EPANET (Lessons + Exercises)

Use epanet to determine minimum diameters for an 800-ft pipe from A to B, applying Hazen-Williams headloss to meet 50 psi at B for 300–7200 gpm.

Apply initial pipe statuses—open, closed, or check valve—within an epanet model, run simulations, and adjust with a bypass and additional pipes to satisfy all junction demands.

Demonstrate how to add and configure a pressure reducing valve in EPANET, run a 24-hour simulation, and compare downstream pressure to a set point around 80 psi.

Demonstrate how a pressure sustaining valve in EPANET maintains a minimum upstream pressure by restricting flow, using a simple reservoir-to-junction network and a bypass.

Show how a pressure breaking valve in EPANET creates a set pressure drop across the valve. Explore downstream results, correct valve orientation, and how excessive settings trigger errors.

Explore how EPANET flow control valves regulate flow at a set rate to meet downstream demand, demonstrated with a reservoir, junctions, pipes, and a 12-hour simulation.

Replace pipe with a general purpose valve in EPANET using a custom head loss curve. Learn to set up the model, run the analysis, and interpret head loss versus flow.

This epanet exercise demonstrates sizing a pipe from reservoir to junction B to meet a minimum pressure of 45 psi and a velocity of 5 ft/s, yielding 5 inches.

Explore pump operations in EPANET by building a reservoir and tank network, configuring a pump curve and controls, then run a 24-hour simulation to analyze tank head and pump flow.

Build and simulate a simple parallel pumps model in EPANET using simple controls, linking a reservoir, tank, and junction to manage pump on/off based on tank level.

Learn to create controls for a pressure reducing valve and a pressure sustaining valve in EPANET, run a 24-hour simulation, and apply time-based valve setting changes.

Learn to build an EPANET network and control a pump with rule-based logic, using logical operators to throttle pump speed by time of day based on tank levels.

Explore rule-based controls in EPANET to maintain steady velocity through parallel pipes by opening or closing multiple pipes, targeting 1.5–3.5 ft/s across flows from 330 to 2100 GPM.

Explore how conflicting rule-based controls in an EPANET water distribution model are resolved by priorities, demonstrating how higher-priority rules override lower ones to set valve flow.

Explore filling three differently shaped tanks in EPANET, using volume curves and a pump setup to determine fill times and observe pump power and efficiency effects.

Explore how epanet models chlorine concentrations, water age, and decay coefficients in a reservoir network with a 48-hour simulation across junctions.

In this EPANET exercise, use set point booster and flow pace booster sources to maintain a minimum chemical concentration (0.2 mg/L) across a network, exploring patterns and scenario adjustments.

Explore contaminant tracing in a two-reservoir network with EPANET, building a plume, running a 24-hour water quality simulation, and analyzing concentration and percent trace from reservoir 1.

Blend fresh water with a secondary source in EPANET to limit contaminant concentration. Use a 24-hour metric model with patterns and flow adjustments to achieve seven milligrams per liter.

Explore EPANET's water quality modeling, including quality tolerance, wall coefficient correlation, and limiting concentration, through hands-on network simulations and parameter effects.

this EPANET exercise compares four tank mixing models—mixed, two-compartment, first in, first out, and first in, last out—through a guided setup of tank, junctions, patterns, and simulations.

Explore how flow rate, reaction coefficient, and tank size influence chemical decay in a mixed tank model in epanet, illustrating instantaneous mixing and concentration changes at junctions.

Examine how the two compartment mixing model and varying mixing fractions affect water age and downstream chemical concentration in EPANET, using 0.5, 0.25, and 0.1 cases.

Explore epanet lifo tank mixing, applying last in, first out. The exercise demonstrates how aging water lowers chemical concentration at junction two under varying demand patterns.

Demonstrates using EPANET to model FIFO tank mixing, starting from 2 mg/L and targeting a 0.5–0.8 mg/L chlorine residual at 140 gpm in a 30‑ft diameter tank.

Compare first-order and zero-order decay in epanet, demonstrate decay equations and coefficients, and calibrate model outputs using observed data to fit a tank concentration plot.

