• Journal of the Korea Society for Simulation
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• v.25 no.3
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• pp.97-105
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• 2016

This paper discusses the effect of numerical reinforcement modeling methods on the penetration performance of a penetrator into a concrete target. AUTODYN-3D has been used to conduct the numerical penetration analyses. In order to validate the computational approach, experimental data of Hanchak have been compared to a computation result and a reasonably good agreement could be obtained. The strength and the diameter of a reinforcement have been changed to find out the effect of reinforcement modeling methods on the penetration performance. The impact locations and velocities of a penetrator are also changed to investigate the effect of reinforcement modeling methods. Residual velocities of a penetrator are quantitatively compared in detail for the evaluation of reinforcement modeling effects on the penetration performance.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.30 no.1
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• pp.87-94
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• 2017

Reliability analysis(RA) and Reliability-based design optimization(RBDO) require statistical modeling of input random variables, which is parametrically or nonparametrically determined based on experimental data. For the parametric method, goodness-of-fit (GOF) test and model selection method are widely used, and a sequential statistical modeling method combining the merits of the two methods has been recently proposed. Kernel density estimation(KDE) is often used as a nonparametric method, and it well describes a distribution function when the number of data is small or a density function has multimodal distribution. Although accurate statistical models are needed to obtain accurate RA and RBDO results, accurate statistical modeling is difficult when the number of data is small. In this study, the accuracy of two statistical modeling methods, SSM and KDE, were compared according to the number of data. Through numerical examples, the RA results using the input models modeled by two methods were compared, and appropriate modeling method was proposed according to the number of data.

• Journal of the Korean Data and Information Science Society
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• v.17 no.2
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• pp.269-278
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• 2006

This paper presents the new hybrid data mining technique using error pattern, modeling of improving classification accuracy. The proposed method improves classification accuracy by combining two different supervised learning methods. The main algorithm generates error pattern modeling between the two supervised learning methods(ex: Neural Networks, Decision Tree, Logistic Regression and so on.) The Proposed modeling method has been applied to the simulation of 10,000 data sets generated by Normal and exponential random distribution. The simulation results show that the performance of proposed method is superior to the existing methods like Logistic regression and Discriminant analysis.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.29 no.8 s.239
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• pp.1153-1160
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• 2005

Many methods of spot weld modeling have been developed to increase efficiency and guarantee the robustness fer the CAE analysis. In this research they are introduced and the performance is compared in a viewpoint of stiffness and durability. For evaluating the performance a multi-spot welded specimen as well as two single welded specimen is used. The results show that the CWELD element considering 'patch to patch' is stiffer than other modeling methods in stiffness and more conservative in durability. It also offers simple modeling and since it is much easier to maintain the orrhogonality of the BAR element expressing a nugget, we can obtain more exact reaction forces and moments in a nugget. Therefore the CWELD element is the most excellent in the assessment of durability.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.1145-1150
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• 1993

In this paper, two methods of fuzzy modeling are prsented to describe the input-output relationship effectively based on relation characteristics utilizing simplified reasoning and neuro-fuzzy reasoning. The methods of modeling by the simplified reasoning and the neuro-fuzzy reasoning are used when the input-output relation of a system is 'crisp' and 'fuzzy', respectively. The structure and the parameter identification in the modeling method by the simplified reasoning are carried out by means of FCM clustering and the proposed GA hybrid scheme, respectively. The structure and the parameter identification in the modeling method by the neuro-fuzzy reasoning are carried out by means of GA and BP algorithm, respectively. The feasibility of the proposed methods are evaluated through simulation.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.28 no.12
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• pp.1931-1936
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• 2004

In this paper, modeling methods for the structural dynamic analysis employing single reference frame are presented and their modal and transient analysis results are compared. The geometric stiffening effects often occur when structures undergo large overall motion. These effects were considered in several structural previous modeling methods but the role of reference frame has never been scrutinized. In this study, modeling methods employing single reference frame are presented, and their numerical results are compared. The results show that discrepancy between the two modeling methods increases as the eccentricity of the structural system and the magnitude of the large overall motion increase.

• Proceedings of the KSME Conference
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• 2004.04a
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• pp.834-839
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• 2004

In this paper, modeling methods for the structural dynamic analysis employing single reference frame are presented and their modal and transient analysis results are compared. The geometric stiffening effects often occur when structures undergo large overall motion. These effects were considered in several structural previous modeling methods but the role of reference frame has never been scrutinized. In this study, modeling methods employing single reference frame are presented, and their numerical results are compared. The results show that discrepancy between the two modeling methods increases as the eccentricity of the structural system and the magnitude of the large overall motion increase.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2002.04a
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• pp.300-307
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• 2002

Damage estimation methods are classified into two groups according to the dependence on the FE model : signal-based and model-based methods. Signal-based damage estimation methods are generally appropriate for detection of damage location, whereas not effective for estimation of damage severities. Model-based damage estimation methods are difficult to apply directly to the structures with a large number of the probable damaged members. It is difficult to obtain the exact model representing the real bridge behavior due to the modeling errors. The modeling errors even may exceed the modal sensitivity on damage. In this study, Model-based damage detection method which can effectively consider the modeling errors is suggested. Two numerical example analyses on a simple beam and a multi-girder bridge are presented to demonstrate the effectiveness of the presented method.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.56 no.12
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• pp.2098-2102
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• 2007

The conventional CCPU(Cable Covering Protective Unit) modeling methods which are used by Type 99 and Type 92 in EMTP simulation are firstly discussed in this paper, and then some problems of them are also investigated. Modeling method using Type 99 causes unstable characteristic at initial transient of CCPU operation. Time delay has also effect on the output of CCPU modeling. So, in this paper, a new modeling method for CCPU is designed by DBM(Data Base Module) of EMTP. The characteristic of Type 92 is used for the new modeling method. The reliability of this one is proved by comparing simulation with measured data. Specially, at initial transient, the waveform of the new method shows more stable result than that of conventional method using Type 99. Also, the peak value is similar to measured data.

• Journal of Environmental Science International
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• v.11 no.7
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• pp.697-703
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• 2002

In the present study, space allocation methods of pollutant emission from area and mobile sources are assessed by the actual application to air quality modeling of Pohang area. It is found that the TM-based modeling which allocates emission onto the 1km x 1km sized TM-grid system predicts almost the same mean ground-level concentration as that by the GIS-based modeling which uses geographical information of area and mobile sources directly, while maximum ground-level concentration by the TM-based modeling is predicted considerably lower than that by the GIS-based modeling. Moreover, the problem is found that the TM-based modeling causes deviation of mobile roads. In conclusion, it is anticipated to applying GIS-based modeling for a more accurate assessment of air quality in local scale.