• Nuclear Engineering and Technology
• /
• v.56 no.9
• /
• pp.3594-3601
• /
• 2024

It is difficult to detect a small-scale leakage in a nuclear power plant (NPP) quickly and take appropriate action. Delaying these procedures can have adverse effects on NPPs. In this paper, we propose leak flow rate prediction using the bidirectional gated recurrent unit (Bi-GRU) method to detect leakage quickly and accurately in small-scale leakage situations because large-scale leak rates are known to be predicted accurately. The data were acquired by simulating small loss-of-coolant accidents (LOCA) or small-scale leakage situations using the modular accident analysis program (MAAP) code. In addition, to improve prediction performance, data were collected by distinguishing the break sizes in more detail. In addition, the prediction accuracy was improved by performing both LOCA diagnosis and leak flow rate prediction in small LOCA situations. The prediction model developed using the Bi-GRU showed a superior prediction performance compared with other artificial intelligence methods. Accordingly, the accurate and effective prediction model for small-scale leakage situations proposed herein is expected to support operators in decision-making and taking actions.

• The KSFM Journal of Fluid Machinery
• /
• v.11 no.4
• /
• pp.14-21
• /
• 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
• /
• v.53 no.8
• /
• pp.2547-2555
• /
• 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.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.26 no.7
• /
• pp.963-971
• /
• 2022

Water pipeline network in local and metropolitan area is buried underground, by which it is hard to know the degree of pipe aging and leakage. In this study, assuming various sensor combinations installed in the water pipeline network, the optimal algorithm was derived by predicting the water flow rate and pressure through artificial intelligence algorithms such as linear regression and neuro fuzzy analysis to examine the possibility of detecting pipe leakage according to the data combination. In the case of leakage detection through water supply pressure prediction, Neuro fuzzy algorithm was superior to linear regression analysis. In case of leakage detection through water supply flow prediction, flow rate prediction using neuro fuzzy algorithm should be considered first. If flow meter for prediction don't exists, linear regression algorithm should be considered instead for pressure estimation.

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

• Journal of Korea Water Resources Association
• /
• v.54 no.8
• /
• pp.599-606
• /
• 2021

Water Distribution Networks, one of the social infrastructures buried underground, has the function of transporting and supplying purified water to customers. In recent years, as measurement capability is improved, a number of studies related to leak recognition and detection by applying a deep learning technique based on flow rate data have been conducted. In this study, a cognitive model for leak occurrence was developed using an LSTM-based deep learning algorithm that has not been applied to the waterworks field until now. The model was verified based on the assumed data, and it was found that all cases of leaks of 2% or more can be recognized. In the future, based on the proposed model, it is believed that more precise results can be derived in the prediction of flow data.

• Journal of Korea Water Resources Association
• /
• v.46 no.10
• /
• pp.969-976
• /
• 2013

Leakage in water distribution system causes social and economic losses by direct water loss into the ground, and additional energy demand for water supply. This research suggests a leak detection model of using adaptive Kalman filtering on real-time data of pipe flow. The proposed model takes into account hourly and daily variations of water demand. In addition, the model's prediction accuracy is improved by automatically calibrating the covariance of noise through innovation sequence. The adaptive Kalman filtering shows more accurate result than the existing Kalman method for virtual sine flow data. Then, the model is applied to data from two real district metered area in JE city. It is expected that the proposed model can be an effective tool for operating water supply system through detecting burst leakage and abnormal water usage.

• Journal of Hydrogen and New Energy
• /
• v.34 no.3
• /
• pp.292-296
• /
• 2023

Until recently, ships, automobiles, and drones using hydrogen energy are being actively researched. In addition, stations and facilities for hydrogen supply are being developed widely. Among them, a hydrogen pump is necessary for compressing it and transfer to other stations. The liquid hydrogen pump is operated at very high pressure up to 90 MPa. In our research, a reciprocating plunger pump is studied. Especially, a leakage in a liquid hydrogen pump is predicted using a finite element method. As a result, it was found that leak mass flow rates changed from 0.09 to 2.20 kg/h, when the gaps were given from 2 to 6 ㎛. Thus pump efficiencies were calculated from 99.9 to 97.9%, when the gaps changed from 2 to 6 ㎛. These results are useful for the design of the liquid hydrogen pump.

• Journal of the Korean Institute of Gas
• /
• v.27 no.2
• /
• pp.7-15
• /
• 2023

The electric submersible pump (ESP) has been operating in production wells around the world because of its high applicability and operational efficiency among artificial lift techniques. When operating an ESP in a reservoir, variables such as temperature, pressure, gas/oil ratio, and flow rate are factors that affect ESP performance. In particular, free gas in the production fluid is a major factor that reduces the life and operational efficiency of ESP. This study presents the flow loop system which can implement the performance and damage tests of ESP considering field operating conditions to quantitatively analyze the variables that affect ESP performance. The developed apparatus in an integrated system that can diagnose the failure and causes of ESP, and detect leak of tubing by linking ESP and tubing as one system. In this study, the flow conditions for stable operation of ESP were identified through single phase and two phase flow experiments related to evaluation for the performance of ESP. The results provide the basic data to develop the failure prediction and diagnosis program of ESP, and are expected to be used for real-time monitoring for optimal operating conditions and failure diagnosis for ESP operation.