Examining the Influence of Data Uncertainty and Hydraulic Simulation Method on the Results of Modeling and Performance Evaluation of Water Distribution Networks

Document Type : Research Paper

Authors

1 Prof., Center of Excellence for Engineering and Management of Civil Infrastructures, School of Civil Engineering, College of Engineering, University of Tehran, Tehran, Iran

2 MSc Student, School of Civil Engineering, College of Engineering, University of Tehran, Tehran, Iran

3 Assist. Prof., Faculty of Civil Engineering, Urmia University of Technology, Urmia, Iran

Abstract

Water distribution networks (WDNs) are complicated infrastructures which their construction, operation and maintenance have considerable costs. Since most of the variables effective on the design and operation of WDNs cannot be computed and achieved accurately and definitely, uncertainty subject should be considered as an inseparable issue in the calculation of these networks. In this study, using the fuzzy logic concept and genetic optimization algorithm, the impact of uncertainties of input variables (nodal demands and pipe roughness coefficients) on the results of hydraulic analysis of two sample networks have been examined. In this regard, first, the fuzzy membership functions of input variables have been determined and by considering the simultaneous impacts of these variables' uncertainties, the output variables of hydraulic analysis have been calculated more accurately. Afterwards, variables of pressure, velocity and energy loss have been considered as representers for evaluating the hydraulic performance of network elements (nodal demand and pipes). In order to calculate the hydraulic performance indices of these elements, after analyzing the network based on the pressure driven simulation method, penalty curves defined according to the available standards, have been employed and the obtained results have been compared to the results of the demand driven simulation method. In addition, a new relation for combining the performance indices of network elements and obtaining an index for evaluating the total pipe performance and calculating the total hydraulic performance index of network has been introduced. According to the obtained results, slight uncertainties in the input variables of hydraulic analysis lead to high uncertainties in the outputs of the hydraulic analysis of WDNs. Meanwhile, velocity in pipes more than nodal pressures are affected by the uncertainties of input variables of hydraulic analysis. Also, implementing the pressure driven simulation method in performance evaluation of WDNs in their operation period leads to more reasonable and real results. For instance the total performance of network was 0.56 for 9-loop network and was 0.26 and 0.59 for 2-loop network, respectively, based on demand and pressure driven simulation methods.

