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Record W3039845163 · doi:10.1080/19942060.2020.1784798

Numerical simulation of the aerodynamic characteristics of double unit train

2020· article· en· W3039845163 on OpenAlexaff
Zijian Guo, Tanghong Liu, Hassan Hemida, Zhengwei Chen, Hongkang Liu

Bibliographic record

VenueEngineering Applications of Computational Fluid Mechanics · 2020
Typearticle
Languageen
FieldEngineering
TopicAerodynamics and Fluid Dynamics Research
Canadian institutionsMinistry of Education and Child Care
Fundersnot available
KeywordsTrainAerodynamicsDragDetached eddy simulationVortexLift (data mining)Unit (ring theory)Lift-to-drag ratioAerodynamic forceLarge eddy simulationStructural engineeringEngineeringMechanicsSimulationAerospace engineeringPhysicsComputer scienceComputational fluid dynamicsTurbulenceMathematicsReynolds-averaged Navier–Stokes equations

Abstract

fetched live from OpenAlex

Double unit trains running at high speeds may create additional aerodynamic challenges due to two streamlined structures with close proximity, exploring the aerodynamic performance of double unit trains is now critical. In this study, detached eddy simulation (DES) approach was employed to study the aerodynamic performance and the nearby flow patterns of a double unit train, whose results were compared and analyzed with that of a single-unit train with a same length. The results showed that the coupling method could change the aerodynamic drag on each car and tended to increase the overall drag of the double unit train. The lift force of the front car near the coupler was significantly increased. Similar slipstream distributions were found around the front half single and double-unit train except in a region close to the coupler. Due to the coupling structure, the slipstream of the rear half of double unit train was much stronger compared to single unit train. The vortex region behind the double-unit train was much wider than that of the single-unit train and was accompanied by greater vortex-shedding.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.013
GPT teacher head0.238
Teacher spread0.225 · 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

Citations24
Published2020
Admission routes1
Has abstractyes

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