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GP-CV-0QCDMF7CivilReady

EPANET intermittent water-network resilience

A 24-case EPANET study of continuous and intermittent water supply, central and distributed storage, rising demand, pipe restriction, pressure, service equity, and water age.

EPANET intermittent water-network resilience project visual
GP-CV-0QCDMF7 · Civil
  • EPANET 2.2
  • WNTR 1.5
  • Python
  • NumPy
  • Pandas
  • Matplotlib
  • Jupyter

Project definition

Problem statement

Intermittent water supply can leave high or distant nodes with inadequate pressure and reduced demand delivery during long source closures.

Storage can improve service but can also increase water age, so pressure, delivery, equity, and age must be assessed together.

Project objectives

  • Compare continuous, intermittent, central-storage, and distributed-storage operation over forty-eight hours.
  • Use pressure-dependent demand to measure realistic delivery during deficient pressure.
  • Test base, 1.25 times, and 1.5 times demand with healthy and restricted pipe conditions.
  • Measure demand satisfaction, critical-node service, pressure exposure, equity, recovery, and water age.
  • Retain reproducible evidence and explain the limits of applying a synthetic network to field decisions.

Project structure

Project components

01

Network model

Defines nine demand junctions, thirteen base pipes, a reservoir, daily demand, elevations, controls, and optional storage.

02

Hydraulic study

Runs EPANET 2.2 pressure-dependent hydraulics and water age at thirty-minute resolution for forty-eight hours.

03

Service analysis

Calculates demand delivery, critical and worst-node service, pressure adequacy, low-pressure exposure, equity, age, and recovery.

04

Experiment

Runs four layouts, three demand factors, and two pipe conditions for twenty-four controlled cases.

05

Evidence

Writes case and time-series CSV files, structured JSON, and eight labelled figures.

Methodology

Project workflow

  1. 01
    Select case

    Choose the supply layout, demand factor, and healthy or restricted P7 condition.

  2. 02
    Build network

    The model applies pressure thresholds, daily demand, source controls, and the selected storage layout.

  3. 03
    Run EPANET

    Hydraulic and water-age results are calculated for two daily cycles.

  4. 04
    Calculate indicators

    Delivered demand, node service, pressure exposure, equity, water age, and recovery are calculated.

  5. 05
    Compare evidence

    The retained tables and figures show service benefit, weak nodes, and water-age tradeoffs.

Demonstration scenario

Under base demand, unstored intermittent supply delivers 41.27 percent of requested demand and creates 292.5 low-pressure node-hours. Central storage raises delivery to 96.39 percent and removes pressure below 3 m, while its 95th percentile water age rises to 27.66 hours. The student explains why hydraulic improvement and water age must be considered together.

Engineering

Tools and method

Tools
The project uses EPANET 2.2, WNTR 1.5, Python, NumPy, Pandas, Matplotlib, Jupyter for subject analysis, simulation, and results.
Hydraulic engine
EPANET 2.2 supplies extended-period pressure-dependent hydraulic and water-age simulation.
Network interface
WNTR 1.5 constructs the network, controls, storage layouts, and simulator inputs.
Analysis
Python, NumPy, and Pandas calculate expected demand and the retained performance measures.
Figures
Matplotlib produces the network schematic, timelines, comparisons, and service-age tradeoff.
Verification
Automated tests cover inputs, network objects, simulations, trends, evidence files, plots, and commands.

Testing

Evaluation

Evaluation measures

  • Demand satisfaction across all twenty-four cases
  • Critical and worst-node service
  • Pressure adequacy and node-hours below 3 m
  • Weighted low-pressure exposure index
  • Service equity across nine demand nodes
  • Second-day 95th percentile and maximum water age
  • Mean recovery time after supply onset

Project boundaries

  • The network, demands, tank sizes, elevations, and schedule are declared teaching assumptions.
  • The low-pressure exposure index is not a contaminant concentration, pathogen dose, or water-safety result.
  • The model does not represent pipe filling fronts, trapped air, rapid transients, leakage, household storage, or disinfectant decay.
  • Field use requires surveyed assets, measurements, calibration, validation, water-quality sampling, and qualified engineering review.

Included

  1. 01Complete EPANET and WNTR model
  2. 02Twenty-four controlled simulation cases
  3. 03CSV, JSON, and eight result figures
  4. 04Sixty-three automated tests with 99 percent statement coverage
  5. 05Complete project files, models, calculations, and analysis material in a private GitHub repository
  6. 06113-page project documentation in PDF and editable Word formats
  7. 0717-page setup and usage guide in PDF and editable Word formats
  8. 08Forty-six annotated references

Project record

No information is collected on this page.

Permanent project ID
GP-CV-0QCDMF7
Catalogued
21 Aug 2026
Completed
24 Aug 2026
Verified
24 Aug 2026
Demonstration
Included in repository

Handover

After purchase

  1. 01
    Payment is confirmed

    The project is marked unavailable and cannot be purchased again.

  2. 02
    Repository access is granted

    The buyer's submitted GitHub account receives access to the private repository.

  3. 03
    The purchase record is delivered

    The certification sheet is prepared from the reviewed buyer details and sent privately by email.