Bibliographic record
Abstract
High Cycle Fatigue (HCF) cracking of a Canadian Forces (CF) turbofan engine fuel tube resulted in a six year, multinational effort to identify the root cause and to ultimately develop and implement a solution. The second of three fuel tube failures experienced by the CF during this timeframe resulted in an engine fire that caused significant damage to a military aircraft, underlining the seriousness of the issue at hand. With HCF identified as the mechanism of failure, efforts focused on vibration input to the fuel tube during engine operation. An experiment was developed to instrument an in-service fuel tube and perform comprehensive flight tests to quantify the strains experienced by the tube and identify vibration frequencies that required mitigation in order to eliminate HCF failures. A damper bracket was developed to shift the modal response of the fuel tube away from damaging frequencies experienced in-flight. The prototype bracket then required the addition of an adjustable feature that would allow it to accommodate slight variability in the location of mounting points from engine to engine. In addition, modification of some existing hardware was also required in order to implement the final solution. Production damper brackets were installed on all in-service engines, and to date have prevented any further fuel tube failures.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".