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Record W4297625979 · doi:10.1061/9780784484265.289

A Study of the Pressure Waves Induced by Two 600 km/h High-Speed Maglev Trains Intersecting in a Tunnel

2022· article· en· W4297625979 on OpenAlexaff
Shuai Han, Jie Zhang, Zhan-Hao Guo, Fan Wang, Yuge Wang, Jia-Bin Wang, Guangjun Gao

Bibliographic record

VenueCICTP 2022 · 2022
Typearticle
Languageen
FieldEngineering
TopicAerodynamics and Fluid Dynamics Research
Canadian institutionsMinistry of Education and Child Care
Fundersnot available
KeywordsMaglevTrainTurbulenceCompressibilityHigh speed trainTransient (computer programming)MechanicsComputer simulationEngineeringGeologyMarine engineeringStructural engineeringPhysicsComputer scienceElectrical engineering

Abstract

fetched live from OpenAlex

When two high-speed maglev trains intersect in a tunnel, the pressure rises sharply, which causes serious damage on the train and tunnel structure. In this paper, the three-dimensional, compressible, unsteady, k-ɛ two-equation turbulence model, and sliding grid technology were used to study the influence of speed on the pressure waves induced by two maglev trains intersecting in a tunnel. The numerical simulation method used in this paper was validated against the results from moving model tests. The main conclusions in this paper are as follows: The transient pressures on the maglev train and tunnel surface show a significant relationship with the train speed which will provide strong support for the design of the high-speed maglev train and tunnel.

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)0.000
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.507
Threshold uncertainty score0.565

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.016
GPT teacher head0.250
Teacher spread0.235 · 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
Published2022
Admission routes1
Has abstractyes

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