• Structural Engineering and Mechanics
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• 제65권2호
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• pp.173-181
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• 2018

In this paper, finite element (FE) model updating based on multi-objective optimization with the surrogate model for a steel plate girder bridge is investigated. Conventionally, FE model updating for bridge structures uses single-objective optimization with finite element analysis (FEA). In the case of the conventional method, computational burden occurs considerably because a lot of iteration are performed during the updating process. This issue can be addressed by replacing FEA with the surrogate model. The other problem is that the updating result from single-objective optimization depends on the condition of the weighting factors. Previous studies have used the trial-and-error strategy, genetic algorithm, or user's preference to obtain the most preferred model; but it needs considerable computation cost. In this study, the FE model updating method consisting of the surrogate model and multi-objective optimization, which can construct the Pareto-optimal front through a single run without considering the weighting factors, is proposed to overcome the limitations of the single-objective optimization. To verify the proposed method, the results of the proposed method are compared with those of the single-objective optimization. The comparison shows that the updated model from the multi-objective optimization is superior to the result of single-objective optimization in calculation time as well as the relative errors between the updated model and measurement.

• Smart Structures and Systems
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• 제17권4호
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• pp.611-629
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• 2016

This study presents a new approach of surrogate modeling for time-consuming finite element analysis. A surrogate model is widely used to reduce the computational cost under an iterative computational analysis. Although a variety of the methods have been widely investigated, there are still difficulties in surrogate modeling from a practical point of view: (1) How to derive optimal design of experiments (i.e., the number of training samples and their locations); and (2) diagnostics of the surrogate model. To overcome these difficulties, we propose a sequential surrogate modeling based on Gaussian process model (GPM) with self-adaptive sampling. The proposed approach not only enables further sampling to make GPM more accurate, but also evaluates the model adequacy within a sequential framework. The applicability of the proposed approach is first demonstrated by using mathematical test functions. Then, it is applied as a substitute of the iterative finite element analysis to Monte Carlo simulation for a response uncertainty analysis under correlated input uncertainties. In all numerical studies, it is successful to build GPM automatically with the minimal user intervention. The proposed approach can be customized for the various response surfaces and help a less experienced user save his/her efforts.

• Structural Engineering and Mechanics
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• 제41권1호
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• pp.25-41
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• 2012

Kriging surrogate model provides explicit functions to represent the relationships between the inputs and outputs of a linear or nonlinear system, which is a desirable advantage for response estimation and parameter identification in structural design and model updating problem. However, little research has been carried out in applying Kriging model to crack identification. In this work, a scheme for crack identification based on a Kriging surrogate model is proposed. A modified rectangular grid (MRG) is introduced to move some sample points lying on the boundary into the internal design region, which will provide more useful information for the construction of Kriging model. The initial Kriging model is then constructed by samples of varying crack parameters (locations and sizes) and their corresponding modal frequencies. For identifying crack parameters, a robust stochastic particle swarm optimization (SPSO) algorithm is used to find the global optimal solution beyond the constructed Kriging model. To improve the accuracy of surrogate model, the finite element (FE) analysis soft ANSYS is employed to deal with the re-meshing problem during surrogate model updating. Specially, a simple method for crack number identification is proposed by finding the maximum probability factor. Finally, numerical simulations and experimental research are performed to assess the effectiveness and noise immunity of this proposed scheme.

• Smart Structures and Systems
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• 제22권5호
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• pp.561-574
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• 2018

