Digital twin technology development and demonstration for aircraft structural life-cycle management
Bibliographic record
Abstract
This paper presents the Airframe Digital Twin (ADT) framework and key technologies for aircraft structural life-cycle management, developed by the National Research Council (NRC) of Canada, with the aim of significantly reducing maintenance cost and extending the remaining useful life of aircraft components. The NRC ADT technologies include high-fidelity structural modelling, probabilistic usage/loads forecasting, probabilistic crack growth modelling, Bayesian updating based on non-destructive inspection (NDI) results, and advanced risk/reliability analysis. To demonstrate the NRC ADT framework, a CF-188 full-scale life-extension test was used as a physical platform to simulate the remaining lifespan of an aircraft component. A series of eddy-current NDI results, obtained during the CF-188 full-scale test, were processed using a Bayesian inference algorithm to update the ADT model. The updated ADT model was then used to predict the remaining service life of the component and to determine the next inspection interval based on the acceptable probability of failure defined by risk-based airworthiness management policies. The ADT-based methods and results were compared with the existing CF-188 lifing approach, which revealed advantages and gaps of the ADT framework for the future aircraft structural life-cycle management in the digital age. This work demonstrated the unique capability of the ADT framework to quantify the effect of NDI capability and reliability, which is crucial to update the ADT model and achieve its benefits for structural life assessment and maintenance scheduling.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".