Picture of athlete cycling

Open Access research with a real impact on health...

The Strathprints institutional repository is a digital archive of University of Strathclyde's Open Access research outputs. Strathprints provides access to thousands of Open Access research papers by Strathclyde researchers, including by researchers from the Physical Activity for Health Group based within the School of Psychological Sciences & Health. Research here seeks to better understand how and why physical activity improves health, gain a better understanding of the amount, intensity, and type of physical activity needed for health benefits, and evaluate the effect of interventions to promote physical activity.

Explore open research content by Physical Activity for Health...

Practical application of the head dependent gradient method for water distribution networks

Siew, Calvin and Tanyimboh, Tiku (2011) Practical application of the head dependent gradient method for water distribution networks. Water Science and Technology: Water Supply, 11 (4). pp. 444-450. ISSN 1606-9749

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

Abstract

Critical pressure-deficient operating conditions frequently occur in water distribution networks. Head driven analysis (HDA) of water distribution networks allows for the pressure-dependent nature of nodal flows and, therefore, yields the actual nodal flows and heads for both normal and subnormal pressure conditions. Hence, HDA simulation models are more practical than demand driven analysis models that assume that all demands are satisfied in full even under pressure-deficient conditions. This paper describes an approach in which a new pressure-dependent demand function that has no discontinuities has been incorporated in the Gradient Method. The procedure has been tested extensively and demonstrated to be capable of effectively simulating both normal and pressure-deficient operating conditions. The algorithm's convergence is smooth and rapid thanks to a line search and backtracking procedure that ensures progress towards the solution in each iteration. The results presented herein would appear to confirm that the proposed method is robust and efficient. Results for a real network are also included.