Data Replay and Reanalysis
Storing raw data lets a twin be re-run later with better models, extracting new insight from old operation.
Operation, kept for later
A twin makes its best estimate in real time under strict deadlines, using the models it had at the moment. But if the raw sensor data is stored faithfully, the whole operation can be replayed later, through improved models, better assimilation, and hindsight smoothing, to extract more than was possible live. Reanalysis turns a historian archive into a renewable source of insight.
Why reanalysis pays
- Later models are more accurate, so old data yields better reconstructions
- Smoothing uses future data to sharpen past estimates the real-time filter could not
- New questions can be asked of past operation without re-running the machine
- It produces clean, consistent training data for surrogates
The requirement: store the raw data
Reanalysis is only possible if the original measurements, with full metadata and provenance, are preserved. Storing only derived estimates throws away the ability to reconsider them. This is why the twin's storage layer keeps raw signals, not just conclusions, and why data standards and time alignment matter: a future reanalysis must be able to reconstruct exactly what the sensors saw and when. See data standards.
Reanalysis and validation
Reanalysis is also how a twin's earlier predictions are checked against what actually happened, a direct form of validation. Comparing a past forecast to the smoothed reconstruction of the real outcome measures the twin's skill and reveals where its models need work, feeding the change-controlled improvement cycle. See validation.
In the Kronos program
For the Hyperion breeder and the burner, faithful raw-data retention is designed in from the start, so that every pulse and campaign can be reanalyzed as models mature. Before the machines operate, reanalysis is rehearsed on simulated campaigns; afterward it becomes a primary way the twins learn from real operation and the burner fleet compares experience across units. See smoothing versus filtering and twin governance.