Fuzzy Analysis of Pressure-Deficient Water Distribution Networks

Abstract

Future water demands and pipe roughness coefficients used in the design of water distribution networks (WDNs) have a high degree of uncertainty. Fuzzy analysis of WDNs provide how the uncertainties in independent or basic parameters (such as nodal demands and pipe roughness coefficients) are propagated to dependent or derived parameters (such as pipe flows, pipe velocities and available pressure heads). Usually demand dependent analysis is used for such analysis. A WDN may be pressure-deficient or may become pressure-deficient because of high pipe roughness or inadequate pump pressure and may not be able to meet desired demands at all nodes. Thus, it is desirable to consider the nodal outflows as unknown and as a function of available pressure heads under pressure-deficient conditions. An approach for fuzzy analysis of a WDN which takes into account pressure-deficient condition using node flow analysis is presented in this paper. The proposed methodology is illustrated with example network taken from literature. The methodology is useful in identifying vulnerable zones in WDNs and can be extended for reliability analysis under uncertainty of nodal demands and pipe roughness coefficients. Comparison of results with usual demand-dependent analysis shows that proposed method is better in identifying vulnerable zones.

History of Pressure-Dependent Analysis of Water Distribution Networks and Its Applications

“…Gupta et al (2014) carried out fuzzy-analysis to identify vulnerable areas under uncertainty of nodal demands and pipe-roughness coefficients using PDA. Ozger and Mays (2004) considered the problem of location of n-1 number of valves at a junction with n connected pipes and obtained their location to maximize the reliability defined as available demand fraction which is obtained using PDA.…”

Uncertainty Analysis of WDN Pipes Repair and Replacement Optimal Instruction Using EPANET Simulation Model and Fuzzy Cut Approach Combination

“…and the highest performance during the operation period (Bozorg-Haddad et al 2017;Donger and Gupta 2017;Shibu and Reddy 2012;Farmani et al 2005aFarmani et al , 2005bDridi et al 2009;Amirabdollahian et al 2011;Mosavian and Lence 2018). In the fuzzy uncertainty analysis, the extraction of output parameters membership functions such as nodes demand and pressure and pipes flow velocity and their analysis is considered (Sabzkouhi and Haghighi 2016;Branisavljevic and Ivetic 2006;Revelli and Ridolfi 2002;Spiliotis and Tsakiris 2012;Shibu and Reddy 2011;Gupta and Bhave 2007;Moosavian and Lence 2018;Geranmehr et al 2019;Gupta et al 2014). Revelli and Ridolfi (2002) performed the uncertainty analysis of WDN based on the uncertain input variables include the nodes demand and pipes roughness coefficient.…”