SOURCE IDENTIFICATION OF A GAS TURBINE ENGINE USING AN INVERSE METHOD WITH BEAMFORMING
Bibliographic record
Abstract
This paper addresses the discrimination of inlet / exhaust noise of aero-engines in free-field static tests using far-field semi-circular microphone arrays. Three approaches are considered for this problem: focused beamforming, inverse method with Tikhonov regularization and inverse method with beamforming matrix regularization (called hybrid method). The classical beamforming method is disadvantaged due to need for a high number of measurement microphones in accordance to the requirements. Similarly, the Inverse methods are disadvantaged due to their need of having an a-priori source information. The classical Tikhonov regularization provides improvements in solution stability, however continues to be disadvantaged due to its requirement of imposing a stronger penalty for undetected source positions. The proposed hybrid method builds upon the beneficial attributes of both the beam-forming and inverse methods, and has been validated using experiments conducted in hemi-anechoic conditions with a small-scale waveguide system simulating a gas turbine engine. The method has further been applied to the measured noise data from a Pratt & Whitney Canada turbo-fan engine and has been observed to provide better spatial resolution and solution robustness with a limited number of measurement microphones compared to the existing methods. More validation work is ongoing.
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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 | 0.001 |
| 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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".