Name: Pattern Recognition for Reliability Assessment of Water Distribution Networks: Unesco-Ihe PhD Thesis (en Inglés)
Price: 242200.00 CLP
Availability: InStock
Author: Trifunovic, N.
ISBN: 9781138474444

portada Pattern Recognition for Reliability Assessment of Water Distribution Networks: Unesco-Ihe PhD Thesis (en Inglés)

Formato

Libro Físico

Autor

N. Trifunovic

Editorial

CRC Press

Idioma

Inglés

N° páginas

310

Encuadernación

Tapa Dura

ISBN13

9781138474444

N° edición

Categorías

Ingeniería civil, topografía y construcción
Suministro y tratamiento de aguas

Pattern Recognition for Reliability Assessment of Water Distribution Networks: Unesco-Ihe PhD Thesis (en Inglés)

N. Trifunovic (Autor) · CRC Press · Tapa Dura

Libro Físico

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Reseña del libro "Pattern Recognition for Reliability Assessment of Water Distribution Networks: Unesco-Ihe PhD Thesis (en Inglés)"

This study investigates the patterns that describe reliability of water distribution networks focusing to the node connectivity, energy balance, and economics of construction, operation and maintenance. A number of measures to evaluate the network resilience has been developed and assessed to arrive at more accurate diagnostics of regular and irregular demand scenarios. These measures have been proposed as a part of the methodology for snap-shot assessment of network reliability based on its configuration and hydraulic performance. Practical outcome of the research is the decision support tool for reliability-based design of water distribution networks. This computer package named NEDRA (Network Design and Reliability Assessment) consists of the modules for network generation, filtering, initialisation, optimisation, diagnostics and cost calculation, which can be used for sensitivity analyses of single network layout or assessments of multiple layouts.The study concludes that none of the analysed aspects develops clear singular patterns. Nevertheless, the proposed network buffer index (NBI) and the hydraulic reliability diagram (HRD) as visual representation of the network resilience give sufficient snap-shot pointing the composition of the index value, and displaying possible weak points in the network that can be hidden behind the averaged values of various reliability measures.