Experimental Validation Of An Acceleration Power Spectral Density Aircraft Panel Model Given Different Excitations
Bibliographic record
Abstract
The noise and vibration in an aircraft cabin during cruise conditions is primarily caused by external flow excitations from the turbulent boundary layer (TBL). The TBL causes the fuselage panels on the aircraft to vibrate. These vibrations radiate sound energy in the form of noise. It is of interest to be able to predict the response of these panels to different excitations using an analytical model, so that expensive wind tunnel and flight tests can be minimized when doing noise research. Two existing analytical models were modified to account for different excitations: one with simply supported boundary conditions and the other with arbitrary boundary conditions. These models were programmed, using Matlab, to calculate the acceleration power spectral density (PSD), and radiated sound power (RSP). Models were validated against experimental data, obtained by the authors, for a thin rectangular panel with boundary conditions between simply supported and clamped conditions. It is shown that the modified analytical models accurately predict the panel response for a point force excitation, for a TBL excitation, and for an oscillating piezoelectric patch excitation.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".