Assessment of the Indirect Combustion Noise Generated in a Transonic High-Pressure Turbine Stage
Bibliographic record
Abstract
Indirect combustion noise, generated by the acceleration and distortion of entropy waves through the turbine stages, has been shown to be the dominant noise source of gas turbines at low-frequencies and to impact the thermoacoustic behavior of the combustor. In the present work, indirect combustion noise generation is evaluated in the realistic, fully 3D transonic high-pressure turbine stage MT1 using large eddy simulations (LESs). An analysis of the basic flow and the different turbine noise generation mechanisms is performed for two configurations: one with a steady inflow and a second with a pulsed inlet, where a plane entropy wave train at a given frequency is injected before propagating across the stage generating indirect noise. The noise is evaluated through the dynamic mode decomposition (DMD) of the flow field. It is compared with the previous 2D simulations of a similar stator/rotor configuration, as well as with the compact theory of Cumpsty and Marble. 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 to the 2D case, highlighting the potential impact of indirect combustion noise on the overall noise signature of the engine.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".