MétaCan
Menu
Back to cohort
Record W2084938823 · doi:10.1080/00423110903078794

Cross-wind effects on road and rail vehicles

2009· article· en· W2084938823 on OpenAlexaff
Chris Baker, Federico Cheli, Alexander Orellano, Nicolas Paradot, Carsten Proppe, Daniele Rocchi

Bibliographic record

VenueVehicle System Dynamics · 2009
Typearticle
Languageen
FieldEngineering
TopicAerodynamics and Fluid Dynamics Research
Canadian institutionsBombardier (Canada)
Fundersnot available
KeywordsAerodynamicsWind tunnelEngineeringComputational fluid dynamicsSet (abstract data type)Moment (physics)Aerodynamic forceVehicle dynamicsMarine engineeringComputer scienceAerospace engineering

Abstract

fetched live from OpenAlex

This paper presents a review of recent research that has been carried out on the cross-wind effects on road and rail vehicles. After a brief introduction to the issues involved, the risk analysis framework is set out. All risk analysis methods require some knowledge of cross-wind aerodynamic force and moment coefficients, and methods of obtaining these through full scale and wind tunnel testing and through Computational Fluid Dynamics methods are then described. The picture of the flow fields around vehicles that is suggested by these measurements and calculations is then presented, and the steady and the unsteady aerodynamic force characteristics described. The detailed methodology for using this information to predict accident risk is then set out, including details of the vehicle dynamics system models that can be used. Finally potential alleviation methods are described and suggestions made for further works.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.005
GPT teacher head0.231
Teacher spread0.226 · 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 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

Citations189
Published2009
Admission routes1
Has abstractyes

Explore more

Same venueVehicle System DynamicsSame topicAerodynamics and Fluid Dynamics ResearchFrench-language works237,207