• 품질경영학회지
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• 제47권4호
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• pp.701-711
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• 2019

Purpose: The problem of optimizing redundancy allocation in multi-level systems is considered when each item in a multi-level system has alternative items with the same function. The number of redundancy of multi-level system is allocated to maximize the reliability of the system under path set and cost limitation constraints. Methods: Based on cost limitation and path set constraints, a mathematical model is established to maximize system reliability. Particle swarm optimization is employed for redundant allocation and verified by numerical experiments. Results: Comparing the particle swarm optimization method and the memetic algorithm for the 3 and 4 level systems, the particle swarm optimization method showed better performance for solution quality and search time. Particularly, the particle swarm optimization showed much less than the memetic algorithm for variation of results. Conclusion: The proposed particle swarm optimization considerably shortens the time to search for a feasible solution in MRAP with path set constraints. PS optimization is expected to reduce search time and propose the better solution for various problems related to MRAP.

• 한국전산유체공학회:학술대회논문집
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• 한국전산유체공학회 2000년도 춘계 학술대회논문집
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• pp.159-165
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• 2000

In this paper, numerical study has been done for the improvement of the superdetonative ram accelerator performance and for the design optimization of the system. The objective function to optimize the premixture composition is the ram tube length required to accelerate projectile from initial velocity $V_o$ to target velocity $V_e$. The premixture is composed of $H_2,\;O_2,\;N_2$ and the mole numbers of these species are selected at design variables. RSM(Response Surface Methodology) which is widely used for the complex optimization problems is selected as the optimization technique. In particular, to improve the non-linearity of the response and to consider the accuracy and efficiency of the solution, design space stretching technique has been applied. Separate sub-optimization routine is introduced to determine the stretching position and clustering parameters which construct the optimum regression model. Two step optimization technique has been applied to obtain the optimal system. With the application of stretching technique, we can perform system optimization with a small number of experimental points, and construct precise regression model for highly non-linear domain. The error to compared with analysis result is only $0.01\%$ and it is demonstrated that present method can be applied more practical design optimization problems with many design variables.

• 대한기계학회논문집A
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• 제28권1호
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• pp.48-54
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• 2004

Generally, structural optimization is carried out based on external static loads. All forces have dynamic characteristics in the real world. Mathematical optimization with dynamic loads is extremely difficult in a large-scale problem due to the behaviors in the time domain. In practical applications, it is customary to transform the dynamic loads into static loads by dynamic factors, design codes, and etc. But the optimization results with the unreasonably transformed loads cannot give us good solutions. Recently, a systematic transformation has been proposed as an engineering algorithm. Equivalent static loads are made to generate the same displacement field as the one from dynamic loads at each time step of dynamic analysis. Thus, many load cases are used as the multiple loading conditions which are not costly to include in modem structural optimization. In this research, the proposed algorithm is applied to the optimization of flexible multibody dynamic systems. The equivalent static load is derived from the equations of motion of a flexible multibody dynamic system. A few examples that have been solved before are solved to be compared with the results from the proposed algorithm.

• 한국생산제조학회지
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• 제19권5호
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• pp.589-595
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• 2010

This paper presents a reliability-based shape optimization (RBSO) using the evolutionary structural optimization (ESO). An actual design involves uncertain conditions such as material property, operational load, poisson's ratio and dimensional variation. The deterministic optimization (DO) is obtained without considering of uncertainties related to the uncertainty parameters. However, the RBSO can consider the uncertainty variables because it has the probabilistic constraints. In order to determine whether the probabilistic constraint is satisfied or not, simulation techniques and approximation methods are developed. In this paper, the reliability-based shape design optimization method is proposed by utilization the reliability index approach (RIA), performance measure approach (PMA), single-loop single-vector (SLSV), adaptive-loop (ADL) are adopted to evaluate the probabilistic constraint. In order to apply the ESO method to the RBSO, a sensitivity number is defined as the change of strain energy in the displacement constraint. Numerical examples are presented to compare the DO with the RBSO. The results of design example show that the RBSO model is more reliable than deterministic optimization.

• Advances in Computational Design
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• 제2권4호
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• pp.313-331
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• 2017

In this study, teaching-learning based optimization (TLBO) is improved by incorporating model of multiple teachers, adaptive teaching factor, self-motivated learning, and learning through tutorial. Modified TLBO (MTLBO) is applied for simultaneous topology, shape, and size optimization of space and planar trusses to study its effectiveness. All the benchmark problems are subjected to stress, displacement, and kinematic stability constraints while design variables are discrete and continuous. Analyses of unacceptable and singular topologies are prohibited by seeing element connectivity through Grubler's criterion and the positive definiteness. Performance of MTLBO is compared to TLBO and state-of-the-art algorithms available in literature, such as a genetic algorithm (GA), improved GA, force method and GA, ant colony optimization, adaptive multi-population differential evolution, a firefly algorithm, group search optimization (GSO), improved GSO, and intelligent garbage can decision-making model evolution algorithm. It is observed that MTLBO has performed better or found nearly the same optimum solutions.

