Active noise control simulation of tonal turbofan noise in aero engines
Bibliographic record
Abstract
Active Noise Control (ANC) of the tonal noise of a turbofan engine is introduced using active stator vanes and rings of loudspeakers as control actuators. It involves a unique coupling of two original analytical and numerical models for the primary and secondary sources respectively, and two different control strategies that attempt to independently control the propagative acoustic modes upstream and downstream for the first time. The primary noise source is based on the analytical model recently developed by De Laborderie that deals with rectilinear cascade responses due to wake-interaction applied in a strip theory framework. Good agreement with both numerical and experimental data is found on the blade pressure jumps. The secondary noise sources induced by the piezo-actuators embedded in the stator blade are shown to behave as compact dipoles that are radiating in an annular duct. The corresponding radiation can be obtained either numerically or analytically. Here the propagation is computed numerically using COMSOL. The noise control strategy can either Singular Values Decomposition (SVD) or Generalized Singular Values Decomposition (GSVD) without any knowledge on the physical form of the acoustic field. Simulation results are shown to achieve some significant tonal noise reduction and it is demonstrated that the GSVD is a great avenue to separate inlet and outlet radiation. Introduction Tonal noise from rotor/stator interaction in aircraft engines is predominant during landing and take-off phases of civil aircraft. Active Noise Control (ANC) is one of the solutions developed since the early 1990s to reduce tonal turbofan noise. Currently, most of published results rely on experimental data1,2, 3, 4, 5 but some analytical and numerical models were also developed.6,7, 8, 9 Sound Pressure Level (SPL) reductions from 3 to 20 dB at the Blade Passing Frequency (BPF) were obtained, most of them by using modal decomposition with feedforward control for low radial mode orders. Yet these configurations were too constrained in terms of weight and energy consumption to complete industrial implementation. Also, the available processing power was unsufficient at that time to reach the requested sampling rate for active control implementation. The next decade brought new advances in digital signal processing, especially the Field Programmable Gate Array (FPGA) hardware10 and new control algorithms like the Principal value Orthogonal Decomposition (POD). In 2006, ANC with aeroacoustic control sources provided a SPL reduction of up to 20.5 dB at the BPF.11 The latest advances were pursued in 2009 by the German Aerospace Center (DLR).12 An Ultra High Bypass Ratio (UHBR) turbofan was tested with a rotating rake for the modal decomposition, microphone rings as error sensors and loudspeaker rings as secondary sources, all among the stator vane cascade. Active control was implemented with a feedforward algorithm and POD for up to 32 dB in Sound Power Level (PWL) reduction for the azimuthal mode m = 6. ∗Research associate, GAUS, Mechanical Engineering Department, yann.pasco@usherbrooke.ca †Post-doctorate fellow, Mechanical Engineering Department, thomas.guedeney@usherbrooke.ca ‡MSc student, GAUS, Mechanical Engineering Department, arnaud.leung-tack@usherbrooke.ca §Professor, GAUS, Mechanical Engineering Department ¶Professor, Mechanical Engineering Department, AIAA Lifetime Member
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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".