Characterisation of blade out events using powder bed fusion of polymers
Bibliographic record
Abstract
The release of a blade, hereafter referred to as a blade-out event, in an aviation gas turbine engine (GTE) represents a serious safety hazard. Such an event can result in severe dynamic imbalance, engine failure, and potential fire hazards. Full-scale blade-out testing of GTEs is extremely costly, hazardous, and time-consuming. To address these challenges, we developed a novel, instrumented, scaled-down test rig to perform multiple blade-out tests using dynamic similarity principles based on Buckingham’s theorem. In this design, the rotating disk assembly comprises sixteen pre-twisted blades, each 50[Formula: see text]mm in length and attached via dovetail roots. Both the blades and disk are composed of Polyamide 12 and were fabricated using the powder bed fusion of polymers with laser beam (PBF-LB/P), a 3D additive manufacturing technique that employs high-power lasers to fuse fine powder materials into solid structures with complex geometries. The application of PBF-LB/P was crucial for achieving accurate, rapid, and cost-effective manufacturing of the blades, allowing numerous blade-out tests to be performed efficiently. This is particularly significant since blade-out tests typically cause extensive damage not only to the released blade but also to the trailing blades. The use of PBF-LB/P therefore enabled the execution of a large number of blade-out tests, the results of which were compared with finite element (FE) simulations of blade release in real GTEs. The findings demonstrate excellent agreement between the two approaches, confirming that the scaled-down rig can accurately capture the trajectories of released blades and their interactions with trailing blades.
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 | 0.000 | 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) | 0.001 | 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 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".