Reduced order modeling of cyclically symmetric bladed disks with geometric and contact nonlinearities
Bibliographic record
Abstract
In the current economic and environmental context, aircraft engines manufacturers try to design more efficient engines in order to reduce their fuel consumption. First, aerodynamic losses are decreased by reducing the clearance between the blades and the surrounding casing. This can lead to contact events between the blades and the casing even in nominal operating conditions. Then, the engine weight is reduced by designing lighter, and therefore more flexible, blades. As a consequence, the blades can undergo large displacements and deformations. A methodology has been recently derived to study the contact interactions of single blades undergoing large displacements. In this article, this methodology is extended to full bladed disks with cyclic symmetry. In order to be computationally efficient and compatible with the use of large industrial models, the methodology is based on a reduction procedure. Each sector of the high fidelity model is projected onto a basis composed of Craig-Bampton modes and a selection of their modal derivatives. The internal nonlinear forces due to large displacements are evaluated in the reduced basis with the stiffness evaluation procedure. Contact is numerically handled with Lagrange multipliers. The numerical strategy is applied on an open industrial compressor model, the NASA rotor 37, in order to promote reproducibility of results. This work demonstrates that reduced order models provide a computationally efficient alternative to full order finite element models for the accurate prediction of the time response of structures with both distributed and localized nonlinearities.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".