• Journal of the Korean Society for Precision Engineering
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• v.30 no.11
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• pp.1179-1185
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• 2013

In this study, the design optimization of the automotive side member is performed to maximize the crash energy absorption efficiency per unit weight. Design parameters which seriously influence on the frontal crash performance are selected through the sensitivity analysis using the Plackett-Burman design method. And also the design variables, which are determined from the sensitivity analysis, are optimized by two methods. One is conventional approximate optimization method which uses the statistical design of experiments (DOE) and response surface method (RSM). The other is a methodology derived from previous work by the authors, which is called sequential design of experiments (SDOE), to reduce a trial and error procedure and to find an appropriate condition for using micro-genetic algorithm. The proposed optimization technique shows that the automotive side member structure can be designed considering the frontal crash performance.

• Transactions of the Korean Society of Automotive Engineers
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• v.20 no.6
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• pp.1-8
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• 2012

Multidisciplinary design optimization (MDO) for a suspension component of the vehicle front suspension was performed in this research. Shapes and thicknesses of the subframe were optimized to satisfy multi-disciplinary design requirements; weight, fatigue, crash, noise, vibration, and harshness (NVH), and kinematic and compliance (K&C). Analyses procedures of the performance disciplines were integrated and automated by using the process integration and design optimization (PIDO) technique, and the integrated and automated analyses environments enabled various types of analytic design methodologies for solving the MDO problem. We applied an approximate optimization technique which involves sequential sampling and metamodeling. Since the design variables for thicknesses should be dealt as discrete variables. the evolutionary algorithm is selected as optimization technique. The MDO problem was formulated three types of problems according to the order of priorities among the performance disciplines, and the results of MDO provided design alternatives for various design situations.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.33 no.12
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• pp.1464-1470
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• 2009

RBDO approach based on a sampling method with the Kriging metamodel and Constraint Boundary Sampling (CBS), which is sequential sampling method to generate metamodels is proposed. The major advantage of the proposed RBDO approach is that it does not require Most Probable failure Point (MPP) which is essential for First-Order Reliability Method (FORM)-based RBDO approach. The Monte Carlo Sampling (MCS), most well-known method of the sampling methods for the reliability analysis is used to assess the reliability of constraints. In addition, a Cumulative Distribution Function (CDF) of the constraints is approximated using Moving Least Square (MLS) method from empirical distribution function. It is possible to acquire a probability of failure and its analytic sensitivities by using an approximate function of the CDF for the constraints. Moreover, a concept of inactive design is adapted to improve a numerical efficiency of the proposed approach. Computational accuracy and efficiency of the proposed RBDO approach are demonstrated by numerical and engineering problems.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.42 no.4
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• pp.284-290
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• 2014

To successfully explore the moon by lunar lander, it is essential to guarantee the safe landing of lunar lander. Therefore, efficient shock absorption system of lunar lander should be designed in order to reduce landing impact force. Also, for more practical design of lunar lander, it is important to consider the effect of lunar regolith. In the line of thought, finite element model of lunar lander considering the effect of lunar regolith is developed. To reduce landing impact force, optimization of shock absorption system for lunar lander has been carried out. In optimization, sequential approximate optimization method based on meta-model is used. Through the result of optimization, it is verified that landing impact force on lunar lander can be efficiently reduced by the present optimization procedure.

• Proceedings of the Korean Society Of Semiconductor Equipment Technology
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• 2006.10a
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• pp.35-38
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• 2006

디스플레이 산업에 이용되는 적층판(layered plates)의 결합공정 중 냉각공정에서 열팽창계수의 차이로 인해 잔류응력이 발생하고 심하면 적층판에 크랙(crack)이 발생한다. 본 연구에서는 적층판의 결합공정을 대상으로 현상을 분석하고 이 과정을 시뮬레이션하는 해석 프로그램을 개발하였다. 또한 이를 토대로 향후의 새로운 제품에 대해서도 크랙과 같은 문제점을 최소화 할 수 있는 신뢰성 있는 공정 셋업을 제시하기 위해 차원감소법(dimension reduction method)과 근사화 방법인 반응표면법(response surface method), 순차적 근사최적화 기법(Sequential Approximate Optimization, SAO)을 이용하여 신뢰성기반의 강건최적설계를 하였다.

• Structural Engineering and Mechanics
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• v.5 no.1
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• pp.59-68
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• 1997

A two-step method is presented for the optimum design of trusses with available sections under stress and Euler buckling constraints. The shape design of the truss is used as a means to convert the discrete solution into a continuous one. In the first step of the method, a continuous solution is obtained by sizing and shape design using an approximate polynomial expression for the buckling coefficients. In the second step, the member sizes obtained are changed to the nearest available sections and the truss is reconfigured by using the exact values for the buckling coefficients. The optimizer used is based on the sequential quadratic programming and the gradients are evaluated in closed form. The method is illustrated by two numerical examples.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.37 no.11
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• pp.1397-1405
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• 2013

The optimal rib pattern design of B-pillar trim considering occupant head protection can be determined by two methods. One is the conventional approximate optimization method that uses the statistical design of experiments (DOE) and response surface method (RSM). Generally, approximated optimum results are obtained through the iterative process by trial-and-error. The quality of results strongly depends on the factors and levels assigned by a designer. The other is a methodology derived from previous work by the authors, called the sequential design of experiments (SDOE), to reduce the trial-and-error procedure and to find an appropriate condition for using artificial neural network (ANN) systematically. An appropriate condition is determined from the iterative process based on the analysis of means. With this new technique and ANN, it is possible to find an optimum design accurately and efficiently.

• International conference on construction engineering and project management
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• 2015.10a
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• pp.540-543
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• 2015

Solar energy is one of the most important alternative energy sources which have been shown to meet high levels of heating and cooling demands in buildings. However, the efficiencies to satisfy these demands using solar energy significantly vary based on the characteristics of individual building. Therefore, this paper is focused on developing the methodology which can help to design optimal solar system for heating and cooling to be in cooperated within the existing buildings according to their load profiles. This research has established the Solar Heating and Cooling (SHC) system which is composed of collectors, absorption chiller, boiler and heat storage tank. Each component of SHC system is analyzed and made by means of Modelica Language and Pistache tool is verified the results. Sequential approximate optimization (SAO) and meta-models determined to 15 design parameters to optimize SHC system. Finally, total coefficient of performance (COP) of the entire SHC system is improved approximately 7.3% points compared to total COP of the base model of the SHC system.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.40 no.6
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• pp.587-593
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• 2016

Reliability-based design optimization (RBDO) requires a high computational cost owing to its reliability analysis. A surrogate model is introduced to reduce the computational cost in RBDO. The accuracy of the reliability depends on the accuracy of the surrogate model of constraint boundaries in the surrogated-model-based RBDO. In earlier researches, constraint boundary sampling (CBS) was proposed to approximate accurately the boundaries of constraints by locating sample points on the boundaries of constraints. However, because CBS uses sample points on all constraint boundaries, it creates superfluous sample points. In this paper, efficient constraint boundary sampling (ECBS) is proposed to enhance the efficiency of CBS. ECBS uses the statistical information of a kriging surrogate model to locate sample points on or near the RBDO solution. The efficiency of ECBS is verified by mathematical examples.