Twin Governance and Lifecycle
A twin is a long-lived asset that must be versioned, audited, and maintained across the whole life of the machine it represents.
A twin outlives any release
A twin is not a project that ships once; it lives as long as the machine, evolving as models improve, sensors change, and the machine ages. Governance is the discipline that keeps a long-lived twin trustworthy: versioning, provenance, change control, and clear ownership, so that every decision the twin informs can be traced and defended years later.
Provenance and traceability
Every estimate, forecast, and recommendation must be traceable to the model version, data window, and parameters that produced it. When a twin advises a safety-relevant action, or when an incident is later reviewed, the ability to reconstruct exactly what the twin knew and computed is essential. This demands immutable logging and disciplined versioning of code, models, and data together.
Change control
- Model updates are reviewed and validated before deployment, not slipped in silently
- Changes are versioned so results before and after remain distinguishable
- The validated domain is re-checked when models change
- Rollback is possible if an update degrades performance
The lifecycle stages
A twin passes through design, where it is built from simulation before hardware exists; commissioning, where it is calibrated to the real machine; operation, where it is used and continuously updated; and decommissioning, where its accumulated history informs the end of the machine's life. Each stage has different validation status and different obligations, and honest governance states which stage a twin is in.
In the Kronos program
The Hyperion breeder and the burner twins are in the design stage: built on validated simulation, not yet calibrated to hardware. Governance now means version-controlling the models and data schemas, recording the provenance of every design-stage result, and setting the change-control process before it is stress-tested by real operation. For the burner fleet in Aegis and MetroVolt housings, governance also defines how per-unit twins and fleet-level learning coexist without confusing the record. See the roadmap and data standards.