6d19038784
12 pages covering architecture, findings, and metrics from the rotatingMachine + machineGroupControl hardening work: - Overview: node inventory, what works/doesn't, current scale - Architecture: 3D pump curves, group optimization algorithm - Findings: BEP-Gravitation proof (0.1% of optimum), NCog behavior, curve non-convexity, pump switching stability - Metrics: test counts, power comparison table, performance numbers - Knowledge graph: structured YAML with all data points and provenance - Session log: 2026-04-07 production hardening - Tools: query.py, search.sh, lint.sh Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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title, created, updated, status, tags, sources
| title | created | updated | status | tags | sources | ||||
|---|---|---|---|---|---|---|---|---|---|
| Pump Switching Stability | 2026-04-07 | 2026-04-07 | proven |
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Pump Switching Stability
Concern
Frequent pump on/off cycling causes mechanical wear, water hammer, and process disturbance.
Test Method
Sweep demand from 5% to 95% in 2% steps, count combination changes. Repeat in reverse to check for hysteresis.
Results — Mixed Station (2x H05K + 1x C5)
Rising 5→95%: 1 switch at 27% (H05K-1+C5 → all 3) Falling 95→5%: 1 switch at 25% (all 3 → H05K-1+C5)
Same transition zone, no hysteresis.
Results — Equal Station (3x H05K)
Rising 5→95%: 2 switches
- 19%: 1 pump → 2 pumps
- 37%: 2 pumps → 3 pumps
Clean monotonic transitions, no flickering.
Why It's Stable
The marginal-cost refinement only adjusts flow distribution WITHIN a combination — it never changes which pumps are selected. Combination selection is driven by total power comparison, which changes smoothly with demand.