• Proceedings of the KSR Conference
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• 2003.05a
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• pp.567-573
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• 2003

Korean High Speed Train (KHST) has been tested on high speed line in JungBu site since it was developed in 2002. The data acquisition system was developed to accomplish successfully this on-line test for proving the dynamic Performance of KHST. This system was consist of the personal computers based on National Instrument PXI modules and the test programs based on Labview 6i. This paper shows that this system is efficient to acquire the test data through the multi-channels connected the accelerometers which located in long distance places and flexible to change and add channels for data acquisition. The dynamic analysis of an on-line test is very complicate because the environmental conditions, as examples radius of curve, inclination of the track, tunnels, bridges, and so forth, and running conditions, as examples driving, braking, the number of working motors, and so forth, have an effect on the results. Therefor, the analysis method is important and this paper proposes the efficient procedure graphically, showing the proposed method simplify the accelerations of 5th bogie frame acquired during the on-line test for KHST.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.36 no.8
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• pp.921-927
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• 2012

Reliability-based design optimization (RBDO) can be used to determine the reliability of a system by means of probabilistic design criteria, i.e., the possibility of failure considering stochastic features of design variables and input parameters. To assure these criteria, various reliability analysis methods have been developed. Most of these methods assume that distribution functions are continuous. However, in real problems, because real data is often discrete in form, it is important to estimate the distributions for discrete information during reliability analysis. In this study, we employ the Akaike information criterion (AIC) method for reliability analysis to determine the best estimated distribution for discrete information and we suggest an RBDO method using AIC. Mathematical and engineering examples are illustrated to verify the proposed method.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.36 no.8
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• pp.873-879
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• 2012

Metamodels have been developed with a variety of design optimization techniques in the field of structural engineering over the last decade because they are efficient, show excellent prediction performance, and provide easy interconnections into design frameworks. To construct a metamodel, a sequential procedure involving steps such as the design of experiments, metamodeling techniques, and validation techniques is performed. Because validation techniques can measure the accuracy of the metamodel, the number of presampled points for an accurate kriging metamodel is decided by the validation technique in the sequential kriging metamodel. Because the interpolation model such as the kriging metamodel based on computer experiments passes through responses at presampled points, additional analyses or reconstructions of the metamodels are required to measure the accuracy of the metamodel if existing validation techniques are applied. In this study, we suggest a sensitivity validation that does not require additional analyses or reconstructions of the metamodels. Fourteen two-dimensional mathematical problems and an engineering problem are illustrated to show the feasibility of the suggested method.