Comparison of Methods for Predicting Deficient-Network Performance

Gupta, Rajesh; Bhave, Pramod R.

doi:10.1061/(asce)0733-9496(1996)122:3(214)

Cited by 188 publications

(111 citation statements)

References 14 publications

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“…The demand satisfaction ratio, derived by pressure-driven analysis (see e.g. Gupta and Bhave 1996;Giustolisi et al 2008;Gorev and Kodzhespirova 2013;Kovalenko et al 2014;Ciaponi et al 2015), is the ratio of the flow delivered to the flow required (Ackley et al 2001;Tanyimboh et al 2003;Kalungi and Tanyimboh 2003;Tanyimboh and Templeman 2010). The demand satisfaction ratio is also known as the available demand fraction (see e.g.…”

Section: Formulation Of the Optimization Modelmentioning

confidence: 99%

Penalty-Free Multi-Objective Evolutionary Approach to Optimization of Anytown Water Distribution Network

Siew

Tanyimboh

Seyoum

2016

Water Resour Manage

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This paper describes the development and application of a new multiobjective evolutionary optimization approach for the design and upgrading of water distribution systems with multiple pumps and service reservoirs. The optimization model employs a pressure-driven analysis simulator that accounts for the minimum node pressure constraints and conservation of mass and energy. Pump scheduling, tank siting and tank design are integrated seamlessly in the optimization without introducing additional heuristic procedures. The computational solution of the optimization problem is entirely penalty-free, thanks to pressure-driven analysis and the inclusion of explicit criteria for tank depletion and replenishment. The model was applied to the Anytown network that is a benchmark optimization problem. Many new solutions were achieved that are cheaper and offer superior performance compared to previous solutions in the literature. Detailed and extensive simulations of the solutions achieved were carried out. Spatial and temporal variations in water quality were investigated by simulating the chlorine residual and disinfection by-products in addition to water age. The hydraulic requirements were satisfied; efficiency of pumps was consistently high; effective operation of the new and existing tanks was achieved; water quality was improved; and overall computational efficiency was high. The formulation is entirely generic.Keywords Demand-driven analysis . Pressure-driven analysis . Penalty-free constrained multiobjective evolutionary optimization . Water distribution system . Optimal pump scheduling . Service reservoir design and operation Water Resour Manage (2016) 30:3671-3688

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Section: Formulation Of the Optimization Modelmentioning

confidence: 99%

Penalty-Free Multi-Objective Evolutionary Approach to Optimization of Anytown Water Distribution Network

Siew

Tanyimboh

Seyoum

2016

Water Resour Manage

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“…Differently from the usual problem of analysis of water networks where it is assumed a-priori the ability of the system to satisfy the demand, Qdemand, and flow losses are evaluated as a fixed percentage of the circulating flow, in this case both these quantities cannot be assumed known, and must be evaluated as a function of the network response. Indeed, the ability to serve the users has been expressed as a function of hydraulic head according the following expression (Gupta & Bhave, 1996) (8) where h is the water head respect to the ground level, hdemand is the water head needed to assure to the consumers their demand, Qdemand, and hmin is the minimum water head to be assured in order to have a discharge greater than zero. In this case hdemand can be assumed to be equal to the average building height plus 3÷7 m (in order to account for the head losses due to domestic water system), whilst n is a model coefficient controlling the shape of the head dependent demand and to be assumed into the recommended range between 0.5 and 2.0.…”

Section: Flow Equationsmentioning

confidence: 99%

Reliability Analysis of Urban Water Systems Under Seismic Action

Rasulo¹

2017

Proceedings of the 6th International Conference on Computational Methods in Structural Dynamics and Earthquake Engineering (COM

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“…The evaluation of the hydraulic reliability requires multiple analyses of the distribution system under both normal and subnormal operating conditions. This requires pressure-dependent modelling (Bhave 1981;Germanopoulos 1985;Gupta and Bhave 1996;Ackley et al 2001). Pressure-dependent modelling differs from demand-driven modelling in that pressure-dependent modelling treats the available flow at a demand node as a function of the nodal pressure (e.g.…”

Section: Hydraulic Reliability Measuresmentioning

confidence: 99%

Comparison of Surrogate Measures for the Reliability and Redundancy of Water Distribution Systems

Tanyimboh

Siew

Saleh

et al. 2016

Water Resour Manage

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An investigation into the effectiveness of surrogate measures for the hydraulic reliability and/or redundancy of water distribution systems is presented. The measures considered are statistical flow entropy, resilience index, network resilience and surplus power factor. Looped network designs that are maximally noncommittal to the surrogate reliability measures were considered. In other words, the networks were designed by multi-objective evolutionary optimization free of any influence from the surrogate measures. The designs were then assessed using each surrogate measure and two accurate but computationally intensive measures namely hydraulic reliability and pipe-failure tolerance. The results indicate that by utilising statistical flow entropy, the reliability of the network can be reasonably approximated, with substantial savings in computational effort. The results for the other surrogate measures were often inconsistent. Two networks in the literature were considered. One example involved a range of alternative network topologies. In the other example, based on wholelife cost accounting, alternative design and upgrading schemes for a 20-year design horizon were considered. Pressure-dependent hydraulic modelling was used to simulate pipe failures for the reliability calculations.

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Comparison of Methods for Predicting Deficient-Network Performance

Cited by 188 publications

References 14 publications

Penalty-Free Multi-Objective Evolutionary Approach to Optimization of Anytown Water Distribution Network

Penalty-Free Multi-Objective Evolutionary Approach to Optimization of Anytown Water Distribution Network

Reliability Analysis of Urban Water Systems Under Seismic Action

Comparison of Surrogate Measures for the Reliability and Redundancy of Water Distribution Systems

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