• Journal of the Korean Society of Safety
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• v.12 no.3
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• pp.120-131
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• 1997

This paper presents an approximate reanalysis methods of structures based on substructuring for an effective optimization of large-scale structural systems. In most optimal design procedures the analysis of the structure must be repeated many times. In particular, one of the main obstacles in the optimization of structural systems are involved high computational cost and expended long time in the optimization of large-scale structures. The purpose of this paper is to evaluate efficiently the structural behavior of new designs using information from previous ones, without solving basic equations for successive modification in the optimal design. The proposed reanalysis procedure is combined Taylor series expansions which is a local approximation and reduced basis method which is a global approximation based on substructuring. This technique is to choose each of the terms of Taylor series expansions as the basis vector of reduced basis method in substructuring system which is one of the most effective analysis of large -scale structures. Several numerical examples illustrate the effectiveness of the solution process.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.25 no.3
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• pp.537-547
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• 2001

The paper proposes the fuzzy logic global approximate optimization strategies in optimal sizing of automotive A-pillar trim with rib structures for occupant head protection. Two different strategies referred to as evolutionary fuzzy modeling (EFM) and neuro-fuzzy modeling (NFM) are implemented in the context of global approximate optimization. EFM and NFM are based on soft computing paradigms utilizing fuzzy systems, neural networks and evolutionary computing techniques. Such approximation methods may have their promising characteristics in a case where the inherent nonlinearity in analysis model should be accommodated over the entire design space and the training data is not sufficiently provided. The objective of structural design is to determine the dimensions of rib in A-pillar, minimizing the equivalent head injury criterion HIC(d). The paper describes the head-form modeling and head impact simulation using LS-DYNA3D, and the approximation procedures including fuzzy rule generation, membership function selection and inference process for EFM and NFM, and subsequently presents their generalization capabilities in terms of number of fuzzy rules and training data.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.30 no.6 s.249
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• pp.691-697
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• 2006

Metamodel, model of model, has been widely used to improve an efficiency of optimization process in engineering fields. However, global metamodels of constraints in a constrained optimization problem are required good accuracy around neighborhood of optimum point. To satisfy this requirement, more sampling points must be located around the boundary and inside of feasible region. Therefore, a new sampling strategy that is capable of identifying feasible domain should be applied to select sampling points for metamodels of constraints. In this research, we suggeste sequential feasible domain sampling that can locate sampling points likely within feasible domain by using penalty function method. To validate the excellence of feasible domain sampling, we compare the optimum results from the proposed method with those form conventional global space-filling sampling for a variety of optimization problems. The advantages of the feasible domain sampling are discussed further.

• Journal of applied mathematics & informatics
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• v.15 no.1_2
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• pp.173-184
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• 2004

In this paper, we find the approximate solution of a second order nonlinear partial differential equation on a simple connected region in $R^2$. We transfer this problem to a new problem of second order nonlinear partial differential equation on a rectangle. Then, we transformed the later one to an equivalent optimization problem. Then we consider the optimization problem as a distributed parameter system with artificial controls. Finally, by using the theory of measure, we obtain the approximate solution of the original problem. In this paper also the global error in $L_1$ is controlled.

• International Journal of Fluid Machinery and Systems
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• v.10 no.3
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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.

• Korean Management Science Review
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• v.28 no.2
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• pp.1-16
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• 2011

In this study we suggested two optimization models to answer a question from an investor standpoint : how many convertible bonds should one convert, and how many keep? One model minimizes certain risk to the minimum required expected return, the other maximizes the expected return subject to the maximum acceptable risk. In comparison with Markowitz portfolio models, which use the variance of return, our models used Conditional Value-at-Risk(CVaR) for risk measurement. As a coherent measurement, CVaR overcomes the shortcomings of Value-at-Risk(VaR). But there are still difficulties in solving CVaR including optimization models. For this reason, we adopted Rockafellar and Uryasev's[18, 19] approach. Then we could approximate the models as linear programming problems with scenarios. We also suggested to extend the models with credit risk, and applied examples of our models to Hynix 207CB, a convertible bond issued by the global semiconductor company Hynix.

