다봉임)가 제안되었고 몬테카를로 시뮬레이션을 이용하여 알고리즘의 성능을 평가하였다. 단순 테스트 함수에 대하여는 모든 알고리즘이 유사한 성능을 보이고 있다. 복잡한 다봉의 테스트 함수에 대하여는 랜덤재출발 랜덤최적화, 담금질 기법과 군집 기반의 입자군집 알고리즘이 훨씬 더 좋은 성능을 보임을 알 수 있다.

• 한국소성가공학회:학술대회논문집
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• 한국소성가공학회 2007년도 추계학술대회 논문집
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• pp.344-347
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• 2007

For modeling the non-periodic and randomly scattered powder particles, the quasi-random multi-particle array is introduced. The multi-scale process simulation, which enables to formulate a regression model with a response surface method, is performed by employing a homogenization method. The size of ${Al_2}{O_3}$ particle, amplitude of cyclic compaction pressure, and friction coefficient are considered as optimal process parameters. The optimal conditions of process parameters providing the highest relative density are finally found by using the grid search method.

• Steel and Composite Structures
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• 제32권4호
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• pp.455-466
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• 2019

Despite the rapid advancement in computing resources, many real-life design and optimization problems in structural engineering involve huge computation costs. To counter such challenges, approximate models are often used as surrogates for the highly accurate but time intensive finite element models. In this paper, surrogates for first-order shear deformation based finite element models are built using a polynomial regression approach. Using statistical techniques like Box-Cox transformation and ANOVA, the effectiveness of the surrogates is enhanced. The accuracy of the surrogate models is evaluated using statistical metrics like $R^2$, $R^2{_{adj}}$, $R^2{_{pred}}$ and $Q^2{_{F3}}$. By combining these surrogates with nature-inspired multi-criteria decision-making algorithms, namely multi-objective genetic algorithm (MOGA) and multi-objective particle swarm optimization (MOPSO), the optimal combination of various design variables to simultaneously maximize fundamental frequency and frequency separation is predicted. It is seen that the proposed approach is simple, effective and good at inexpensively producing a host of optimal solutions.

• Structural Engineering and Mechanics
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• 제90권2호
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• pp.189-208
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• 2024

Artificial intelligence is one of the efficient methods that can be developed to simulate nonlinear behavior and predict the response of building structures. In this regard, an adaptive method based on optimization algorithms is used to train the TSK model of the fuzzy inference system to estimate the seismic behavior of building structures based on analytical data. The optimization algorithm is implemented to determine the parameters of the TSK model based on the minimization of prediction error for the training data set. The adaptive training is designed on the feedback of the results of previous time steps, in which three training cases of 2, 5, and 10 previous time steps were used. The training data is collected from the results of nonlinear time history analysis under 100 ground motion records with different seismic properties. Also, 10 records were used to test the inference system. The performance of the proposed inference system is evaluated on two 3 and 20-story models of nonlinear steel moment frame. The results show that the inference system of the TSK model by combining the optimization method is an efficient computational method for predicting the response of nonlinear structures. Meanwhile, the multi-vers optimization (MVO) algorithm is more accurate in determining the optimal parameters of the TSK model. Also, the accuracy of the results increases significantly with increasing the number of previous steps.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2002년도 추계학술대회논문집
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• pp.623-629
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• 2002

The spectrum of impulse response signal from an impulse hammer testing is widely used to obtain frequency response function(FRF) of the structure. However the FRFs obtained from impact hammer testing have not only leakage errors but also finite record length errors when the record length for the signal processing is not sufficiently long. The errors cannot be removed with the conventional signal analyzer which treats the signals as if they are always steady and periodic. Since the response signals generated by the impact hammer are transient and have damping, they are undoubtedly non-periodic. It is inevitable that the signals be acquired for limited recording time, which causes the finite record length error and the leakage error. In this paper, the errors in the frequency response function of multi degree of freedom system are formulated theoretically. And the method to remove these errors is also suggested. This method is based on the optimization technique. A numerical example of 3-dof model shows the validity of the proposed method.

• Journal of Electrical Engineering and Technology
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• 제9권1호
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• pp.184-189
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• 2014

This paper presents a multi-objective optimization approach to design rotor slot geometry of three-phase squirrel cage induction machine to achieve NEMA design D torque-speed (T-S) characteristics with high efficiency. The multi-objective Particle Swarm Optimization (MOPSO) algorithm combined with the adaptive response surface method and Latin hypercube sampling strategy is applied to obtain the Pareto optimal designs. In order to demonstrate the validity of the suggested optimal algorithm, an application to rotor slot design of three-phase induction motor is presented.

• 한국전산구조공학회:학술대회논문집
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• 한국전산구조공학회 2000년도 가을 학술발표회논문집
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• pp.115-124
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• 2000

An efficient multi-level (EML) optimization algorithm using discrete variables of framed structures is proposed in this paper. For the efficiency of the proposed algorithm multi-level optimization techniques using a decomposition method that separates both system-level and element-level are incorporated in the algorithm In the system-level, to save the numerical efforts an efficient reanalysis technique through approximated structural responses such as moments and frequencies with respect to intermediate variables is proposed in the paper. Sensitivity analysis of dynamic structural response is executed by automatic differentiation (AD) that is a powerful technique for computing complex or implicit derivatives accurately and efficiently with minimal human effort. In the element-level, to use AISC W-sections a section search algorithm is introduced. The efficiency and robustness of the EML algorithm, compared with a conventional multi-level (CML) algorithm and single-level genetic algorithm is successfully demonstrated in the numerical examples.