• The KSFM Journal of Fluid Machinery
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• v.11 no.4
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• pp.14-21
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• 2008

The prediction for gaseous leak flows through a narrow crack is important for a leak-before-break (LBB) analysis. Therefore, the methodology to obtain the flow characteristics of gaseous leak flow in a narrow crack for the wide range by using the product of friction factor and Reynolds number correlations (fRe) for a micro-channel is developed and presented. The correlation applied here was proposed by the previous study. The fourth-order Runge-Kutta method was employed to integrate the nonlinear ordinary differential equation for the pressure and the regular-Falsi method was also employed to find the inlet Mach number. A narrow crack whose opening displacement ranges from 10 to $100{\mu}m$ with a crack length in the range from 2 to 200mm was chosen for sample prediction. The present results are compared with both numerical simulation results and available experimental measurements. The results are in excellent agreement with them. The leak flow rate can be approximately predicted by using proposed methodology.

• Nuclear Engineering and Technology
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• v.53 no.8
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• pp.2547-2555
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• 2021

The frequency of reactor coolant leakage is expected to increase over the lifetime of a nuclear power plant owing to degradation mechanisms, such as flow-acceleration corrosion and stress corrosion cracking. When loss of coolant accidents (LOCAs) occur, several parameters change rapidly depending on the size and location of the cracks. In this study, leak flow during LOCAs is predicted using a deep fuzzy neural network (DFNN) model. The DFNN model is based on fuzzy neural network (FNN) modules and has a structure where the FNN modules are sequentially connected. Because the DFNN model is based on the FNN modules, the performance factors are the number of FNN modules and the parameters of the FNN module. These parameters are determined by a least-squares method combined with a genetic algorithm; the number of FNN modules is determined automatically by cross checking a fitness function using the verification dataset output to prevent an overfitting problem. To acquire the data of LOCAs, an optimized power reactor-1000 was simulated using a modular accident analysis program code. The predicted results of the DFNN model are found to be superior to those predicted in previous works. The leak flow prediction results obtained in this study will be useful to check the core integrity in nuclear power plant during LOCAs. This information is also expected to reduce the workload of the operators.

• Nuclear Engineering and Technology
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• v.56 no.5
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• pp.1725-1732
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• 2024

In nuclear power plants, reactor coolant leakage can occur due to various reasons. Early detection of leaks is crucial for maintaining the safety of nuclear power plants. Currently, a detection system is being developed in Korea to identify reactor coolant system (RCS) leakage of less than 0.5 gpm. Typically, RCS leaks are detected by monitoring temperature, humidity, and radioactivity in the containment, and a water level in the sump. However, detecting small leaks proves challenging because the resulting changes in the containment humidity and temperature, and the sump water level are minimal. To address these issues and improve leak detection speed, it is necessary to quantify the leaks and develop an artificial intelligence-based leak detection system. In this study, we employed bidirectional long short-term memory, which are types of neural networks used in artificial intelligence, to predict the relative humidity in the leakage area for leak quantification. Additionally, an optimization technique was implemented to reduce learning time and enhance prediction performance. Through evaluation of the developed artificial intelligence model's prediction accuracy, we expect it to be valuable for future leak detection systems by accurately predicting the relative humidity in a leakage area.

• Journal of the Korea Convergence Society
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• v.10 no.10
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• pp.7-13
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• 2019

The natural gas (NG), mostly methane leaks into the air, it is a big problem for the climate. detected NG leaks under U.S. city streets and collected data. In this paper, we introduced a Deep Neural Network (DNN) classification of prediction for a level of NS leak. The proposed method is OrdinalEncoder(OE) based K-means clustering and Multilayer Perceptron(MLP) for predicting NG leak. The 15 features are the input neurons and the using backpropagation. In this paper, we propose the OE method for labeling target data using k-means clustering and compared normalization methods performance for NG leak prediction. There five normalization methods used. We have shown that our proposed OE based MLP method is accuracy 97.7%, F1-score 96.4%, which is relatively higher than the other methods. The system has implemented SPSS and Python, including its performance, is tested on real open data.

