2009
DOI: 10.1002/j.1551-8833.2009.tb09877.x
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Effect of pressure‐sensitive demand on surge analysis

Abstract: Traditional water distribution models solve the network problem by considering instantaneous demands lumped as nodal outflows. Such demand‐driven analysis assumes that demands are independent of pressures and can be met under all operating conditions. Under transient conditions, however, the resulting positive‐ or negative‐pressure surges can drastically alter the local pressures and affect the demand magnitude that can be extracted. A pressuresensitive demand representation is needed to assess the effect of p… Show more

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Cited by 22 publications
(18 citation statements)
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“…7 nodes) [14], (2) real-world network (incl. 29 nodes) [248]. Objective (1) (7) ratio between diameter and height (real), (8) ratio between emergency volume and total volume (real).…”
mentioning
confidence: 99%
“…7 nodes) [14], (2) real-world network (incl. 29 nodes) [248]. Objective (1) (7) ratio between diameter and height (real), (8) ratio between emergency volume and total volume (real).…”
mentioning
confidence: 99%
“…Otra desventaja relevante del algoritmo es que su funcionamiento óptimo depende de la discretización adoptada para la red, la cual debe ser establecida de forma tal que pueda cumplir con la condición de Courant y, dependiendo del caso, ésta debe ser tal que permita incorporar los nodos internos de interés dentro del análisis. Además, el algoritmo supone que la DUD es insensible a la presión en los nodos internos de las tuberías, lo cual puede llevar a sobreestimar (o subestimar) las condiciones transientes, con riesgo de afectar el dimensionamiento o funcionamiento de los artefactos supresores de ondas y, por ende, de los costos asociados (Jung et al, 2009). 26, 43-50…”
Section: Discussionunclassified
“…Under transient conditions, the pressure may drop to or below zero, so that the demand in the actual system will not be met. Therefore, a pressure-sensitive demand formulation was utilized for all surge analyses, as recommended in Jung et al (2009a) and McInnis and Karney (1995).…”
Section: Transient Modelingmentioning
confidence: 99%