Computational Aeroacoustic Predictions of the 30P30N High-Lift Airfoil using RANS Simulations
Bibliographic record
Abstract
A series of two-dimensional Reynolds-Averaged Navier-Stokes (RANS) simulations are conducted on the McDonnell Douglas 30P30N high-lift airfoil placed inside a hard-wall wind tunnel to predict the Kevlar wall angle of attack correction required for the University of Toronto Hybrid Anechoic Wind Tunnel (HAWT), and to evaluate the accuracy of RANS simulations in predicting the acoustic signature of high-lift devices during the landing phase. The dissertation begins with an extensive steady RANS study conducted at a Reynolds number of $1.35\times 10^{6}$ and a freestream Mach number of $0.14$. Using the $k$-$\omega$ SST turbulence model, mean surface pressure results are validated at angles $\alpha = 0^{\circ},\,3^{\circ},\,6^{\circ}$ and $9^{\circ}$ via comparisons with data collected from other research institutions. In all, the results align with the University of Toronto HAWT when a $3^{\circ}$ correction is applied to account for the Kevlar wall deflections, which subsequently modify the wind tunnel flow conditions. To assess its noise prediction capabilities, an unsteady RANS (uRANS) simulation of the model at $3^{\circ}$ is performed using identical flow conditions as the steady RANS cases. The application of density dilatation iso-contours, surface pressure spectra comparisons, frequency-wavenumber analyses, and an empirical cavity mode prediction model successfully confirm the solver's ability to capture near-field tonal content, including Rossiter cavity modes in the slat and low-frequency acoustic waves traveling between high-lift coves. Lastly, time-averaged turbulent kinetic energy and velocity field uRANS data are used as input for Amiet’s analytical noise prediction models. The computed sound levels compared with experimental data demonstrate that this procedure is an effective means to predict far-field broadband content measured $90^{\circ}$ relative to mid-chord, with a deviation of $\pm$2.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".