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Record W2898957705 · doi:10.1115/detc2018-85628

Experimental and Computational Methods for Investigating Automotive Door Closure Sounds

2018· article· en· W2898957705 on OpenAlexaff
Giuseppe T. V. Garro, Chris K. Mechefske

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicVehicle Noise and Vibration Control
Canadian institutionsQueen's University
Fundersnot available
KeywordsAcousticsAnechoic chamberSound pressureMicrophoneClosure (psychology)Computer scienceComputationOctave (electronics)Automotive industryEngineeringAlgorithmPhysicsAerospace engineering

Abstract

fetched live from OpenAlex

The focus of this investigation was to examine the acoustic trends present during operation of an automotive door closure at two impact speeds through experimental methods. Transient sound pressure of five different door closure mechanisms were collected in a semi-anechoic chamber using a three-element condenser microphone array. Post-processing methodologies such as Sound Pressure Level versus 1/3 Octave band and Continuous Wavelet Transform computations were conducted. These procedures provided an in-depth analysis on the overall generated sound in addition to identifying which frequencies dominate the response at specific impact events during latch operation. Computational model analyses of the closure system using Rigid Body Dynamic and Explicit Dynamic methods using ANSYS to obtain a clearer understanding of the latch component interactions. Recorded average sound pressure level, frequency decomposition, and impact reaction forces are presented in addition to the notable trends between both impacting speeds.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.840
Threshold uncertainty score0.281

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 head0.023
GPT teacher head0.335
Teacher spread0.312 · 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 designBench or experimental
Domainnot available
GenreMethods

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
Published2018
Admission routes1
Has abstractyes

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