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Record W3125646813 · doi:10.1504/ijad.2021.10034929

Effectiveness of tail devices for wake control of road heavy vehicles

2021· article· en· W3125646813 on OpenAlexaff
Kefu Liu, Mohammad Saeedi, Ali Tarokh

Bibliographic record

VenueInternational Journal of Aerodynamics · 2021
Typearticle
Languageen
FieldEngineering
TopicAerodynamics and Fluid Dynamics Research
Canadian institutionsLakehead University
Fundersnot available
KeywordsWakeAutomotive engineeringEnvironmental scienceControl (management)Transport engineeringMarine engineeringAeronauticsEngineeringAerospace engineeringComputer science

Abstract

fetched live from OpenAlex

In the current research, a series of numerical simulations of the flow field around a typical tractor-trailer combination with and without a drag reduction device (i.e., boat tail) have been performed to study the effect of the device on wake structures and flow evolution behind the trailer. The results of the simulations have been validated through comparison of the drag coefficient against available wind-tunnel measurement data. Based on the evolution profiles and flow visualisation behind the trailer, it has been shown that the trailer wake region in proximity of the trailer rear surface is significantly influenced by the trailer boat tail. As such a quicker flow recovery, smaller recirculation bubble and lower level of turbulent kinetic energy is obtained when the boat tail is utilised. This resulted in about 8.6% decrease in the vehicle aerodynamic drag and 4.6% fuel saving based on the travelling speed of 65 mph.

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)0.000
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0020.000

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 head0.008
GPT teacher head0.268
Teacher spread0.260 · 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

Citations0
Published2021
Admission routes1
Has abstractyes

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