RnD/EVOLV

Files

CI / lint-and-test (push) Has been cancelled

Details

Pumping-station demo overhaul + cross-node test harness + bumps

Submodule bumps land the deadlock fix (state.js residue unpark + MGC
optimalControl dispatch reorder) and pumpingStation stopLevel hysteresis.

- Renames examples/pumpingstation-3pumps-dashboard →
  pumpingstation-complete-example with regenerated flow.json. New
  dashboard groups, demand-broadcast wiring, S88 placement rule
  applied, ui-chart trend-split and link-channel naming follow
  .claude/rules/node-red-flow-layout.md.
- New cross-node test harness under test/: end-to-end-pumpingstation
  drives PS + MGC + 3 pumps + physics simulator end-to-end and
  verifies the ~5/15 min cycle.
- Adds Grafana provisioning dashboards (pumping-station.json) and a
  helper sync-example.sh script for export/import to live Node-RED.
- Docker entrypoint + settings + compose tweaks for the persistent
  user dir layout used by the demo.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

2026-05-08 11:21:21 +02:00

3.8 KiB

Raw Blame History

EVOLV — End-to-End Example Flows

Working with these examples? See WORKFLOW.md — the canonical guide for editing, switching projects, persistence, and debugging.

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-complete-example/`	End-to-end stack: pumpingStation + MGC + 3 pumps + 12 measurement nodes (4 per pump, physics-coupled), operator-driven inflow with scenario buttons (Constant / Sine / Diurnal / Storm), FlowFuse dashboard (realtime + 1h trends), and provisioned Grafana dashboard backed by InfluxDB.

How it loads

Each subfolder here is a Node-RED project. The Docker stack has Node-RED's Projects feature enabled and bootstraps each examples/<name>/ into /data/projects/<name>/ on first container start.

To run:

docker compose up -d from the EVOLV root.
Open Node-RED at http://localhost:1880.
Menu → Projects → Open Project → pick one.
Open the FlowFuse dashboard at http://localhost:1880/dashboard.

The default active project is pumpingstation-complete-example (override via DEFAULT_PROJECT env var on the nodered service). Switching is two clicks; persistence is handled by the evolv_nodered_data named volume — docker compose down && up doesn't lose the active flow.

Each example uses a unique dashboard path so they can coexist if you load multiple in the same runtime.

Adding new examples

When you create a new end-to-end example:

Make a subfolder under examples/ named <scenario>-<focus>.
Include at least flow.json and README.md. A build_flow.py (or equivalent generator) is recommended so the JSON stays diff-friendly.
docker compose restart nodered — the entrypoint will bootstrap your new folder as a Node-RED project (synthesizes package.json, git init, initial commit) under /data/projects/<name>/.
Editor → Projects → Open Project → pick your new one.
Add a row to the catalogue table above.

The bootstrap skips folders that already exist in the volume. To force a refresh of an existing project from the repo source (e.g. after editing build_flow.py), use ./scripts/sync-example.sh <name>.

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.

3.8 KiB Raw Blame History