• Title, Summary, Keyword: Kriging Metamodel

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Reliability Estimation Using Kriging Metamodel (크리깅 메타모델을 이용한 신뢰도 계산)

  • Cho Tae-Min;Ju Byeong-Hyeon;Jung Do-Hyun;Lee Byung-Chai
    • Transactions of the Korean Society of Mechanical Engineers A
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    • v.30 no.8
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    • pp.941-948
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    • 2006
  • In this study, the new method for reliability estimation is proposed using kriging metamodel. Kriging metamodel can be determined by appropriate sampling range and sampling numbers because there are no random errors in the Design and Analysis of Computer Experiments(DACE) model. The first kriging metamodel is made based on widely ranged sampling points. The Advanced First Order Reliability Method(AFORM) is applied to the first kriging metamodel to estimate the reliability approximately. Then, the second kriging metamodel is constructed using additional sampling points with updated sampling range. The Monte-Carlo Simulation(MCS) is applied to the second kriging metamodel to evaluate the reliability. The proposed method is applied to numerical examples and the results are almost equal to the reference reliability.

Reliability Estimation Using Two-Staged Kriging Metamodel and Genetic Algorithm (2단 크리깅 메타모델과 유전자 알고리즘을 이용한 신뢰도 계산)

  • Cho, Tae-Min;Ju, Byeong-Hyeon;Jung, Do-Hyun;Lee, Byung-Chai
    • Transactions of the Korean Society of Mechanical Engineers A
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    • v.30 no.9
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    • pp.1116-1123
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    • 2006
  • In this study, the effective method for reliability estimation is proposed using tow-staged kriging metamodel and genetic algorithm. Kriging metamodel can be determined by appropriate sampling range and the number of sampling points. The first kriging metamodel is made based on the proposed sampling points. The advanced f'=rst order reliability method is applied to the first kriging metamodel to determine the reliability and most probable failure point(MPFP) approximately. Then, the second kriging metamodel is constructed using additional sampling points near the MPFP. These points are selected using genetic algorithm that have the maximum mean squared error. The Monte-Carlo simulation is applied to the second kriging metamodel to estimate the reliability. The proposed method is applied to numerical examples and the results are almost equal to the reference reliability.

Failure Probability Calculation Method Using Kriging Metamodel-based Importance Sampling Method (크리깅 근사모델 기반의 중요도 추출법을 이용한 고장확률 계산 방안)

  • Lee, Seunggyu;Kim, Jae Hoon
    • Transactions of the Korean Society of Mechanical Engineers A
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    • v.41 no.5
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    • pp.381-389
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    • 2017
  • The kernel density was determined based on sampling points obtained in a Markov chain simulation and was assumed to be an important sampling function. A Kriging metamodel was constructed in more detail in the vicinity of a limit state. The failure probability was calculated based on importance sampling, which was performed for the Kriging metamodel. A pre-existing method was modified to obtain more sampling points for a kernel density in the vicinity of a limit state. A stable numerical method was proposed to find a parameter of the kernel density. To assess the completeness of the Kriging metamodel, the possibility of changes in the calculated failure probability due to the uncertainty of the Kriging metamodel was calculated.

Sensitivity Validation Technique for Sequential Kriging Metamodel (순차적 크리깅 메타모델의 민감도 검증법)

  • Huh, Seung-Kyun;Lee, Jin-Min;Lee, Tae-Hee
    • Transactions of the Korean Society of Mechanical Engineers A
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    • v.36 no.8
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    • pp.873-879
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    • 2012
  • Metamodels have been developed with a variety of design optimization techniques in the field of structural engineering over the last decade because they are efficient, show excellent prediction performance, and provide easy interconnections into design frameworks. To construct a metamodel, a sequential procedure involving steps such as the design of experiments, metamodeling techniques, and validation techniques is performed. Because validation techniques can measure the accuracy of the metamodel, the number of presampled points for an accurate kriging metamodel is decided by the validation technique in the sequential kriging metamodel. Because the interpolation model such as the kriging metamodel based on computer experiments passes through responses at presampled points, additional analyses or reconstructions of the metamodels are required to measure the accuracy of the metamodel if existing validation techniques are applied. In this study, we suggest a sensitivity validation that does not require additional analyses or reconstructions of the metamodels. Fourteen two-dimensional mathematical problems and an engineering problem are illustrated to show the feasibility of the suggested method.

Comparison of global models for calculation of accurate and robust statistical moments in MD method based Kriging metamodel (크리깅 모델을 이용한 곱분해 기법에서 정확하고 강건한 통계적 모멘트 계산을 위한 전역모델의 비교 분석)

  • Kim, Tae-Kyun;Lee, Tae-Hee
    • Proceedings of the KSME Conference
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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.

