Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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 it