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Record W2280330614 · doi:10.4271/2009-01-0175

Downstream Blockage Corrections of Automotive Cooling Fan Module Performance

2009· article· en· W2280330614 on OpenAlexafffund
Allen G. Hunt, Eric Savory, N. L. Gifford, Robert J. Martinuzzi

Bibliographic record

VenueSAE technical papers on CD-ROM/SAE technical paper series · 2009
Typearticle
Languageen
FieldEngineering
TopicAerodynamics and Fluid Dynamics Research
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsAutomotive industryDownstream (manufacturing)Automotive engineeringAutomotive engineMechanical engineeringComputer scienceEnvironmental scienceEngineeringAerospace engineering

Abstract

fetched live from OpenAlex

A novel, simple and rapid method for predicting the performance effects of blockage downstream of an automotive cooling fan is presented. Fans are often tested without downstream blockage and, thus, the performance is considerably different when the fan is mounted in a vehicle as part of a cooling system. An easy to use tool is needed that can quickly predict fan performance modifiers. The suggested approach is able to predict the significant effects of a blockage behind the fan. Two fans were analyzed with this new method. Comparison charts between tests and predictions showed good agreement over a range of blockage distances. Based on the results the method is considered beneficial for initial design procedures. Although the agreement was poorer at very close separation distances such small gaps are less commonly utilized in practice. Further work is needed to include those distances in the present model.

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)0.002
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 score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.008
GPT teacher head0.234
Teacher spread0.226 · 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

Citations4
Published2009
Admission routes2
Has abstractyes

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