In this paper, for efficiently reducing the computational cost of the model updating during the optimization process of damage detection, the structural response is evaluated using properly trained surrogate model. Furthermore, in practice uncertainties in the FE model parameters and modelling errors are inevitable. Hence, an efficient approach based on Monte Carlo simulation is proposed to take into account the effect of uncertainties in developing a surrogate model. The probability of damage existence (PDE) is calculated based on the probability density function of the existence of undamaged and damaged states. The current work builds a framework for Probability Based Damage Detection (PBDD) of structures based on the best combination of metaheuristic optimization algorithm and surrogate models. To reach this goal, three popular metamodeling techniques including Cascade Feed Forward Neural Network (CFNN), Least Square Support Vector Machines (LS-SVMs) and Kriging are constructed, trained and tested in order to inspect features and faults of each algorithm. Furthermore, three wellknown optimization algorithms including Ideal Gas Molecular Movement (IGMM), Particle Swarm Optimization (PSO) and Bat Algorithm (BA) are utilized and the comparative results are presented accordingly. Furthermore, efficient schemes are implemented on these algorithms to improve their performance in handling problems with a large number of variables. By considering various indices for measuring the accuracy and computational time of PBDD process, the results indicate that combination of LS-SVM surrogate model by IGMM optimization algorithm have better performance in predicting the of damage compared with other methods.

• Nuclear Engineering and Technology
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• 제52권1호
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• pp.109-114
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• 2020

This work presents the results of laboratory scale tests of the CARBOFLUOREX (CARBOnate FLUORide EXtraction) process - a novel technology for the recovery of U and Pu from the solid fluorides residue (fluorination ash) of Fluoride Volatility Method (FVM) reprocessing of spent nuclear fuel (SNF). To study the oxidative leaching of U from the fluorination ash (FA) by Na₂CO₃ or Na₂CO₃-H₂O₂ solutions followed by solvent extraction by methyltrioctylammonium carbonate in toluene and purification of U from the fission products (FPs) impurities we used a surrogate of FA consisting of UF₄ or UO₂F₂, and FPs fluorides with stable isotopes of Ce, Zr, Sr, Ba, Cs, Fe, Cr, Ni, La, Nd, Pr, Sm. Purification factors of U from impurities at the solvent extraction refining stage reached the values of 10⁴-10⁵, and up to 10⁶ upon the completion of the processing cycle. Obtained results showed a high efficiency of the CARBOFLUOREX process for recovery and separating of U from FPs contained in FA, which allows completing of the FVM cycle with recovery of U and Pu from hardly processed FA.

• 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.

• 한국음향학회지
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• 제40권1호
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• pp.64-72
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• 2021

본 논문에서는 유체의 이동에 의한 배관의 진동을 저감시키기 위해 동조질량감쇠기(Tuned Mass Damper, TMD)의 최적 설계를 수행하였다. 배관 설비의 정확한 진원과 배관의 사양을 알지 못하는 상황에서 TMD 설계를 하기 위해 MATLAB을 이용하여 배관시스템 모델을 설계하고, 이를 바탕으로 최적 설계 방법을 개발하였다. 개발된 최적화 방법은 ANSYS Workbench에서 유한요소 모델을 이용해 최적 설계 방법을 검증했다. 그리고 실제 배관 시스템의 측정값을 바탕으로 진동수를 보정할 수 있도록 TMD를 설계 및 제작하고 실제 배관 시스템에 설치해 감쇠 진폭이 95% 수준으로 줄어든 것을 확인했다.

• Structural Engineering and Mechanics
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• 제83권3호
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• pp.293-304
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• 2022

In this work, a novel reliability approach for combined high and low cycle fatigue (CCF) estimation is developed by combining active learning strategy with least squares support vector machines (LS-SVM) (named as ALS-SVM) surrogate model to address the multi-resources uncertainties, including working loads, material properties and model itself. Initially, a new active learner function combining LS-SVM approach with Monte Carlo simulation (MCS) is presented to improve computational efficiency with fewer calls to the performance function. To consider the uncertainty of surrogate model at candidate sample points, the learning function employs k-fold cross validation method and introduces the predicted variance to sequentially select sampling. Following that, low cycle fatigue (LCF) loads and high cycle fatigue (HCF) loads are firstly estimated based on the training samples extracted from finite element (FE) simulations, and their simulated responses together with the sample points of model parameters in Coffin-Manson formula are selected as the MC samples to establish ALS-SVM model. In this analysis, the MC samples are substituted to predict the CCF reliability of turbine blades by using the built ALS-SVM model. Through the comparison of the two approaches, it is indicated that the reliability model by linear cumulative damage rule provides a non-conservative result compared with that by the proposed one. In addition, the results demonstrate that ALS-SVM is an effective analysis method holding high computational efficiency with small training samples to gain accurate fatigue reliability.

