Combustion Noise Modelling Re-Examined for Thermally Perfect and Multi-Species Gas Flows
Bibliographic record
Abstract
Combustion noise has now become a significant noise contribution of turboshaft engine due to the current and past noise reduction efforts mainly focused on jet and fan noise. It is basically differentiated into two mechanisms: direct noise, generated by the unsteady turbulent flame, and indirect noise, generated by the acceleration/deceleration of entropy spots, vortical and compositional heterogeneities produced in the combustion chamber through the turbine. To predict these noise generation mechanisms, several hypotheses are used to describe fluctuation motions through nozzles. One of them is the calorifically perfect gas mixture assumption, i.e. $c_p = cst$, which is re-examined in this study using thermally perfect gas mixture flow, i.e. $c_p = c_p(T)$. To do so, the quasi one-dimensional Euler equations for multi-species, isentropic and non-reactive flow are considered within nozzle. Their linearization yields a new prediction model in addition to show a new entropy-to-entropy coupling mechanism. In the assumption of low-frequency perturbation, a solution of that system is derived. By performing parametric studies, the fixing of the constant heat capacity $c_p$ in calorifically perfect flow is shown to have significant impact on the noise transfer function modulus prediction compared to thermally perfect flow. Finally, it is shown that acoustic-to-acoustic and composition-to-acoustic mechanisms are less impacted by a change in $c_p(T)$, whereas entropy-to-entropy, entropy-to-acoustic and composition-to-entropy mechanisms undergo important variations up to $10\%$ in choked nozzle, representative of actual take-off conditions in a turboengine.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 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 teacher head, 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".