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Field testing

Overview​

Field testing is the systematic process of gathering accurate, real-world data directly from the water distribution system. This empirical data forms the bedrock of reliable hydraulic modeling, providing the essential "ground truth" needed to build, calibrate, and validate your model. Without robust field data, a hydraulic model, no matter how complex, remains largely a theoretical exercise with unverified assumptions. This chapter will explore the various methodologies for collecting critical information about your network's performance, from passive data logging to active system tests.

We will look into techniques like extensive pressure logging to capture the dynamic behavior of the system over time, active tests like fire flow measurements to assess network capacity under stress, and the utilization of existing telemetry and supervisory control and data acquisition (SCADA) systems to understand ongoing operational performance. You will learn about the instrumentation involved, the planning and execution of different tests, and how to manage and interpret the data collected. The primary goal is to equip you with the knowledge to design and implement effective field testing programs that yield high-quality data crucial for developing trustworthy hydraulic models.

Why is field testing important?​

Field testing is a non-negotiable component of credible hydraulic modeling for several fundamental reasons:

  1. Provides "ground truth": Models are representations of reality. Field testing provides the actual data — pressures, flows, tank levels, and valve statuses — against which the model's assumptions and calculations can be compared and adjusted. It anchors the model to the real system.

  2. Enables accurate model calibration: Calibration is impossible without reliable field data. Measured pressures and flows are essential benchmarks for adjusting model parameters (like pipe roughness or demands) until the model accurately replicates observed conditions.

  3. Validates model construction and assumptions: The data collected can reveal discrepancies in the initial model build, like incorrect pipe connectivity, unknown closed valves, or inaccurate asset information, prompting necessary corrections.

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