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Record W3123528779 · doi:10.4271/2021-01-0959

Near-to-Far Wake Characteristics of Road Vehicles Part 3: Influence of Multi-Vehicle Interactions

2021· article· en· W3123528779 on OpenAlexaff
Brian McAuliffe, Bhargav Sowmianarayanan, Hali Barber

Bibliographic record

VenueSAE International Journal of Advances and Current Practices in Mobility · 2021
Typearticle
Languageen
FieldEngineering
TopicAerodynamics and Fluid Dynamics Research
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsWakeContext (archaeology)Wind tunnelAerodynamicsCrosswindComputer scienceAutomotive engineeringAerospace engineeringEngineeringGeology

Abstract

fetched live from OpenAlex

Conventional assessments of the aerodynamic performance of ground vehicles have, to date, been considered in the context of a vehicle that encounters a uniform wind field in the absence of surrounding traffic. Recent vehicle-platooning studies have revealed measurable fuel savings when following other vehicles at inter-vehicle distances experienced in every-day traffic. These energy savings have been attributed in large part to the air-wakes of the leading vehicles. This set of three papers documents a study to examine the moderate-to-far regions of ground-vehicle wakes (one to ten vehicle lengths), in the context of their potential influence on other vehicles. Part Three of this three-part paper documents the wake characteristics for multi-vehicle scenarios of two or three vehicles, in single-lane or two-lane arrangements. A set of high-fidelity numerical simulations, based on a Lattice-Boltzmann approach, and a series of scaled-model wind-tunnel measurements are presented to examine some multi-vehicle scenarios with four types of vehicle shapes: a sedan, an SUV, a medium-duty vehicle, and a heavy-duty vehicle. The wake-merging process of a two-lane longitudinally-staggered sedan-and-SUV configuration is examined in detail with and without cross winds. Experimental-results are presented only for the SUV, medium- and heavy-duty vehicle shapes due to challenges described in Part One with respect to the sedan-model and the stationary-floor arrangement. Complex wake patterns emerge under multi-lane scenarios, particularly under cross-wind conditions, but the results suggest a level of predictability in the aggregate wake properties. Only for the cases with close lateral proximity (side-by-side configurations) do the wake patterns change substantially such that the characteristics of the individual wakes are not perceptible. Additionally, the merged wakes in cross winds are shown to potentially influence vehicles up to three or four lane widths away. The results suggest that superposition of moderate-to-far-wake profiles may be possible to generate on-road wind conditions associated with a range of traffic scenarios.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.025
GPT teacher head0.364
Teacher spread0.339 · 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

Citations8
Published2021
Admission routes1
Has abstractyes

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