Combustion noise modelling of thermally perfect and multi-species gas flow in nozzles
Bibliographic record
Abstract
Several hypotheses are employed to describe the fluctuating motions within nozzles and to analytically predict combustion noise generation mechanisms. One of these assumptions is that of a calorifically perfect gas mixture, where $c_p$ is constant. Nonetheless, a realistic flow rather encompasses heat capacities $c_p$ that vary with temperature, i.e. $c_p = c_p(T)$ , such that the mixture is called thermally perfect. The influence of the mixture assumptions on noise generation mechanisms is re-examined in this paper. To do so, the quasi one-dimensional Euler equations for multi-species, isentropic and non-reactive flow are considered within the nozzle. Their linearisation yields a new prediction model in addition to showing a new entropy-to-entropy coupling mechanism. Relying on either the assumption of low frequencies or the Magnus’ expansion methodology, two analytical solutions are derived and studied. Validation of these two prediction models is then provided relying on unsteady simulations of axisymmetric nozzles with superimposed incident waves. To generalise previous results, parametric studies are performed considering various nozzle flow geometries. Variations of up to $10\,\,\%$ are exhibited in a choked flow nozzle between the two mixtures, especially for the indirect entropy noise and the entropy-to-entropy transmission moduli.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".