RnD/EVOLV

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wip: sinus-driven pumping station demo + PS levelbased control to MGC

Architecture change: demo is now driven by a sinusoidal inflow into the
pumping station basin, rather than a random demand generator. The basin
fills from the sinus, and PS's levelbased control should start/stop
pumps via MGC when level crosses start/stop thresholds.

Changes:
- Demo Drivers tab: sinus generator (period 120s, base 0.005 + amp 0.03
  m³/s) replaces the random demand. Sends q_in to PS via link channel.
- PS config: levelbased mode, 10 m³ basin, startLevel 1.2 m / stopLevel
  0.6 m. Volume-based safeties on, time-based off.
- MGC scaling = normalized (was absolute) so PS's percent-based level
  control maps correctly.
- Dashboard mode toggle now drives PS mode (levelbased ↔ manual) instead
  of per-pump setMode. Slider sends Qd to PS (only effective in manual).
- PS code (committed separately): _controlLevelBased now calls
  _applyMachineGroupLevelControl + new Qd topic + forwardDemandToChildren.

KNOWN ISSUE: Basin fills correctly (visible on dashboard), but pumps
don't start when level exceeds startLevel. Likely cause: _pickVariant
for 'level' in _controlLevelBased may not be resolving the predicted
level correctly, or the safetyController is interfering despite
time-threshold being 0. Needs source-level tracing of the PS tick →
_safetyController → _controlLogic → _controlLevelBased path with
logging enabled. To be debugged in the next session.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

2026-04-14 08:42:22 +02:00

pumpingstation-3pumps-dashboard

wip: sinus-driven pumping station demo + PS levelbased control to MGC

2026-04-14 08:42:22 +02:00

README.md

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

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

README.md

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.