Numerical Simulation of Internal Cavities Acoustic Trapped Modes With Mean Flow
Bibliographic record
Abstract
Flow-excited acoustic resonance of trapped modes in ducts has been reported in different engineering applications. The excitation mechanism of these modes results from the interaction between the hydrodynamic flow field and the acoustic particle velocity, and is therefore dependent on the mode shape of the resonant acoustic field, including the amplitude and phase distributions of the acoustic particle velocity. For a cavity-duct system, the aerodynamic excitation of the trapped modes can generate strong pressure pulsations at moderate Mach numbers (M>0.1). This paper investigates numerically the effect of mean flow on the characteristics of the acoustic trapped modes for a cavity-duct system. Numerical simulations are performed for a two-dimensional planar configuration and different flow Mach numbers up to 0.3. A two-step numerical scheme is adopted in the investigation. A linearized acoustic perturbation equation is used to predict the acoustic field. The results show that as the Mach number is increased, the acoustic pressure distribution develops an axial phase gradient, but the shape of the amplitude distribution remains the same. Moreover, the amplitude and phase distributions of the acoustic particle velocity are found to change significantly near the cavity shear layer with the increase of the mean flow Mach number. These results demonstrate the importance of considering the effects of the mean flow on the flow-sound interaction mechanism.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".