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Record W4243861788 · doi:10.36884/jafm.13.05.31080

A Numerical Approach for Simulating a High-Speed Train Passing through a Tornado-Like Vortex

2020· article· en· W4243861788 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 · 2020
Typearticle
Languageen
FieldEngineering
TopicAerodynamics and Fluid Dynamics Research
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsTornadoVortexMechanicsComputer scienceMeteorologyAerospace engineeringPhysicsEngineering

Abstract

fetched live from OpenAlex

Tornados are one of the most common natural disasters, but their occurrence can be sudden and unpredictable. For trains operating in the areas where tornadoes frequently happen, the operation safety is challenged. Tornado generator was recently proposed as a method of numerical investigation of tornado-like vortex flows. This paper presents a numerical approach for the simulation of train passing through a tornado-like vortex on realistic scale. It is found that the tornado-like vortex causes appearance of localized regions of a negative pressure on the train and transient variations of the aerodynamic loads acting on the train. As a result, the tornado-like vortex causes swings on the lateral force, and subsequently on the rolling moment, which affect the passenger comfort and operation safety of the train. The method presented herein can be further applied to the study of train behavior and real time response while encountering tornadoes of different types and strength, which is significant for evaluating the operation safety of high-speed trains.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.835
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0000.000
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 head0.024
GPT teacher head0.243
Teacher spread0.219 · 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