A graphic partition method based on nodes learning for energy pipelines network simulation

Abstract

The network of energy pipelines is extremely important for the proper functioning of a modern society. Even though measurement instruments and sensors are currently widely used to monitor the network’s operational status in real time, it is expensive to deploy these extra devices for a large city-scale network. Numerical simulation of fluid networks offers a rather easy and effective technique to track the work performance of pipelines. Because of the computational intensiveness of the pipeline network, it is most suitable to deploy such a massive simulation program on a supercomputer with a parallel technique. The performance of this kind of parallel simulation may undoubtedly be enhanced if the pipeline network is reasonably divided into several subnetworks in which the fluid flows are resolved independently. In this paper, we concentrate on how to achieve a computationally balanced subnetwork partition scheme and propose an acceleration method for energy pipeline network simulations based on a graph partition algorithm. In our approach, each node of the network takes part in the decision of dividing the entire network by learning the weight of its neighbors. Moreover, we utilize graph-tree transformations to merge locally related components as much as possible, and together with subgraph rebalancing, the pipeline network division quality is improved considerably. Numerical simulations show that our approach is much better than others for the degree of balance, and the cut edge ratio is also lower than others when it is compared with the random, k-means, and METIS methods.