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Record W4411826885 · doi:10.20855/ijav.2025.30.22097

Computational Modal Analysis and Free Size Optimization of an Aircraft Hydraulic Pump Support Structure

2025· article· en· W4411826885 on OpenAlexafffund
Simon Kersten, John M. Sekijoba, Chris K. Mechefske

Bibliographic record

VenueThe International Journal of Acoustics and Vibration · 2025
Typearticle
Languageen
FieldEngineering
TopicHydraulic and Pneumatic Systems
Canadian institutionsQueen's University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsModalModal analysisComputer scienceComputational fluid dynamicsAerospace engineeringEngineeringStructural engineeringMaterials scienceFinite element method

Abstract

fetched live from OpenAlex

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%.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.608
Threshold uncertainty score0.238

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.004
GPT teacher head0.224
Teacher spread0.219 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes2
Has abstractyes

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