Effects of Scalloping Depth on the Sound Generated by Turbofan Engine Lobed Mixers
Bibliographic record
Abstract
The growing stringency of community noise regulations for commercial turbo-fan engine requires the development of effective jet noise suppression configurations. The large number of geometrical design parameters for lobed mixers precludes trial and error experimental studies. In this study, a robust computational tool was used to investigate the effects of the scalloping depth on sound radiated from a lobed mixer. The near field sound and flow were simulated using a flow solver based on the Lattice Boltzmann Method (LBM). The far field radiated sound was predicted using the Ffwocs William-Hawkings (FWH) surface integral method. One baseline confluent nozzle and three nozzles with varying scalloping depth were considered. Low Mach number flow was assumed, with operating conditions selected to best approach conditions for actual engines. The effects of an outer mean flow to simulate forward flight were not included. This results suggest that scalloping doesn't always bring benefit to noise reduction, and that there exists an optimal value for scalloping depth. Results were in qualitative agreement with available experimental results.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".