• 한국전산유체공학회:학술대회논문집
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• 2005.10a
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• pp.163-167
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• 2005

A research to evaluate efficiency of design optimization was performed for aerodynamic design optimization problem in distributed computing environment. The aerodynamic analyses which take most of computational work during design optimization were divided into several jobs and allocated to associated PC clients through network. This is not a parallel process based on domain decomposition rather than a simultaneous distributed-analyses process using network-distributed computers. GBOM(gradient-based optimization method), SAO(Sequential Approximate Optimization) and RSM(Response Surface Method) were implemented to perform design optimization of transonic airfoil and to evaluate their efficiencies. One dimensional minimization followed by direction search involved in the GBOM was found an obstacle against improving efficiency of the design process in distributed computing environment. The SAO was found quite suitable for the distributed computing environment even it has a handicap of local search. The RSM is apparently the fittest for distributed computing environment, but additional trial and error works needed to enhance the reliability of the approximation model are annoying and time-consuming so that they often impair the automatic capability of design optimization and also deteriorate efficiency from the practical point of view.

• Korean Journal of Computational Design and Engineering
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• v.17 no.5
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• pp.375-386
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• 2012

Most engineering design problems require analyses or simulations to evaluate objective functions. However, a single simulation can take many hours or even days to finish for many real world problems. As a result, design optimization becomes impossible since they require hundreds or thousands of simulation evaluations. The surrogate-based optimization (SBO) strategy became a remedy for such computationally expensive analyses and simulations. A surrogate-based optimization strategy has been developed in this study in order to improve global optimization performance. The strategy is a heuristic algorithm and it exploits not only multiple surrogates, but also multiple optimizers. Multiple optimizations of multiple surrogate models yield multiple candidate design points of optima. During the sequential sampling process, the algorithm ranks candidate design points, selects the points as many as specified, and builds the improved surrogate model. Various mathematical functions with different numbers of design variables are chosen to compare the proposed method with the other most recent algorithm, MSEGO. The proposed method shows superior performance to the other method.

• Proceedings of the KSME Conference
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• 2003.04a
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• pp.570-573
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• 2003

Wheels for passenger car support the car weight with tires. and they transmit rolling and braking power into the ground. Whittliing away at wheel weight is more effective to boost fuel economy that lighting vehicle body structure. A shape of hole in disk is optimized for minimizing the weight of steel wheel. Pro/ENGINEER program is used for formulating the design model. and ANSYS package is selected for analyzing the design model. It has difficulties 10 interface these commercial software directly. For combining both programs. response surface methodology is applied to construct approximation functions for maximum stresses and maximum displacements are obtained by full factorial design of five levels. This steel wheel is modeled in 14-inch diameter of rim. and wide parameter of hole in disk is only selected as design variable for reducing the weight of steel wheel. PLBA(Pshenichny·Lim-Belegundu_arora) algorithm. which uses the second-order information in the direction finding problem and uses the active set strategy. is used for solving optimization problems.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.19 no.11
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• pp.1151-1157
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• 2009

Ensuring both product quality and reducing material cost are important issue for the design of the piping system of an air conditioner outdoor unit. This paper describes a shape optimization that achieves mass reduction of an air conditioner piping system while satisfying two design constraints on resonance avoidance and the maximum stress in the pipes. In order to obtain optimized design results with various analysis fields considered simultaneously, an automated multidisciplinary analysis system was constructed using PIAnO v.2.4, a commercial process integration and design optimization(PIDO) tool. As the first step of the automated analysis system, a finite element model is automatically generated corresponding to the specified shape of the pipes using a morphing technique included in HyperMesh. Then, the performance indices representing various design requirements (e.g. natural frequency, maximum stress and pipe mass) are obtained from the finite element analyses using appropriate computer-aided engineering(CAE) tools. A sequential approximate optimization(SAO) method was employed to effectively obtain the optimum design. As a result, the pipe mass was reduced by 18 % compared with that of an initial design while all the constraints were satisfied.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.34 no.7
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• pp.18-27
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• 2006

