docs: initialize project wiki from production hardening session

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>

wiki/architecture/3d-pump-curves.md

@@ -0,0 +1,56 @@
+---
+title: 3D Pump Curve Architecture
+created: 2026-04-07
+updated: 2026-04-07
+status: proven
+tags: [predict, curves, interpolation, rotatingMachine]
+sources: [nodes/generalFunctions/src/predict/predict_class.js, nodes/rotatingMachine/src/specificClass.js]
+---
+
+# 3D Pump Curve Prediction
+
+## Data Structure
+A family of 2D curves indexed by pressure (f-dimension):
+- **X-axis**: control position (0-100%)
+- **Y-axis**: flow (nq) or power (np) in canonical units
+- **F-dimension**: pressure (Pa) — the 3rd dimension
+
+Raw curves are in curve units (m3/h, kW, mbar). `_normalizeMachineCurve()` converts to canonical (m3/s, W, Pa).
+
+## Interpolation
+Monotonic cubic spline (Fritsch-Carlson) in both dimensions:
+- **X-Y splines**: at each discrete pressure level
+- **F-splines**: across pressure levels for intermediate pressure interpolation
+
+## Prediction Flow
+```
+predict.y(x):
+  1. Clamp x to [currentFxyXMin, currentFxyXMax]
+  2. Normalize x to [normMin, normMax]
+  3. Evaluate spline at normalized x for current fDimension
+  4. Return y in canonical units (m3/s or W)
+```
+
+## Unit Conversion Chain
+```
+Raw curve (m3/h, kW, mbar)
+  → _normalizeMachineCurve → canonical (m3/s, W, Pa)
+  → predict class → canonical output
+  → MeasurementContainer.getCurrentValue(outputUnit) → output units
+```
+
+No double-conversion. Clean separation: specificClass handles units, predict handles normalization/interpolation.
+
+## Three Predict Instances per Machine
+- `predictFlow`: control % → flow (nq curve)
+- `predictPower`: control % → power (np curve)
+- `predictCtrl`: flow → control % (reversed nq curve)
+
+## Boundary Behavior
+- Below/above curve X range: flat extrapolation (clamped)
+- Below/above f-dimension range: clamped to min/max pressure level
+
+## Performance
+- `y(x)`: O(log n), effectively O(1) for 5-10 data points
+- `buildAllFxyCurves`: sub-10ms for typical curves
+- Full caching of normalized curves, splines, and calculated curves