A Novel Numerical Approach for Generation and Propagation of Rotor-Stator Interaction Noise
Bibliographic record
Abstract
As turbofan engine designs move towards bypass ratios >12 and corresponding low pressure ratios, fan rotor blade tip Mach numbers are reduced, leading to rotor-stator interaction becoming an important contributor to tonal fan noise. For future aircraft configurations employing boundary layer ingestion, non-uniform flow enters the fan. The impact of such non-uniform flows on the generation and propagation of rotor-stator interaction tones has yet to be assessed. In this thesis, a novel approach is proposed to numerically predict the generation and propagation of rotor-stator interaction noise with distorted inflow. The approach enables a 42% reduction in computational cost compared to traditional approaches employing a sliding interface between the rotor and stator. Such an interface may distort rotor wakes and can cause non-physical acoustic wave reflections if time steps are not sufficiently small. Computational costs are reduced by modelling the rotor using distributed, volumetric body forces. This eliminates the need for a sliding interface and thus allows a larger time step size. The force model responds to local flow conditions and thus can capture the effects of long-wavelength flow distortions. Since interaction noise is generated by the incidence of the rotor wakes onto the stator vanes, the key challenge is to produce the wakes using a body force field since the rotor blades are not directly modelled. It is shown that such an approach can produce wakes by concentrating the viscous forces along streamtubes in the last 15% chord. The new approach to rotor wake generation is assessed on the GE R4 fan from NASA's Source Diagnostic Test, for which the computed overall aerodynamic performance matches the experiment to within 1%. The rotor blade wakes are generated with widths in excellent agreement and depths in fair agreement with the experiment. An assessment of modal sound power levels computed in the exhaust duct indicates that this approach can be used for predicting downstream propagating interaction 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".