• 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.

• 융합정보논문지
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• 제11권1호
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• pp.109-117
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• 2021

본 논문에서는 해양자동채염기의 구조중량 최소화를 위해 구조설계에 대한 메타모델 기반 근사최적화를 수행하였다. 구조해석은 해양자동채염기의 초기설계에 대한 강도성능을 평가하기 위해 유한요소법을 이용하여 수행하였다. 구조해석에서는 설계하중조건에 대한 강도성능을 평가하였다. 최적설계문제는 강도성능 제한조건을 만족하면서 중량을 최소화할 수 있는 구조두께의 설계변수를 결정하도록 정식화하였다. 근사최적화에는 반응표면법, 크리깅 모델 및 체비쇼프 직교 다항식의 메타모델을 사용하였다. 수치계산 특성을 검토하기 위해 근사최적화 결과는 비근사최적화 결과와 비교하였다. 근사최적화에 사용된 메타모델 중 체비쇼프 직교 다항식이 해양자동채염기의 구조설계에 가장 적합한 최적설계 결과를 나타내었다.

• 한국산업융합학회 논문집
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• 제24권1호
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• pp.31-43
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• 2021

In this study, approximate design optimization using various meta-models was performed for the structural design of active type deck support frame. The active type deck support frame was newly developed to facilitate both transportation and installation of 20,000 ton class offshore plant topside. Structural analysis was carried out using the finite element method to evaluate the strength performance of the active type deck support frame in its initial design stage. In the structural analysis, the strength performances were evaluated for various design load conditions that were regulated in ship classification organization. The approximate optimum design problem based on meta-model was formulated such that thickness sizing variables of main structure members were determined by achieving the minimum weight of the active type deck support frame subject to the strength performance constraints. The meta-models used in the approximate design optimization were response surface method, Kriging model, and Chebyshev orthogonal polynomials. The results from approximate design optimization were compared to actual non-approximate design optimization. The Chebyshev orthogonal polynomials among the meta-models used in the approximate design optimization represented the most pertinent optimum design results for the structure design of the active type deck support frame.

• 한국지리정보학회지
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• 제25권4호
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• pp.49-62
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• 2022

시추데이터는 작업자가 작업현장에서 안전하게 공사를 수행할 수 있도록 제공하는 지반정보로, 현재 시추데이터를 3차원 데이터로 만들어서 3차원 형태의 이미지로 볼 수 있도록 지원하고 있다. 지반정보의 3차원 가시화를 이용하여 다양한 프로그램을 개발하는 국내의 기업들은 지반 정보의 3차원 가시화를 위해 C Tech Development Corporation에서 개발한 MVS 프로그램을 사용하고 있다. 그러나 MVS 프로그램은 유료 프로그램이고 국내에서 개발하는 3차원 관련 프로그램에서 사용하기에는 어려움이 있다. 본 논문에서는 파이썬의 Gempy 오프 소스를 이용하여 군집화된 시추정보로 부터 3차원 지층모델을 생성할 수 있도록 MVS를 대체할 수 있는 라이브러리를 개발하고자 한다. 3차원 지층모델 프로그램은 각 지층별 포인트 데이터를 생성하고 보간을 통해서 지층별 표면을 생성한다. 그리고 각 지층별 표면을 합하여 3차원 지층모델 프로그램을 완성한다. 고양 지역의 시추데이터로부터 MVS 프로그램과 제안하는 프로그램으로 3차원 모형을 생성하여 비교하였을 때 큰 차이가 없음을 확인하였다.

• 대한기계학회논문집A
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• 제36권6호
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• pp.653-663
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• 2012

본 논문에서는 높은 기동성을 목적으로, 적절한 양력과 추진력이 발생하도록 스트로크 평면의 경사각을 고려하여 경로최적화를 수행한다. 기동성비행은 추진력을 최대화하는 비행, 양력을 최대화하는 비행, 양력과 추진력을 동시에 최대화하는 비행 세 가지로 정의하고 날갯짓운동은 단순한 사인함수로 이루어진 플런징과 피칭운동으로 정의하였다. 경로최적화 과정에서 직교배열표를 이용하여 후보점을 생성하고, 그 후보점에서 2 차원 비정상 유동해석을 하였다. 유동해석 결과를 바탕으로 크리깅방법을 이용하여 근사모델을 생성하였다. 그리고 설계정식화를 정의하고 유전알고리즘을 이용하여 최적화를 수행하였다. 세 가지 목적의 날갯짓 경로의 최적화를 통해 기동성비행을 위한 날갯짓 경로를 제시하였다. 또한 날갯짓 운동으로 인해 생성되는 와류를 분석함으로써 양력과 추진력의 발생원리를 확인하였다.

• 한국기계가공학회지
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• 제20권6호
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• pp.33-43
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• 2021

A60 class bulkhead penetration piece is a fire resistance system installed on a bulkhead compartment to protect lives and to prevent flame diffusion in a fire accident on a ship and offshore plant. This study focuses on the approximate optimization of the fire resistance design of the A60 class bulkhead penetration piece using a multi-island genetic algorithm. Transient heat transfer analysis was performed to evaluate the fire resistance design of the A60 class bulkhead penetration piece. For approximate optimization, the bulkhead penetration piece length, diameter, material type, and insulation density were considered discrete design variables; moreover, temperature, cost, and productivity were considered constraint functions. The approximate optimum design problem based on the meta-model was formulated by determining the discrete design variables by minimizing the weight of the A60 class bulkhead penetration piece subject to the constraint functions. The meta-models used for the approximate optimization were the Kriging model, response surface method, and radial basis function-based neural network. The results from the approximate optimization were compared to the actual results of the analysis to determine approximate accuracy. We conclude that the radial basis function-based neural network among the meta-models used in the approximate optimization generates the most accurate optimum design results for the fire resistance design of the A60 class bulkhead penetration piece.

