Infrastructure Modeling, Simulation, and Analysis
Critical infrastructure protection is a recognized problem of national importance. Infrastructure interdependence research and applications require a seamless and unified view of infrastructure as a “system of systems,” but existing infrastructure modeling efforts, however, have been hampered by an inflexible software technology base. The Simulation Object Framework for Infrastructure Analysis (SOFIA), Energy Interdependence Simulator (EISIM), and Interdependence Energy Infrastructure Simulation System (IEISS) projects at Los Alamos National Laboratory (LANL) aim to research and develop a high-quality, flexible, and extensible actor-based software framework for the modeling, simulation, and analysis of interdependent infrastructures. We intend to have a state-of-the-art tool for the analysis of interdependent infrastructures and to have applied it to innovative research questions of national importance. This integrated, holistic approach handles multiple problem domains, varying levels of component aggregation, disparate time scales, heterogeneous simulation algorithms, sensitivity to uncertainties, and data visualization to study issues related to the characterization of infrastructure system complexity, sensitivity, robustness, and degree of interdependence, and to the identification of critical components, synergistic behavior, and fundamental limits on the predictability of these systems. Present work focuses on the simulation of interconnected electric power and natural gas infrastructure, along with their associated control systems. This effort supports LANL’s strategic goal of developing tools that enable the nation to accurately assess the vulnerability of critical infrastructures and. developing next-generation tools to model interdependency among infrastructures.
Selected Publications
M. Blue and B. W. Bush, “Information content in the Nagel-Schreckenberg cellular automaton traffic model,” Phys. Rev. E, vol. 67, no. 4, p. 047103. <http://link.aps.org/doi/10.1103/PhysRevE.67.047103>
We estimate the set dimension and find bounds for the set entropy of a cellular automaton model for single lane traffic. Set dimension and set entropy, which are measures of the information content per cell, are related to the fractal nature of the automaton [S. Wolfram, Physica D 10, 1 (1989); Theory and Application of Cellular Automata, edited by S. Wolfram (World Scientific, Philadelphia, 1986)] and have practical implications for data compression. For models with maximum speed vmax, the set dimension is approximately log(vmax+2)2.5, which is close to one bit per cell regardless of the maximum speed. For a typical maximum speed of five cells per time step, the dimension is approximately 0.47.
M. P. Blue and B. W. Bush, “Set Entropy of Block Configurations That Appear in the TRANSIMS Simulation,” Los Alamos National Laboratory, Report LA-UR-01-4277.
M. P. Blue and B. W. Bush, “Critical Energy Infrastructure Contingency Screening Heuristics Status Report,” Los Alamos National Laboratory, Report LA-UR-04-7649.
M. Blue, B. Bush, and J. Puckett, “Applications of Fuzzy Logic to Graph Theory,” Los Alamos National Laboratory, Report LA-UR-96-4792.
Graph theory has numerous applications to problems in systems analysis, operations research, transportation, and economics. In many cases, however, some aspects of the graph-theoretic problem are uncertain. In these cases, it can be useful to deal with this uncertainty using the methods of fuzzy logic. This paper discusses the taxonomy of fuzzy graphs, formulates some standard graph-theoretic problems (shortest paths, maximum flow, minimum cut, and articulation points) in terms of fuzzy graphs, and provides algorithmic solutions to these problems, with examples.
M. Blue, B. Bush, and J. Puckett, “Unified approach to fuzzy graph problems,” Fuzzy Sets and Systems, vol. 125, no. 3, pp. 355–368. <http://www.sciencedirect.com/science/article/pii/S0165011401000112>
We present a taxonomy of fuzzy graphs that treats fuzziness in vertex existence, edge existence, edge connectivity, and edge weight. Within that framework, we formulate some standard graph-theoretic problems (shortest paths and minimum cut) for fuzzy graphs using a unified approach distinguished by its uniform application of guiding principles such as the construction of membership grades via the ranking of fuzzy numbers, the preservation of membership grade normalization, and the “collapsing” of fuzzy sets of graphs into fuzzy graphs. Finally, we provide algorithmic solutions to these problems, with examples.
B. W. Bush, C. R. Files, and D. R. Thompson, “Empirical Characterization of Infrastructure Networks,” Los Alamos National Laboratory, Report LA-UR-01-5784.
Critical infrastructure protection is a recognized problem of national importance. Infrastructure networks such as electric power, natural gas, communications, and transportation systems have an inherent graph-theoretic structure. Quantitatively characterizing the essential properties of infrastructure networks for various domains lays a valuable foundation for studying the universal features (especially criticality, robustness, etc.) and specific characteristics of such networks. We construct an extensive reference data set of infrastructure network graphs: 44 graphs of 13 types with nearly one million vertices and over one million edges. After regularizing these graphs, we compute more than fifty metrics related to connectivity, distance scale, cyclicity, cliquishness, and redundancy. We contrast these metrics for different types of infrastructures, study their interrelationship, and use them to cluster and classify systems. We consider both intact networks and networks that have been degraded by the removal of some vertices or edges either at random or systematically–this provides insight as to the robustness of the network if it were subject to a natural disaster or an attack.