Extracting the Acoustic Pressure Field from Large Eddy Simulation of Confined Reactive Flows
Bibliographic record
Abstract
Today, much of the current effort in the field of combustion noise is the development of efficient numerical tools to calculate the noise radiated by flames. Although unsteady CFD methods such as LES or DNS can directly provide the acoustic field radiated by noise sources, this evaluation is limited to small domains due to high computational costs. Hybrid methods have been developed to overcome this limitation. In these schemes, the noise sources are decoupled from the radiated sound. The sources are still calculated by DNS or LES codes whereas the radiated sound is evaluated by acoustic codes. These two approaches (direct and hybrid) have been widely used to assess combustion noise radiated by open flames. On the contrary, not significative work has been done for evaluating noise radiated by confined flames. Moreover, several problems can arise when attempting to compare direct to hybrid methods results from confined combustors. While almost a pure acoustic pressure field is obtained from direct computations when considering an open flame, for confined flames this statement does not hold: hydrodynamic pressure fluctuations play an important role. In the present paper the assessment of combustion noise is conducted by both direct (LES) and hybrid computations in a premixed swirled combustor. A method to extract the acoustic field from the entire pressure field given by LES is proposed.
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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 | 0.000 | 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) | 0.000 | 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".