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 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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".