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Record W2142220974 · doi:10.4271/2014-01-2451

Measurement of the On-Road Turbulence Environment Experienced by Heavy Duty Vehicles

2014· article· en· W2142220974 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.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueSAE International journal of commercial vehicles · 2014
Typearticle
Languageen
FieldEngineering
TopicAerodynamics and Fluid Dynamics Research
Canadian institutionsTransport CanadaNational Research Council Canada
Fundersnot available
KeywordsHeavy dutyTurbulenceAutomotive engineeringTransport engineeringEnvironmental scienceAeronauticsMarine engineeringEngineeringAerospace engineeringMeteorologyPhysics

Abstract

fetched live from OpenAlex

<div class="section abstract"><div class="htmlview paragraph">Terrestrial winds play an important role in affecting the aerodynamics of road vehicles. Of increasing importance is the effect of the unsteady turbulence structure of these winds and their influence on the process of optimizing aerodynamic performance to reduce fuel consumption. In an effort to predict better the aerodynamic performance of heavy-duty vehicles and various drag reduction technologies, a study was undertaken to measure the turbulent wind characteristics experienced by heavy-duty vehicles on the road.</div><div class="htmlview paragraph">To measure the winds experienced on the road, a sport utility vehicle (SUV) was outfitted with an array of four fast-response pressure probes that could be arranged in vertical or horizontal rake configurations that provided measurements up to 4.0 m from the ground and spanning a width of 2.4 m. To characterize the influence of the proximity of the vehicle on the pressure signals of the probes, the SUV and its measurements system was calibrated in a large wind tunnel. On-road measurements of the turbulence intensities, turbulence length scales, wind spectra, and spatial correlations were performed. Eight days of testing over a two month period in late 2012 were conducted over roads in Eastern Ontario and Western Quebec, Canada. Dates and test routes were selected to provide a variety of conditions. The time-series of on-road turbulence data were segmented and classified based on differences in the terrain roughness, traffic density, and wind strength experienced during the measurements.</div><div class="htmlview paragraph">Of the three classification categories, traffic density provided the greatest influence on the measured turbulence characteristics by modifying the strength of the high-frequency/small-scale turbulence structures in the wind. Conversely, the strength of the terrestrial winds provided a strong influence on the low-frequency/large-scale turbulence. In the near-ground region of the current study (0.5 to 4.0 m), which represents the vertical extent of typical heavy duty vehicles on the road, the turbulence length scales showed a much greater sensitivity to vertical distance than did the turbulence intensity, resulting from damping of the large-scale/low-frequency wind fluctuations near the ground.</div><div class="htmlview paragraph">Based on the frequency of the various conditions experienced on the road, a target set of wind conditions, (intensities, length scales, spectra, correlation lengths) are recommended that differ from previously-published recommendations.</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.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.641
Threshold uncertainty score0.388

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 integrity0.0000.000
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 head0.013
GPT teacher head0.242
Teacher spread0.228 · 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