Numerical Investigation Into Non-Synchronous Vibrations of Axial Flow Compressors by the Resonant Tip Clearance Flow
Bibliographic record
Abstract
This work investigates Non-Synchronous Vibrations (NSV) encountered in a turbine engine axial flow compressor using a Computational Fluid Dynamics (CFD) approach. It has been proposed that the resonance of the tip clearance flow in compressor blades could be the physical mechanism behind NSV. This work’s emphasis is on being able to computationally capture this resonance and predict the critical NSV speed using CFD. This would considerably reduce the costs involved in future hardware design and testing. The model uses the same compressor blade geometry on which experimental validation of the proposed NSV theory was conducted. The flow interaction with blade vibratory motion is modeled using a moving mesh capability and a SAS-SST turbulence model is used for computation. A review of the proposed theory on NSV is done. The CFD model is first verified with experimental data and then characterized to ensure that the simulations are conducted at the proper NSV conditions, in order to assess the resonance of the tip clearance flow. Evidence of this resonance behavior is presented and critical NSV speeds are identified based on numerical results for two different inlet temperature cases and are validated against experimental data. Further study of the actual flow structure associated with NSV is done. Additional remarks on the numerical results are discussed. An iterative design methodology to account for NSV is also proposed based on the current numerical study.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".