The conventional reliability based design optimization(RBDO) methods require high computational cost compared with the deterministic design optimization(DO) methods, therefore it is hard to apply directly to large-scaled problems such as an aerodynamic shape design optimization. In this study, to overcome this computational limitation the efficient RBDO procedure with the two-point approximation(TPA) and adjoint sensitivity analysis is proposed, that the computational requirement is nearly the same as DO and the reliability accuracy is good compared with that of RBDO. Using this, the 3-D aerodynamic shape design optimization is performed very efficiently.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2011.10a
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• pp.279-284
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• 2011

The calculation of Zwicker's loudness which is needed for multiple frequency response with a fine frequency resolution using the finite element (FE) procedure usually requires significant computation time since a numerical solution must be obtained for each considered frequency. Furthermore, if the analysis is the basis for an iterative optimization procedure this approach imposes high computational cost. In this work, we present an efficient approach for obtaining Zwicker's loudness via the Pad$\acute{e}$ approximants and applying in an acoustical topology optimization procedure. The paper is focused on an efficient and accurate calculation of Zwicker's loudness, design sensitivity analysis, and the acoustical topology optimization method by using Pad$\acute{e}$ approximants. The paper compares the efficient algorithm to results obtained by a standard FEM. Comparison are made both in terms of accuracy and in terms of CPU-times needed for the calculation.

• Journal of Korean Society of Steel Construction
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• v.12 no.4 s.47
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• pp.397-406
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• 2000

This study presents an effective optimal technique to control quantitatively lateral drift for tall steel braced frames subject to horizontal loads. In this paper, the displacement sensitivity depending on behavior characteristics of steel braced frames is established, and also the approximation concept that has the generality of the mathematical programming and can efficiently solve large scale problems is introduced. Especially, the commercially available standard steel sections are used for the discrete selection of member sizes. Three types of 12-story braced frames and a 30-story braced framework are presented to illustrate the features of the quantitative lateral drift control technique proposed in this study.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.18 no.3
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• pp.74-81
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• 2019

Herein, we performed a comparative study on approximate multi-objective design optimization, to realize a structural design to improve the weight and vibration performances of the knuckle - a car suspension component - considering various load conditions and vibration characteristics. In the approximate multi-objective optimization process, a regression meta-model was generated using the response surfaces method (RSM), while Kriging and back-propagation neural network (BPN) methods were applied for interpolation meta-modeling. The Pareto solutions, multi-objective optimal solutions, were derived using the non-dominated sorting genetic algorithm (NSGA-II). In terms of the knuckle design considered in this study, the characteristics and influence of the meta-model on multi-objective optimization were reviewed through a comparison of the approximate optimization results with the meta-models and the actual optimization.

• Journal of the Korean Society of Propulsion Engineers
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• v.18 no.1
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• pp.33-41
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• 2014

In a supersonic turbine, A rotor overlap technique reduced the chance of chocking in the rotor passage, and made the design pressure ratio satisfied. However, the technique also made additional losses, like a pumping loss, expansion loss, etc. Therefore, an approximate optimization technique was appled to find the optimal shape of overlap which maximizes the improvement of the turbine performance. The design variables were shape factors of a rotor overlap. An optimal design for rotor overlap reduces leakage mass flow rate at tip clearance by about 50% and increases about 4% of total-static efficiency compared with the base model. It was found that the most effective design variable is the tip overlap and that the hub overlap size is the lowest.

• Journal of Korea Society of Industrial Information Systems
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• v.28 no.1
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• pp.27-34
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• 2023

The optimal assignment problem between agents and tasks is known as one of the representative problems of combinatorial optimization and an NP-hard problem. This paper covers multi agent-multi task assignment problems with uncertain completion probability. The completion probabilities are generally uncertain due to endogenous (agent or task) or exogenous factors in the system. Assignment decisions without considering uncertainty can be ineffective in a real situation that has volatility. To consider uncertain completion probability mathematically, a mathematical formulation with stochastic programming is illustrated. We also present an algorithm by using the sample average approximation method to solve the problem efficiently. The algorithm can obtain an assignment decision and the upper and lower bounds of the assignment problem. Through numerical experiments, we present the optimality gap and the variance of the gap to confirm the performances of the results. This shows the excellence and robustness of the assignment decisions obtained by the algorithm in the problem with uncertainty.