Blackbox Optimization for Aircraft Engine Blades With Contact Interfaces
Bibliographic record
Abstract
In modern aircraft engines, reduced operating clearances between rotating blade tips and the surrounding casing increase the risk of blade/casing structural contacts, which may lead to high blade vibration levels. Therefore, structural contacts must now be accounted for as early as in the engine design stage. As the vibrations resulting from contact are intrinsically nonlinear, direct optimization of blade shapes based on vibration simulation is not realistic in an industrial context. A recent study on a blade featuring significantly lower vibration levels following contact event identified a potential criterion to estimate a blade sensitivity to contact interactions. This criterion is based on the notion of dynamic clearance, a quantity describing the evolution of the blade/casing clearance as the blade vibrates along one of its free-vibration modes. This paper presents an optimization procedure, which minimizes the dynamic clearance as a first step toward the integration of structural criteria in blade design. A dedicated blade geometry parameterization is introduced to allow for an efficient optimization of the blade shape. The optimization procedure is applied to the three-dimensional (3D) properties of two different blades. In both cases, initial and optimized blades are compared by means of an in-house numerical tool dedicated to the simulation of structural contact events with a surrounding casing. The simulations focus on rubbing phenomena, involving the vibration of a single blade. Simulation results show a significant reduction of vibration levels following contact interactions for the optimized blades. Critical speeds related to the mode on which the dynamic clearance is computed are successfully eliminated by the blade shape optimization. For the investigated blade geometries, backward sweep and backward lean angles are associated with reduced contact interactions compared to forward sweep and forward lean angles.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".