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Record W2047506307 · doi:10.4271/2013-01-0802

Design of an Actively Controlled Aerodynamic Wing to Increase High-Speed Vehicle Safety

2013· article· en· W2047506307 on OpenAlexafffund
Yuping He

Bibliographic record

VenueSAE technical papers on CD-ROM/SAE technical paper series · 2013
Typearticle
Languageen
FieldEngineering
TopicAerodynamics and Fluid Dynamics Research
Canadian institutionsOntario Tech University
FundersOntario Centres of Excellence
KeywordsAerodynamicsWingAutomotive engineeringAeronauticsComputer scienceAerospace engineeringEngineering

Abstract

fetched live from OpenAlex

<div class="section abstract"><div class="htmlview paragraph">This paper presents the design of airfoil and briefly introduces a real physical prototype for an actively controlled wing to improve high speed vehicle safety. Conventionally, active safety systems of road vehicles, including active steering and differential braking, mainly manipulate the tire/road forces to enhance the lateral stability of vehicles. However, this active safety technology is hindered by the saturation of tire/road forces at high lateral accelerations and on icy slippery roads. In contrast, the use of controlled aerodynamic forces has received little attention. In this paper, the actively controlled wing is proposed to manipulate the negative lift force (downforce) to enhance handling capabilities of vehicles at high speeds. Various wings are examined in terms of airfoil shapes, coefficient of drag and lift, resulting yaw/roll moments, effect of wing attack angle at different Reynolds numbers using numerical simulations with X-Foil, Gambit and Fluent software packages. A prototype of the proposed actively controlled wing is briefly introduced. The essential design parameters, functionality and features of the active wing are addressed. In a companion paper that will follow, the stability control analysis of the vehicle with the actively controlled wing will be offered.</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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.991
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.010
GPT teacher head0.236
Teacher spread0.227 · 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
Published2013
Admission routes2
Has abstractyes

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