Rotorcraft Digital Twin: Exploiting On-board Data for Enhancing Sustainment and Operational Availability
Bibliographic record
Abstract
Rotorcraft Digital Twin (RDT) is a Digital Transformation initiative to leverage on-board flight data, design data, maintenance records, and advanced analytical models to enhance the current sustainment paradigm, increase availability, reduce life-cycle cost, and to enable optimization of future designs. Many core capabilities have been integrated into RDT, such as load estimation, advanced regime recognition, gross weight estimation, fatigue damage accrual calculations, among others, which leverage a rich body of prior work. Multiple algorithms enable component life extensions, predictive maintenance, selection of the optimal set of assets for a particular mission or deployment to minimize the likelihood of unscheduled maintenance, supporting the Rotorcraft Structural Integrity Program (RSIP) requirements, and feeding field data back into the design process for continuous product improvements. A robust, generic, and reusable suite of algorithms and associated framework has been developed utilizing a common data model where possible. RDT has been developed with a modern cloud-native microservice software architecture which glues together carefully selected Free and Open-Source Software (FOSS) components.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".