• Journal of the Korean Society of Industry Convergence
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• v.26 no.6_3
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• pp.1279-1288
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• 2023

Recently, domestic leisure boats have been actively researching eco-friendly product development to enter the global market. Since the hulls of existing leisure boats are mainly made of fiber reinforced plastic (FRP) or aluminum, design techniques for securing structural safety by applying related materials have been mainly studied. In this study, an initial structural design safety assessment of a trimaran pontoon leisure boat with a modular hull structure and eco-friendly high-density polyethylene (HDPE) material was conducted, and sensitivity evaluation and optimization analysis for lightweight design were performed. The initial structural design safety assessment was carried out by creating a finite element analysis model and applying the loading conditions specified in the ship classification regulation to check whether the specified allowable stresses are satisfied. For the sensitivity evaluation, the influence of stress and weight of each hull structural member was evaluated using the orthogonal array design of experiments method, and an approximate model based on the response surface method was generated using the results of the design of experiments. The optimization analysis set the thickness of the hull structural members as the design variable and considered the optimal design formulation to minimize the weight while satisfying the allowable stress. The algorithm of the optimization analysis applied the Gradient-population Based Optimizer (GBO) to improve the accuracy of the optimal solution convergence while reducing the numerical cost. Through this study, the optimal design of a newly developed eco-friendly trimaran pontoon leisure boat with a weight reduction of 10% was presented.

• International Journal of Aeronautical and Space Sciences
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• v.17 no.1
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• pp.29-36
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• 2016

Tensairity girder is a light weight inflatable fabric structural concept which can be used in road emergency transportation. It uses low pressure air to stabilize compression elements against buckling. With the purpose of obtaining the comprehensive target of minimum deflection and weight under ultimate load, the cross-section and the inner pressure of tensairity girder was optimized in this paper. The Variable Complexity Modeling (VCM) method was used in this paper combining the Kriging approximate method with the Finite Element Analysis (FEA) method, which was implemented by ABAQUS. In the Kriging method, the sample points of the surrogate model were outlined by Design of Experiment (DOE) technique based on Optimal Latin Hypercube. The optimization framework was constructed in iSIGHT with a global optimization method, Multi-Island Genetic Algorithm (MIGA), followed by a local optimization method, Sequential Quadratic Program (SQP). The result of the optimization gives a prominent conceptual design of the tensairity girder, which approves the solution architecture of VCM is feasible and efficient. Furthermore, a useful trend of sensitivity between optimization variables and responses was performed to guide future design. It was proved that the inner pressure is the key parameter to balance the maximum Von Mises stress and deflection on tensairity girder, and the parameters of cross section impact the mass of tensairity girder obviously.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.33 no.8
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• pp.753-759
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• 2009

Feed-forward neural networks have been widely used as function approximation tools in the context of global approximate optimization. In the present study, a wavelet neural network (WNN) which is based on wavelet transform theory is suggested as an alternative to a traditional back-propagation neural network (BPN). The basic theory of wavelet neural network is briefly described, and approximation performance is tested using a nonlinear multimodal function and a composite rotor blade analysis problem. Laplacian of Gaussian function, Mexican function, and Morlet function are considered during the construction of WNN architectures. In addition, approximation results from WNN are compared with those from BPN.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.34 no.9
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• pp.33-44
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• 2006

As many scientific phenomena are now investigated using complex computer models, the effective use of Kriging on physical problems has been expanded to provide global approximations for optimization problems. This paper is focused on the two types of strategies to improve efficiency and accuracy of approximate optimization models using Kriging. These methods are performed by the stochastic process, stochastic-localization method(SLM), as the criterion to move the local domains and the design of experiments(DOE), the classical design and space-filling design. The proposed methodology is applied to the designs of 3-bar truss, Sandgren's pressure vessel, and honeycomb upper platform of a satellite structure.