Noise mechanisms in a transonic high-pressure turbine stage
Bibliographic record
Abstract
In modern ultra-high by-pass ratio turboengines, the noise contribution of both turbine stages and combustion chamber is expected to increase drastically. In the present work, both noise sources are evaluated in the realistic, fully three-dimensional transonic high-pressure turbine stage MT1 using large-eddy simulations (LES). An analysis of the basic flow field and the different turbine noise generation mechanisms is performed for two configurations: one with a steady inflow and one with an unsteady inflow, where a plane entropy wave train at a given frequency is injected at the inlet and propagates across the stage generating indirect noise. The noise is evaluated through Fourier analysis, dynamic mode decomposition of the flow field, and estimates of propagation coefficients. The steady case show three different dominant noise mechanisms: the usual stator wake brushing of the rotor blades, the several pulsating shocks in the stage and the vortex shedding. The forced results have additional tonal noise caused by the indirect noise mechanism generated by the acceleration and distortion of entropy spots. They are compared with previous two-dimensional (2D) simulations of a similar stator/rotor configuration, as well as with the compact theory. Results show that the upstream propagating entropy noise is reduced due to the choked turbine nozzle guide vane. Downstream acoustic waves are found to be of similar strength as the 2D case and larger than the blade passing frequency of the wake-interaction mechanism, highlighting the potential impact of indirect combustion noise on the overall noise signature of the engine.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 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".