Prediction of the Sound radiated from Low-Mach Internal Mixing Nozzles with Forced Mixers using the Lattice Boltzmann Method
Bibliographic record
Abstract
Numerical simulations were performed to investigate the transient compressible flow through lobed mixers inside the internal mixing nozzles of turbofan engines. The Lattice Boltzmann Method (LBM) was used. A detailed model of the mixer and the nozzle was created to capture both the internal flow through the mixer and the external jet plume. The mean flow characteristics were obtained both inside the nozzle and within the jet plume. The transient behavior of the streamwise vortices at the nozzle exit was visualized and quantified. A confluent mixer was selected as a baseline to investigate the performance of both the low and high penetration lobed mixers. The goal was to better understand the detailed noise reduction mechanisms of lobed mixers, as well as thrust enhancements. The Reynolds number based on nozzle exit diameter was 1.36×10 6 and the peak Mach number reached 0.5. The low-Mach setting is to abide by the constraints of the 19-stage LBM algorithm used in this study. The sub grid scales were modeled using the renormalization group (RNG) forms of the standard k-e equations. Far-field sound was computed using the porous Ffwocs William-Hawkings (FWH) surface integral method. The results suggested an increase in thrust coefficient as expected for the lobed mixers. The far-field sound analysis showed considerable low-frequency noise reduction (i.e. ~4-5 dB) for the lobed mixers, as well as about 3dB reduction in the overall sound pressure level (OASPL) in comparison with the confluent nozzle. Both near field and far field results and trends were as expected based on available subsonic experimental data.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".