• Journal of the Korean Society of Safety
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• v.37 no.6
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• pp.81-88
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• 2022

It is necessary to better understand the effect of age-related degradation on the performance of reinforced concrete shear walls in nuclear power plants in order to ensure their structural safety in the event of earthquakes. Therefore, this paper studies seismic fragility of the typical shear wall in nuclear power plants under earthquake excitation Reinforced concrete shear wall is composed of wall, horizontal and vertical flanges. Due to characteristics of its geometry, it is difficult to predict the ultimate behavior of shear wall under earthquake excitation. In this study, for more realistic numerical simulation, the Latin Hyper-Cube (LHC) simulation technique was used to generate uncertain variables for the material properties of concrete shear walls. The effects of crack, characteristics of inelastic behavior of concrete, and loss of cross section were considered in the nonlinear finite element analysis. The effects of aging-related deterioration were investigated on the performance of reinforced concrete shear walls through analysis of undegraded concrete shear walls and degraded concrete shear walls. The resulting seismic fragility curves present the change of performance of concrete shear wall due to age-related degradation.

• Proceedings of the Korea Concrete Institute Conference
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• 2005.05a
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• pp.235-238
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• 2005

The seismic fragility analyses of reinforced concrete propelled beam are performed to evaluate safety margin. The models were simulated by Latin Hyper-Cube (LHC) method considering various aging-related deterioration of RC beam. Fragility curves under various condition subjected to static load are compared. It is found that the 20$\%$ loss of top and bottom steel 15$\%$ lower than the undegraded beam in the ultimate strength. Seismic fragility analyses were performed to find out the effect of aging-related deterioration on the dynamic behaviour of RC beam.

• Journal of the Korean Society for Precision Engineering
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• v.28 no.7
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• pp.814-820
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• 2011

It takes long time in numerical simulation because structural design for tire requires the nonlinear material property. Neural networks has been widely studied to engineering design to reduce numerical computation time. The numbers of hidden layer, hidden layer neuron and training data have been considered as the structural design variables of neural networks. In application of neural networks to optimize design, there are a few studies about arrangement method of input layer neurons. To investigate the effect of input layer neuron arrangement on neural networks, the variables of tire contour design and tension in bead area were assigned to inputs and output for neural networks respectively. Design variables arrangement in input layer were determined by main effect analysis. The number of hidden layer, the number of hidden layer neuron and the number of training data and so on have been considered as the structural design variables of neural networks. In application to optimization design problem of neural networks, there are few studies about arrangement method of input layer neurons. To investigate the effect of arrangement of input neurons on neural network learning tire contour design parameters and tension in bead area were assigned to neural input and output respectively. Design variables arrangement in input layer was determined by main effect analysis.

• Transactions of Materials Processing
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• v.29 no.2
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• pp.89-96
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• 2020

The finite element method has been widely applied in the sheet metal forming process. However, the finite element method is computationally expensive and time consuming. In order to tackle this problem, surrogate modeling methods have been proposed. An artificial neural network (ANN) is one such surrogate model and has been well studied over the past decades. However, when it comes to ANN with two or more layers, so called deep neural networks (DNN), there is distinct a lack of research. We chose to use DNNs our surrogate model to predict the behavior of sheet metal in the deep drawing process. Thickness variation is selected as an output of the DNN in order to evaluate workpiece feasibility. Input variables of the DNN are radius of die, die corner and blank holder force. Finite element analysis was conducted to obtain data for surrogate model construction and testing. Sampling points were determined by full factorial, latin hyper cube and monte carlo methods. We investigated the performance of the DNN according to its structure, number of nodes and number of layers, then it was compared with a radial basis function surrogate model using various sampling methods and numbers. The results show that our DNN could be used as an efficient surrogate model for the deep drawing process.

• Fire Science and Engineering
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• v.26 no.2
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• pp.40-52
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• 2012

This paper presents the uncertainty analysis results of fire modeling input parameters for motor control center in switchgear room of nuclear power plants. FDS (Fire Dynamics simulator) 5.5 was used to simulate the fire scenario and Latin Hyper Cube Monte Carlo simulations were employed to generate random samples for FDS input parameters. The uncertainty analysis results of input parameters are compared with those of the model uncertainty analysis and sensitivity analysis approaches of NUREG-1934. The study results show that the input parameter uncertainty analysis approach may lead to more conservative results than the uncertainty analysis and sensitivity analysis methods of NUREG-1934.

• The KSFM Journal of Fluid Machinery
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• v.20 no.1
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• pp.21-28
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• 2017

Optimization process of pre-swirl nozzle geometry was conducted to improve the discharge coefficient of pre-swirl system by using CFD. The optimization of pre-swirl nozzle shape covered the converging angle and the location of the converging nozzle. Optimization process included Optimal Latin Hyper-cube Design method to get the experimental points and the Kriging method to create the response surface which gives candidate points. The process was finished when the difference between the predicted value and CFD value of candidate point was less than 0.1 %. This paper compared the Reference model, Initial model which is the first model of optimization and Optimized model to study flow characteristics. Finally, the discharge coefficient of Optimized model is improved about 17 % to the Reference model.

• Journal of the Earthquake Engineering Society of Korea
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• v.22 no.2
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• pp.45-54
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• 2018

The seismically isolated nuclear power plants shall be designed for design basis earthquake (DBE) and considered to ensure safety against beyond design basis earthquake (BDBE). In order to limit the excessive displacement of the seismic isolation system of the seismically isolated structure, the moat is installed at a certain distance from the upper mat supporting the superstructure. This certain distance is called clearance to stop (CS) and is calculated from the 90th percentile displacement of seismic isolation system subjected to BDBE. For design purposes, the CS can be obtained simply by multiplying the median displacement of the seismic isolation system against DBE by scale factor with a value of 3. The DBE and BDBE used in this study were generated by using 30 sets of artificial earthquakes corresponding to the nuclear standard design spectrum. In addition, latin hyper cube sampling was applied to generate 30 sets of artificial earthquakes corresponding to maximum - minimum spectra. For the DBE, the median displacement and the 99th percentile displacement of the seismic isolation system were calculated. For the BDBE, the suitability of the scale factor was assessed after calculating the 90th percentile displacement of the seismic isolation system.