• Journal of the Korean housing association
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• v.13 no.6
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• pp.89-99
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• 2002

The purpose of this research is to analyze the characteristics of flow rate distribution in hot-water piping networks in the apartment building. A 14-story apartment house was selected as a sample building and analyzed numerically by Hardy-Cross method. Two different piping networks, one has three vertical zones and the other of a single zone with automatic balancing valves, were compared. Some of research results are as follows; As the temperature of supply hot-water increases, the flow rate of it does by buoyancy effect, but this effect is not found in the piping network with automatic balancing valves. Non-uniformity in hot-water flow distributions to all stories in the piping system of single vertical zone can be completely reformed by the installation of either manually operated or automatic balancing valves in every story.

• Journal of Korean Society of Water and Wastewater
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• v.27 no.4
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• pp.479-487
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• 2013

This study was to investigate characteristics for the pressure wave propagation and the maximum pressure near a rapid closure valve which was installed the end of multi piping network. The multi piping network consists of one inlet and three outlet with straight pipes. The diameter of the pipes including the valve was 100 mm, 80 mm, 80 mm respectively. The valve was rapidly closed with the instantaneous time which was 0.023s in the level for the water hammer. For the simulation, the influence of the pipe thickness and deformation due to pressure-wave-propagation was not considered. CFD was conducted under the following condition : the initial pressure was 1bar in the inlet and the mass flow rate was 7.83 kg/s in the outlet(the velocity in the pipe with 100 mm diameter was 1 m/s). As the valve have conditions that were status with and without fluid flow in the pipe after valve closing, the maximum pressure change and the frequency analysis were examined. As the results, the case that was status with fluid flow appeared the higher maximum pressure than another's, the maximum frequency band was about 10 ~ 11 Hz.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.5 no.3
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• pp.157-168
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• 1993

Pipe network of hot water heat supply system in an apartment house was analyzed. Flowrate and supply heat capacity of each household in which constant flowrate balancing valve is installed in a single zone system were calculated and the results were investigated. In the existing piping system, the non-uniformity of heat supply with floors due to the static pressure and temperature difference between supply main and return main can not be avoided and this tendency get intense with the increase of the height of building. The non-uniformity of heat supply can be prevented by the installation of balancing valve at each household, however if the performance of supply pump is not sufficient to overcome the energy loss due to the installation of balancing valve for constant flow rate or if the selection of the valve capacity is not adequate, the valves will may lose their controllability.

• Journal of the Korean Society of Marine Environment & Safety
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• v.28 no.1
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• pp.184-192
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• 2022

Valve internal leakage is caused by damage to the internal parts of the valve, resulting in accidents and shutdowns of the piping system. This study investigated the possibility of a real-time leak detection method using the acoustic emission (AE) signal generated from the piping system during the internal leakage of a butterfly valve. Datasets of raw time-domain AE signals were collected and postprocessed for each operation mode of the valve in a systematic manner to develop a data-driven model for the detection and classification of internal leakage, by applying machine learning algorithms. The aim of this study was to determine whether it is possible to treat leak detection as a classification problem by applying two classification algorithms: support vector machine (SVM) and convolutional neural network (CNN). The results showed different performances for the algorithms and datasets used. The SVM-based binary classification models, based on feature extraction of data, achieved an overall accuracy of 83% to 90%, while in the case of a multiple classification model, the accuracy was reduced to 66%. By contrast, the CNN-based classification model achieved an accuracy of 99.85%, which is superior to those of any other models based on the SVM algorithm. The results revealed that the SVM classification model requires effective feature extraction of the AE signals to improve the accuracy of multi-class classification. Moreover, the CNN-based classification can be a promising approach to detect both leakage and valve opening as long as the performance of the processor does not degrade.