PS subscribes to MGC's flow.predicted.downstream and uses it as the
outflow estimate for net-flow computation. MGC was only writing to
DOWNSTREAM inside optimalControl (the optimizer's bestFlow TARGET, not
the achieved aggregate), and to AT_EQUIPMENT in handlePressureChange.
During transients — e.g. demand dropping to dead-band keep-alive while
pumps are still ramping down from full throttle — PS saw a stale 25 m³/h
target on DOWNSTREAM while pumps were physically delivering 500+ m³/h.
NetFlow looked small and stable when the basin was actually draining
fast.
flow.act = sum of every pump's current predicted output = achieved
aggregate. Mirror it onto DOWNSTREAM so PS gets a live signal on every
pump flow/pressure update, not just every MGC.handleInput.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
The priority-control codepath had the same stale dispatch shape that
caused the live deadlock in optimalControl: only handling idle and
operational states, and chaining flowmovement after execsequence
startup. Aligns it with the optimalControl fix so a future mode switch
to prioritycontrol doesn't reintroduce the bug.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Scenario 5 covers 100% → 0% → 100% with the second 100% landing
mid-shutdown (stopping/coolingdown) — exercises the path where
delayedMove must NOT be saved on a non-idle non-residue state without
a follow-up startup, since transitionToState('idle') doesn't fire it.
Scenario 6 walks 10%→100%→10% monotonically and asserts the down-sweep's
final demand is honoured (catches the user's observed "stuck around
60% going up, no reaction going down" symptom — where pumps would
otherwise freeze at a stale setpoint from the up-sweep).
Both pass with the current MGC dispatch fix.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Previously the dispatch loop only fired flowmovement for pumps in
'operational' or transitioned 'idle' pumps via execsequence-startup-then-flowmovement.
Pumps mid-startup (starting/warmingup) were silently skipped. With PS
sending demand every tick, intermediate setpoints during the startup
window never reached the pump — it locked onto the very first
snapshot's flowmovement and froze there.
Now flowmovement is sent regardless of state and rotatingMachine's
state.moveTo handles the queueing (delayedMove for transients, unpark
for residue, immediate for operational). Crucially, flowmovement runs
BEFORE execsequence-startup so the FIRST call's stale setpoint can't
land on an already-operational pump and overwrite the latest
delayedMove that fires at end of startup.
Adds three integration tests:
- demand-cycle-walkthrough: 0..100% sweep with clean per-step table
- idle-startup-deadlock: four scenarios that pin the dispatch behaviour
including the regression guard for varying-demand-during-startup
- optimizer-combination-choice: physical-validity invariants
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
### Bug fix — stale flow cache on shutdown (specificClass.js)
When turnOffAllMachines() fires (negative demand, zero flow demand, or
safety trip), the MGC was only shutting pumps down. The pumps' last
emitted predicted flow / power stayed in the MeasurementContainer,
so the parent pumpingStation kept computing net flow from cached
non-zero values — reading the MGC as "still draining" when it wasn't.
Net: net-flow direction and safety triggers misfired during and
shortly after an MGC shutdown.
Fix: after shutting down all machines, write 0 to the predicted
flow (downstream + atEquipment) and predicted power (atEquipment)
slots so the cache reflects reality immediately.
### Correctness — async/await on shutdown (specificClass.js)
Two call sites invoked turnOffAllMachines() without awaiting it, so
the subsequent `return` raced the shutdown promises. Now awaited.
Also DRY'd one inline shutdown loop into a call to
turnOffAllMachines().
### Physics correction — NCog for centrifugal pumps (integration tests)
The previous tests asserted NCog > 0 for centrifugal pumps. That's
physically wrong: for variable-speed centrifugal pumps P ∝ n³ and
Q ∝ n, so Q/P ∝ 1/n² is monotonically decreasing with speed. Peak
efficiency (peak Q/P) is always at minimum speed → cogIndex = 0 →
NCog = 0 by the current formula.
Tests now:
- Assert NCog == 0 for all centrifugal configurations
- Assert distributeByNCog() falls back to equal distribution when
NCog == 0 (confirmed by the existing tests 4-6 that slope-based
redistribution is what actually differentiates pumps with different
BEPs — not NCog)
This matches the actual implementation; the previous tests were
asserting an idealised COG model that doesn't apply here.
### Editor hygiene (mgc.html, nodeClass.js)
- mgc.html: add missing asset-menu defaults (uuid, supplier, category,
assetType, model, unit) — brings MGC in line with rotatingMachine
and pumpingStation editor shapes.
- nodeClass.js: clear node status badge on close.
All 13 tests (basic + integration) pass.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
References the flow-layout rule set in the EVOLV superproject
(.claude/rules/node-red-flow-layout.md) so Claude Code sessions working
in this repo know the S88 level, colour, and placement lane for this node.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
- Fix flowmovement unit mismatch: MGC computed flow in canonical (m³/s)
but rotatingMachine expects output units (m³/h). All flowmovement calls
now convert via _canonicalToOutputFlow(). Without this fix, every pump
stayed at minimum flow regardless of demand.
- Fix absolute scaling: demandQout vs demandQ comparison bug, reorder
conditions so <= 0 is checked first, add else branch for valid demand.
- Fix empty Qd <= 0 block: now calls turnOffAllMachines().
- Add empty-machines guards on optimalControl and equalizePressure.
- Add null fallback (|| 0) on pressure measurement reads.
- Fix division-by-zero in calcRelativeDistanceFromPeak.
- Fix missing flowmovement after startup in equalFlowControl.
- Add marginal-cost refinement loop in BEP-Gravitation: after slope-based
redistribution, iteratively shifts flow from highest actual dP/dQ to
lowest using real power evaluations. Closes gap to brute-force optimum
from 2.1% to <0.1% without affecting combination selection stability.
- Add NCog distribution comparison tests and brute-force power table test.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
