Methodology for the Redesign of Compressor Blades Undergoing Nonlinear Structural Interactions: Application to Blade-Tip/Casing Contacts
Bibliographic record
Abstract
Abstract Over the past decade, the drive towards more efficient aircraft engines has pushed the boundaries of operating ranges far beyond a linear structural context. Nonlinear interfaces, such as blade-tip/casing contacts, are to be expected in nominal operating conditions. However, current blade design methodologies still rely on empirical structural considerations, often linear, which may lead to costly redesign operations. This work aims at proposing a methodology for the redesign of blades undergoing nonlinear structural interactions. A three-step redesign process is considered: (1) parameterization of an existing blade, (2) update of blade parameters with respect to a surrogate performance criterion, and (3) performance check of the optimized blade. An original two-way parameterization method is proposed to parameterize existing blades and generate models from blade parameters. As a proof-of-concept, the redesign of the NASA compressor blade rotor 37 and fan blade rotor 67 with respect to blade-tip/casing contacts is considered. High-fidelity parameterized models of the initial blades are obtained and their dynamic response to contact interactions are analyzed. Geometries are updated with respect to their clearance consumption, as its minimization has shown beneficial effects on the considered contact interactions. The proposed methodology allows us to better assess the relevance of this performance criterion in the context of blade-tip/casing contacts.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".