Network Models and the Impact of Modeling Assumptions

Eggener, Charles L.; Polkowski, Lawrence B.

doi:10.1002/j.1551-8833.1976.tb02385.x

Cited by 30 publications

(6 citation statements)

References 3 publications

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“…Once the network equation has been established, it is solved iteratively with the Newton-Raphson numerical method. However, several numerical methods can be used to solve the mathematical model of a water network and they can be classified in the following major groups: a) the numerical minimization methods [42] [43]; b) the Hardy-Cross method [44] [45]; c) the Newton-Raphson method [46][47][48]; and d) the Linear Theory method [49].…”

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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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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“…In fact, many of today's distribution system modeling and analysis packages contain some type of skeletonization module or routine for rapid system simplification. Although published literature describing the effects of network simplification on modeling and water distribution system analysis accuracy has been limited, some quantitative analyses are available (Grayman & Rhee, 2004; Grayman et al, 1991; Characklis, 1988; Eggener & Polkowski, 1976). The results of these quantitative analyses appear to indicate that water quality analyses can be more sensitive to network skeletonization than standard hydraulic analyses.…”

Section: Literature Reviewmentioning

confidence: 99%

Using aggregation/skeletonization network models for water quality simulations in epidemiologic studies

Perelman¹,

Maslia²,

Ostfeld³

et al. 2008

Journal AWWA

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In recent years, epidemiologic studies and vulnerability assessments have come to rely on water distribution system models for refined estimates of street‐level exposure scenarios in conducting health studies and assessing potential contamination occurrences. A water distribution system network analysis at the street level can result in thousands to tens of thousands of hydraulic appurtenances such as pipelines, hydrants, and valves, making model calibration, verification, and system operation simulations time‐consuming and complex. Methods of aggregation, skeletonization, and network simplification have been used to reduce the complexity of the networks being analyzed. The issue then is whether network aggregation based on hydraulic characteristics and controls conserves mass so that the simplified network provides reliable results for the water quality analyses needed for epidemiologic studies and contamination assessment. In this study, a network aggregation methodology based on both hydraulic and water quality aggregation of an all‐pipes network was applied to a complex allpipes network described in the literature and used for an epidemiologic study. Simulation results obtained using the quality aggregation method, closely resembled both hydraulic (i.e., pressures) and water quality (i.e., concentrations) behavior of the original full‐sized (all‐pipes) system, while reducing system size by almost half.

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“…Reasonable agreement of the model is usually judged in terms of the differences in the observed and predicted pressures or heads at the test nodes (e.g., Cesario and Davis 1984;Eggener and Polkowski 1976;Rahal et al 1980;Walski 1983b), and depends on the accuracy of the data set and the effort and cost the model user is prepared to spend in fine-tuning the model. Walski (1983b) states that an average difference of ± 1.5 m with a maximum value of ± 5.0 m for a good data set and the corresponding values of ± 3.0 m and ± 10 m for a poor data set would be a reasonable target.…”

Section: Introductionmentioning

confidence: 99%

“…On the other hand, Eggener and Polkowski (1976) state that the weakest piece of input information is not the assumed loading condition, but the pipe friction factor. From these conflicting views, it is clear that, in some cases, the adjustment in the pipe head-loss coefficients may be more predominant than the adjustment in the nodal consumptions, while the opposite may be the situation in other cases (Shamir and Howard 1977).…”

Section: Introductionmentioning

confidence: 99%

Calibration of water distribution network of the Ramnagar zone in Nagpur City using online pressure and flow data

Jadhao

Gupta

2018

Appl Water Sci

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Calibration of hydraulic model of a water distribution network is required to match the model results of flows and pressures with those obtained in the field. This is a challenging task considering the involvement of a large number of parameters. Having more precise data helps in reducing time and results in better calibration as shown herein with a case study of one hydraulic zone served from the Ramnagar Ground Service Reservoir in Nagpur City. Flow and pressure values for the entire day were obtained through data loggers. Network details regarding pipe lengths, diameters, installation year and material were obtained with the largest possible accuracy. Locations of consumers on the network were noted and average nodal consumptions were obtained from the billing records. The non-revenue water losses were uniformly allocated to all junctions. Valve positions and their operating status were noted from the field and used. The pipe roughness coefficients were adjusted to match the model values with field values of pressures at observation nodes by minimizing the sum of square of difference between them. This paper aims at describing the entire process from collection of the required data to the calibration of the network.

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Network Models and the Impact of Modeling Assumptions

Cited by 30 publications

References 3 publications

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

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

Using aggregation/skeletonization network models for water quality simulations in epidemiologic studies

Calibration of water distribution network of the Ramnagar zone in Nagpur City using online pressure and flow data

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