Two-Way Blade Modeling Method for Structural Redesign of Compressor Blades
Bibliographic record
Abstract
Over the past few years, stringent environmental requirements and the need for increased overall efficiency have forced designers to bring turbomachine components closer to their operating limits. To address lifespan issues, costly redesign operations are thus unavoidable. These operations face many roadblocks, especially when they are triggered by nonlinear phenomena for which there exists no design guidelines. For aircraft engine blades, the handling of nonlinear structural interactions is a major challenge. This works proposes a proof of concept for the redesign of compressor blades undergoing structural contact interactions at the blade-tip/casing interface. The redesign process involves the modeling of an existing input blade, followed by a shape update based on an iterative optimization algorithm. A two-way modeling method is proposed to parameterize the input blade and generate a computer-aided design model from blade parameters describing several conical blade sections. The fidelity of the parameterized blade with respect to the input blade is assessed for the NASA blades rotor 37 and rotor 67. A high fidelity is observed with respect to geometric and dynamic characteristics. The modeling method is fully compatible with an iterative redesign process: it is applied to the redesign of rotor 37 to increase its robustness to contact interactions.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".