Modeling water distribution networks facilitates assessment of system resiliency, improvements for demand forecasting, and overall optimization of limited system resources. This report serves as an introduction to the fundamentals of Water Distribution Networks (WDN) and provides insight into modern approaches of modeling these critical infrastructures. We provide an overview of core components within a WDN and a literature review and summary of current modeling approaches. We investigate and compare three unique vulnerability assessment methodologies based upon a graph theoretic approach. We assess the merits of each approach and the associated analytics implemented to identify the critical nodes and edges within a network. The first method utilizes a topological approach and segments the network into valve-enclosed sections. Analysis is centered on a depth-first search to identify nodes which would impact the most downstream nodes. The second method fuses topological and hydraulic data calculated using software such as EPANET. Various centrality measures corresponding to portion of network flow are used to assess vulnerability. The last method focuses on pipe (edge) vulnerability, incorporating information such as the average daily flow through each pipe as key parameters to algorithmically assess vulnerability.
KEYWORDS: Optimization (mathematics), Detection and tracking algorithms, Algorithm development, Data modeling, Java, Defense and security, Complex systems, Chemical elements
In recovering from cyber attacks on power grids, restoration steps have also included disconnecting parts of the network to prevent failures from propagating. Inspired by these reports we investigate how this may be performed optimally. We show how throughput can indeed be increased by selectively disconnecting links when the network is currently stressed and unable to meet all of the demands. We also consider the impact of this option on critical node analysis. For defensive as well as offensive scenario planning it is important to be able to identify the critical nodes in a given network. We show how ignoring this option of disconnecting links can lead to misidentifying critical nodes, overstating the impact of these nodes. We outline an iterative procedure to address this problem and correctly identify critical nodes when link disconnection is included in the recovery scheme.
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