• 대한환경공학회지
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• 제34권9호
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• pp.590-596
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• 2012

고도응집 공정은 DBP 전구물질인 NOM을 제거하는 최적기법이다. 본 연구에서는 낙동강 원수를 대상으로 $FeCl_3$, alum, PSOM 및 PACl 응집제를 대상으로 고도응집 공정의 적용시 가장 효과적인 응집제와 응집조건을 DOC, THMFP, HAAFP 및 제타전위 변화를 중심으로 평가하였다. 탁도 제거율은 고도응집을 적용시 기존응집에 비해 제거율의 상승은 없었으며, 일정 응집제 주입량 이상에서는 제거율이 더욱 저하되었으나 DOC, THMFP 및 HAAFP 제거율은 응집제 종류별로 기존응집에 비해 각각 13~18%, 9~18% 및 9~18% 정도 증가하였다. 응집 pH 변화에 따른 탁도 제거특성은 $FeCl_3$와 PACl이 pH 4~10 범위에서 비교적 높은 탁도 제거율을 나타내었고 alum과 PSOM의 경우는 pH 5~8의 범위에서 안정적인 제거율을 나타내었다. DOC는 4종의 응집제 모두 pH 5~7 범위에서 안정적인 제거율을 나타내었다. 고도응집 공정을 적용시 1 kDa 이하 및 10 kDa 이상의 용존 유기물질의 제거율은 각각 11~21% 및 16% 정도 기존응집 공정에 비해 증가하였으며, 소수성 및 친수성 유기물질의 제거율은 각각 27~38% 및 11~15% 정도 증가하였다. 낙동강 원수의 고도응집에 가장 효과적인 응집제로는 $FeCl_3$로 나타났으며, 다음으로 PSOM, PACl 및 alum 순이었다.

• 방사성폐기물학회지
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• 제3권4호
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• pp.309-317
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• 2005

방사성 폐기물 핵종 재고량 평가에 필요한 핵종분석을 위하여 다양한 매질의 방사성 폐기물 시료로부터 $^{99}Tc,\;^{94}Ng,\;^{55}Fe,\;^{90}Sr$ 및 $^{59/63}Ni$의 분리에 관한 연구가 수행되고 있다. Ni은 음이온교환 수지와 Sr-Spec 추출 크로마토그래피 수지로 Re($^{99}Tc$의 대용물), Nb, Fe 및 Sr을 차례로 분리하는 과정에서 Ca, Mg, Al, Cr, Ti, Mn, Ce, Na, K 및 Cu와 함께 회수되었다. 본 연구에서는 Ni의 선택적 분리기술을 확립하기 위하여 Ni-Spec 추출 크로마토그래피 및 양이온교환수지법으로 이들의 분리거동을 비교하였다. 또한 Ni의 정제와 기체비례계수법으로 방사능을 측정하기에 적합한 계측시료 준비를 위하여 ammonium $citrate/ethanol-H_2O$ 및 tartaric $acid/acetone-H_2O$에서 dimethylglyoxime(DMG)에 의한 Ni의 침전거동을 조사하였다 원자력발전소로부터 채취한 폐이온교환수지 시료 용해용액의 화학조성을 모사하여 만든 모의 폐이온교환수지 용액을 사용하여 Re, Nb, Fe 및 Sr 분리과정을 거쳐 최종적으로 분리한 Ni의 회수율은 $92.1\%\;(RSD:\;0.9\%)$이었다. 또한 tartaric $acid/acetone-H_2O$에서 DMG에 의한 Ni의 회수율은 $85.6\%\;(RSD:\;1.9\%)$이었다.