Quantitative risk analysis of urban natural gas pipeline networks using geographical information systems

2013 ◽  
Vol 26 (6) ◽  
pp. 1183-1192 ◽  
Author(s):  
Lei Ma ◽  
Liang Cheng ◽  
Manchun Li
Keyword(s):  
Information Systems ◽  
Natural Gas ◽  
Risk Analysis ◽  
Geographical Information Systems ◽  
Gas Pipeline ◽  
Geographical Information ◽  
Quantitative Risk Analysis ◽  
Natural Gas Pipeline ◽  
Pipeline Networks

scholarly journals QRA-Grid: Quantitative Risk Analysis and Grid-based Pre-warning Model for Urban Natural Gas Pipeline

2019 ◽  
Vol 8 (3) ◽  
pp. 122 ◽  
Author(s):  
Shuang Li ◽  
Chengqi Cheng ◽  
Guoliang Pu
Keyword(s):  
Natural Gas ◽  
Risk Analysis ◽  
Gas Pipeline ◽  
Gas Pipelines ◽  
Gis Technology ◽  
Quantitative Risk Analysis ◽  
Natural Gas Pipeline ◽  
Fire And Explosion ◽  
Warning Model ◽  
Grid Based

With the increasing use and complexity of urban natural gas pipelines, the occurrence of accidents owing to leakage, fire, explosion, etc., has increased. Based on Quantitative Risk Analysis (QRA) models and Geographic Information System (GIS) technology, we put forward a quantitative risk simulation model for urban natural gas pipeline, combining with a multi-level grid-based pre-warning model. We develop a simulation and pre-warning model named QRA-Grid, conducting fire and explosion risk assessment, presenting the risk by using a grid map. Experiments show that by using the proposed method, we can develop a fire and explosion accident pre-warning model for gas pipelines, and effectively predict areas in which accidents will happen. As a result, we can make a focused and forceful policy in areas which have some potential defects in advance, and even carry out urban planning once more, rebuilding it to prevent the risk.

scholarly journals A quantitative risk analysis method for the natural gas pipeline network

2010 ◽  
Author(s):  
Y.-D. Jo ◽  
K.-S. Park ◽  
H.-S. Kim ◽  
J.-J. Kim ◽  
J.-Y. Kim ◽  
...  
Keyword(s):  
Natural Gas ◽  
Risk Analysis ◽  
Gas Pipeline ◽  
Analysis Method ◽  
Quantitative Risk Analysis ◽  
Pipeline Network ◽  
Natural Gas Pipeline

Example of Quantitative Risk Analysis of a Gas Pipeline

2002 ◽  
Author(s):  
Chunyong Huo ◽  
Chuanjing Zhuang ◽  
Minxu Lu ◽  
Helin Li ◽  
Guoxing Li
Keyword(s):  
High Risk ◽  
Natural Gas ◽  
Risk Analysis ◽  
Preliminary Data ◽  
Gas Pipeline ◽  
Quantitative Risk Analysis ◽  
Risk Levels ◽  
Natural Gas Pipeline ◽  
Calculated Risk ◽  
Risk Areas

Quantitative risk analysis was carried out on a proposed natural gas pipeline based on preliminary data using the methods and models implemented in the PIRAMID™ software program. The high-risk areas were identified and the main causes of risk were analyzed. Measures to lower the calculated risk levels were developed, which will be taken into consideration in the design and construction of the pipeline.

scholarly journals GPU-accelerated hydraulic simulations of large-scale natural gas pipeline networks based on a two-level parallel process

2020 ◽  
Vol 75 ◽  
pp. 86
Author(s):  
Yue Xiang ◽  
Peng Wang ◽  
Bo Yu ◽  
Dongliang Sun
Keyword(s):  
Natural Gas ◽  
Large Scale ◽  
Parallel Simulation ◽  
Simulation Software ◽  
Gas Pipeline ◽  
Processing Unit ◽  
Pipeline Network ◽  
Natural Gas Pipeline ◽  
Pipeline Networks ◽  
Speedup Ratio

The numerical simulation efficiency of large-scale natural gas pipeline network is usually unsatisfactory. In this paper, Graphics Processing Unit (GPU)-accelerated hydraulic simulations for large-scale natural gas pipeline networks are presented. First, based on the Decoupled Implicit Method for Efficient Network Simulation (DIMENS) method, presented in our previous study, a novel two-level parallel simulation process and the corresponding parallel numerical method for hydraulic simulations of natural gas pipeline networks are proposed. Then, the implementation of the two-level parallel simulation in GPU is introduced in detail. Finally, some numerical experiments are provided to test the performance of the proposed method. The results show that the proposed method has notable speedup. For five large-scale pipe networks, compared with the well-known commercial simulation software SPS, the speedup ratio of the proposed method is up to 57.57 with comparable calculation accuracy. It is more inspiring that the proposed method has strong adaptability to the large pipeline networks, the larger the pipeline network is, the larger speedup ratio of the proposed method is. The speedup ratio of the GPU method approximately linearly depends on the total discrete points of the network.

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