An experimental study of the wall-pressure fluctuations beneath low Reynolds number turbulent boundary layers
Bibliographic record
Abstract
A more complete understanding of the physical relationships, between wall-pressure and turbulence, is required for modeling flow-induced noise and developing noise reduction strategies. In this study, the wall-pressure fluctuations, induced by low Reynolds number turbulent boundary layers, are experimentally studied using a high-resolution microphone array. Statistical characteristics obtained using traditional cross-correlation and cross-spectra analyses are complimented with wall-pressure-velocity cross-spectra and wavelet cross-correlations. Wall-pressure-velocity correlations revealed that turbulent activity in the buffer layer contributes at least 40% of the energy to the wall-pressure spectrum at all measured frequencies. As Reynolds number increases, the low-frequency energy shifts from the buffer layer to the logarithmic layer, as expected for regions of uniform streamwise momentum formed by hairpin packets. Conditional cross-spectra suggests that the majority of broadband wall-pressure energy is concentrated within the packets, with the pressure signatures of individual hairpin vortices estimated to decay on average within traveling ten displacement thicknesses, and the packet signature is retained for up to seven boundary layer thicknesses on average.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".