Uncertainty Quantification for the Trailing-Edge Noise of a Controlled-Diffusion Airfoil
Bibliographic record
Abstract
Two deterministic incompressible flow solvers are coupled with a nonintrusive stochastic collocation method to propagate several aerodynamic uncertainties of the same type encountered in a standard trailing-edge noise experiment of a low-speed controlled-diffusion airfoil to predict the far-field noise. Reynolds-averaged Navier–Stokes and large-eddy simulations are applied to a common restricted domain surrounding the airfoil embedded in the potential core of the jet in the anechoic wind-tunnel experiment. Both simulation methods provide the wall-pressure fluctuations near the airfoil trailing edge, which are then used in Amiet’s acoustic analogy for trailing-edge noise. In the Reynolds-averaged Navier–Stokes simulations, two different representative models are used to reconstruct the wall-pressure fluctuations: Rozenberg’s deterministic model directly based on integral boundary-layer parameters, and Panton and Linebarger’s statistical model based on the velocity field in the boundary layer. The nonintrusive stochastic model is solved in a stochastic collocation framework, with the inlet velocity profiles as random variables. This framework is found to be two orders of magnitude more efficient than a classical Monte Carlo simulation for the same accuracy. Comparisons of the mean and standard deviations of the wall-pressure spectra and the far-field acoustic pressure with experiment stress that Rozenberg’s model is more accurate at low frequencies and has larger uncertainties at high frequencies because of the uncertainty on the wall shear stress and that Panton and Linerbarger’s is less accurate at low frequencies because of the slow statistical convergence of the integration involved in the model.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".