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Record W1862917528 · doi:10.4271/2009-01-0777

The Effects of Detailed Tire Geometry on Automobile Aerodynamics - a CFD Correlation Study in Static Conditions

2009· article· en· W1862917528 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueSAE International Journal of Passenger Cars - Mechanical Systems · 2009
Typearticle
Languageen
FieldEngineering
TopicAerodynamics and Fluid Dynamics Research
Canadian institutionsChrysler (Canada)
Fundersnot available
KeywordsAerodynamicsComputational fluid dynamicsAerospace engineeringMechanicsGeometryEngineeringPhysicsMathematics

Abstract

fetched live from OpenAlex

<div class="htmlview paragraph">A correlation study was performed between static wind tunnel testing and computational fluid dynamics (CFD) for a small hatchback vehicle, with the intent of evaluating a variety of different wheel and tire designs for aerodynamic forces. This was the first step of a broader study to develop a tool for assessing wheel and tire designs with real world (rolling road) conditions. It was discovered that better correlation could be achieved when actual tire scan data was used versus traditional smooth (CAD) tire geometry.</div> <div class="htmlview paragraph">This paper details the process involved in achieving the best correlation of the CFD prediction with experimental results, and describes the steps taken to include the most accurate geometry possible, including photogrammetry scans of an actual tire that was tested, and the level of meshing detail utilized to capture the fluid effects of the tire detail. The effects of this scanned tire geometry were significant, and improved the results for drag, lift, and the cooling drag for the vehicle relative to experimental full scale testing.</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.

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)0.001
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.171
Threshold uncertainty score0.533

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0010.000
Research integrity0.0000.001
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 head0.006
GPT teacher head0.271
Teacher spread0.265 · 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