Abstract
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.
Similar content being viewed by others
References
N. Joukowsky, Über den hydraulischen Stoss in Wasserleitungsröhren (On the hydraulic hammer in water supply pipes) Mémoires de l’AcadémieImpériale des Sciences deSt.-Pétersbourg, Series 8, 9(5) (1898) 1–71 (in German).
A. Bergant, A. Tijsseling, J. P. Vítkovský, D. Covas, A. Simpson and M. Lambert, Further investigation of parameters affecting water hammer wave attenuation, shape and timing. Part 1: Mathematical tools, Proc. of the 11th International Meeting of the IAHR Work Group on the Behaviour of Hydraulic Machinery under Steady Oscillatory Conditions, Stuttgart, Germany (2003) 1–12.
M. S. Ghidaoui, M. Zhao, D. A. McInnis and D. H. Axworthy, A review of water hammer theory and practice, Applied Mechanics Review, ASME, 58(1) (2005) 49–76.
W. Tian, G. H. Su, G. Wang, S. Qiu and Z. Xiao, Numerical simulation and optimization on valve-induced water hammer characteristics for parallel pump feed water system, Annals of Nuclear Energy, 35(12) (2008) 2280–2287.
W. Barten, A. Jasiulevicius, A. Manera, R. Macian-Juan and O. Zerkak, Analysis of the capability of system codes to model cavitation water hammers: Simulation of UMSCIT water hammer experiments with TRACE and RELAP5, Nuclear Engineering and Design, 238 (2008) 1129–1145.
A. Ismaier and E. Schlücker, Fluid dynamic interaction between water hammer and centrifugal pumps, Nuclear Engineering and Design, 239(12) (2009) 3151–3154.
K. Deb, S. Agrawal, A. Pratap and T. Meyarivan, A fast elitist non-dominated sorting genetic algorithm for multiobjective optimization: NSGA-II, KANGAL Rep. No. 200001, Indian Institute of Technology, Kanpur, India (2000).
K. Deb, A. Pratap, S. Agarwal and T. Meyarivan, A Fast and elitist multiobjective genetic algorithm: NSGA-II, IEEE Trans actions on Evolutionary Computation, 6(2) (2002) 182–197.
M. R. Reed and B. S. Minsker, Striking the balance longterm groundwater monitoring design for conflicting objectives, Journal of Water Resource Planning and Management, ASCE, 130(2) (2004) 140–149.
S. R. M. Yandamuri, K. Srinivasan and S. M. Bhallamudi, Multiobjective optimal waste load allocation models for rivers using Non-dominated sorting genetic algorithm-II, Journal of Water Resources Planning and Management, ASCE; 132(3) (2006) 133–43.
M. R. Bazargan-Lari, R. Kerachian and A. Mansoori, A conflict resolution model for conjunctive use of surface and groundwater resources considering the water-quality issues: A case study, Environmental Management, 43(3) (2009) 470–482.
R. Kerachian, M. Fallahnia, M. R. Bazargan-Lari, A. Mansoori and H. Sedghi, A fuzzy game theoretic approach for groundwater resources management: Application of Rubinstein Bargaining Theory, Journal of Resource, Conservation and Recycling, 54(10) (2010) 673–682.
H. Afshar, R. Kerachian, M. R. Bazargan-Lari and A. R. Niktash, Developing a closing rule curve for valves in pipelines to control the water hammer impacts: Application of the NSGA-II optimization model, Proc. of International Pipelines Conference 2008, American Society of Civil Engineering (ASCE), Atlanta, Georgia, USA (2008) 1–10.
J. S. Lee, B. K. Kim, W. R. Lee and K. Y. Oh, Analysis of water hammer in pipelines by partial fraction expansion of transfer function in frequency domain, Journal of Mechanical Science and Technology, 24(10) (2010) 1975–1980.
M. Rohani and M. H. Afshar, Simulation of transient flow caused by pump failure: Point-Implicit Method of Characteristics. Annals of Nuclear Energy, 37(12) (2010) 1742–1750.
K. Hariri-Asli, F. B. O. Naghiyeh and K. A. Haghi, Some aspects of physical and numerical modeling of water hammer in pipelines, International Journal of Nonlinear Dynamics and Chaos in Engineering Systems, 60(4) (2010) 677–701.
E. B. Wylie and V. L. Streeter, Fluid Transients, First Ed. McGraw-Hill, New York, USA (1978).
E. B. Wylie and V. L. Streeter, Fluid Transients in Systems, First Ed., Prentice Hall, Englewood Cliffs, New Jersey, USA (1993).
D. Mokeddem and A. Khellaf, Optimal solutions of multiproduct batch chemical process using multiobjective genetic algorithm with expert decision system, Journal of Automated Methods and Management in Chemistry, Volume 2009, Article ID 927426 (2009) doi:10.1155/2009/927426.
