Computational Modal Analysis and Free Size Optimization of an Aircraft Hydraulic Pump Support Structure
Bibliographic record
Abstract
This study examines computational modal analysis using FEA modeled structural components on a rear-mounted pump support structure in an aircraft. The goal is to understand the vibratory transmission path to the aircraft cabin, and information regarding dynamic modifications. The frequency response functions were used to validate the FE model by visual and analytical comparison against experimental data. A modal frequency response analysis was used to estimate the dynamic response at a discrete set of points on the structure. A model validation study showed excellent correlation for frequencies between 100--2000 Hz with an average percent difference of the centre frequencies of 9.4% that deteriorated slightly to 11.7% between 20--4000 Hz. Next, modifications were made to the webbing of the pump support yoke and the in-board and out-board isolator plates. These tests confirmed that differences between the frequency responses of the original and modified finite element models can be justified using the modal analysis theory. Webbing removal did not significantly influence the modal response of the structure while isolator plate removal increased the mobility response by 22%. More structural modifications were made using free size optimization, which is advantageous due to minimal changes in structure topology. This reduced the average velocity response magnitude by 6%.
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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 | 0.001 |
| 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) | 0.002 | 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".