Keywords

Main Subjects


Ayyub, B.M. 1998. Uncertainty analysis in engineering and scinces: Fuzzy logic statistics and neural network approach, Kluwer Academic Pub., USA.
Babayan, A.V., Kapelan, Z., Savic, D.A. & Walters, G.A. 2005. Least-cost design of water distrubution networks under uncertainty. Journal of Water Resources Planning and Management, 131(5), 375-382.
Bao, Y. & Mays, L.W. 1990. Model for water distribution system reliability. Journal of Hydraulic Engineering, 116(9), 1119-1137.
Bozorg-Haddad, O., Adams, B.J. & Marino, M.A. 2008. Optimum rehabilitation strategy of water distribution systems using the HBMO algorithm. Journal of Water Supply Research and Technology, 57(5),337-350.
Branisavljevic, N. & Ivetic, M. 2006. Fuzzy approach in the uncertainty analysis of the water distribution network of Becej, Civil Engineering and Environmental Systems, 23(3), 221-236.
Cullinane, M.J., Lansey, K.E. & Mays, L.W. 1992. Optimization availability-based design of water distribution networks. Journal of Hydraulic Engineering, 118(3), 420-441.
Gupta, R. & Bhave, P.R. 2007. Fuzzy parameters in pipe network analysis. Civil Engineering and Environmental System, 24(1), 33-54.
Haghighi, A., Samani, H.M.V. & Samani, Z.M.V. 2011. GA-ILP method for optimization of water distribution networks. Journal of Water Resources Management, 25(7), 1791-1808.
Haghighi, A. & Zahedi Asl, A. 2014. Uncertainty analysis of water supply network using the fuzzy set theory and NSGA-II. Journal of Engineering Applications of Artificial Intelligence, 32, 270-282.
Hudson, W.D. 1966. Studies of distribution system capacity in seven cities. Journal of American Water Works Association, 58(2), 157-164.
Islamic Republic of Iran Vice Presidency for Strategic Planning and Supervision (IRIVPSPS). 2011. Guidelines for design of urban and rural water supply and distribution systems, Report No. 117-3 (First Revision), Islamic Republic of Iran Vice Presidency for Strategic Planning and Supervision Press, Iran. (In Persian)
Kadu, M.S., Gupta, R. & Bhave, P.R. 2008. Optimal design of water networks using a modified genetic algorithm with reduction in search space. Journal of Water Resources Planning and Management, 134(2), 147-160.
Kapelan, Z., Savic, D.A. & Walters, G.A. 2005. Multiobjective design of water distribution systems under uncertainty. Water Resources Research, 41(11), 11407-11415.
Karmakar, S. 2011. Propagation of uncertainty in water distribution systems modeling. Desalination and Water Treatment, 33(13), 107-117.
Revelli, R. & Ridolfi, L. 2002. Fuzzy approach for analysis of pipe networks. Journal of Hydraulic Engineering, 128(1), 93-101.
Ross, T. 2004. Fuzzy logic with engineering applications, 2nd Ed., John Wiley & Sons, NY.
Sabzkouhi A.M. & Haghighi A. 2016. Uncertainty analysis of pipe-network hydraulics using a many-objective particle swarm optimization, Journal of Hydraulic Engineering, 10.1061/(ASCE)HY.1943-7900.0001148.
Seifollahi-Aghmiuni, S., Bozorg-Haddad. O., Omid, M.H. & Marino, M.A. 2013a. Effect of pipe roughness on water distribution network performance during its operational period. Water Resource Management, 27(5), 1581-1599.
Seifollahi-Aghmiuni, S., Bozorg-Haddad, O. & Marino, M.A. 2013b. Water distribution network risk analysis under simultaneous consumption and roughness uncertainties. Water Resource Management,27(7), 2595-2610.
Shirzad, A., Tabesh, M., Farmani, R. & Mohammadi, M. 2013. Pressure-discharge relations with application in head driven simulation of water distribution networks. Journal of Water Resources Planning and Management, 139(6), 660-670.
Shirzad, A. & Tabesh, M. 2016. New indices for reliability assessment of water distribution networks. Journal of Water Supply: Research and Technology, 65(5), 384-395.
Spiliotis, M. & Tsakiris, G. 2012. Water distribution network analysis under fuzzy demands, Civil Engineering and Environmental Systems, 29(2), 107-122.
Tabesh, M., Tanyimboh, T.T. & Burrows, R. 2002. Head driven simulation of water supply networks. Engineering, Transactions A: Basics, 15(1), 11-22.
Tabesh, M. & Zia, A. 2003. Dynamic management of water distribution networks based on hydraulic performance analysis of the system. Water Science and Technology, 13(1), 95-102.
Tabesh, M. & Dolatkhahi, A. 2006. Effects of pressure dependent analysis on quality performance assessment of water distribution networks. Iranian Journal of Science and Technology, 30(B1), 119-128.
Tabesh, M. 2016. Advanced modeling of water distribution networks, 1st Ed., University of Tehran Press, Tehran, Iran. (In Persian).
Taebi, A. & Chamani, M.R. 2008. Water distribution systems, 2nd Edition, Isfahan University of Technology Press, Isfahan, Iran. (In Persian)
Todini, E. 2000. Looped water distribution networks design using a resilience index based heuristic approach. Urban Water, 2(3), 115-122.
Tung, Y.K. 1996. Unecrtainty and reliability analysis, Edited by L. Mays, McGraw-Hill, New York.
Xu, C. & Goulter, I.C. 1996. Uncertainty analysis of water distribution networks. Tickle, K.S., Goulter, I.C., Wasimi S.A. & Bouchart, F. (Eds). In Stochastic hydraulics 96, Proceeding of the 7th IAHR International Symposium, Balkema, Rotterdam, Netherland.