Acoustic-entropy coupling behavior and acoustic scattering properties of a Laval nozzle
Bibliographic record
Abstract
Combustion noise of stationary gas turbines or aero engines is associated with unsteady heat release that creates temperature fluctuations or so-called entropy waves (hot-spots). When accelerated in the turbine located downstream of the combustor, these temperature fluctuations radiate sound, the indirect noise. This gives reason to investigate the acousticentropy coupling in a generic convergent-divergent nozzle configuration as a simplified model of the turbine flow and its corresponding entropy sound generation. A two-step approach is applied for that purpose: First, the mean flow is computed by performing a stationary Reynolds-averaged Navier-Stokes (RANS) simulation. Then, the propagation of acoustic and entropy waves is superimposed to the mean flow and modeled by linearized Navier-Stokes equations (LNSEs). These equations are solved numerically in frequency space by a stabilized finite-element approach. The acoustic pressure responses to the excited entropy waves correlate well with experimental measurements indicating that the RANS/LNSEs method includes all physical transport and coupling mechanisms relevant to entropy noise. Furthermore, the acoustic scattering properties of the nozzle are determined. The comparison with analytical models and numerical solutions show good quantitative agreement.
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 | 0.000 | 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".