• Structural Engineering and Mechanics
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• 제36권1호
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• pp.79-94
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• 2010

This study shows how uncertainties of data like material properties quantitatively have an influence on structural topology optimization results for dynamic problems, here such as both optimal topology and shape. In general, the data uncertainties may result in uncertainties of structural behaviors like deflection or stress in structural analyses. Therefore optimization solutions naturally depend on the uncertainties in structural behaviors, since structural behaviors estimated by the structural analysis method like FEM need to execute optimization procedures. In order to quantitatively estimate the effect of data uncertainties on topology optimization solutions of dynamic problems, a so-called interval analysis is utilized in this study, and it is a well-known non-stochastic approach for uncertainty estimate. Topology optimization is realized by using a typical SIMP method, and for dynamic problems the optimization seeks to maximize the first-order eigenfrequency subject to a given material limit like a volume. Numerical applications topologically optimizing dynamic wall structures with varied supports are studied to verify the non-stochastic interval analysis is also suitable to estimate topology optimization results with dynamic problems.

• Structural Engineering and Mechanics
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• 제66권4호
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• pp.465-476
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• 2018

In this paper, a new meta-heuristic optimization method is presented. This new method is named "Numbers Cup Optimization" (NCO). The NCO algorithm is inspired by the sport competitions. In this method, the objective function and the design variables are defined as the team and the team members, respectively. Similar to all cups, teams are arranged in groups and the competitions are performed in each group, separately. The best team in each group is determined by the minimum or maximum value of the objective function. The best teams would be allowed to the next round of the cup, by accomplishing minor changes. These teams get grouped again. This process continues until two teams arrive the final and the champion of the Numbers Cup would be identified. In this algorithm, the next cups (same iterations) will be repeated by the improvement of players' performance. To illustrate the capabilities of the proposed method, some standard functions were selected to optimize. Also, size optimization of three benchmark trusses is performed to test the efficiency of the NCO approach. The results obtained from this study, well illustrate the ability of the NCO in solving the optimization problems.

• 한국시뮬레이션학회논문지
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• 제27권2호
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• pp.83-90
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• 2018

본 연구에서는 선형의 기본 파라메타가 주어졌을 때, 선형 최적화를 통하여 고속으로 주행하는 활주선의 저항성능을 개선하는 기법을 제안하였다. 먼저 선행연구 된 활주선형을 기준 선형으로 채택한 뒤, 선형 변경지점을 정의해 설계변수로 하여 최적화 문제를 수립하였다. 계산 효율을 위하여 탐색공간을 이산화하고, 최적화 문제를 풀기위하여 DPSO(Discrete binary version of Particle Swarm Optimization) 알고리즘을 사용하였다. 최적화 수행 후 기준 선형과 수정 선형의 목적함수 출력의 비교를 수행하였고, 이를 통해 고속영역에서의 저항성능의 개선을 확인하였다.

• International Journal of Naval Architecture and Ocean Engineering
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• 제9권6호
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• pp.693-704
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• 2017

Blended-Wing-Body Underwater Glider (BWBUG), which has excellent hydrodynamic performance, is a new kind of underwater glider in recent years. In the shape optimization of BWBUG, the lift to drag ratio is often used as the optimization target. However this results in lose of internal space. In this paper, the energy reserve is defined as the direct proportional function of the internal space of BWBUG. A motion model, which relates gliding range to steady gliding motion parameters as well as energy consumption, is established by analyzing the steady-state gliding motion. The maximum gliding range is used as the optimization target instead of the lift to drag ratio to optimizing the shape of BWBUG. The result of optimization shows that the maximum gliding range of initial design is increased by 32.1% though an Efficient Global Optimization (EGO) process.

• 대한기계학회논문집A
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• 제26권7호
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• pp.1408-1415
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• 2002

In this paper, we investigate the effect and the importance of the accuracy of finite element analysis in the shape optimization based on the finite element method and improve the existing finite element which has inaccuracy in some cases. And then, the shape optimization is performed by using the improved finite element. One of the main stream to improve finite element is the prevention of locking phenomenon. In case of bending dominant problems, finite element solutions cannot be reliable because of shear locking phenomenon. In the process of shape optimization, the mesh distortion is large due to the change of the structure outline. So, we have to raise the accuracy of finite element analysis for the large mesh distortion. We cannot guarantee the accurate result unless the finite element itself is accurate or the finite elements are remeshed. So, we approach to more accurate shape optimization to diminish these inaccuracies by improving the existing finite element. The shape optimization using the modified finite element is applied to a two and three dimensional simple beam. Results show that the modified finite element has improved the optimization results.