A robust numerical approach for prediction of turbofan engine noise
Bibliographic record
Abstract
Noise from the aircraft jet engines is still the dominant source in takeoff condition. Government regulations of community noise are getting more stringent, creating significant challenges for aircraft manufacturers to meet these requirements. Expensive nature of experiments as well as accessing to powerful computers have provided new impetus for computational studies. In this paper, an efficient and robust numerical scheme, namely the Lattice Boltzmann Method was used to simulate the sound created by typical internal mixing nozzles with forced mixers. The simulation includes capturing the time-resolved flow characteristics and large scale turbulent structures. The sub grid scales were modeled using the renormalization group (RNG) forms of the standard k-ε equations. Several test cases including cold and hot core flow experiments conducted by NASA were selected for computational setup and validation purposes. The far field sound was predicted using a surface integral method. The near-field simulation results such as the jet centerline velocity decay and turbulence intensities as well as far-field sound were qualitatively in agreement with experimental results. The far-field sound analysis suggested significant low-frequency noise reduction for the lobed mixers, as well as significant reduction in overall sound pressure level (OASPL) in comparison with the simple confluent nozzle configurations.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".