Towards the Structural Optimization of Bladed Components Featuring Contact Interfaces
Bibliographic record
Abstract
In order to maximize their efficiency, modern aircraft engines feature reduced nominal clearances between rotating (such as bladed disks) and static (casing) components. As a consequence, structural contacts between these components are now more likely to occur and must be accounted for as early as in the design stage of the engine. To this day, there exists no relevant criterion to discriminate contacting components, such as blades, according to their sensitivity to contact events. In a recent study, it was found that a redesigned blade, featuring significant improvements with respect to its vibratory response following structural contacts, essentially differed from the original design with respect to its clearance consumption — a quantity that characterizes the evolution of blade/casing clearance as the blade vibrates over its first free-vibration modes. From this observation, it was decided to carry out a thorough investigation on the possible relation between clearance consumption and the nonlinear vibratory response following structural contacts. This paper presents an automated structural optimization procedure of a blade design using an objective-function related to the blade’s clearance consumption. Thus, optimized blades feature a lower clearance consumption. By means of an in-house numerical tool dedicated to the simulation of structural contact events with a surrounding casing, it is found that optimized blades feature lower amplitudes of vibration following contacts in comparison with the original blades. Overall, it is evidenced that the blade sensitivity to contact has been lowered as certain critical speeds vanish for the optimized profile. The current paper aims at establishing a proof-of-concept and thus only considers structural aspects. Aerodynamic considerations that are key for designing efficient blades are purposely left aside in order to focus on the feasibility of blade dynamics optimization.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".