• Journal of the Computational Structural Engineering Institute of Korea
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• v.21 no.5
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• pp.413-419
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• 2008

Deterministic design optimization (DO) does not explicitly deal with a variety of factors from inherent randomness and uncertainties. Reliability based design optimization(RBDO) is necessary to use in engineering systems in order to guarantee quality and performance of product. In this paper, design variables are considered as random variables. Standard deviation according to change of design variables have changed as much as coefficient of variation. And, if the standard deviation is error of manufacturing, standard deviation-mean relation is concave form. We obtain reliability index using advanced first order second moment method(AFOSM). This paper is examined by solving two examples and the results are compares with DO, RBDO and suggested RBDO.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.37 no.3
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• pp.255-266
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• 2009

In this study, a robust aerospace system design process for the aerospace system is developed by considering the uncertainties of user requirements, manufacturing errors, and operational environment variation. User requirements are analyzed and quantified by decision making models and system engineering methods to select alternative concepts which satisfies the various requirements. Robust design and optimization method is applied to derive the robust solution of the selected system. First, a variance of objective function is calculated, and a mean value and a variance of target value are determined by the deterministic design optimization results of the system. A robust optimum design formulation is then needed to derive the robust solution that minimizes the variance of the response and moves the mean values to the target value. It is applied to Very Light Jet (VLJ) aircraft to which much attention is paid recently in civil aerospace market.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.19 no.4
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• pp.529-538
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• 2010

This paper presents a reliability-based topology optimization (RBTO) based on bidirectional evolutionary structural optimization (BESO). In design of a structure, uncertain conditions such as material property, operational load and dimensional variation should be considered. Deterministic topology optimization (DTO) is performed without considering the uncertainties related to the design variables. However, the RBTO can consider the uncertainty variables because it can deal with the probabilistic constraints. The reliability index approach (RIA) and the performance measure approach (PMA) are adopted to evaluate the probabilistic constraints in this study. In order to apply the BESO to the RBTO, sensitivity number for each element is defined as the change in the reliability index of the structure due to removal of each element. Smoothing scheme is also used to eliminate checkerboard patterns in topology optimization. The limit state indicates the margin of safety between the resistance (constraints) and the load of structures. The limit State function expresses to evaluate reliability index from finite element analysis. Numerical examples are presented to compare each optimal topology obtained from RBTO and DTO each other. It is verified that the RBTO based on BESO can be effectively performed from the results.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2002.05a
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• pp.651-659
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• 2002

Component Mode Synthesis (CMS) is a dynamic substructuring technique to get an approximate eigensolutions of large degree-of-freedom structures divisible into several components. But, In practice. most of large structures are modeled by different teams of engineers. and their respective finite element models often require different mesh resolutions. As a result, the finite element substructure models can be non-conforming and/or incompatible. In this work, A hybrid version of component mode synthesis using a localized lagrange multiplier to treat the non-conforming mesh problem was derived. Evolution Strategies (ESs) is a stochastic numerical optimization technique and has shown a robust performance for solving deterministic problems. An ESs conducts its search by processing a population of solutions for an optimization problem based on principles from natural evolution. An optimization example for raising the first natural frequency of a plate structure using beam stiffeners was presented using hybrid component mode synthesis and robust evolution strategies (RES) optimization technique. In the example. the design variables are the positions and lengths of beam stiffeners.

• Geomechanics and Engineering
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• v.7 no.5
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• pp.525-537
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• 2014

The typical design of ground improvement with prefabricated vertical drains (PVD) and surcharge preloading involves a series of deterministic analyses using averaged or mean soil properties for the various combination of the PVD spacing and surcharge preloading height that would meet the criteria for minimum consolidation time and required degree of consolidation. The optimum design combination is then selected in which the total cost of ground improvement is a minimum. Considering the variability and uncertainties of the soil consolidation parameters, as well as considering the effects of soil disturbance (smear zone) and drain resistance in the analysis, this study presents a stochastic cost optimization of ground improvement with PVD and surcharge preloading. Direct Monte Carlo (MC) simulation and importance sampling (IS) technique is used in the stochastic analysis by limiting the sampled random soil parameters within the range from a minimum to maximum value while considering their statistical distribution. The method has been verified in a case study of PVD improved ground with preloading, in which average results of the stochastic analysis showed a good agreement with field monitoring data.

