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Record W2190514569 · doi:10.1504/ijhvs.2015.070415

Numerical and experimental characterisation of the dynamic behaviour of a passenger aircraft seat during a takeoff condition

2015· article· en· W2190514569 on OpenAlexafffund
Mohammed Alziadeh, Salim El Bouzidi, Atef Mohany, Marwan Hassan

Bibliographic record

VenueInternational Journal of Heavy Vehicle Systems · 2015
Typearticle
Languageen
FieldMedicine
TopicEffects of Vibration on Health
Canadian institutionsMcMaster UniversityUniversity of GuelphOntario Tech University
FundersUniversity of Ontario Institute of Technology
KeywordsTakeoffModalEngineeringStructural engineeringVibrationFinite element methodTakeoff and landingTransmissibility (structural dynamics)Modal analysisExperimental dataVehicle dynamicsAirplaneAutomotive engineeringSimulationAerospace engineeringAcoustics

Abstract

fetched live from OpenAlex

This paper investigates the dynamic response of an aircraft seat structure. The study involved the use of a finite element software to predict aircraft seat vibrations during takeoff-like flight conditions. The described model is validated by performing experimental dynamic response measurements of an aircraft seat subjected to actual take off excitation recordings obtained onboard an aircraft. The accuracy of the model is investigated by comparing the simulated properties with experimental modal analysis and seat plate dynamics data. Moreover, the severity of the transmitted vibrations to the seat surface is assessed using the seat effective amplitude transmissibility (SEAT) method. Results show that the numerical model is able to replicate the dynamic and modal properties of the aircraft seat. The numerical and experimental SEAT values computed through different methods were also found to be in good agreement. This developed model can be used to predict and improve the dynamic comfort of aircraft seats.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0020.000

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 head0.017
GPT teacher head0.327
Teacher spread0.310 · 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 designBench or experimental
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
Published2015
Admission routes2
Has abstractyes

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