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Record W1608455432 · doi:10.4271/2008-01-0820

Analysis of Air Flow and Heat Dissipation from a High Performance GT Car Front Brake

2008· article· en· W1608455432 on OpenAlexaff
Edward Palmer, Rakesh Mishra, John D. Fieldhouse, Jonathan Layfield

Bibliographic record

VenueSAE technical papers on CD-ROM/SAE technical paper series · 2008
Typearticle
Languageen
FieldEngineering
TopicAerodynamics and Fluid Dynamics Research
Canadian institutionsBentley (Canada)
Fundersnot available
KeywordsBrakeThermal management of electronic devices and systemsFront (military)DissipationAutomotive engineeringFlow (mathematics)AirflowMechanicsMechanical engineeringEnvironmental scienceEngineeringPhysicsThermodynamics

Abstract

fetched live from OpenAlex

Within this paper the convective heat dissipation from the front brake discs fitted to the left and right hand side of a high performance passenger car has been compared. The tools used in this investigation include computational fluid dynamics (CFD) and vehicle testing. The results show that although identical discs are fitted to both sides of the vehicle the disc fitted to the left hand side shows better thermo-aerodynamic properties than that fitted to the right due to the different direction of rotation. The computational model shows strong agreement with the test results; over predicting the average heat transfer coefficient by 4% for the left hand disc and 7.6% for the right disc. The CFD analysis enabled a detailed insight into the air flow and heat transfer distributions that was not possible during the vehicle test regime.

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 score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
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 head0.008
GPT teacher head0.220
Teacher spread0.212 · 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

Citations23
Published2008
Admission routes1
Has abstractyes

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