• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2002.11a
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• pp.352.2-352
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• 2002

In this paper, Power Flow Analysis (PFA) method has been applied to the prediction of vibration energy density and intensity of coupled shell structures in the medium-to-high frequency ranges. To consider the wave transformation at joint between shell elements, power transmission and reflection coefficients are investigated for various joint angles, and here Donnell-Mushtari thin shell theory has been used. (omitted)

• Structural Engineering and Mechanics
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• v.32 no.3
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• pp.459-475
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• 2009

This paper presents analytical methodologies for remaining life prediction of plain concrete structural components considering tension softening and size effects. Non-linear fracture mechanics principles (NLFM) have been used for crack growth analysis and remaining life prediction. Various tension softening models such as linear, bi-linear, tri-linear, exponential and power curve have been presented with appropriate expressions. Size effect has been accounted for by modifying the Paris law, leading to a size adjusted Paris law, which gives crack length increment per cycle as a power function of the amplitude of a size adjusted stress intensity factor (SIF). Details of tension softening effects and size effect in the computation of SIF and remaining life prediction have been presented. Numerical studies have been conducted on three point bending concrete beams under constant amplitude loading. The predicted remaining life values with the combination of tension softening & size effects are in close agreement with the corresponding experimental values available in the literature for all the tension softening models.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.62 no.7
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• pp.913-917
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• 2013

During the restoration process of primary restorative transmission system, some over voltages may happen due to nonlinear interaction between unloaded transformers and transmission systems. These over voltages caused by harmonic resonance can be suppressed by inserting damping loads before energizing transformers. But it is very difficult to predict the occurrence possibility of harmonic resonance and complex simulation must be repeated to estimate the sufficient damping loads. This paper presents a damping loads prediction system to prevent harmonic resonance. Detailed analysis of the relationship between harmonic resonance and the amount of damping loads is discussed. The prediction system is developed using a curve fitting and a neural network based on this relationship. A curve fitting used a Gaussian function based on non-linear least square method and multi-layer back-propagation neural network is applied. The system is applied to primary restorative transmission lines in korean power system and the result showed satisfactory performance.

• Journal of Power System Engineering
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• v.19 no.5
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• pp.45-51
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• 2015

Grade 91 steel is used for the major structural components of Generation-IV reactor systems such as a very high temperature reactor (VHTR) and sodium-cooled fast reactor (SFR). Since these structures are designed for up to 60 years at elevated temperatures, the prediction of long-term creep life is very important to determine an allowable design stress of elevated temperature structural component. In this study, a large body of creep rupture data was collected through world-wide literature surveys, and using these data, the long-term creep life was predicted in terms of three methods: Larson-Miller (L-M), Manson-Haferd (M-H) and Wilshire methods. The results for each method was compared using the standard deviation of error. The L-M method was overestimated in the longer time of a low stress. The Wilshire method was superior agreement in the long-term life prediction to the L-M and M-H methods.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.21 no.11
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• pp.1796-1808
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• 1997

In order to perform leak-before-break design of nuclear piping systems and integrity evaluation of reactor vessels, full stress-strain curves and fracture resistance(J-R) curves are required. However it is time-consuming and expensive to obtain J-R curves experimentally. To resolve these problems, three different methods for predicting J-R curves from tensile data were proposed by the authors previously. The objective of this paper is to develop a computer program based on those J-R curve prediction methods. The program consists of two major parts ; the main program part for the J-R curve prediction and the database part. Several case studies were performed to verify the program, and it was shown that the predicted results were, in general, in good agreement with the experimental ones.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.21 no.11
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• pp.1786-1795
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• 1997

In order to perform leak-before-break design of nuclear piping systems and integrity evaluation of reactor vessels, full stress-strain curves and fracture resistance (J-R) curves are required. However it is time-consuming and expensive to obtain J-R curves experimentally. The objective of this paper is to modify two J-R curve prediction methods previously proposed by the authors and to propose an additional J-R curve prediction method for nuclear piping materials. In the first method which is based on the elastic-plastic finite element analysis, a blunting region handling procedure is added to the existing method. In the second method which is based on the empirical equation, a revised general equation is proposed to apply to both carbon steel and stainless steel. Finally, in the third method, both full stress-strain curve and finite element analysis results are used for J-R curve prediction. A good agreement between the predicted results based on the proposed methods and the experimental ones is obtained.

