Experimental Modal Analysis of a Half-Scale Business Jet Fuselage Tail Section
Bibliographic record
Abstract
Structure borne noise caused by the engine is a significant source of discomfort for business aircraft passengers. Aircraft with fuselage mounted engines have increased structure borne noise levels in the cabin due to the shortened vibration transmission path. Working with a prominent Canadian aircraft manufacturer, the research objective was to characterize the structural dynamics of the aircraft rear cabin bulkhead experimentally. This is an initial step for structural dynamic modifications (SDM) which will yield reduced noise output and increased passenger comfort. Experimental modal analysis (EMA) identified dynamic properties of the fuselage (i.e. natural frequencies, mode shapes) which were used to validate a FE model. This information will guide structural dynamic modifications, reducing vibration levels in the structure. This research involved using modal testing with an electromechanical shaker to excite a half-scale model aft fuselage tail section at the engine support yokes and measuring the response at the cabin rear bulkhead. The fuselage was excited in three different directions and the results compared with the various driving points and the FE model. The bandwidth of interest was 50 to 400 Hz. This thesis reviews relevant literature and details modal analysis theory, fuselage construction, methodology of excitation, validation techniques, and results. This thesis provides a method for conducting EMA on large, complex structures. The FE and experimental model exhibited five correlated mode pairs. Furthermore, by model updating the FE model, the eigenvalue discrepancy with the experimental model was reduced from 10% to 8%.
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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 | 0.000 | 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) | 0.003 | 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 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".