Picture of UK Houses of Parliament

Leading national thinking on politics, government & public policy through Open Access research

Strathprints makes available scholarly Open Access content by researchers in the School of Government & Public Policy, based within the Faculty of Humanities & Social Sciences.

Research here is 1st in Scotland for research intensity and spans a wide range of domains. The Department of Politics demonstrates expertise in understanding parties, elections and public opinion, with additional emphases on political economy, institutions and international relations. This international angle is reflected in the European Policies Research Centre (EPRC) which conducts comparative research on public policy. Meanwhile, the Centre for Energy Policy provides independent expertise on energy, working across multidisciplinary groups to shape policy for a low carbon economy.

Explore the Open Access research of the School of Government & Public Policy. Or explore all of Strathclyde's Open Access research...

Augmented gradient method for head dependent modelling of water distribution networks

Siew, Calvin and Tanyimboh, Tiku (2009) Augmented gradient method for head dependent modelling of water distribution networks. In: World Environmental and Water Resources Congress 2009. American Society of Civil Engineers. ISBN 978-0-7844-1036-3

Full text not available in this repository.Request a copy from the Strathclyde author

Abstract

When analysing a pressure deficient network, it is crucial that the pressure dependent nature of nodal outflows be taken into account. The head dependent analysis (HDA) produces an accurate representation of the nodal outflows and network hydraulic performance. This is essential when modelling pipe leakages, network redundancy and reliability. These are vital aspects often considered in the optimization of a water distribution system (WDS). This paper describes an approach for head dependent analysis in which an embedded function for the head-outflow relationship is incorporated in the Gradient Method (GM). The procedure is capable of simulating both normal and deficient network operating conditions effectively. Results based on networks from the literature show that the proposed method is robust and converges smoothly and rapidly.