• 대한원격탐사학회지
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• 제33권1호
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• pp.25-35
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• 2017

Spatial downscaling with fine resolution auxiliary variables has been widely applied to predict precipitation at fine resolution from coarse resolution satellite-based precipitation products. The spatial downscaling framework is usually based on the decomposition of precipitation values into trend and residual components. The fine resolution auxiliary variables contribute to the estimation of the trend components. The main focus of this study is on quantitative analysis of impacts of trend component estimates on predictive performance in spatial downscaling. Two regression models were considered to estimate the trend components: multiple linear regression (MLR) and geographically weighted regression (GWR). After estimating the trend components using the two models,residual components were predicted at fine resolution grids using area-to-point kriging. Finally, the sum of the trend and residual components were considered as downscaling results. From the downscaling experiments with time-series Tropical Rainfall Measuring Mission (TRMM) 3B43 precipitation data, MLR-based downscaling showed the similar or even better predictive performance, compared with GWR-based downscaling with very high explanatory power. Despite very high explanatory power of GWR, the relationships quantified from TRMM precipitation data with errors and the auxiliary variables at coarse resolution may exaggerate the errors in the trend components at fine resolution. As a result, the errors attached to the trend estimates greatly affected the predictive performance. These results indicate that any regression model with high explanatory power does not always improve predictive performance due to intrinsic errors of the input coarse resolution data. Thus, it is suggested that the explanatory power of trend estimation models alone cannot be always used for the selection of an optimal model in spatial downscaling with fine resolution auxiliary variables.

• 전기학회논문지
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• 제62권11호
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• pp.1528-1534
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• 2013

This paper proposes the optimal design of stator shape for cogging torque reduction of single-phase brushless DC (BLDC) motor with asymmetric notch. This method applied size and position of asymmetric notches to tapered teeth of stator for single-phase BLDC motor. Which affects the variation of the residual flux density of the permanent magnet. The process of optimal design included the extraction of the sampling point by using Latin Hypercube Sampling(LHS), and involved the creation of an approximation model by using kriging method. Also, the optimum point of the design variables were discovered by using the Genetic Algorithm(GA). Finite element analysis was used to calculate the characteristics analysis and cogging torque. As a result of finite element analysis, cogging torque were reduced approximately 39.2% lower than initial model. Also experimental result were approximately 38.5% lower than initial model. The period and magnitude of the cogging torque were similar to the results of FEA.

• International Journal of Fluid Machinery and Systems
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• 제10권3호
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• pp.240-253
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• 2017

Latin hypercube sampling is widely used design-of-experiment technique to select design points for simulation which are then used to construct a surrogate model. The exploration/exploitation properties of surrogate models depend on the size and distribution of design points in the chosen design space. The present study aimed at evaluating the performance characteristics of various surrogate models depending on the Latin hypercube sampling (LHS) procedure (sample size and spatial distribution) for a diverse set of optimization problems. The analysis was carried out for two types of problems: (1) thermal-fluid design problems (optimizations of convergent-divergent micromixer coupled with pulsatile flow and boot-shaped ribs), and (2) analytical test functions (six-hump camel back, Branin-Hoo, Hartman 3, and Hartman 6 functions). The three surrogate models, namely, response surface approximation, Kriging, and radial basis neural networks were tested. The important findings are illustrated using Box-plots. The surrogate models were analyzed in terms of global exploration (accuracy over the domain space) and local exploitation (ease of finding the global optimum point). Radial basis neural networks showed the best overall performance in global exploration characteristics as well as tendency to find the approximate optimal solution for the majority of tested problems. To build a surrogate model, it is recommended to use an initial sample size equal to 15 times the number of design variables. The study will provide useful guidelines on the effect of initial sample size and distribution on surrogate construction and subsequent optimization using LHS sampling plan.

• 대한기계학회논문집A
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• 제35권10호
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• pp.1323-1328
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• 2011

자동차 로워암과 같이 다양한 형상설계변수를 갖는 부품모듈의 최근 설계경향은 설계자가 관심을 갖는 설계영역을 선형 및 2 차 다항식으로 근사화시키는 반응표면모델로 탐색하고, 다음 단계로서 최적설계를 수행하는 것이다. 본 연구에서는 로워암의 설계변수 변화에 따른 작용응력과 중량의 비선형적 변화뿐만 아니라 이의 예측에 적합한 신경망모델로 직교성과 균형성을 모두 만족시키는 다수준 전산실험계획법으로 설계영역을 탐색하였다. 구축된 신경망모델에 형상 설계변수의 공차도 같이 고려할 수 있는 식스시그마 제약조건을 적용하여 로워암의 공차 최적설계를 수행하고, 최적해의 공차 강건성을 확보하였다.