Global Gradient Algorithm Extension to Distributed Pressure Driven Pipe Demand Model

Abstract

It has been proved that the standard representation of water demand in a Water Distribution Network (WDN) leads to pipe head loss errors as well that the fully satisfied demand regardless water pressure assumption is misleading. This follows that different algorithms have been developed in order to overcome these two drawbacks although separately and independently. Consequently, this paper introduces an alternative formulation of the Global Gradient Algorithm (GGA), referred to as UD-PD, which is able to solve uniformly distributed pressure driven demands along the pipes of a WDN in extended period simulations. In addition, this new scheme is tested against reference solutions and its performances are compared with the standard WDN models. Finally, the UD-PD is applied to a real WDN under pressure deficit conditions. Numerical results show that the hydraulic heads computed with the UD-PD result higher than those simulated with standard demand driven models and that the UD-PD is able both to capture the non linear behavior of the hydraulic head along the network and to correctly compute the flow inversion even in pressure driven conditions.

Performance between the Hydraulic Gradient Method and the Perturbation Method for the Analysis of Water Supply Networks

“…Once the head loss has been calculated, the new flow rates of the pipes are determined with the following matrix equation [11][12][13][14][15][16][17][18][19]:…”

Burst Detection in Water Distribution Systems: The Issue of Dataset Collection

“…In this work, both the three coefficients of p ij (h(x)) and the nodal q ij (h(x)) consumption are evaluated by Siew and Tanyimboh [33]. The solver used for performing the hydraulic simulations is based on a Menapace and Avesani numerical scheme [34]. This hydraulic solver enables simulating both distributed along a pipe and lumped at nodes water withdrawals with a pressure-driven approach using the GGA method [35].…”

“…Since the mathematical equations on the basis of the DFPDM have been presented, the modeling of the different components of the water losses is herewith specified. Firstly, the water demand is modeled with the pressure-demand relationship [33] and the distributed along pipe demand scheme [34]. The accuracy of this approach, which has been proved in [36,37], is given by the uniformly widespread withdrawals along each pipe of the network depending on the pressure along it, as follows:…”

An artificial intelligence approach for managing water demand in water supply systems

“…Keywords such as systems optimization, smart systems, and digital water are just some of the research challenges for sustainable urban water management [2]. In the latest years, hydro informatics research played a crucial role in developing new techniques and methodologies for supporting the sustainable management of urban water, dealing with this discipline that "can be thought of as a continuous process of developing and using water data, models and tools, to understand the environment, to engage all stakeholders, and help make decisions that improve society" [3,4]. In the latest decades, artificial intelligence and machine learning have been the object of increased research activity in the hydraulic field [5][6][7][8].…”