Development of Aircraft Cabin Sound Environment Reproduction Facility for Passenger Comfort Research
Bibliographic record
Abstract
The Cabin Comfort and Environment Research (CCER) facility is a flexible cabin laboratory at National Research Council of Canada (NRC), which was built to investigate the effects of integrating new cabin technology and designs on passenger comfort and travel experience. The cabin sound field reproduction system is one of the capabilities being developed within the CCER in order to accurately reproduce the spatial distribution of the sound field environment within an aircraft cabin that passengers experience during flight. Sound environment reproduction in aircraft cabin mock-up can be used to demonstrate novel cabin interior technologies, and is also critical for maximizing the realism of flight experience when human subjects are used in experiments. This paper describes the progress of the current capability development to reproduce aircraft cabin interior sound field inside a full-scale cabin mock-up. A 40 channel microphone array was built and used to capture in-flight sound recordings of representative flight segments of the NRC Falcon 20 aircraft. The CCER cabin mock-up will be used to reproduce the spatial distribution of the cabin sound field using mini-actuators mounted on the cabin trim panels. Material characterization and modal analysis of the cabin trim panels were conducted through simulations on LMS VirtualLab and validated through experimental tests. The spatial distribution of the aircraft cabin sound pressure levels reproduced using the developed system will be compared with the original recorded sound field within the NRC Falcon 20 aircraft cabin.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".