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Reliability Estimation and RBDO Using Kriging Metamodel and Genetic Algorithm (크리깅 메타모델과 유전알고리즘을 이용한 신뢰도 계산 및 신뢰도기반 최적설계)

  • Cho, Tae-Min;Lee, Byung-Chai
    • Transactions of the Korean Society of Mechanical Engineers A
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    • v.33 no.11
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    • pp.1195-1201
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    • 2009
  • In this study, effective methods for reliability estimation and reliability-based design optimization(RBDO) are proposed using kriging metamodel and genetic algorithm. In our previous study, we proposed the accurate method for reliability estimation using two-staged kriging metamodel and genetic algorithm. In this study, the possibility of applying the previously proposed method to RBDO is investigated. The efficiency and accuracy of that method were much improved than those of the first order reliability method(FORM). Finally, the effective method for RBDO is proposed and applied to numerical examples. The results are compared to the existing RBDO methods and shown to be very effective and accurate.

Feasibility study of improved particle swarm optimization in kriging metamodel based structural model updating

  • Qin, Shiqiang;Hu, Jia;Zhou, Yun-Lai;Zhang, Yazhou;Kang, Juntao
    • Structural Engineering and Mechanics
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    • v.70 no.5
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    • pp.513-524
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    • 2019
  • This study proposed an improved particle swarm optimization (IPSO) method ensemble with kriging model for model updating. By introducing genetic algorithm (GA) and grouping strategy together with elite selection into standard particle optimization (PSO), the IPSO is obtained. Kriging metamodel serves for predicting the structural responses to avoid complex computation via finite element model. The combination of IPSO and kriging model shall provide more accurate searching results and obtain global optimal solution for model updating compared with the PSO, Simulate Annealing PSO (SimuAPSO), BreedPSO and PSOGA. A plane truss structure and ASCE Benchmark frame structure are adopted to verify the proposed approach. The results indicated that the hybrid of kriging model and IPSO could serve for model updating effectively and efficiently. The updating results further illustrated that IPSO can provide superior convergent solutions compared with PSO, SimuAPSO, BreedPSO and PSOGA.

Global Optimization Using Kriging Metamodel and DE algorithm (크리깅 메타모델과 미분진화 알고리듬을 이용한 전역최적설계)

  • Lee, Chang-Jin;Jung, Jae-Jun;Lee, Kwang-Ki;Lee, Tae-Hee
    • Proceedings of the KSME Conference
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    • pp.537-542
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    • 2001
  • In recent engineering, the designer has become more and more dependent on computer simulation. But defining exact model using computer simulation is too expensive and time consuming in the complicate systems. Thus, designers often use approximation models, which express the relation between design variables and response variables. These models are called metamodel. In this paper, we introduce one of the metamodel, named Kriging. This model employs an interpolation scheme and is developed in the fields of spatial statistics and geostatistics. This class of interpolating model has flexibility to model response data with multiple local extreme. By reason of this multi modality, we can't use any gradient-based optimization algorithm to find global extreme value of this model. Thus we have to introduce global optimization algorithm. To do this, we introduce DE(Differential Evolution). DE algorithm is developed by Ken Price and Rainer Storn, and it has recently proven to be an efficient method for optimizing real-valued multi-modal objective functions. This algorithm is similar to GA(Genetic Algorithm) in populating points, crossing over, and mutating. But it introduces vector concept in populating process. So it is very simple and easy to use. Finally, we show how we determine Kriging metamodel and find global extreme value through two mathematical examples.

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Shape Optimization of a CRT based on Response Surface and Kriging Metamodels (반응표면과 크리깅메타모델을 이용한 CRT 형상최적설계)

  • Lee, Tae-Hee;Lee, Chang-Jin;Lee, Kwang-Ki
    • Transactions of the Korean Society of Mechanical Engineers A
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    • v.27 no.3
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    • pp.381-386
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    • 2003
  • Gradually engineering designers are determined based on computer simulations. Modeling of the computer simulation however is too expensive and time consuming in a complicate system. Thus, designers often use approximation models called metamodels, which represent approximately the relations between design and response variables. There arc general metamodels such as response surface model and kriging metamodel. Response surface model is easy to obtain and provides explicit function. but it is not suitable for highly nonlinear and large scaled problems. For complicate case, we may use kriging model that employs an interpolation scheme developed in the fields of spatial statistics and geostatistics. This class of into interpolating model has flexibility to model response data with multiple local extreme. In this study. metamodeling techniques are adopted to carry out the shape optimization of a funnel of Cathode Ray Tube. which finds the shape minimizing the local maximum principal stress Optimum designs using two metamodels are compared and proper metamodel is recommended based on this research.

Selection Method of Global Model and Correlation Coefficients for Kriging Metamodel (크리깅 메타모델의 전역모델과 상관계수 선정 방법)

  • Cho, Su-Kil;Byun, Hyun-Suk;Lee, Tae-Hee
    • Transactions of the Korean Society of Mechanical Engineers A
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    • v.33 no.8
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    • pp.813-818
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    • 2009
  • Design analysis and computer experiments (DACE) model is widely used to express efficiently nonlinear responses in the field of engineering design. As a DACE model, kriging model can approximately replace a simulation model that is very expensive or highly nonlinear. The kriging model is composed of the summation of a global model and a local model representing deviation from the global model. The local model is determined by correlation coefficient with the pre-sampled points, where the accuracy and robustness of the kriging model depends on the selection of proper correlation coefficients. Therefore, to achieve the robust kriging model, the range of the correlation coefficients is explored with respect to the degrees of the global model. Based on this study we propose the proper orders of the global model and range of parameters to make accurate and robust kriging model.