Advanced damage tolerance and risk assessment methodology and tool for aircraft structures containing MSD/MED
Bibliographic record
Abstract
This paper presents results from NRC's development of advanced damage tolerance analysis (DTA) and risk assessment methods and tools for aircraft structures, including build-up structures containing multi-site fatigue damage (MSD) and multi-element damage (MED), to support the Canadian Forces (CF) aircraft structural life cycle management. The DTA methods developed include new closedform solutions and generic finite element (FE) based tools to calculate the stress intensity factor (SIF) and the ß-solutions for build-up structural configurations. To facilitate the risk assessment, an in-house crack growth analysis program, CanGROW, was developed to simultaneously grow multiple cracks. Guidelines were established to calculate the residual strength of MSD/MED structures using global and/or local FE models, considering load redistribution among adjacent components. For the MSD risk analysis, an efficient Monte Carlo simulation technique was developed to determine the crack size distributions at different inspection intervals, which were then used in NRC's risk analysis code ProDTA to calculate the single flight hour probability of failure. Case studies on critical locations of the CC-130 centre wing structure are presented to demonstrate the capability of the developed methods and tools.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".