Skip to content
Technology How it works Breeder — Hyperion Burner — Aegis Burner — MetroVolt AI-Native Architecture Magnets Fuel cycle Safety Roadmap
Solutions AI & Data Centers Defense & Government Grid & Baseload Neutron Detection Quantum
Learn Technical Library
Proof Publications Whitepapers Technical Library Open Science & Reproducibility The Honest Gates
Company About / Mission Leadership Environment Health & Safety Investors Careers Press Contact
3D Model
AI Architecture › Physical Interfaces
Physical Interfaces

Breeder Diagnostic Port Map

Hyperion's 60+ diagnostic ports are mapped to flux coordinates so every reading lands at a known plasma location for the twin.

STRATEGY / SLOW ▲ ▼ MICROSECOND REAL-TIMEL7Ecosystem & Strategytelemetry ▲ control ▼open ▸L6Experience & Visualizationtelemetry ▲ control ▼open ▸L5Applications & Copilotstelemetry ▲ control ▼open ▸L4Orchestrationtelemetry ▲ control ▼open ▸L3Twin Modeling & AItelemetry ▲ control ▼open ▸L2Data Fabrictelemetry ▲ control ▼open ▸L1Control Planetelemetry ▲ control ▼open ▸L0Foundationtelemetry ▲ control ▼open ▸PHYSICAL S.M.A.R.T. GENERATOR PLANTBREEDER · HYPERION1R0 1.2 m · A 2.5 · 16.84 T · δ −0.30BURNER · TANDEM MIRROR2317 T throat · 26.49 T plug · fₙ 5.44% · DEC1 center stack + plasma · 2 high-field plug · 3 expander → direct converterCOLOR GRAMMAR strategy AI-workflow infra/data models reactor/DECLINE SEMANTICStelemetry (µs)controlKRONOS FUSION ENERGYAI-NATIVE S.M.A.R.T. GENERATORMASTER BLUEPRINTSHEET 01REV. 2026-08L0-L7 · 2 MACHINES
The AI-Native S.M.A.R.T. Generator Master Blueprint — eight layers (L0→L7), one control stack, wired to both machines. Telemetry rises in microseconds; control descends the same path.

Why the map matters

A diagnostic reading is meaningless without knowing where it looks. The breeder carries 60+ diagnostic ports — magnetics, interferometer chords, Thomson points, neutron sightlines, bolometry, probes — and the port map ties each channel to a geometric line-of-sight and, through the equilibrium, to a flux-surface coordinate.

Port classes

python
# map a port channel to flux coordinate
for ch in ports:
    los = geometry[ch]                       # line-of-sight in (R,Z)
    rho = flux_coord(los, equilibrium)        # normalized flux label
    channel_map[ch] = rho                     # feeds twin ingestion

Spatial normalization

Because the equilibrium moves, the mapping from a fixed port to a flux coordinate is time-varying: it is recomputed as the reconstruction updates, so a chord that crosses the core at one instant may sample the edge at another. This normalization is what lets the twin fuse dozens of heterogeneous ports into one state estimate.

Owner: L2 for channel registration and timestamping; L3 for the flux-coordinate mapping. Calibration of geometry (port pointing) is tracked separately from signal calibration. Design-and-simulation specification, validated against the twin before commissioning.

Content reviewed August 2026 · design-and-simulation stage