• Structural Engineering and Mechanics
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• v.56 no.5
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• pp.707-726
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• 2015

There is a growing trend of considering uncertainty in optimization process since last few decades. In this regard, Robust Design Optimization (RDO) scheme has gained increasing momentum because of its virtue of improving performance of structure by minimizing the variation of performance and ensuring necessary safety and feasibility of constraint under uncertainty. In the present study, RDO of reinforced concrete folded plate and shell structure has been carried out incorporating uncertainty in the relevant parameters by Monte Carlo Simulation. Folded plate and shell structures are among the new generation popular structures often used in aesthetically appealing constructions. However, RDO study of such important structures is observed to be scarce. The optimization problem is formulated as cost minimization problem subjected to the force and displacements constraints considering dead, live and wind load. Then, the RDO is framed by simultaneously optimizing the expected value and the variation of the performance function using weighted sum approach. The robustness in constraint is ensured by adding suitable penalty term and through a target reliability index. The RDO problem is solved by Sequential Quadratic Programming. Subsequently, the results of the RDO are compared with conventional deterministic design approach. The parametric study implies that robust designs can be achieved by sacrificing only small increment in initial cost, but at the same time, considerable quality and guarantee of the structural behaviour can be ensured by the RDO solutions.

• 한국전산유체공학회:학술대회논문집
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• 2010.05a
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• pp.50-54
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• 2010

In this study, reliability-based design optimizations of airfoils were performed. PARSEC function was used to consider the uncertainty of the aerodynamic shape for the reliability-based shape optimization of airfoils. Among aerodynamic performance. The accuracy of the reliability analysis was compared with other method and it was found that the moment method predicts the probability accurately. Deterministic and reliability-based optimizations were performed for shape of the RAE2822 airfoil and it was demonstrated that reliability-based optimizations the aerodynamic performances under uncertainties of the shape of the airfoil.

• Transactions of the Korean Society of Mechanical Engineers
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• v.17 no.12
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• pp.3063-3072
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• 1993

The layout is an important and difficult problem in industrial applications like sheet metal manufacturing, garment making, circuit layout, plant layout, and land development. The module layout problem is known to be non-deterministic polynomial time complete(NP-complete). To efficiently find an optimal layout from a large number of candidate layout configuration a heuristic algorithm could be used. In recent years, a number of researchers have investigated the combinatorial optimization problems by using neural network principles such as traveling salesman problem, placement and routing in circuit design. This paper describes the application of Self-organizing Feature Maps(SOM) of the Kohonen network and Simulated Annealing Algorithm(SAA) to the layout problem of the two-dimensional rectangular shapes.

• Journal of Industrial Technology
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• v.23 no.A
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• pp.15-20
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• 2003

Stochastic simulation technique has advantages over deterministic simulation in various engineering analysis, since stochastic simulation can take into consideration of scattering of various design variables, which is inherent characteristics of physical world. In this work, Monte-Carlo simulation mothod in ADAMS/Insight for steady-state cornering and J-turn behavior of a truck with design variables like hard points and busing stiffnesses have performed to achieve better dynamic performance. The main purpose is to improve understeer gradient at steady-state cornering and minimize peak lateral acceleration and peak yaw rate at J-turn. Through correlation analysis, design variables that have high impacts on the cornering behavior were selected, and significant performance improvement has been achieved by appropriately changing the high impact design variables.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.8 no.4
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• pp.110-121
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• 2009

It is essential to embed product quality in the design process to win the global competition. Many components found in many products including automobiles and electronic devices are fabricated using sheet metal forming processes. Wrinkle and fracture are two types of defects frequently found in the sheet metal forming process. Reducing such defects is a hard problem as they are affected by many uncontrollable factors. Attempts to solve the problem based on traditional deterministic optimization theories are often led to failures. Furthermore, the wrinkle and fracture are conflicting defects in such a way that reducing one defect leads to increasing the other. Hence, it is a difficult task to reduce both of them at the same time. In this research, a new design method for reducing the rates of conflicting defects under uncontrollable factors is presented by using operating window and a sequential search procedure. A new SN ratio is proposed to overcome the problems of a traditional SN ratio used in the operating window technique. The method is applied to optimizing the robust design of a sheet metal forming process. To show the effectiveness of the proposed method, a comparison is made between the traditional and the proposed methods using simulation software, applied to a design of particular sheet metal forming process problem. The results show that the proposed method always gives a more robust design that is less sensitive to noises than the traditional method.