Publications

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Drinking water systems face multiple challenges, including aging infrastructure, water quality concerns, uncertainty in supply and demand, natural disasters, environmental emergencies, and cyber and terrorist attacks. All of these incidents have the potential to disrupt a large portion of a water system causing damage to critical infrastructure, threatening human health, and interrupting service to customers. Recent incidents, including the floods and winter storms in the southern United States, highlight vulnerabilities in water systems and the need to minimize service loss. Simulation and analysis tools can help water utilities better understand how their system would respond to a wide range of disruptive incidents and inform planning to make systems more resilient over time. The Water Network Tool for Resilience (WNTR) is a new open source Python package designed to meet this need. WNTR integrates hydraulic and water quality simulation, a wide range of damage and response options, and resilience metrics into a single software framework, allowing for end-Toend evaluation of water network resilience. WNTR includes capabilities to 1) generate and modify water network structure and operations, 2) simulate disaster scenarios, 3) model response and repair strategies, 4) simulate pressure dependent demand and demand-driven hydraulics, 5) simulate water quality, 6) calculate resilience metrics, and 7) visualize results. These capabilities can be used to evaluate resilience of water distribution systems to a wide range of hazards and to prioritize resilience-enhancing actions. Furthermore, the flexibility of the Python environment allows the user to easily customize analysis. For example, utilities can simulate a specific incident or run stochastic analysis for a range of probabilistic scenarios. The U.S. Environmental Protection Agency and Sandia National Laboratories are working with water utilities to ensure that WNTR can be used to efficiently evaluate resilience under different use cases. The software has been used to evaluate resilience under earthquake and power outage scenarios, run fire-fighting capacity and pipe criticality analysis, evaluate sampling and flushing locations, and prioritize repair strategies. This paper includes discussion on WNTR capabilities, use cases, and resources to help get new users started using the software. WNTR can be downloaded from the U.S. Environmental Protection Agency GitHub site at https://github.com/USEPA/WNTR. The GitHub site includes links to software documentation, software testing results, and contact information.

Drinking water systems face multiple challenges, including aging infrastructure, water quality concerns, uncertainty in supply and demand, natural disasters, environmental emergencies, and cyber and terrorist attacks. All of these incidents have the potential to disrupt a large portion of a water system causing damage to critical infrastructure, threatening human health, and interrupting service to customers. Recent incidents, including the floods and winter storms in the southern United States, highlight vulnerabilities in water systems and the need to minimize service loss. Simulation and analysis tools can help water utilities better understand how their system would respond to a wide range of disruptive incidents and inform planning to make systems more resilient over time. The Water Network Tool for Resilience (WNTR) is a new open source Python package designed to meet this need. WNTR integrates hydraulic and water quality simulation, a wide range of damage and response options, and resilience metrics into a single software framework, allowing for end-Toend evaluation of water network resilience. WNTR includes capabilities to 1) generate and modify water network structure and operations, 2) simulate disaster scenarios, 3) model response and repair strategies, 4) simulate pressure dependent demand and demand-driven hydraulics, 5) simulate water quality, 6) calculate resilience metrics, and 7) visualize results. These capabilities can be used to evaluate resilience of water distribution systems to a wide range of hazards and to prioritize resilience-enhancing actions. Furthermore, the flexibility of the Python environment allows the user to easily customize analysis. For example, utilities can simulate a specific incident or run stochastic analysis for a range of probabilistic scenarios. The U.S. Environmental Protection Agency and Sandia National Laboratories are working with water utilities to ensure that WNTR can be used to efficiently evaluate resilience under different use cases. The software has been used to evaluate resilience under earthquake and power outage scenarios, run fire-fighting capacity and pipe criticality analysis, evaluate sampling and flushing locations, and prioritize repair strategies. This paper includes discussion on WNTR capabilities, use cases, and resources to help get new users started using the software. WNTR can be downloaded from the U.S. Environmental Protection Agency GitHub site at https://github.com/USEPA/WNTR. The GitHub site includes links to software documentation, software testing results, and contact information.

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Continuous or regularly scheduled monitoring has the potential to quickly identify changes in the environment. However, even with low - cost sensors, only a limited number of sensors can be deployed. The physical placement of these sensors, along with the sensor technology and operating conditions, can have a large impact on the performance of a monitoring strategy. Chama is an open source Python package which includes mixed - integer, stochastic programming formulations to determine sensor locations and technology that maximize monitoring effectiveness. The methods in Chama are general and can be applied to a wide range of applications. Chama is currently being used to design sensor networks to monitor airborne pollutants and to monitor water quality in water distribution systems. The following documentation includes installation instructions and examples, description of software features, and software license. The software is intended to be used by regulatory agencies, industry, and the research community. It is assumed that the reader is familiar with the Python Programming Language. References are included for addit ional background on software components. Online documentation, hosted at http://chama.readthedocs.io/, will be updated as new features are added. The online version includes API documentation .

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One of the major challenges of simulating flow and transport in the far field of a geologic repository in crystalline host rock is related to reproducing the properties of the fracture network over the large volume of rock with sparse fracture characterization data. Various approaches have been developed to simulate flow and transport through the fractured rock. The approaches can be broadly divided into Discrete Fracture Network (DFN) and Equivalent Continuum Model (ECM). The DFN explicitly represents individual fractures, while the ECM uses fracture properties to determine equivalent continuum parameters. We compare DFN and ECM in terms of upscaled observed transport properties through generic fracture networks. The major effort was directed on making the DFN and ECM approaches similar in their conceptual representations. This allows for separating differences related to the interpretation of the test conditions and parameters from the differences between the DFN and ECM approaches. The two models are compared using a benchmark test problem that is constructed to represent the far field (1 × 1 × 1 km3) of a hypothetical repository in fractured crystalline rock. The test problem setting uses generic fracture properties that can be expected in crystalline rocks. The models are compared in terms of the: 1) effective permeability of the domain, and 2) nonreactive solute breakthrough curves through the domain. The principal differences between the models are mesh size, network connectivity, matrix diffusion and anisotropy. We demonstrate how these differences affect the flow and transport. We identify the factors that should be taken in consideration when selecting an approach most suitable for the site-specific conditions.

Drinking water systems face multiple challenges, including aging infrastructure, water quality concerns, uncertainty in supply and demand, natural disasters, environmental emergencies, and cyber and terrorist attacks. All of these have the potential to disrupt a large portion of a water system causing damage to infrastructure and outages to customers. Increasing resilience to these types of hazards is essential to improving water security. As one of the United States (US) sixteen critical infrastructure sectors, drinking water is a national priority. The National Infrastructure Advisory Council defined infrastructure resilience as “the ability to reduce the magnitude and/or duration of disruptive events. The effectiveness of a resilient infrastructure or enterprise depends upon its ability to anticipate, absorb, adapt to, and/or rapidly recover from a potentially disruptive event”. Being able to predict how drinking water systems will perform during disruptive incidents and understanding how to best absorb, recover from, and more successfully adapt to such incidents can help enhance resilience.

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