Computational aeroacoustics for low Mach number flow using the lattice Boltzmann method
Bibliographic record
Abstract
Computational aeroacoustics at low Mach number is challenging because of the comparative order of magnitudes between the acoustic perturbations and the numerical errors. The lattice Boltzmann method has been proven to be advantageous for aeroacoustics with a lower dissipation. The recursive and regularized LBM scheme (LBM-rrBGK), which has only been developed recently and tested in aeroacoustic problems, shows advantages over the traditional LBM-BGK. In this paper, results of computational aeroacoustics for low Mach number flow using rrBGK are presented. Two benchmark problems are tested, including the flow passing around a circular cylinder and the sound radiation of a cylindrical duct with uniform flow. The results are compared with either simulation, experiment or analytical results in the literature, and show a good agreement. Finally, the sound radiation directivity of a horn in the presence of mean flow is studied.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".