Computational aeroacoustic noise predictions of a 30P30N three-elementhigh-lift device
Bibliographic record
Abstract
In recent decades, it was discovered that airframe-induced aircraft noise poses a significant threat to public health due to its negative physiological effects that are imposed through chronic noise exposure. Particularly, high lift devices (HLD) are one of the most prominent broadband and narrowband sources of airframe noise which has made it an active subject of research. Generally comprised of slats, a main element, and flaps, recent HLD research has limited its scope of interest to the slat cove and slat trailing edge as they are the loudest recorded noise sources. However, multi-dimensional flap noise has become of particular interest given the growing popularity of two-element HLD configurations (i.e., only flaps and the main element) which are generally found in smaller commercial aircraft and business jetsthereby making flaps the biggest source of HLD noise. In this paper, the most recent computational aeroacoustic (CAA) results of an ongoing simulation campaign series to benchmark a fully configured 30P30N high lift device inside the University of Toronto's Hybrid Anechoic Wind Tunnel (HAWT) will be presented and discussed. First, the subject matter will feature aerodynamic comparisons between quasi-two-dimensional unsteady Reynolds-Averaged Navier-Stokes (uRANS) simulations and Large Eddy Simulations (LES), and the current experimental results gathered at the HAWT. These statistically converged results will investigate Reynolds number dependency via surface pressure measurements and integral force characteristics such as lift and dragall while comparing CAA results to the experimental results subjected to the installation effects of the HAWT. Second, computational acoustic results will be compared with experiments wherein dominant acoustic modes, tones and noise sources will be examined. Lastly, low-fidelity two-dimensional steady RANS simulations using a modified version of Amiet's acoustic model will be compared with high-fidelity unsteady RANS (uRANS) and wall-resolved LES featuring a Ffowcs-Williams and Hawkings' (FW-H) far-field acoustics code. The emphasis of this section will be to contrast the flap selfnoise induced by edge scattering at the trailing edge, and to analyze the feedback loop induced by laminar recirculation bubble instability against the flap suction surface into the far-field. The topics covered in this paper will be further refined and expanded by the group, and the goal of fully benchmarking the HAWT while investigating the capacities of numerical schemes in CAA solvers will be attained in subsequent papers.
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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) | 0.001 | 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) | 0.003 | 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".