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Record W2933955336 · doi:10.4271/2019-01-0649

Heavy-Duty Aerodynamic Testing for CO<sub>2</sub> Certification: A Methodology Comparison

2019· article· en· W2933955336 on OpenAlexaboutno aff
J. Felipe Rodriguez, Oscar Delgado, Berk Demirgök, Cem Baki, Marc Besch, Arvind Thiruvengadam, Martin Rexeis, Martin Röck

Bibliographic record

VenueSAE technical papers on CD-ROM/SAE technical paper series · 2019
Typearticle
Languageen
FieldEngineering
TopicAerodynamics and Fluid Dynamics Research
Canadian institutionsnot available
Fundersnot available
KeywordsCertificationAerodynamicsHeavy dutyAerodynamic diameterEnvironmental scienceComputer scienceAerospace engineeringAutomotive engineeringEngineeringPhysicsMeteorology

Abstract

fetched live from OpenAlex

<div class="section abstract"><div class="htmlview paragraph">Aerodynamic drag testing is a key component of the CO<sub>2</sub> certification schemes for heavy-duty vehicles around the world. This paper presents and compares the regulatory approaches for measuring the drag coefficient of heavy-duty vehicles in Europe, which uses a constant-speed test, and in the United States and Canada, which use a coastdown test. Two European trucks and one North American truck were tested using the constant-speed and coastdown methods. When corrected to zero yaw angle, a difference of up to 12% was observed in the measured drag coefficients from the US coastdown procedure and the EU constant-speed test. The differences in the measured drag coefficient can be attributed, among others, to the assumptions in the speed-dependence of the tire rolling resistance and axle spin losses, the data post-processing required by each methodology, unaccounted frictional losses in the transmission, the behavior of the automated manual transmission during the coastdown run, and the yaw angle correction.</div></div>

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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.970
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0020.000
Research integrity0.0020.002
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 head0.051
GPT teacher head0.313
Teacher spread0.262 · 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.

Study designObservational
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
Published2019
Admission routes1
Has abstractyes

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