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Record W1824205191

Trends in aero-acoustic wind tunnel testing

2009· article· en· W1824205191 on OpenAlexvenueno aff
Gary Elfstrom

Bibliographic record

VenueCanadian acoustics · 2009
Typearticle
Languageen
FieldEngineering
TopicAerodynamics and Fluid Dynamics Research
Canadian institutionsnot available
Fundersnot available
KeywordsAerodynamicsWind tunnelNoise (video)Automotive industryAcousticsEngineeringAerospace engineeringNoise controlSubsonic and transonic wind tunnelMarine engineeringComputer scienceNoise reductionPhysics
DOInot available

Abstract

fetched live from OpenAlex

Some of the latest trends in new aero-acoustic wind tunnels, upgraded aerodynamic wind tunnels, and climatic wind tunnels are presented in terms of innovation to serve as aero-acoustic tools for the vehicle designer. The goal for better simulation in a wind tunnel is to have a signal to noise ratio of about 10dB between vehicle noise and background noise across the full frequency spectrum for a given vehicle. GIE S2A 24M2 automotive aero-acoustic wind tunnel and General Motors Aerodynamic Lab (GMAL) 56.2m2 are two of the latest innovations in the field of aero-acoustic tools for vehicle designers.

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)0.010
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.010
Threshold uncertainty score0.053

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.014
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.003
Science and technology studies0.0000.002
Scholarly communication0.0020.005
Open science0.0020.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0050.002

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 head0.233
Teacher spread0.216 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations1
Published2009
Admission routes1
Has abstractyes

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