Determination of CFD Turbulence Scales for Lobed Mixer Jet Noise Prediction
Bibliographic record
Abstract
Many turbofan engine exhaust designs feature internal forced mixers to rapidly mix the hot core flow with the cold bypass flow before the nozzle exit, primarily to enhance mixing and thus improve Specific Fuel Consumption (SFC). Although the design is intended for performance improvement, it may also considerably reduce low frequency noise because of the lower relative mixed jet velocity compared to a confluent nozzle. In reality, the presence of the mixer adds complexity to the jet flow fields and additional high frequency source noise commonly labeled “mixer excess noise”. There is no industry standard on predicting such jet noise contribution. As a remedy to this, a new method was recently developed by the Institute of Sound and Vibration Research (ISVR), UK, and Purdue University, USA, under the AeroAcoustics Research Consortium (AARC) contract to predict jet noise of lobed mixers. The method essentially relies on SAE ARP876D or ESDU98019 far field noise spectra predicted for single stream jets, with appropriate filtering to decompose the spectrum into an enhanced jet spectrum and a fully mixed jet spectrum. The process is similar to the four source model earlier developed for the coplanar separate flow jets. In addition to mixer flow parameters, the prediction method requires the knowledge of two parameters related to mixer excess noise: a turbulence factor Fm, defined as the ratio of the turbulence in a forced mixer to the ‘normal’ turbulence in a single-stream mixed jet at equal distances downstream of the nozzle; and LenJ that represents the axial length of the effective jet over which Fm exceeds unity. Extensive analysis of NASA scale model lobed mixers noise data showed that the method is promising. RANS CFD was also performed to numerically determine equivalent turbulence scales based on the turbulent kinetic energy in forced mixer jets relative to confluent mixer jets. The present paper extends this work, refining the prediction method and providing validation of the new method with full-scale engine noise data. In addition, the potential of CFD to enhance noise prediction for lobed mixer jets by providing the turbulence scales needed for the empirical model is further investigated. A new definition of the equivalent CFD turbulence parameters is proposed that agrees well with those derived from empirical jet noise model. Comparison of the CFD results with NASA PIV data for a confluent mixer configuration showed that the CFD methodology is not yet fully mature and additional work is required. However, the resolution of the mixer turbulence scales predicted by CFD analysis is sufficient to identify noise trends between two mixer designs. As a result, CFD is seen as a tool with the potential to identify mixer designs that result in lower jet noise.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".