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Record W3121253990 · doi:10.4271/2021-01-0949

Near-to-Far Wake Characteristics of Road Vehicles Part 2: Influence of Cross Winds and Free-Stream Turbulence

2021· article· en· W3121253990 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
KeywordsWakeCrosswindFreestreamContext (archaeology)Wind tunnelAerodynamicsTurbulenceTruckMeteorologyPhysicsAerospace engineeringEngineeringMechanicsGeographyReynolds number

Abstract

fetched live from OpenAlex

<div class="section abstract"><div class="htmlview paragraph">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 near-to-far regions of ground-vehicle wakes (one to ten vehicle lengths), in the context of their potential influence on other vehicles.</div><div class="htmlview paragraph">Part two of this three-part paper documents the influence of the ambient winds on the development of the wake behind a vehicle. A series of scaled-model wind-tunnel measurements, supplemented by some high-fidelity numerical simulations, based on a Lattice-Boltzmann approach, are presented to examine the effects cross-wind magnitude, by means of changes in yaw angle, on the wakes behind four vehicle shapes: a sedan, an SUV, a pickup truck, a medium-duty vehicle and a heavy-duty vehicle. The influence of road-representative freestream turbulence is also examined.</div><div class="htmlview paragraph">The results of these investigations show that, under yaw conditions, the distinct differences between the wake structures of slant/step-back and square-back shapes, documented in Part 1, are eliminated. At yaw, the moderate-to-far wake region is dominated by a large vortex structure of similar size to the vehicle itself that generates significant sidewash, analogous to the downwash in the wake of a wing in pitch. All vehicle shapes studied demonstrate this vortex structure which increases in strength with yaw angle. For vehicles following in the wake, not only do they experience a wind-speed deficit associated with the wake, but they experience a twisted wind profile with reduced yaw angles near the ground. The introduction of freestream turbulence is shown to generate a large wake with reduced shear, but without changing the dominant flow characteristics.</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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.348
Threshold uncertainty score0.378

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.015
GPT teacher head0.347
Teacher spread0.332 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations12
Published2021
Admission routes1
Has abstractyes

Explore more

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