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Record W2126926642 · doi:10.1061/9780784413623.020

Effect of Cutting Slope Angle on Aerodynamic Performance of High-Speed Trains

2014· article· en· W2126926642 on OpenAlexaff
Jie Zhang, Tanghong Liu

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAerodynamics and Fluid Dynamics Research
Canadian institutionsMinistry of Education and Child Care
FundersCentral South UniversityNational Natural Science Foundation of China
KeywordsCrosswindTrainAerodynamicsComputational fluid dynamicsDerailmentDetached eddy simulationWork (physics)Flow (mathematics)Wind tunnelEnvironmental scienceMarine engineeringGeologyStructural engineeringAerospace engineeringEngineeringMechanicsMechanical engineeringPhysicsReynolds-averaged Navier–Stokes equations

Abstract

fetched live from OpenAlex

Research on aerodynamic performance of high-speed trains in cutting with different slope angles would complement the operation safety management under strong winds. This study was conducted to investigate the flow structure around a train using computational fluid dynamics (CFD). The accuracy of the numerical method was validated, combined with a wind tunnel test. This work shows that the surroundings of cutting along the railway line have great effect on the crosswind stability of the train. With the increase of slope angle, the coefficients of aerodynamic forces tend to reduce. Comparing the angle 0° with 90°, the Cs, Cl and Cm of head car fall by 96.7%, 108%, and 96.8% to the maximum respectively, while those of middle car by 108%, 98.8%, and 105%. For the shield of applicable cutting, the whole body is in a minor positive pressure environment. Thus, an appropriate slope angle for the cutting can largely improve its windbreak performance.

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)0.001
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 score0.386
Threshold uncertainty score0.552

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.004
GPT teacher head0.219
Teacher spread0.214 · 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

Citations1
Published2014
Admission routes1
Has abstractyes

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