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feat(examples): pumpingstation-3pumps-dashboard end-to-end demo + bump generalFunctions

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>

2026-04-13 15:53:47 +02:00

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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 -d from 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) and README.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 optimalcontrol and prioritycontrol modes 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 — reactor upstream feeding settler, return sludge controlled by a small pumpingStation.
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 measurement node 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.

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