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Record W4401884024 · doi:10.1063/5.0218270

Mitigation of crosswind effects on high-speed trains using vortex generators

2024· article· en· W4401884024 on OpenAlex
Bin Xu, Tanghong Liu, Xuan Shi, Pierre E. Sullivan, Zhengwei Chen, Xiaodong Chen

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.

Bibliographic record

VenuePhysics of Fluids · 2024
Typearticle
Languageen
FieldEngineering
TopicAerodynamics and Fluid Dynamics Research
Canadian institutionsUniversity of Toronto
FundersNatural Science Foundation of Hunan ProvinceChina Postdoctoral Science Foundation
KeywordsCrosswindPhysicsTrainVortexAerospace engineeringVortex generatorMeteorologyMechanicsAeronauticsEngineering

Abstract

fetched live from OpenAlex

Vortex generators can enhance the operational safety of high-speed trains and offer effective anti-rolling performance. This paper investigates the influence of vortex generator installation angles on the aerodynamic characteristics of trains. The Improved Delayed Detached Eddy Simulation method is used to analyze the leeward side vortex structure. It is found that when the angle between the vortex generators and the relative wind is 30°, the rolling moment of the train is minimized, as it significantly reduces side forces while preventing excessive growth of lift force inducing rolling moment. The reduction in rolling moment of the train by vortex generators is attributed to the suppression of leeward side trailing vortices of the train, which delays flow separation at the roof of the train, inducing a downward trend in the separated flow. Dynamic Mode Decomposition reveals that vortex generators do not alter the stability of near-body trailing vortices but enhance the pulsatile characteristics of far-body trailing vortices, which do not affect the pressure distribution on the leeward side of the train.

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)0.000
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.660
Threshold uncertainty score0.591

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 science0.0000.000
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 head0.012
GPT teacher head0.262
Teacher spread0.250 · 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