S. E. Fienberg, When did Bayesian inference become Bayesian?, Bayesian Analysis, 1(1) (2006) 1–40.
W. Buntine, A guide to the literature on learning probabilistic network from data, IEEE Transactions on Knowledge and Data Engineering, 8(2) (1996) 195–210.
F. V. Jensen, Bayesian Networks and Decision Graphs, First Ed. Springer-Verlag, New York, USA (2001).
P. Congdon, Bayesian Statistical Modelling, Second Ed. Wiley, New York, USA (2001).
S. Dorner, J. Shi and D. A. Swayne, Multi-objective modelling and decision support using a Bayesian network approximation to a non-point source pollution model, Environmental Modelling and Software, 22(2) (2007) 211–222.
D. N. Barton, T. Saloranta, S. J. Moe, H. O. Eggestad and S. Kuikka, Bayesian belief networks as a meta-modelling tool in integrated river basin management — Pros and cons in evaluating nutrient abatement decisions under uncertainty in a Norwegian river basin, Ecological Economics, 66 (2008) 91–104.
S. M. Mesbah, R. Kerachian and M. R. Nikoo, Developing real time operating rules for trading discharge permits in rivers: Application of Bayesian Networks, Environmental Modeling and Software, 24(2) (2009) 238–246.
B. Malekmohammadi, R. Kerachian and B. Zahraie, Developing Monthly Operating Rules for a Cascade System of Reservoirs: Application of Bayesian Networks, Environmental Modelling & Software, 24(12) (2009) 1420–1432.
I. Malekmohamadi, M. R. Bazargan-Lari, R. Kerachian, M. R. Nikoo and M. Fallahnia, Evaluating the efficacy of SVMs, BNs, ANNs and ANFIS in wave height prediction, Ocean Engineering, 38(2–3) (2011) 487–497.
P. A. Aguilera, A. Fernandez, R. Fernandez, R. Rumi and A. Salmeron, Bayesian networks in environmental modeling, Environmental Modeling and Software, 26(12) (2011) 1376–1388.
A. Abed-Elmdoust and R. Kerachian, Wave height prediction using the rough set theory, Ocean Engineering, 54 (2012), 244–250, DOI: 10.1016/j.oceaneng.2012.07.020.
R. E. Neapolitan, Learning Bayesian networks, First Ed. Prentice Hall Series in Artificial Intelligence (2003).
Author information
Authors and Affiliations
Corresponding author
Additional information
Recommended by Associate Editor Kyongsu Yi
Mohammad Reza Bazargan-Lari received his B.Sc. in Civil Engineering from Shiraz University, Shiraz, Iran in 2002. He obtained his M.Sc. and Ph.D from the Islamic Azad University in 2004 and 2009, respectively. Dr. Bazargan-Lari is currently an Assistant Professor at the Department of Civil Engineering, East Tehran Branch, Islamic Azad University, Tehran, Iran. His areas of research interest include the design and operation of hydraulic structures and water resource management.
Reza Kerachian is an Associate Professor at the School of Civil Engineering, University of Tehran, Tehran, Iran. He received his Ph.D in Civil Engineering from Tehran Polytechnic in 2004. His research and teaching interests include civil and environmental system analyses, water quality management and application of game theory in water and environmental system management. He has published more than 60 articles in peer-reviewed journals.
Hossein Afshar received his B.Sc. in Mechanical Engineering from K. N. Toosi University of Technology, Tehran, Iran in 2002. He obtained his M.Sc. in Aerospace Engineering from Shiraz University, Shiraz, Iran in 2005 and his Ph.D in Mechanical Engineering from K. N. Toosi University of Technology in 2011. Dr. Afshar is currently an Assistant Professor at the Department of Mechanical Engineering, East Tehran Branch, Islamic Azad University, Tehran, Iran. His areas of research interest include computational fluid dynamics, hydraulics, and micro- and nanofluidics.
Seyyed Nasser Bashi-Azghadi is a Ph.D. candidate at the School of Civil Engineering, Iran University of Science and Technology (IUST), Tehran, Iran. He received his B.Sc. from the Islamic Azad University of Mashhad in 2006 and his M.Sc. from the University of Tehran in 2010. His main research interests include water quality management, especially groundwater and water distribution systems, water quality simulation, and the application of meta-models in civil and environmental system management.
Rights and permissions
About this article
Cite this article
Bazargan-Lari, M.R., Kerachian, R., Afshar, H. et al. Developing an optimal valve closing rule curve for real-time pressure control in pipes. J Mech Sci Technol 27, 215–225 (2013). https://doi.org/10.1007/s12206-012-1208-7
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12206-012-1208-7