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Record W2794352216 · doi:10.1504/ijvsmt.2017.10011079

Aerodynamic analysis of an active rear split spoiler for improving lateral stability of high-speed vehicles

2017· article· en· W2794352216 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

VenueInternational Journal of Vehicle Systems Modelling and Testing · 2017
Typearticle
Languageen
FieldEngineering
TopicAerodynamics and Fluid Dynamics Research
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsAerodynamicsEngineeringDragStructural engineeringWind tunnelAccelerationAutomotive engineeringAerodynamic forceAerospace engineeringAerodynamic dragPhysics

Abstract

fetched live from OpenAlex

This paper examines an active rear split spoiler designed for improving lateral stability of high-speed vehicles under high lateral acceleration (high-g) scenarios, such as a tight cornering manoeuvre at high speeds. Downforces produced by the spoiler can enhance the lateral stability of the vehicle under a high-g cornering manoeuvre. On the other hand, the spoiler may introduce additional drags on the vehicle. Aerodynamic analysis and wind tunnel testing are conducted to evaluate the dynamic effects of the active spoiler on a high-speed car. The downforce and drag, as well as their relationship, are investigated using CFD simulations of the car with the active rear split spoiler at different spoiler angles of attack and at different speeds. Then, the achieved CFD simulation results are compared with the experimental data derived from the wind tunnel on the physical car and the spoiler prototype. The observations and findings achieved from the study may provide valuable guidelines for developing active aerodynamic control systems to increase safety of high-speed vehicles.

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 score0.095
Threshold uncertainty score0.486

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 science0.0000.000
Research integrity0.0000.000
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 head0.041
GPT teacher head0.286
Teacher spread0.245 · 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