In-Duct Measurement of Gas Turbine Noise Emmisions Using a Cross Spectrum Method
Bibliographic record
Abstract
It is often desirable to measure the sound power radiated by the inlet or exhaust of a gas turbine in an installation that includes filters and silencers. A direct measurement can only be accomplished within the empty duct between the inlet or exhaust flange and the silencer. For a number of reasons ISO 5136 [1] cannot be used directly to perform these measurements. Consequently, some other means must be found. The primary requirement for an in-duct sound power measurement is a valid in-duct sound pressure measurement. In order to accomplish this, the effect of the turbulent pressure fluctuations inherent in the flow, or induced by the microphone probes, must be removed from the dynamic pressure signals sensed by the microphone. This paper describes the use of a two-microphone cross amplitude spectrum (CAS) technique to perform accurate measurements of the autospectra of sound fields embedded in turbulent duct flows. This technique is based on the assumption that the acoustic and turbulence signals detected by any microphone are uncorrelated with each other and also with the turbulence signals detected by another microphone. The paper gives the measurement parameters required for various degrees of turbulence rejection as well as the accuracy and convergence of the estimate of the embedded acoustic spectrum. The paper also provides a means to determine the microphone separation distance required to ensure that the turbulence signals at the two microphones are uncorrelated. These results are based on both the simulation of acoustic fields embedded in turbulence and on laboratory experiments using both loudspeakers and an aeroacoustic wind tunnel. Typical results show that for practical measurement durations the proposed method will provide a turbulence rejection in octave bands of approximately 15 dB at 31.5 Hz rising smoothly to 25 dB at 8 kHz. The method thus appears to have some distinct advantages over ISO 5136 methodology.
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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 | 0.001 |
| 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".