Bibliographic record
Abstract
The present review covers the latest evolution of computational aeroacoustics, the field that deals with the noise generated by fluid flows and its propagation in the medium. It highlights the latest findings in both free flows (jet noise) and wall-bounded flows (airfoil, airframe, and turbomachinery noise) in more and more complex environments. Among the computational aero-acoustics methods, high-order schemes of the Navier–Stokes equations on unstructured grids and the lattice Boltzmann method on Cartesian grids have emerged as excellent candidates to tackle noise problems in realistic complex geometries. The latter is also shown to be particularly efficient for both noise generation and propagation, allowing to directly estimate the noise in the far field. Two examples of application of such methods to complex jet noise and to installed airfoil noise are first presented. The first one involves compressible subsonic and supersonic flows in dual-stream nozzles and the second one subsonic flow around an airfoil embedded in the potential core of the open-jet anechoic wind tunnel as in the actual trailing-edge noise experiment. For airframe noise, large eddy simulations of scaled nose landing gear noise and three-element high-lift devices can be tackled to decipher noise sources. For turbomachinery noise, simulations of installed low-speed fans have already unveiled a wealth of details on their noise sources, whereas high-speed turbofans remain a challenge giving the high Reynolds numbers and small tip gaps involved.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".