MétaCan
Menu
Back to cohort
Record W4226453568 · doi:10.1504/ijvp.2022.122138

A comparative study of tail air-deflector designs on aerodynamic drag reduction of medium-duty trucks

2022· article· en· W4226453568 on OpenAlexaff
Wei Gao, Zhaowen Deng, Yuping He

Bibliographic record

VenueInternational Journal of Vehicle Performance · 2022
Typearticle
Languageen
FieldEngineering
TopicAerodynamics and Fluid Dynamics Research
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsTruckAerodynamic dragDragAerodynamicsAirflowAutomotive engineeringFuel efficiencyComputational fluid dynamicsReduction (mathematics)Marine engineeringHeavy dutyEnvironmental scienceEngineeringAerospace engineeringMechanical engineering

Abstract

fetched live from OpenAlex

With the rapid development of highway networks and logistics industry in China, medium-duty trucks have been increasingly used for highway and urban area freight transportation. The aerodynamic drag reduction of medium-duty trucks is of significant importance for improving highway transportation efficiency, enhancing fuel economy, and reducing greenhouse emissions. This paper proposes, compares and evaluates seven designs of tail air-deflector, which are devised to reduce aerodynamic drag of a medium-duty truck. The impacts of the shapes and/or configurations of tail air-deflector on the aerodynamic characteristics of the truck are studied using computational fluid dynamics (CFD) simulation. The results attained from the comparative study will provide guidelines for the design of airflow control devices for commercial vehicles to effectively reduce aerodynamic drag.

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)0.000
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.190
Threshold uncertainty score0.483

Codex and Gemma teacher scores by category

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 science0.0010.000
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 head0.030
GPT teacher head0.301
Teacher spread0.271 · 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 designSimulation or modeling
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

Citations7
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Vehicle PerformanceSame topicAerodynamics and Fluid Dynamics ResearchFrench-language works237,207