• Progress in Superconductivity and Cryogenics
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• v.22 no.3
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• pp.25-30
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• 2020

The diminishing of heat leak into cryogenic vessels can prolong the storage time of cryogenic liquid. With the storage of cryogenic liquid reducing, the heat leak decreases, while the actual storage time increases. Regarding to the theoretical analysis, the obtained results seems to be constructive for the cryogenic insulation system applications. This study presents a predictive assessment of heat leak occurring in non-vacuum tanks with a single layer of insulation. A Radial steady-state heat transfer, based on heat conduction equation, is taken into consideration. Graphical results show the thermal performance of the insulation used, they also allow us to choose the appropriate insulation thickness according to the shape and diameter of the storage tank.

• Transactions of the Korean Society of Pressure Vessels and Piping
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• v.16 no.2
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• pp.35-46
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• 2020

In this paper, a fracture mechanics evaluation system which can be used to assess the leak-before-break (LBB) of nuclear piping is developed. Existing solutions for calculating the fracture mechanics parameters (J-integral and crack opening displacement) required for LBB evaluation were firstly presented. Then a module for calculating J-integral and COD was developed, with an additional module for predicting the critical load based on the crack driving force diagram to finally develop a fracture mechanics evaluation system. To confirm the validity of the proposed evaluation system, finite element (FE) analysis was performed, and the FE J-integral and COD results were compared with prediction results using the J-integral and COD estimations program. Furthermore, the critical load assessment module was verified by comparing the actual pipe test results (Battelle test data) with prediction results using the proposed program.

• Progress in Superconductivity and Cryogenics
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• v.24 no.1
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• pp.23-28
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• 2022

A sorption cooler, which utilizes helium-4 as a working fluid, was previously developed and tested in KAIST. The cooler consists of a sorption pump and a thermosyphon. The developed sorption cooler aims to pre-cool a certain amount of the magnetic refrigerant of an adiabatic demagnetization refrigerator (ADR) from 4.5 K to 2.5 K. To simulate the high heat capacitance of the magnetic refrigerant, liquid helium was utilized not only as a refrigerant for the sorption cooling but also as a thermal capacitor. The previous experiment, however, showed that the lowest temperature of 2.7 K which was slightly higher than the target temperature (2.5 K) was achieved due to the radiation heat leak. This excessive heat leak would not occur when the sorption cooler is completely integrated with the ADR. Thus, based on the experimentally obtained pumping speed, the prediction model for the sorption cooler is developed in this study. The presented model in this paper assumes the sorption cooler is integrated with the ADR and the heat leak is negligible. The model predicts the amount of the liquid helium and the required time for the sorption cooling process. Furthermore, it is confirmed that the performance of the sorption cooler is enhanced by reducing the volume of the thermosiphon. The detailed results and discussions are summarized.

• 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.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.26 no.2
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• pp.91-96
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• 2014

An acoustical model for detecting the leak location in a buried gas pipeline has been developed. This model is divided into an experimental model for sound diagnosis, and a theoretical model for sound prediction, which is based on the transfer matrix method, representing the sound pressure and the volume velocity as state variables. The power spectrum is measured by attaching only one microphone to the closed end pipe. It has been shown that the response magnitude of acoustic pressure signals calculated by the acoustical model depends upon the thickness and diameter of a pinhole. The validity for the acoustical model has been verified through a comparison between the measured and calculated results.

• Journal of the Korean Institute of Gas
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• v.11 no.4
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• pp.47-53
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• 2007

The safe operation of high pressure pipe line systems is of significant importance. Leaks due to faulty operation from the pipelines can lead to considerable product losses and to exposure of community to dangerous gases. There are several leak detection methods, which have been recently suggested on pipeline network. The negative pressure wave detection technology, which has advantages of short time detection availability, accurate leaking location estimate capability and cost effective, is concentrated in this study. Theoretical analysis of the flow characteristics for leaking through a hole on the pipe wall has been performed by using CFD++, commercial CFD package. The results of 3-dimensional analysis near leaking hole confirm the occurrence of negative pressure wave and verify the characteristics of propagation of the wave which travels with speed equal to the speed of sound in the pipeline contents. For the application of long pipe line system. The method of 1-dimensional analysis has been suggested and verified with results of CFD++.