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Modeling the Interaction of the Rolling Stock and the Track in the Emergency Braking Mode of a Passenger Train

2023· preprint· en· W4386697317 on OpenAlexaff
Vladimir Solonenko, Janat Musayev, Seitbek Zhunisbekov, Algazy Zhauyt, Batyrkhan Kyrykbayev, Gulbarshyn Smailova, Ulbala Murzakhmetova, Saltanat Yussupova

Bibliographic record

VenuePreprints.org · 2023
Typepreprint
Languageen
FieldEngineering
TopicRailway Engineering and Dynamics
Canadian institutionsTransport Canada
Fundersnot available
KeywordsBogieVibrationStructural engineeringEngineeringStiffnessAutomotive engineeringSuspension (topology)Passenger trainRigidity (electromagnetism)PhysicsAcoustics

Abstract

fetched live from OpenAlex

The article analyzes the transitional mode of movement of a passenger train caused by emergency braking by the driver. To study the possibility of derailment of passenger cars, a simulation model of the movement of a train consisting of a locomotive and twenty passenger cars was developed in the Universal Mechanism software environment, designed to study the dynamics and kinematics of mechanical systems, which include railway rolling stock. The developed model allows taking into account the longitudinal, transverse and vertical vibrations of all cars and locomotive. All bodies in the model are assumed to be absolutely rigid. The assumption of non-deformability of the bodies is based on the fact that the stiffness of the spring suspension and elastic connections is significantly less than the structural rigidity of the bogie frames and the bolster structure, and the frequency of elastic vibrations of these bodies is much higher than the frequency of their vibrations on the spring suspension. Passengers and cargo in a wagon are considered to be non-deformable and integral with the wagon body, similarly in the case of a locomotive body. Between the elements of the system, connections are involved that regulate certain relative movements of these elements.

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)0.002
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 score0.018
Threshold uncertainty score0.639

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 science0.0010.000
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 head0.084
GPT teacher head0.315
Teacher spread0.232 · 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
Published2023
Admission routes1
Has abstractyes

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