🎓 Lesson 1
D1
Why Water Distribution Network Analysis Matters
Water distribution network analysis is how engineers figure out if clean water will reach every home and building at the right pressure and flow—just like checking if all the pipes in a giant plumbing system actually work together.
🎯 Learning Objectives
- ✓ Calculate nodal pressures and pipe flows using the Hardy-Cross method
- ✓ Design a looped network that meets minimum pressure (≥20 m) and velocity (0.6–3.0 m/s) criteria per AWWA standards
- ✓ Analyze system reliability by identifying critical nodes and evaluating redundancy under single-pipe failure scenarios
- ✓ Apply extended period simulation (EPS) to evaluate diurnal demand variation impacts on tank levels and pump scheduling
- ✓ Explain the trade-offs between energy efficiency, capital cost, and service continuity in network configuration decisions
📖 Why This Matters
Imagine a city where hospitals lose water pressure during peak hours, fire hydrants fail when needed most, or neighborhoods experience chronic low pressure—or worse, contamination due to negative pressure drawing in groundwater. Water distribution network analysis prevents these failures. It’s not just about pipes and pumps; it’s about public health, emergency response readiness, climate resilience, and equitable access. In mining contexts, reliable water supply is critical for dust suppression, ore processing, tailings management, and worker safety—making robust network analysis foundational to sustainable operations.
📘 Core Principles
Analysis begins with mass and energy conservation: continuity (ΣQ_in = ΣQ_out at each node) and energy equilibrium (Σh_f + Σh_p = 0 around each loop). Networks are modeled as directed graphs—nodes (junctions, tanks, reservoirs) and links (pipes, pumps, valves). Steady-state analysis assumes constant demand and pump speed, solved iteratively via methods like Hardy-Cross or global Newton-Raphson. Extended-period simulation (EPS) adds time-varying demand, tank level dynamics, and pump controls. Key assumptions include incompressible flow, fully turbulent regime (Darcy-Weisbach or Hazen-Williams friction), and negligible minor losses unless explicitly modeled. Real-world complexity arises from aging infrastructure, undocumented modifications, and intermittent supply—requiring calibration against field measurements.
📐 Hazen-Williams Head Loss Formula
The Hazen-Williams equation is widely used in water distribution practice for its empirical accuracy in typical pipe materials and flow ranges. It relates head loss to flow rate, pipe diameter, length, and roughness coefficient—avoiding iterative Reynolds number calculations required by Darcy-Weisbach. It applies best for water at 10–25°C in pipes >50 mm diameter with velocities <3 m/s.
Hazen-Williams Head Loss
h_f = 10.67 × L × Q^1.852 / (C^1.852 × D^4.871)Calculates frictional head loss (m) in pipes carrying water under turbulent flow conditions.
Variables:
| Symbol | Name | Unit | Description |
|---|---|---|---|
| h_f | Head loss | m | Energy loss due to pipe friction |
| L | Pipe length | m | Length of pipe segment |
| Q | Volumetric flow rate | m³/s | Flow through the pipe |
| C | Hazen-Williams roughness coefficient | dimensionless | Empirical measure of pipe interior smoothness (PVC: 140–150; ductile iron: 110–130) |
| D | Internal pipe diameter | m | Hydraulic diameter of the pipe |
Typical Ranges:
Distribution main (PVC): 1.5 – 4.0 m per 100 m
Service connection (PE): 0.3 – 1.2 m per 100 m
💡 Worked Example
Problem: A 300-mm-diameter PVC pipe (C = 150) carries 45 L/s over 850 m. Calculate head loss (m).
1.
Step 1: Convert flow Q = 45 L/s = 0.045 m³/s; diameter D = 0.3 m; length L = 850 m; C = 150
2.
Step 2: Apply Hazen-Williams: h_f = 10.67 × L × Q^1.852 / (C^1.852 × D^4.871)
3.
Step 3: Compute: h_f = 10.67 × 850 × (0.045)^1.852 / (150^1.852 × 0.3^4.871) ≈ 10.67 × 850 × 0.00492 / (3,623 × 0.00457) ≈ 44.3 / 16.56 ≈ 2.68 m
Answer:
The head loss is 2.68 m, which falls within the acceptable range of 0.5–5.0 m per 100 m for distribution mains.
🏗️ Real-World Application
At the Oyu Tolgoi copper-gold mine in Mongolia, engineers analyzed a 42-km elevated water network supplying process plants, camps, and dust control systems across 600 m of elevation change. Using EPANET calibrated with SCADA pressure/flow data, they identified undersized 200-mm HDPE lines causing <15 m residual pressure at high-elevation pads during peak shift changes. Redesign included adding a mid-elevation break-pressure tank and re-timing pump starts—increasing minimum pressure to 24 m and reducing peak energy use by 18%. The analysis directly prevented potential process shutdowns and ensured compliance with WHO drinking-water guidelines for camp supply.
🔧 Interactive Calculator
🔧 Open Water Distribution Network Analysis Calculator📋 Case Connection
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