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>
35 lines
1.1 KiB
Markdown
35 lines
1.1 KiB
Markdown
---
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title: Pump Switching Stability
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created: 2026-04-07
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updated: 2026-04-07
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status: proven
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tags: [machineGroupControl, stability, switching]
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sources: [nodes/machineGroupControl/test/integration/ncog-distribution.integration.test.js]
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---
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# Pump Switching Stability
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## Concern
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Frequent pump on/off cycling causes mechanical wear, water hammer, and process disturbance.
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## Test Method
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Sweep demand from 5% to 95% in 2% steps, count combination changes. Repeat in reverse to check for hysteresis.
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## Results — Mixed Station (2x H05K + 1x C5)
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Rising 5→95%: **1 switch** at 27% (H05K-1+C5 → all 3)
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Falling 95→5%: **1 switch** at 25% (all 3 → H05K-1+C5)
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Same transition zone, no hysteresis.
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## Results — Equal Station (3x H05K)
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Rising 5→95%: **2 switches**
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- 19%: 1 pump → 2 pumps
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- 37%: 2 pumps → 3 pumps
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Clean monotonic transitions, no flickering.
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## Why It's Stable
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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.
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