Test which EPANet object properties copy across reservoirs, pumps, tanks, pipes, junctions, and valves using the copy-paste routine. Note that pipe length did not copy, while diameter and roughness did.

Download and import a background map as a backdrop, then load it into EPA net. Convert to emf, set map dimensions, enable auto length, sketch junctions, and pipes with scaling.

Build a five-junction epanet network with varied elevations, define pressure zones, and use pressure reducing valves to keep pressures between 45 and 75 psi over a 48-hour simulation.

Design and simulate a three-zone EPANET model with reservoir, three tanks, demand junctions, and pumps; apply simple controls and COM pattern to analyze head and tank levels.

Demonstrate providing fire flow from a different pressure zone in EPANET by using two zones with fireflow patterns, a 40 psi minimum, and a pressure reducing valve with head-loss modeling.

Compare Hazen-Williams, the Darcy-Weisbach, and Chezy-Manning head loss formulas in an EPANET pipe model by varying roughness and examining unit head loss and friction factors.

Compare demand-driven analysis and pressure-driven analysis in EPANET, showing how demand-driven analysis satisfies all demands even with negative pressure, while pressure-driven analysis honors pressure and may leave some demand unmet.

Learn how relative viscosity influences flow rate in EPANET and how specific gravity governs junction pressure under pressure-driven demand using Darcy-Weisbach.

Calibrate sprinkler heads in EPANET by adjusting emitter coefficients and system head, running analyses to achieve 1.5 to 2 gpm per head across a 3x3 grid.

Calibrate Manning's roughness coefficients for three pipes in an epanet model to match target minimum pressures at junctions over a 48-hour simulation, using minimum pressure reports and iterative adjustments.

Build and simulate an EPANET network to track chemical concentration over time with a contour plot, using hydraulics, patterns, and reaction coefficients for visualization.

Use contour plots of elevation and pressure in epanet to delineate pressure zones, by modeling tanks, junctions, and pipes and refining zones via map browser visuals.

An EPANET exercise compares dead-end and looped networks, showing how adding loops improves robustness and sustains higher system pressure under varying demands.

Explore EPANET frequency plots to analyze chemical concentrations across all nodes or links at a single time, or for a single element over time, with practical steps and interpretation.

Build a simple epanet network to confirm pump flow with English units and validate the energy report, including kilowatt hours per million gallons, average kilowatts, and cost per day.

In EPANET, apply optimal pump scheduling by adjusting tank level controls and energy price patterns to reduce pumping costs, demonstrated by trials that cut costs from 403.66 to 253.84.

Compare costs between a single large pump and multiple smaller pumps in an EPANET network, using pump and efficiency curves and the energy report to optimize head, flow, and cost.

Build and run a simple EPANET model, load calibration data, and verify the calibration report's mean absolute error and root mean squared error using independent calculations in Excel.

Calibrate Hazen-Williams roughness coefficients for pipes P1, P2, and P3 in an epanet model to match measured junction pressures using calibration data and iterative adjustments.

Calibrate residential and commercial base demands in EPANET using measured pipe flows, demand patterns, and solver optimization to minimize calibration error.

Develop a pump curve in epanet from measured head data by building a reservoir-tank network, configuring controls, loading calibration data, and iteratively refining the curve to fit observations.

Develop and calibrate a chemical concentration pattern in EPANet to mimic a set-point booster, adjusting a remote source’s chlorine output to meet a junction target.

Build an EPANet network from a text file (.inp) by defining reservoirs, junctions, pipes, and coordinates, import it, and run a 24-hour hydraulic simulation with commercial and residential patterns.

Analyze four pipe replacement scenarios in epanet to ensure minimum pressure and chlorine concentration are met, by building a simple network, running scenarios, and inspecting minimum values in reports.

Build and run an EPANET network, apply future demand scenarios, and verify junction pressures stay above 30 m with a 3 m range over 72 hours, optimizing pipe sizes.

Debug an EPANET network by inspecting and fixing a .inp file using the input file format documentation and sections, then run an extended period simulation with no chemical analysis.