Darwin Sampler is developed to facilitate the sampling design for water distribution modeling. Sampling design is to determine how many locations and where they are
in a system to collect field data for calibrating a water distribution model. The field data collection includes, but not limited to, logging the pressures, measuring the hydrant flows and recording the chemical (usually chlorine) concentrations.
Current version of the Darwin Sampler performs pressure logger placement, hydrant selection optimization and also water quality sensor/logger placement optimization.
Pressure loggers are commonly used to record pressure data throughout a water distribution system. The measured pressures together with the flows are critical information for modelers to conduct model calibration. Thus determining where and how many pressure loggers to be placed in a distribution system is an important task. Conventionally, pressure loggers are placed by experience. For instance, field data collection practice in UK indicates that one pressure logger is placed for every 200 to 300 households. This rule of thumb can easily result in more than a dozen pressure loggers used for a district meter area (DMA). It is found very difficult to apply this rule of thumb in other countries where household intensity is much different from UK, e.g. the developing countries. Therefore, the need arises for developing the method and tool for water utility engineers to come up with a sound solution for pressure logger placement.
Pressure loggers are generally placed on hydrants or junctions. It is known that if any abnormal event such as water leakage happens somewhere in a distribution system, the pressure at junctions or hydrants may change. Therefore, pressure changes at these locations provide the key evidence for the existence of abnormal events, and also good information for the model calibration. However, for a specific event, the pressure changes can be greater at some locations than the other locations. Since the event may not be detected if the pressure change caused is small, to evaluate a candidate location for the pressure logger to be placed, it is important to account for the number of the events that can be detected. The more events can be detected at one location, the better this location is considered for pressure logger placement. The optimal locations for pressure loggers are determined by simulating water leakage events and maximizing the number of events to be detected or covered. A two-phase method is formulated and developed to determine the pressure logger placement. During the first phase, a database is generated by recording the pressure changes of each candidate location for simulated randomly-generated leakage events, each of which can be represented as pressure dependent demand at one junction or multiple junctions. The database is persisted for the computation in the second phase, during which the optimization of the pressure logger locations are undertaken for the given number of pressure loggers, so that the optimized logger locations are able to cover the maximum number of leakage events. Darwin Optimization Framework, based on competent genetic algorithm, is employed for optimizing the pressure logger locations. The developed method has been tested with several real water systems.
Darwin Sampler Installations
Darwin Sampler Demonstrations