Uncertainty Quantification Applied to Aeroacoustics of Wall-Bounded
Bibliographic record
Abstract
The uncertainty quantification (UQ) related to the self-noise prediction based on a Reynolds-Averaged Navier-Stokes (RANS) flow computation of a low-subsonic axial fan has been achieved.As the methodology used for fan noise prediction is based on airfoil theories, the uncertainty quantification of a low-speed Controlled-Diffusion (CD) airfoil has been first considered.For both applications, deterministic incompressible flow solvers are coupled with a non-intrusive stochastic collocation method, found to be two orders-of-magnitude more efficient than a classical Monte Carlo simulation for the same accuracy.In the case of airfoil UQ, the effective flow angle is used as a random variable.Two wall-pressure reconstruction models are used to obtain necessary inputs of Amiet's trailing-edge noise model: Rozenberg's model has larger uncertainties at high frequencies because of the uncertainty on the wall-shear stress parameter required in the method, and Panton & Linebarger's model is less accurate at low frequencies because of the slow statistical convergence of the integration involved in the model.Similar behaviours are observed in the fan UQ involving the volume flow-rate and the rotational speed as random variables.The stochastic mean sound spectra are found to Uncertainty Quantification in Computational Science be dominated by the tip strip and compare well with experimental data.Larger uncertainties are seen in the hub and tip regions, where large flow detachment and recirculation appear.The known uncertainties on flow rate yield larger uncertainties on sound than those on rotational speed.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".