• Journal of computational fluids engineering
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• v.14 no.4
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• pp.31-40
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• 2009

Although optimization by sequentially refining metamodels is known to be computationally very efficient, the metamodel that can be used for this purpose is limited to Kriging method due to the difficulties related with sample points selections. The present study suggests a novel method for sequentially refining metamodels using Pareto Frontiers, which can be used independent of the type of metamodels. It is shown from the examples that the present method yields more accurate metamodels compared with full-factorial optimization and also guarantees global optimum irrespective of the initial conditions. Finally, in order to prove the generality of the present method, it is applied to a 2D transonic airfoil optimization problem, and the successful design results are obtained.

• Journal of Mechanical Science and Technology
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• v.20 no.8
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• pp.1169-1182
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• 2006

Robust design technology has been applied to versatile engineering problems to ensure consistency in product performance. Since 1980s, the concept of robust design has been introduced to numerical optimization field, which is called the robust optimization. The robustness in the robust optimization is determined by a measure of insensitiveness with respect to the variation of a response. However, there are significant difficulties associated with the calculation of variations represented as its mean and variance. To overcome the current limitation, this research presents an implementation of the approximate statistical moment method based on kriging metamodel. Two sampling methods are simultaneously utilized to obtain the sequential surrogate model of a response. The statistics such as mean and variance are obtained based on the reliable kriging model and the second-order statistical approximation method. Then, the simulated annealing algorithm of global optimization methods is adopted to find the global robust optimum. The mathematical problem and the two-bar design problem are investigated to show the validity of the proposed method.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.34 no.5
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• pp.541-547
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• 2010

The rigorous validation of the accuracy of metamodels is an important topic in research on metamodel techniques. Although a leave-k-out cross-validation technique involves a considerably high computational cost, it cannot be used to measure the fidelity of metamodels. Recently, the mean$_0$ validation technique has been proposed to quantitatively determine the accuracy of metamodels. However, the use of mean$_0$ validation criterion may lead to premature termination of a sampling process even if the kriging model is inaccurate. In this study, we propose a new validation technique based on the mean and variance of the response evaluated when sequential sampling method, such as maximum entropy sampling, is used. The proposed validation technique is more efficient and accurate than the leave-k-out cross-validation technique, because instead of performing numerical integration, the kriging model is explicitly integrated to accurately evaluate the mean and variance of the response evaluated. The error in the proposed validation technique resembles a root mean squared error, thus it can be used to determine a stop criterion for sequential sampling of metamodels.

• Transactions of the Korean Society of Automotive Engineers
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• v.21 no.4
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• pp.16-22
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• 2013

In this study, we propose the method of optimally changing material of BIW for minimizing weight while satisfying vehicle requirements on static stiffness. First, we formulate a material selection optimization problem. Next, we establish the CAE procedure of evaluating static stiffness. Then, to enhance the efficiency of design work, we integrate and automate the established CAE procedure using a commercial process integration and design optimization (PIDO) tool, PIAnO. For effective optimization, we adopt the approach of metamodel based approximate optimization. As a sampling method, an orthogonal array (OA) is used for selecting sampling points. The response values are evaluated at the sampling points and then these response values are used to generate a metamodel of each response using the linear polynomial regression (PR) model. Using the linear PR model, optimization is carried out an evolutionary algorithm (EA) that can handle discrete design variables. Material optimization result reveals that the weight is reduced by 44.8% while satisfying all the design constraints.

• Journal of the Korean Society for Railway
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• v.13 no.2
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• pp.222-228
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• 2010

In this paper, we propose a optimization approach for the Electric Locomotive Overhaul Maintenance Facility (ELOMF), which aims at the simulation optimization so as to meet the design specification. In simulation design, we consider the critical path and sensitivity analysis of the critical (dependent) factors and the design (independent) parameters for the parameter selection and reduction of the metamodel. Therefore, we construct the multi-objective non-linear programming. The objective function is normalized for the generalization of design parameter while the constraints are composed of the simulation-based regression metamodel for the critical factors and design factor's domain. Then the effective solution procedure based on the pareto optimal solution set is proposed. This approach provides a comprehensive approach for the optimization of Train Overhaul Maintenance Facility(TOMF)'s design parameters using the simulation and metamoels.

