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Developing an optimal valve closing rule curve for real-time pressure control in pipes
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  • 作者：Mohammad Reza Bazargan-Lari (1)
    Reza Kerachian (2)
    Hossein Afshar (3)
    Seyyed Nasser Bashi-Azghadi (4)
  • 关键词：Bayesian networks (BNs) ; Method of characteristics ; Non ; dominated sorting genetic algorithms ; II (NSGA ; II) ; Valve closing rule curve (VCRC) ; Water hammer
  • 刊名：Journal of Mechanical Science and Technology
  • 出版年：2013
  • 出版时间：January 2013
  • 年：2013
  • 卷：27
  • 期：1
  • 页码：215-225
  • 全文大小：1574KB
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  • 作者单位：Mohammad Reza Bazargan-Lari (1)
    Reza Kerachian (2)
    Hossein Afshar (3)
    Seyyed Nasser Bashi-Azghadi (4)

    1. Department of Civil Engineering, East Tehran Branch, Islamic Azad University, Tehran, Iran
    2. School of Civil Engineering and Center of Excellence for Engineering and Management of Civil Infrastructures, College of Engineering, University of Tehran, Tehran, Iran
    3. Department of Mechanical Engineering, East Tehran Branch, Islamic Azad University, Tehran, Iran
    4. School of Civil Engineering, Iran University of Science and Technology, Tehran, Iran
  • ISSN：1976-3824
文摘
Sudden valve closure in pipeline systems can cause high pressures that may lead to serious damages. Using an optimal valve closing rule can play an important role in managing extreme pressures in sudden valve closure. In this paper, an optimal closing rule curve is developed using a multi-objective optimization model and Bayesian networks (BNs) for controlling water pressure in valve closure instead of traditional step functions or single linear functions. The method of characteristics is used to simulate transient flow caused by valve closure. Non-dominated sorting genetic algorithms-II is also used to develop a Pareto front among three objectives related to maximum and minimum water pressures, and the amount of water passes through the valve during the valve-closing process. Simulation and optimization processes are usually time-consuming, thus results of the optimization model are used for training the BN. The trained BN is capable of determining optimal real-time closing rules without running costly simulation and optimization models. To demonstrate its efficiency, the proposed methodology is applied to a reservoir-pipe-valve system and the optimal closing rule curve is calculated for the valve. The results of the linear and BN-based valve closure rules show that the latter can significantly reduce the range of variations in water hammer pressures.

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