Effective water system management depends upon knowledge of the current state of a water pipeline system network. For example, in many cases, partial blockages in a water pipeline system are a source of inefficiencies, and result in an increase of pumping costs. These anomalies must be detected and corrected as early as possible. In this study, an algorithm is developed for detecting blockages by means of pressure transient measurements and estimating the diameter distribution resulting from their formation. The algorithm is a stochastic successive linear estimator that provides statistically the best unbiased estimate of diameter distribution due to partial blockages and quantifies the uncertainty associated with these estimates. We first present the theoretical formulation of the algorithm and then test it with a numerical case study.
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Research Article|
April 04 2013
Detection and sizing of extended partial blockages in pipelines by means of a stochastic successive linear estimator
Christian Massari;
Christian Massari
1Dipartimento di Ingegneria Civile Ambientale, Università degli Studi di Perugia, Via Duranti 93, Perugia, 06125, Italy
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Tian Chyi J. Yeh;
Tian Chyi J. Yeh
2Department of Hydrology and Water Resources, University of Arizona, 1133 E James E. Rogers Way, Tucson, AZ 8572, USA
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Marco Ferrante;
1Dipartimento di Ingegneria Civile Ambientale, Università degli Studi di Perugia, Via Duranti 93, Perugia, 06125, Italy
E-mail: [email protected]
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Bruno Brunone;
Bruno Brunone
1Dipartimento di Ingegneria Civile Ambientale, Università degli Studi di Perugia, Via Duranti 93, Perugia, 06125, Italy
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Silvia Meniconi
Silvia Meniconi
1Dipartimento di Ingegneria Civile Ambientale, Università degli Studi di Perugia, Via Duranti 93, Perugia, 06125, Italy
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Journal of Hydroinformatics (2014) 16 (2): 248–258.
Article history
Received:
October 30 2012
Accepted:
March 04 2013
Citation
Christian Massari, Tian Chyi J. Yeh, Marco Ferrante, Bruno Brunone, Silvia Meniconi; Detection and sizing of extended partial blockages in pipelines by means of a stochastic successive linear estimator. Journal of Hydroinformatics 1 March 2014; 16 (2): 248–258. doi: https://doi.org/10.2166/hydro.2013.172
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