Large-Eddy-simulation prediction of indirect combustion noise in the entropy wave generator experiment
Bibliographic record
Abstract
Compact and non-compact analytical solutions of the subsonic operating point of the entropy wave generator experiment are compared with detailed numerical results obtained by large Eddy simulations. Two energy deposition methods are presented to account for the experimental ignition sequence and geometry: a single-block deposition as previously used and a delayed deposition that reproduces the experimental protocle closely. The unknown inlet acoustic reflection coefficient is assumed to be fully reflective to be more physically consistent with the actual experimental setup. The time delay between the activation of the heating modules must be considered to retrieve the temperature signal measured at the vibrometer and pressure signals at the microphones. Moreover, pressure signals extracted from the large Eddy simulations in the outlet duct using the delayed ignition model clearly reproduce the experimental signals better than the analytical models. An additional simulation with actual temperature fluctuations directly injected at the inlet of the computational domain clearly shows that the pressure fluctuations produced by the acceleration of the hot slug yields indirect noise almost entirely. Finally, the entropy spot is shown to be distorted when convecting through the turbulent flow in the entropy wave generator nozzle. Its amplitude decreases and its shape is dispersed, but hardly any dissipation occurs. The distortion appears to be negligible through the nozzle and become important only when convected over a long distance in the downstream duct. As the dominant frequencies of the entropy wave generator entropy forcing are very low, the effects of dispersion by the mean flow are however weak.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".