• Environmental Engineering Research
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• v.22 no.3
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• pp.302-311
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• 2017

Nowadays, with the burgeoning development of economy, $CO_2$ emissions increase rapidly in China. It has become a common concern to seek effective methods to forecast $CO_2$ emissions and put forward the targeted reduction measures. This paper proposes a novel hybrid model combined principal component analysis (PCA) with regularized extreme learning machine (RELM) to make $CO_2$ emissions prediction based on the data from 1978 to 2014 in China. First eleven variables are selected on the basis of Pearson coefficient test. Partial autocorrelation function (PACF) is utilized to determine the lag phases of historical $CO_2$ emissions so as to improve the rationality of input selection. Then PCA is employed to reduce the dimensionality of the influential factors. Finally RELM is applied to forecast $CO_2$ emissions. According to the modeling results, the proposed model outperforms a single RELM model, extreme learning machine (ELM), back propagation neural network (BPNN), GM(1,1) and Logistic model in terms of errors. Moreover, it can be clearly seen that ELM-based approaches save more computing time than BPNN. Therefore the developed model is a promising technique in terms of forecasting accuracy and computing efficiency for $CO_2$ emission prediction.

• The KIPS Transactions:PartB
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• v.9B no.4
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• pp.421-428
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• 2002

The local prediction method utilizing embedding vector loses the prediction power when the parameter r estimation is not exact for predicting the chaos time series induced from the high order differential equation. In spite of the fact that there have been a lot of suggestions regarding how to estimate the delay time ($\tau$), no specific method is proposed to apply to any time series. The inclinded linear model, which utilizes inclinded netter, yields satisfying degree of prediction power without estimating exact delay time ($\tau$). The usefulness of this approach has been indicated not only theoretically but also in practical situation when the method w8s applied to economical time series analysis.

• Nuclear Engineering and Technology
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• v.46 no.3
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• pp.373-380
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• 2014

Safety-related parameters are very important for confirming the status of a nuclear power plant. In particular, the reactor vessel water level has a direct impact on the safety fortress by confirming reactor core cooling. In this study, the reactor vessel water level under the condition of a severe accident, where the water level could not be measured, was predicted using a fuzzy neural network (FNN). The prediction model was developed using training data, and validated using independent test data. The data was generated from simulations of the optimized power reactor 1000 (OPR1000) using MAAP4 code. The informative data for training the FNN model was selected using the subtractive clustering method. The prediction performance of the reactor vessel water level was quite satisfactory, but a few large errors were occasionally observed. To check the effect of instrument errors, the prediction model was verified using data containing artificially added errors. The developed FNN model was sufficiently accurate to be used to predict the reactor vessel water level in severe accident situations where the integrity of the reactor vessel water level sensor is compromised. Furthermore, if the developed FNN model can be optimized using a variety of data, it should be possible to predict the reactor vessel water level precisely.

• Geotechnical Engineering
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• v.13 no.2
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• pp.65-76
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• 1997

It has been a growing concern how to use Nanji-Island landfill and other refuse landfills located around metropolitan areas. In this paper, settlement characteristics of Nanji -Island landfill were studied by analyzing the data collected over the period of two years. The settlement characteristics were similar to the analyzed settlement characteristics of 24 refuse landfills in the United States. The model proposed by Bjarngard and Edger(1990) model is considered to be suitable for the long-term prediction of Wnil -Island landfill. The ten-year -period prediction value of Bjarngard and Edger (1990) model is considerably different from that of Power Creep Model. If existing settlement models used for long-term prediction of the settlement characteristics of landfill are not analyzed thoroughly there remains the possibility of including considerable prediction errors.