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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>
43 lines
2.7 KiB
Markdown
43 lines
2.7 KiB
Markdown
# EVOLV — End-to-End Example Flows
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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.
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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.
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## Catalogue
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| Folder | What it shows |
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| [`pumpingstation-3pumps-dashboard/`](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. |
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## How to import
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1. Bring up the EVOLV stack: `docker compose up -d` from the superproject root.
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2. Open Node-RED at `http://localhost:1880`.
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3. Menu → **Import** → drop in the example's `flow.json` (or paste the contents).
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4. Open the FlowFuse dashboard at `http://localhost:1880/dashboard`.
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Each example uses a unique dashboard `path` so they can coexist in the same Node-RED runtime.
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## Adding new examples
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When you create a new end-to-end example:
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1. Make a subfolder under `examples/` named `<scenario>-<focus>`.
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2. Include `flow.json` (Node-RED export) and `README.md` (topology, control modes, dashboard map, things to try).
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3. Test it on a fresh Dockerized Node-RED — clean import, no errors, dashboard loads.
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4. Add a row to the catalogue table above.
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## Wishlist for future examples
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These are scenarios worth building when there's a session for it:
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- **Pump failure + MGC re-routing** — kill pump 2 mid-run, watch MGC redistribute to pumps 1 and 3.
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- **Energy-optimal vs equal-flow control** — same demand profile run through `optimalcontrol` and `prioritycontrol` modes side-by-side, energy comparison chart.
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- **Schedule-driven demand** — diurnal flow pattern (low at night, peak at 7 am), MGC auto-tuning over 24 simulated hours.
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- **Reactor + clarifier loop** — `reactor` upstream feeding `settler`, return sludge controlled by a small `pumpingStation`.
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- **Diffuser + DO control** — aeration grid driven by a PID controller from a dissolved-oxygen sensor.
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- **Digital sensor bundle** — MQTT-style sensor (BME280, ATAS, etc.) feeding a `measurement` node in digital mode + parent equipment node.
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- **Maintenance window** — entermaintenance / exitmaintenance cycle with operator handover dashboard.
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- **Calibration walk-through** — measurement node calibrate cycle with stable / unstable input demonstrations.
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