Validation of the Numerical Simulation of Rotor/Stator Interactions in Aircraft Engine Low-Pressure Compressors
Bibliographic record
Abstract
Abstract This contribution focuses on the validation of a numerical strategy developed jointly by Safran and Polytechnique Montréal for the simulation and the analysis of blade-tip/casing contact interactions in low-pressure compressor stages. A large experimental campaign provided data (including strain measurements on the blade and abradable coating wear profiles) for several contact configurations involving four distinct blades and one type of abradable coating. The numerical strategy is here improved by introducing a new cutoff criterion to ensure the physical relevance of the presented results, specifically by keeping the maximum stress within the blade below the material's yield stress. Similarly to previous publications involving a single contact configuration, the numerical model is first calibrated for one of the four blades of interest. It is seen that the results using the numerical model—critical speed, relative wear depth between leading edge (LE) and trailing edge (TE), and maximum stress levels within the blade—are in good agreement with the experimental observations. Using the same calibration, numerical simulations are then blindly run for the three other blades. The results demonstrate that numerically predicted key quantities align well with experimental data. Additionally, the numerical model provides an accurate relative assessment of a blade's sensitivity to contact in agreement with experimental observations. This paper thus presents the first blind validation of a numerical strategy dedicated to blade-tip/casing contact interactions. Simultaneously, it also demonstrates that this model may be considered for the early discrimination of blade profiles depending on their sensitivity to contact.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".