Effects of a boundary layer trip in the prediction of noise from flow past tandem cylinders
Bibliographic record
Abstract
The use of boundary layer trips in wind tunnel experiments forces transition to fully turbulent flow, which affects the resulting acoustic signature. Boundary layer trips are often too small to be modeled geometrically in computational simulations, making it difficult to consider their influence. The goal of this study was to determine the best method for representing the effects of a boundary layer trip in the benchmark aeroacoustic problem of flow over tandem cylinders in a wind tunnel. This case is pertinent to the study of aircraft landing gear noise because periodic vortex shedding results in pronounced acoustic tones in addition to broadband noise from turbulent wake interactions. A hybrid Lattice-Boltzmann Method/Ffowcs-Williams Hawkings technique was used to predict the transient flow field and the resulting noise. This study compared four methods for simulating the trip's effects: 1) applying a surface roughness on the upstream cylinder; 2) reducing the viscosity of the fluid; 3) applying a geometric trip on the upstream cylinder that is one volume element thick; and 4) forcing velocity perturbations in the incoming free stream. Of these methods, reducing the fluid's viscosity was the most effective way to reproduce the highly turbulent flow patterns and the acoustic signature.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".