The Gas Transportation in a Pipeline Network

Szoplik, J.

doi:10.5772/36902

Cited by 24 publications

(23 citation statements)

References 15 publications

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“…Stochastic modelling has been applied in [9] to consider different rates of leakage that may occur in a pipe fault and thus predict the impact on pressures and flow rates, supporting the planning of appropriate actions to mitigate risks. In [54], fluid-dynamic analysis of a section of a real gas network is repeated for different configurations of demand, thus reflecting the statistics of usage at different hours of the day and in different seasons. The effects of sequential restoration and constrained network capacity are considered in [29] to support reliability assessment by deriving average measures of interruption rate and outage time experienced by end-users, exemplifying the approach on a small-sized gas network.…”

Section: Modelling Approachesmentioning

confidence: 99%

Survivability Evaluation of Gas, Water and Electricity Infrastructures

Avritzer

Carnevali

Ghasemieh

et al. 2015

Electronic Notes in Theoretical Computer Science

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The infrastructures used in cities to supply power, water and gas are consistently becoming more automated. As society depends critically on these cyber-physical infrastructures, their survivability assessment deserves more attention. In this overview, we first touch upon a taxonomy on survivability of cyber-physical infrastructures, before we focus on three classes of infrastructures (gas, water and electricity) and discuss recent modelling and evaluation approaches and challenges.

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Section: Modelling Approachesmentioning

confidence: 99%

Survivability Evaluation of Gas, Water and Electricity Infrastructures

Avritzer

Carnevali

Ghasemieh

et al. 2015

Electronic Notes in Theoretical Computer Science

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“…Pipe repair. On the basis of conventional contractual requirements with end-users, we assume that the time to repair a pipe has a uniform distribution over [24,72] h.…”

Section: Network Reconfiguration and Undo Reconfigurationmentioning

confidence: 99%

“…Stochastic modeling is applied in [5] to consider dif-ferent rates of leakage that may occur in a pipe fault and thus predict the impact that this may have on pressures and flow rates across the network, supporting the planning of appropriate actions to mitigate risks. In [24] fluid-dynamic analysis of a section of a real gas network is repeated for different configurations of demand reflecting the statistics of usage in different day hours and seasons. The effects of sequential restoration and constrained network capacity are considered in [14] to support reliability assessment by deriving average measures of interruption rate and outage time experienced by end-users, exemplifying the approach on a small-sized gas network.…”

Section: Introductionmentioning

confidence: 99%

Quantitative evaluation of availability measures of gas distribution networks

Carnevali¹,

Paolieri²,

Tarani³

et al. 2014

Proceedings of the 7th International Conference on Performance Evaluation Methodologies and Tools

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Rising competition among gas distribution companies, growing availability of smart metering devices, and increasingly strict requirements on agreed service levels stimulate research on advanced modeling and solution techniques for quantitative evaluation of gas distribution networks. We propose a novel methodology for modeling and evaluation of the transient network behavior after a component failure.The approach relies on a topological model of the fluid dynamics and a stochastic timed model of the actions started after a component failure. Fluid dynamic analysis evaluates the service level of end-users in each possible operating condition of the network, also supporting the derivation of stochastic parameters for the failure management model. In turn, such model is analyzed to evaluate the probability over time of the network operating conditions. Transient probabilities are then aggregated on the basis of the results of fluid dynamic analysis to derive availability measures. Special attention is paid to make the structure of the stochastic model independent of the network topology. To provide a proof of concept, the approach is exemplified on a smallsized network equipped with a backup pipe, evaluating for each end-user the transient probability of not being served after a component failure as well as the mean outage time. These measures comprise a valid ground for the evaluation of different failure management processes and the definition of demand-response strategies.

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“…As all the above-mentioned infrastructures are defined as an LI, their characteristics can be easy translated into graphs. The usage of graphs and graph theory in representation of LI is not a new idea and it was used to represent the LI and detect faults in numerous publications for gas [5,6], district heating [7,8], water [9], and landscape planning [10] Quite interesting idea was presented by the authors of US patent [11] that gives an idea for generating A method and system to generate a network graph representation of a physically connected network. The Smart City systems must merge existing data about smart meters state, readings, and parameters with information about the structure of the infrastructure, such as the topology of the network.…”

Section: Introductionmentioning

confidence: 99%

Performance Tests of Smart City IoT Data Repositories for Universal Linear Infrastructure Data and Graph Databases

Gorawski

Grochla

2019

SN COMPUT. SCI.

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The infrastructure managed by Smart City systems includes mainly linear structures, such as pipelines, electric power lines, railway lines, etc.. In this paper, we propose the transcription of linear infrastructure to graph representation and storing needed meta-data in a graph database, merged with the traditional relational database used for device management. Such an approach enables fast acquisition of data about infrastructure's properties and allows merging information about the structure of the managed infrastructure with information devices' properties and statistics. We develop a graph generator that generates virtual network basing on representative examples of linear infrastructure, incorporation of a data from existing metering systems with the generated structure, which automates the deployment and allows to evaluate the performance of Smart City monitoring and management system. We present results of performance tests that show and the creation time of graph database structures in Neo4j and the difference between the performance of the Microsoft SQL Server database and the Neo4j database in a few Smart City use cases. The results show that the use of the graph database to execute queries related to linear infrastructure allows decreasing the response time up to 9 times.

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The Gas Transportation in a Pipeline Network

Cited by 24 publications

References 15 publications

Survivability Evaluation of Gas, Water and Electricity Infrastructures

Survivability Evaluation of Gas, Water and Electricity Infrastructures

Quantitative evaluation of availability measures of gas distribution networks

Performance Tests of Smart City IoT Data Repositories for Universal Linear Infrastructure Data and Graph Databases

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