Extended Period Simulation of Water Systems—Part B

Rao, H. Suresh; Bree, Don W.

doi:10.1061/jyceaj.0004716

Cited by 20 publications

(1 citation statement)

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“…The two most basic types are: steady-state simulation [66][67][68] which computes the state of the water distribution system (flows, pressures, valve position, etc.) assuming that the water demands and boundary conditions do not change with respect to time or extended period simulation [69][70][71] which determines the dynamic behaviour of a system over a period of time by computing the state of the water distribution system as a series of steady-state simulations in which water demands and boundary conditions do change with respect to time. Other types of simulations are water quality simulations [72][73][74][75][76] used to ascertain chemical or biological constituent levels within a system or to determine the age or source of water, automated fire flows analyses [77] [78] that establish the suitability of a system for fire protection needs or cost analyses that are used for looking at the monetary impact of operations and improvements.…”

Section: A Water Network Simulator Algorithmmentioning

confidence: 99%

Mixed simulation-state estimation of water distribution systems based on a least squares loop flows state estimator

Arsene

Gabrys

2014

Applied Mathematical Modelling

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This paper presents a novel algorithm for uncertainty quantification of water distribution system measurement data including nodal demands/consumptions as well as real pressure and flow measurements. This procedure, referred to as a Confidence Limit Analysis (CLA), is concerned with a deployment of a Least Squares (LS) state estimator based on the loop corrective flows and the variation of nodal demands as independent variables. The confidence limits obtained for the nodal pressures and the inflows/outflows of a water network are determined with the novel algorithm called Error Maximization (EM) method and are evaluated with respect to two other more established CLA algorithms based on an Experimental Sensitivity Matrix (ESM) and on the sensitivity matrix method obtained with the LS nodal heads equations state estimator. The estimated confidence limits obtained for two real water networks show that the proposed EM algorithm is comparable to the other two CLA benchmark algorithms but due to its computational efficiency it is more suitable for online decision support applications in water distribution systems. Both ESM and EM methods work for any operating point whether arbitrarily or randomly chosen for any water network although EM method has the advantage of being computationally superior and working with any sets of measurements.

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