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New top-level examples/ folder for end-to-end demos that show how multiple
EVOLV nodes work together (complementing the per-node example flows under
nodes/<name>/examples/). Future end-to-end demos will live as siblings.
First demo: pumpingstation-3pumps-dashboard
- 1 pumpingStation (basin model, manual mode for the demo so it observes
rather than auto-shutting pumps; safety guards disabled — see README)
- 1 machineGroupControl (optimalcontrol mode, absolute scaling)
- 3 rotatingMachine pumps (hidrostal-H05K-S03R curve)
- 6 measurement nodes (per pump: upstream + downstream pressure mbar,
simulator mode for continuous activity)
- Process demand input via dashboard slider (0-300 m3/h) AND auto random
generator (3s tick, [40, 240] m3/h) — both feed PS q_in + MGC Qd
- Auto/Manual mode toggle (broadcasts setMode to all 3 pumps)
- Station-wide Start / Stop / Emergency-Stop buttons
- Per-pump setpoint slider, individual buttons, full status text
- Two trend charts (flow per pump, power per pump)
- FlowFuse dashboard at /dashboard/pumping-station-demo
build_flow.py is the source of truth — it generates flow.json
deterministically and is the right place to extend the demo.
Bumps:
nodes/generalFunctions 43f6906 -> 29b78a3
Fix: childRegistrationUtils now aliases the production
softwareType values (rotatingmachine, machinegroupcontrol) to the
dispatch keys parent nodes check for (machine, machinegroup). Without
this, MGC <-> rotatingMachine and pumpingStation <-> MGC wiring
silently never matched in production even though tests passed.
Demo confirms: MGC reports '3 machine(s) connected'.
Verified end-to-end on Dockerized Node-RED 2026-04-13: pumps reach
operational ~5s after deploy, MGC distributes random demand across them,
basin tracks net flow direction, all dashboard widgets update each second.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
2.7 KiB
2.7 KiB
EVOLV — End-to-End Example Flows
Demo flows that show how multiple EVOLV nodes work together in a realistic wastewater-automation scenario. Each example is self-contained: its folder has a flow.json you can import directly into Node-RED plus a README.md that walks through the topology, control modes, and dashboard layout.
These flows complement the per-node example flows under nodes/<name>/examples/ (which exercise a single node in isolation). Use the per-node flows for smoke tests during development; use the flows here when you want to see how a real plant section behaves end-to-end.
Catalogue
| Folder | What it shows |
|---|---|
pumpingstation-3pumps-dashboard/ |
Wet-well basin + machineGroupControl orchestrating 3 pumps (each with up/downstream pressure measurements), individual + auto control, process-demand input via dashboard slider or random generator, full FlowFuse dashboard. |
How to import
- Bring up the EVOLV stack:
docker compose up -dfrom the superproject root. - Open Node-RED at
http://localhost:1880. - Menu → Import → drop in the example's
flow.json(or paste the contents). - Open the FlowFuse dashboard at
http://localhost:1880/dashboard.
Each example uses a unique dashboard path so they can coexist in the same Node-RED runtime.
Adding new examples
When you create a new end-to-end example:
- Make a subfolder under
examples/named<scenario>-<focus>. - Include
flow.json(Node-RED export) andREADME.md(topology, control modes, dashboard map, things to try). - Test it on a fresh Dockerized Node-RED — clean import, no errors, dashboard loads.
- Add a row to the catalogue table above.
Wishlist for future examples
These are scenarios worth building when there's a session for it:
- Pump failure + MGC re-routing — kill pump 2 mid-run, watch MGC redistribute to pumps 1 and 3.
- Energy-optimal vs equal-flow control — same demand profile run through
optimalcontrolandprioritycontrolmodes side-by-side, energy comparison chart. - Schedule-driven demand — diurnal flow pattern (low at night, peak at 7 am), MGC auto-tuning over 24 simulated hours.
- Reactor + clarifier loop —
reactorupstream feedingsettler, return sludge controlled by a smallpumpingStation. - Diffuser + DO control — aeration grid driven by a PID controller from a dissolved-oxygen sensor.
- Digital sensor bundle — MQTT-style sensor (BME280, ATAS, etc.) feeding a
measurementnode in digital mode + parent equipment node. - Maintenance window — entermaintenance / exitmaintenance cycle with operator handover dashboard.
- Calibration walk-through — measurement node calibrate cycle with stable / unstable input demonstrations.