Synthesizing urban infrastructure and their interdependencies
The SyNF model was developed to support urban sustainability and resilience researchers who often struggle to gain access to rich infrastructure data. The model was designed to estimate the spatial layout, characteristics, and operational parameters of power and water systems, and facilitate interdependency analysis of power, water, building, and transportation systems. SyNF can estimate not only where infrastructure are and how they're used, but can also be used in conjunction with robust simulation tools (such as EPANET and OpenDSS) to analyze the operations and effects of perturbations on complex urban infrastructure.
Using information on treatment plants, elevation, roadway networks, buildings, and demand (from IUWM), SyNF generates a water distribution network including pipe location, pipe diameter, pipe initial year of construction, pump location and pump size characteristics.
Using substation location and size, demand estimates (from Texas A&M), roadway networks, SynNF generates a power distribution network including the service areas of substations, ampacity of power lines, and substation and line initial year of construction.
SyNF connects the water and power distribution models to establish interdependencies between the systems, including the capacity to assess cascading failures.
SyNF uses standard roadway and building data to estimate water and power distribution networks, including their physical and operational characteristics. Socio-economic data are captured. Interdependencies between power, water, transportation, and building systems is then estimated to capture cascading effects of outages.
Water distribution infrastructure data are often unavailable and rarely capture both infrastructure and operational characteristics. SyNF addresses this challenge by synthetically generating water distribution systems with results useable in simulation packages.
Building data are typically available from assessor databases and are necessary for understanding demand and household characteristics.
Power distribution infrastructure data require both an understanding of the physical layout of infrastructure and how energy is delivered from substations. SyNF estimates these system characteristics and produces results compatible with simulation packages.
Household characteristics are critical for understanding both demand and the implications of failures.
Transportation systems rely on the power system and can be adversely impacted by failure of power and water systems. SyNF connects the transportation system to the other infrastructure both spatially and physically.
SyNF captures the spatial and physical interdependencies of water, power, building, and transportation systems, including the capability to assess cascading failures across the systems.
SyNF is being used to assess extreme heat, flooding, and other climate-related events on infrastructure.
Kinetic attack, EMP, and sabotage scenarios have been assessed with SyNF.
Reliability impacts due to disrepair and their cascading failures, within and across infrastructure, have been assessed with SyNF.
We have developed the SyNF model focusing on the Phoenix, New York, San Juan, and Atlanta regions, through National Science Foundation support (UREx, UWIN, Convergence, and RISE projects). The model has been deployed to military infrastructure in support of base resilience planning.
Phoenix is one of the fast growing cities in the U.S., with a modern infrastructure at risk to extreme heat, precipitation, and flooding.
New York's infrastructure are some of the oldest in the U.S., supporting a major population and massive economic activity.
San Juan's storied infrastructure is in need of major rehabilitation that considers the need for climate adaptation against sea level rise, storm surges, and hurricanes.
Atlanta is a major population center in the U.S. Southeast and is subject to major heat, precipitation, and flooding challenges.
SyNF model development is led by Professor Mikhail Chester and Dr. Nasir Ahmad, with the support of many talented researchers and research labs across partner institutions for several research projects.