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Record W2507429504 · doi:10.4271/2016-01-8015

Aerodynamic Performance of Flat-Panel Boat-Tails and Their Interactive Benefits with Side-Skirts

2016· article· en· W2507429504 on OpenAlexafffund
Brian McAuliffe, Alanna Wall

Bibliographic record

VenueSAE International journal of commercial vehicles · 2016
Typearticle
Languageen
FieldEngineering
TopicAerodynamics and Fluid Dynamics Research
Canadian institutionsNational Research Council Canada
FundersTransport Canada
KeywordsAerodynamicsFlat panelAutomotive engineeringEngineeringComputational fluid dynamicsAeronauticsMarine engineeringComputer scienceStructural engineeringAerospace engineeringComputer graphics (images)

Abstract

fetched live from OpenAlex

This paper describes an investigation of the performance potential of conventional flat-panel boat-tail concepts applied to tractor-trailer combinations. The study makes use of data from two wind-tunnel investigations, using model scales of 10% and 30%. Variations in boat-tail geometry were evaluated including the influence of length, side-panel angle and shape, top-panel angle and vertical position, and the presence of a lower panel. In addition, the beneficial interaction of the aerodynamic influence of boat-tails and side-skirts that provides a larger drag reduction than the sum of the individual-component drag reductions, identified in recent years through wind-tunnel tests in different facilities, has been further confirmed. This confirmation was accomplished using combinations of various boat-tails and side-skirts, with additional variations in the configuration of the tractor-trailer configuration. Key findings include the reduced performance associated with lowering the position of the top panel to retain visibility of marker lights, and an observed change in boat-tail performance based on the overall vehicle configuration.

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 score0.003
Threshold uncertainty score0.010

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)0.0030.001

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 head0.231
Teacher spread0.218 · 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

Citations14
Published2016
Admission routes2
Has abstractyes

Explore more

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