• 제목/요약/키워드: multi piping network

검색결과 4건 처리시간 0.016초

아파트의 온수공급배관망해석 (Analysis on Heat Supply Piping Network for Apartment House)

  • 박윤철;황광일
    • 한국주거학회논문집
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    • 제13권6호
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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.

전산유체해석(CFD)을 이용한 밸브의 급폐쇄에 따른 다중 배관 수격 현상에 관한 연구 (Study on a Multi-pipe Water Hammer Phenomenon by using CFD of Rapid Valve Closing)

  • 박노석;김성수;강문선;최종웅
    • 상하수도학회지
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    • 제27권4호
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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.

공동주택 온수난방 시스템의 적정 열공급을 위한 배관망 시뮬레이션 (Simulation of Pipe Network for Optimum Heat Supply in the Hot Water Heating System of Apartment House)

  • 김주용;민만기;최영돈
    • 설비공학논문집
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    • 제5권3호
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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.

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선박용 밸브의 내부 누설 진단을 위한 음향방출신호의 머신러닝 기법 적용 연구 (Diagnosis of Valve Internal Leakage for Ship Piping System using Acoustic Emission Signal-based Machine Learning Approach)

  • 이정형
    • 해양환경안전학회지
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    • 제28권1호
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    • pp.184-192
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    • 2022
  • 밸브의 내부 누설 현상은 밸브의 내부 부품의 손상에 의해 발생하며 배관 시스템의 사고와 운전정지를 일으키는 주요 요인이다. 본 연구는 버터플라이형 밸브의 내부 누설에 따라 배관계에서 발생하는 음향방출 신호를 이용하여 배관 가동 중 실시간 누설 진단의 가능성을 검토하였다. 이를 위해 밸브의 작동 모드별로 측정한 시간영역의 AE 원시신호를 취득하였으며 이로부터 구축한 데이터셋은 데이터 기반의 인공지능 알고리즘에 적용하여 밸브의 내부 누설 유무를 진단하는 모델을 생성하였다. 누설 유무진단을 분류의 문제로 정의하여 SVM 기반의 머신러닝과 CNN 기반의 딥러닝 분류 알고리즘을 적용하였다. 데이터의 특징 추출에 기반한 SVM 분류 모델의 경우, 이진분류 모델에서 구축된 모델에 따라 83~90%의 정확도를 나타냈으며, 다중 클래스인 경우 분류 정확도가 66%로 감소하였다. 반면, CNN 기반의 다중 클래스 분류 모델의 경우 99.85%의 분류 정확도를 얻을 수 있었다. 결론적으로 밸브 내부 누설 진단을 위한 SVM 분류모델은 다중 클래스의 정확도 향상을 위해 적절한 특징 추출이 필요하며, CNN 기반의 분류모델은 프로세서의 성능 저하만 없다면 누설진단과 밸브 개도 분류에 효율적인 접근방법임을 확인하였다.