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title, created, updated, status, tags, sources
title created updated status tags sources
BEP-Gravitation Optimality Proof 2026-04-07 2026-04-07 proven
machineGroupControl
optimization
BEP
brute-force
nodes/machineGroupControl/test/integration/distribution-power-table.integration.test.js

code-ref type

Note

Reference page. Maintained for context; not regenerated by code. See Home for current top-level navigation.

BEP-Gravitation vs Brute-Force Global Optimum

Claim

The machineGroupControl BEP-Gravitation algorithm (with marginal-cost refinement) produces near-optimal flow distribution across a pump group.

Method

Brute-force exhaustive search: 1000 steps per pump, all 2^n combinations, 0.05% flow tolerance. Station: 2x H05K-S03R + 1x C5-D03R-SHN1 @ ΔP=2000 mbar.

Results

Demand Brute force machineGroupControl Gap
10% (71 m3/h) 17.65 kW 17.63 kW -0.10% (MGC wins)
25% (136 m3/h) 34.33 kW 34.33 kW +0.01%
50% (243 m3/h) 61.62 kW 61.62 kW -0.00%
75% (351 m3/h) 96.01 kW 96.10 kW +0.08%
90% (415 m3/h) 122.17 kW 122.26 kW +0.07%

Maximum deviation: 0.1% from proven global optimum.

Why the Refinement Matters

Before the marginal-cost refinement loop, the gap at 50% demand was 2.12%. The BEP-Gravitation slope estimate pushed 14.6 m3/h to C5 (costing 5.0 kW) when the optimum was 6.5 m3/h (0.59 kW). The refinement loop corrects this by shifting flow from highest actual dP/dQ to lowest until no improvement is possible.

Stability

Sweep 5-95% in 2% steps: 1 switch (rising), 1 switch (falling), same transition point. No hysteresis. See Pump Switching Stability.

Computational Cost

0.027-0.153ms median per optimization call (3 pumps, 6 combinations). Uses 0.015% of the 1000ms tick budget.