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>
57 lines
1.9 KiB
Markdown
57 lines
1.9 KiB
Markdown
---
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title: 3D Pump Curve Architecture
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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: [predict, curves, interpolation, rotatingMachine]
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sources: [nodes/generalFunctions/src/predict/predict_class.js, nodes/rotatingMachine/src/specificClass.js]
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---
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# 3D Pump Curve Prediction
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## Data Structure
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A family of 2D curves indexed by pressure (f-dimension):
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- **X-axis**: control position (0-100%)
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- **Y-axis**: flow (nq) or power (np) in canonical units
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- **F-dimension**: pressure (Pa) — the 3rd dimension
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Raw curves are in curve units (m3/h, kW, mbar). `_normalizeMachineCurve()` converts to canonical (m3/s, W, Pa).
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## Interpolation
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Monotonic cubic spline (Fritsch-Carlson) in both dimensions:
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- **X-Y splines**: at each discrete pressure level
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- **F-splines**: across pressure levels for intermediate pressure interpolation
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## Prediction Flow
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```
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predict.y(x):
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1. Clamp x to [currentFxyXMin, currentFxyXMax]
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2. Normalize x to [normMin, normMax]
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3. Evaluate spline at normalized x for current fDimension
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4. Return y in canonical units (m3/s or W)
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```
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## Unit Conversion Chain
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```
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Raw curve (m3/h, kW, mbar)
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→ _normalizeMachineCurve → canonical (m3/s, W, Pa)
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→ predict class → canonical output
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→ MeasurementContainer.getCurrentValue(outputUnit) → output units
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```
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No double-conversion. Clean separation: specificClass handles units, predict handles normalization/interpolation.
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## Three Predict Instances per Machine
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- `predictFlow`: control % → flow (nq curve)
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- `predictPower`: control % → power (np curve)
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- `predictCtrl`: flow → control % (reversed nq curve)
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## Boundary Behavior
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- Below/above curve X range: flat extrapolation (clamped)
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- Below/above f-dimension range: clamped to min/max pressure level
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## Performance
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- `y(x)`: O(log n), effectively O(1) for 5-10 data points
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- `buildAllFxyCurves`: sub-10ms for typical curves
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- Full caching of normalized curves, splines, and calculated curves
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