Bibliographic record
Abstract
Turbulent flows generate a broadband of acoustic noise, which can be extremely important. So, there is need for modelling the generation and propagation of acoustic energy in fluid flows, especially turbulent. This chapter reviews the research work conducted to identify and quantify the noise field generated in turbulent flows. The story starts with the journey of experimental identification and measurement of noise generated from vortices. Various analytical models there were developed, soon after, the popularity of turbulence generated is discussed. The base path-breaking research on quantifying noise generation from conservation laws including Navier–stokes equations is discussed and further used for approximation of acoustic intensity by acoustic analogy with electrostatic quadrupole near-field and far-field. With the development of computational numerical techniques flow field for complex geometries and higher fidelity became possible. The candidates for relevant computational methods are touched and integration with turbulent models is discussed. Finally, a case of simulation of noise generation for turbulent flow over airfoil using acoustic equations and Reynolds-averaged Navier-Stokes (RANS) turbulent model is reviewed.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".