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Sensor Placement Optimization using Chama

Klise, Katherine A.; Laird, Carl D.; Nicholson, Bethany L.

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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TYPE SAND Report YEAR 2017
OSTIDOI

Water Network Tool for Resilience (WNTR) User Manual

Klise, Katherine A.; Hart, David B.; Moriarty, Dylan; Bynum, Michael L.; Murray, Regan M.; Burkhardt, Jonathan B.; Haxton, Terra H.

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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TYPE Other Report YEAR 2017
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Results 26–50 of 116
Results 26–50 of 116