• Transactions of the Korean Society of Mechanical Engineers
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• v.14 no.2
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• pp.276-287
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• 1990

In this paper research result of transient thermal stress analysis of power plant turbine rotors for life prediction under severs operating conditions is presented. Galerkin's recurrence scheme is used for numerical solution of discretized FEM equation of transient heat conduction equation. Boundary conditions for the equation and operating conditions are intensively investigated for accurate life prediction of turbine rotors in operation. A computer program for on-site application is developed and tested. Distribution of thermal stress in turbine rotors during various operating condition is analyzed with the program and it is found that the peak thermal stress appears during cold stage conditions at the first stage of high pressure rotors.

• 한국전산유체공학회:학술대회논문집
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• 2004.10a
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• pp.33-36
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• 2004

The ballast tank of a ship is a system that realizes the required shipping condition and controls the draft of a ship. The loading/unloading of the ballast tank is frequently operated during navigation and the accurate prediction of the loading/unloading time is very important. A numerical algorithm that predicts the loading/unloading time of the ballast tank has been developed and applied to the prediction of the loading/unloading time of the ballast tank with various piping systems. This algorithm can be useful in optimizing the ballast tank system in early design stage.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1997.04a
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• pp.788-793
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• 1997

The surface,generated by end milling operation, is deteriorated by tool runout,vibration,friction,tool deflection, etc. In many source,deflection of tool affects to surfave accuracy. To develop a surface accracy model,method for the prediction of the topography of machined surfaces has been developed based on models of machine tool kinematics and cutting tool geometry. This model accounts for not only the ideal geometrical surface, but also the deflection of tool resulted in cutting force. For the more accurate prediction of cutting force,flexible end mill model is used to simulate cutting process. Compute simu;ation have shown the feasibility of the surface generation system.

• Structural Engineering and Mechanics
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• v.48 no.2
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• pp.151-171
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• 2013

The current paper presents the numerical blind prediction of nonlinear seismic response of two full-scale, three dimensional, one-story reinforced concrete structures subjected to bidirectional earthquake simulations on shaking table. Simulations were carried out at the laboratories of LNEC (Laboratorio Nacional de Engenharia Civil) in Lisbon, Portugal. The study was motivated by participation in the blind prediction contest of shaking table tests, organized by the challenge committee of the 15th World Conference on Earthquake Engineering. The test specimens, geometrically identical, designed for low and high ductility levels, were subjected to subsequent earthquake motions of increasing intensity. Three dimensional nonlinear analytical models were implemented and subjected to the input base motions. Reasonably accurate reproduction of the measured displacement response was obtained through appropriate modeling. The goodness of fit between analytical and measured results depended on the details of the analytical models.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2004.05a
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• pp.442-445
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• 2004

The acoustic target strength (TS) of submarine is associated with its active detection, positioning and classification. That is, the survivability of submarine depends on its target strength. So it should be managed with all possible means. An anechoic coating to existing submarine or changing of curvature can be considered as major measures to reduce the TS of submarine. It is mainly based on the prediction of its TS. Under this circumstances, a study on the more accurate numerical methods becomes big topic for submarine design. In this paper, Kirchhoff approximation method was adopted as a numerical tool for the physical optics region. Secondly, the scaled models of submarine were built and tested in order to verify its performance. Through the comparison, it was found out that the Kirchhoff approximation method could be good design tool for the prediction of TS of submarine.

• Journal of the Korean Geotechnical Society
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• v.16 no.2
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• pp.71-77
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• 2000

In this paper, consolidation settlements with time at vertical drain sites were predicted by artificial neural networks. Laboratory test results and field measurements of two vertical drain sites were used for training and testing neural networks. Predicted consolidation settlements by trained artificial neural networks were compared with measured settlements by field instrumentation. To improve the prediction accuracy, modular artificial neural networks were studied. From the results of applying artificial neural networks to the same situation, it was shown that modular artificial neural network model was more accurate for the prediction of the consolidation settlements than the general model.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1990.04a
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• pp.245-253
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• 1990

In a regression relationship the independent variables are frequently measured with error when measurements are made in the field under less controlled conditions, or when accurate instruments are not available. This paper deals with the prediction problem for the above situation. The integrated mean square error of prediction (IMSE) is developed as a measure of the effect of the errors in the independent variables on the predicted values. The IMSE may be used for assessing the severeness of measurement errors as well as for comparing competing estimators. An example from the area of work measurement is analyzed.

• Journal of Construction Engineering and Project Management
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• v.5 no.4
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• pp.12-15
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• 2015

Size of building has a direct relationship with building cost, energy use and space maintenance cost. Therefore, minimizing building size during a project development is of paramount importance against such wastes. However, incautious reduction of building size may result in crowded space, and therefore harms the functionality despite the fact that building is supposed to satisfactorily support users' activity. A well-balanced design solution is, therefore, needed at an optimum level that minimizes building size in tandem with providing sufficient space to maintain functionality. For such design, architects and engineers need to be informed accurate and reliable space-use information. We present in this paper a conceptual framework of an agent-based space-use prediction simulation system that provides individual level space-use information over time in a building in consideration of project specific user information and activity schedules, space preference, ad beavioural rules. The information will accordingly assist architects and engineers to optimize space of the building as appropriate.

• Journal of Welding and Joining
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• v.31 no.3
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• pp.44-48
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• 2013

Artificial neural network (ANN) model is applied to predict arc welding process window for automotive steel plate. Target weldment was various automotive steel plate combination with lap fillet joint. The accuracy of prediction was evaluated through comparison experimental result to ANN simulation. The effect of ANN variables on the accuracy is investigated such as number of hidden layers, perceptrons and transfer function type. A static back propagation model is established and tested. The result shows comparatively accurate predictability of the suggested ANN model. However, it restricts to use nonlinear transfer function instead of linear type and suggests only one single hidden layer rather than multiple ones to get better accuracy. In addition to this, obvious fact is affirmed again that the more perceptrons guarantee the better accuracy under the precondition that there are enough experimental database to train the neural network.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1996.10a
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• pp.253-256
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• 1996

Rainfall is one of the major and complicated elements of hydrologic system. Accurate prediction of rainfall is very important to mitigate storm damage. The neural network is a good model to be applied for the classification problem, large combinatorial optimization and nonlinear mapping. In this dissertation, rainfall predictions by the neural network theory were presented. A multi-layer neural network was constructed. The network learned continuous-valued input and output data. The network was used to predict rainfall. The online, multivariate, short term rainfall prediction is possible by means of the developed model. A multidimensional rainfall generation model is applied to Seoul metropolitan area in order to generate the 10-minute rainfall. Application of neural network to the generated rainfall shows good prediction. Also application of neural network to 1-hour real data in Seoul metropolitan area shows slightly good predictions.