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Aerodynamic Study of Two Opposing Moving Trains in a Tunnel Based on Different Nose Contours

2017· article· en· W2760983873 on OpenAlex

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

VenueJournal of Applied Fluid Mechanics · 2017
Typearticle
Languageen
FieldEngineering
TopicAerodynamics and Fluid Dynamics Research
Canadian institutionsMinistry of Education and Child Care
Fundersnot available
KeywordsAerodynamicsTrainAerodynamic forceMechanicsTurbulenceCompressibilityComputational fluid dynamicsWind tunnelAcousticsStructural engineeringComputer scienceEngineeringPhysics

Abstract

fetched live from OpenAlex

It is well known that the train nose shape has significant influence on the aerodynamic characteristics. This study explores the influence of four kinds of nose shapes (fusiform, flat-broad, bulge-broad, ellipsoidal) on the aerodynamic performance of two opposing high-speed trains passing by each other through a tunnel at 250 km/h. The method of three dimensional, compressible, unsteady Reynolds-averaged Navier-Stokes equations and RNG k-ε double equation turbulence model was carried out to simulate the whole process of two trains passing by each other inside a tunnel. Then the pressure variations on tunnel wall and train surface are compared with previous full-scale test to validate the numerical method adopted in this paper. The assessment characteristics, such as transient pressure and aerodynamic loading, are analyzed to investigate the influence of nose shape on these assessment parameters. It is revealed that aerodynamic performance of trains which have longitudinal nose profile line B (fusiform, flat-broad shape) is relatively better when passing by each other in a tunnel. The results can be used as a guideline for high-speed train nose shape design.

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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 categoriesMeta-epidemiology (narrow)
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 score0.486
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.018
GPT teacher head0.272
Teacher spread0.254 · 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