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Record W3093844801 · doi:10.1139/tcsme-2020-0132

Dynamic characteristics of a wheelset–track system under corrugation excitations in the metro operation process

2020· article· en· W3093844801 on OpenAlexvenueno aff
Zhiqiang Wang, Zhenyu Lei

Bibliographic record

VenueTransactions of the Canadian Society for Mechanical Engineering · 2020
Typearticle
Languageen
FieldEngineering
TopicRailway Engineering and Dynamics
Canadian institutionsnot available
FundersNational Natural Science Foundation of China
KeywordsTrack (disk drive)AmplitudeVibrationWavelengthAccelerationStructural engineeringAction (physics)Force dynamicsProcess (computing)AcousticsPhysicsMechanicsEngineeringComputer scienceOpticsMechanical engineeringClassical mechanics

Abstract

fetched live from OpenAlex

There are few systematic researches on the dynamic characteristics of a wheelset–track system under different corrugation excitations. To study the influence of different corrugations on the wheelset–track dynamic characteristics, a three-dimensional wheelset–track rolling contact model is established, and the model rationality is analyzed. Then, the wavelength and wave depth of the initial corrugation irregularity for simulation analysis are determined according to the measured data. Finally, the wheel–rail vertical force and wheel vertical vibration acceleration are selected as the output variables, and the wheelset–track dynamic responses under excitations are studied. The results show that comparing with the middle/long-wavelength irregularity, the short-wavelength irregularity will not increase the amplitude of wheel–rail dynamic action but will increase the frequency of wheel–rail dynamic action. The initial irregularity wave depth will not affect the frequency of wheel–rail dynamic action but will affect the amplitude of wheel–rail dynamic action, and the greater the amplitude of the wave depth is, the greater the impact on the dynamic responses of the wheelset–track system is. The main characteristic frequencies of output variables are close to the initial irregularity passing frequency, and the other peak frequencies are in a frequency multiplication relationship with the initial irregularity passing frequency.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.899
Threshold uncertainty score0.456

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.009
GPT teacher head0.200
Teacher spread0.191 · 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

Citations5
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueTransactions of the Canadian Society for Mechanical EngineeringSame topicRailway Engineering and DynamicsFrench-language works237,207