• Journal of Advanced Marine Engineering and Technology
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• v.37 no.8
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• pp.893-904
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• 2013

In this paper, a Kriging metamodel-based multi-objective optimization strategy in conjunction with an NSGA-II(non-dominated sorted genetic algorithm-II) has been employed to optimize the valve-plate shape of the axial piston pump utilizing 3D CFD simulations. The optimization process for minimum pressure ripple and maximum pump efficiency is composed of two steps; (1) CFD simulation of the piston pump operation with various combination of six parameters selected based on the optimization principle, and (2) applying a multi-objective optimization approach based on the NSGA-II using the CFD data set to evaluate the Pareto front. Our exploration shows that we can choose an optimal trade-off solution combination to reach a target efficiency of the axial piston pump with minimum pressure ripple.

• Journal of KIISE:Software and Applications
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• v.34 no.3
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• pp.179-195
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• 2007

Traditional software development method is not suitable for Web application development because of characteristics of Web application such as frequent requirements change, different architectures and models and quick-to-market delivery. In this paper we propose a Web application development method adaptable to requirements change while we systematically model Web application using extended UML model. The metamodel is independent to specific languages and technologies because we define the metamodel using extended UML model. Proposed process is described by SPEM(Software Process Engineering Metamodel) profile. A process supporting tool execute and customize process. To model Web applications systematically and effectively, a navigation modeling and a component communication modeling tools are provided. In a case study, we show the usefulness of our process and model.

• Journal of KIISE
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• v.42 no.9
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• pp.1147-1161
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• 2015

The ISO/IEC 11179 Metadata registry (MDR) is an international standard that was developed to register and share metadata. ISO/IEC 11179 represents an MDR as a metamodel that is an object model. However, it is difficult to develop an MDR based on ISO/IEC 11179 because the standard has no clear criteria to transform the metamodel into a database. In this paper, we suggest the design of an MDR data model that is based on object-relational transformation methodology (ORTM) for the MDR implementation. Hence, we classify the transformation methods of ORTM according to the corresponding relationships. After classification, we propose modeling rules by defining the standard use of the transformation. This paper builds the relational database tables as an implementation result of an MDR data model. Through experiments and evaluation, we verify the proposed modeling rules and evaluate the suitability of the created table structures. As the result, the proposed method shows that the table structures preserve classes and relationships of the standard metamodel well.

• Journal of Advanced Navigation Technology
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• v.27 no.2
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• pp.228-234
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• 2023

As the increasing demand for high-quality software, there is huge requiring for quality certification of international standards, industrial functional safety (IEC 61508), automotive (ISO 26262), embedded software guidelines for weapon systems, etc., in the industry. Software companies are very difficult to systematically acquire the quality certification in terms of cost and manpower of Startup, venture small-sized companies. For their companies one test case automatic generation is considered as a core technique to evaluate or improve software quality. This paper proposes a test case automatic generation method based on the design decision table for system and software design verification. We apply the proposed method with OMG's standard techniques of metamodel and model transformation for automatically generating test cases. To do this, we design the metamodels of design decision table (Model) and test case document (Text) and define model transformation to automatically generate test cases, which will expect to easily work MC/DC coverage.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.38 no.6
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• pp.557-563
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• 2010

This paper addresses the issue of adaptive global optimization using Kriging metamodel known as EGO(Efficient Global Optimization). The algorithm adaptively chooses where to generate subsequent samples based on an explicit trade-off between reduction of global uncertainty and exploration of the region of the interest. A strategy that saves the computational cost by using expectations derived from probabilistic nature of approximate model is proposed. At every iteration, a candidate test point that seems to be feasible/inactive or has little possibility to improve for minimum is identified and excluded from updating approximate models. By doing that the computational cost is saved without loss of accuracy.