Computational Modal Analysis of an Aircraft Hydraulic Pump Support Structure
Bibliographic record
Abstract
Abstract Computational modal analysis performed using finite element analysis is an established method for estimating the modal parameters of complex structures. This work aims to provide insight into the techniques necessary to accurately model structural components within an aircraft by performing computational modal analysis on a rear-mounted pump support structure. The results of this analysis will be used to further aid in the understanding of the vibratory transmission path from the support structure to the aircraft cabin. The frequency response functions were used to validate the finite element model via a 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 an excellent correlation between the experimental and computational results for frequencies between 100–2000 Hz with an average percent difference of the centre frequencies of 9.4%. The agreement between the two sets of results across the 20–4000 Hz bandwidth deteriorated slightly to 11.7 %. Modifications were made to the webbing of the pump support yoke as well as 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 modal analysis theory. It was found that these particular structural design changes did not significantly influence the modal response of the structure. The methodology presented in this work provides an outline for future modelling and analysis of aircraft structural designs.
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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".