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Record W2766662064 · doi:10.1115/detc2017-67389

Experimental Modal Analysis of a Half-Scale Model Twin-Engine Aircraft Rear Fuselage Engine Mount Support Frame

2017· article· en· W2766662064 on OpenAlexafffund
Diego A. Chamberlain, Chris K. Mechefske

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicTransportation Safety and Impact Analysis
Canadian institutionsQueen's University
FundersEuropean Social FundNatural Sciences and Engineering Research Council of Canada
KeywordsModalModal testingFuselageModal analysisHammerNatural frequencyStructural engineeringAerospaceAcousticsComputer scienceVibrationEngineeringAerospace engineeringFinite element methodPhysicsMaterials science

Abstract

fetched live from OpenAlex

Experimental modal testing using an impact hammer is a commonly used method for obtaining the modal parameters of any structure for which the vibrational behavior is of interest. Natural frequencies and associated mode shapes of the structure can be extracted directly from measured FRFs (Frequency Response Functions) through various curve fitting procedures. This paper provides an overview of the modal testing conducted on an aerospace component. Testing set-up, experimental equipment and the methodology employed are all described in detail. Further validation of the testing procedure was done by ensuring that the experimental results satisfy the requirements of repeatability, reciprocity and linearity. The relevant ISO standard has been referenced and important concepts to modal analysis are expanded upon. Recorded natural frequencies, coherence and a description of the observed mode shapes are presented along with notable trends.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

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)0.0060.001

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.

Opus teacher head0.013
GPT teacher head0.255
Teacher spread0.242 · 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 source (direct Gemma or distilled Codex), 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

Citations5
Published2017
Admission routes2
Has abstractyes

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